Innovative Food Analysis [1 ed.] 0128194936, 9780128194935

Table of contents :
Innovative Food Analysis
Copyright
Contents
List of Contributors
Preface
1 Compositional and nutritional analysis
1.1 Introduction
1.2 Carbohydrates
1.2.1 Definition of dietary carbohydrates and classification thereof
1.2.1.1 Dietary fiber
1.2.2 Labeling of carbohydrates in the EU
1.2.2.1 Labeling of sugars in the EU
1.2.2.2 Labeling of dietary fiber in the EU
1.2.3 The importance of carbohydrate analysis
1.2.4 Traditional and emerging methods for sample preparation in carbohydrate analysis
1.2.4.1 Carbohydrate extraction and fractionation
Liquid–liquid extraction and green solvents
Solid-phase extraction
Supercritical fluid extraction
Pressurized liquid extraction
Field flow fractionation
Chromatography-based methods
Membranes
1.2.4.2 Acid hydrolysis and derivatization for traditional analysis of monosaccharides and oligosaccharides
1.2.5 Emerging technologies for carbohydrate analysis
1.2.5.1 Biosensors
1.2.5.2 Supercritical fluid chromatography and supercritical fluid chromatography-mass spectroscopy
1.2.5.3 Liquid chromatography: high performance anion-exchange chromatography with pulsed amperometric detection
1.2.5.4 Hyperspectral imaging
1.2.6 Dietary fiber analysis
1.3 Fat and fatty acids
1.3.1 Definition of dietary fat and sources thereof
1.3.2 Labeling of fats in the EU
1.3.3 The importance of fat analysis
1.3.4 Traditional methods for fat analysis
1.3.4.1 Total fat content
1.3.4.2 Fat characterization
1.3.5 Emerging methods for fat extraction and fractionation
1.3.5.1 Accelerated solvent extraction
1.3.5.2 Supercritical fluid extraction
1.3.6 Emerging technologies for fat analysis
1.3.6.1 Infrared spectroscopy
1.3.6.2 Raman spectroscopy
1.3.6.3 Nuclear magnetic resonance
1.3.6.4 X-ray microtomography
1.3.6.5 Hyperspectral imaging
1.3.7 Lipid oxidation and food analysis
1.4 Minerals
1.4.1 Definition of minerals and sources thereof
1.4.2 Labeling of minerals in the EU
1.4.3 Ash analysis
1.4.4 Analysis of minerals of nutritional interest
1.4.4.1 Traditional methods
1.4.4.2 New frontiers in analysis of minerals of nutritional interest
1.5 Proteins
1.5.1 Definition of dietary proteins and sources thereof
1.5.2 Labeling of proteins in the EU
1.5.3 The importance of protein analysis
1.5.4 Traditional methods for dietary protein analysis
1.5.5 New priorities in protein analysis
1.5.5.1 Enzyme-linked immunosorbent assays
1.5.5.2 Immuno- and biosensors
1.5.5.3 Mass spectrometry
1.6 Water
1.6.1 Water content in foods
1.6.2 Water content determination
1.6.2.1 Traditional versus emerging methods
1.7 Conclusions
References
2 Bioactive component analysis
2.1 Introduction
2.2 Polyphenols
2.2.1 Sample pretreatment
2.2.2 Extraction of polyphenols
2.2.3 Isolation of polyphenols
2.2.4 Analysis of polyphenols
2.2.4.1 Spectrophotometric methods
2.2.4.2 Chromatographic methods
2.2.4.3 Other analysis methods
2.3 Carotenoids
2.3.1 Extraction and isolation of carotenoids
2.3.2 Analysis of carotenoids
2.4 Vitamins
2.4.1 Pretreatment, extraction, and isolation of samples
2.4.2 Analysis of vitamins
2.4.2.1 Bioassay methods
2.4.2.2 Microbiological methods
2.4.2.3 Chemical methods
2.5 Omega-3 fatty acids
2.5.1 Pretreatment and extraction of samples
2.5.2 Analysis of omega-3 fatty acids
2.5.2.1 GC analysis
2.5.2.2 Other analysis methods
2.6 Organic acids
2.6.1 Extraction and analysis of organic acids
2.7 Nucleosides and nucleotides
2.7.1 Pretreatment and extraction of sample
2.7.2 Analysis of nucleosides and nucleotides
2.7.2.1 Chromatographic analysis
2.7.2.2 Capillary electrophoresis analysis
2.8 Phytosterols
2.8.1 Pretreatment, extraction, and analysis of phytosterols
2.9 Conclusions and future perspectives
References
3 Analytical technologies in sugar and carbohydrate processing
3.1 Introduction
3.2 Analytical techniques for analyzing sugars and carbohydrates in sugar crops
3.2.1 Sugar beet
3.2.2 Sugarcane
3.2.3 Fruits
3.2.4 Maple trees
3.2.5 Sweeteners (fruits, honey, cereals)
3.3 Application of spectroscopy and chemometric data analyses for assessment of quality parameters of sugar commodities
3.3.1 Statistical terms used to measure the accuracy of visible to near-infrared radiation spectroscopy regression models
3.3.2 Procedure and necessary treatment of spectroscopic data
3.3.3 Data collection
3.3.4 Data exploratory methods
3.3.4.1 Outlier detection
3.3.4.2 Principal component analysis
3.3.4.3 Spectral data preprocessing techniques
3.3.4.4 Spectral noise removal techniques
3.3.4.5 Spectral derivatives and transformations
3.3.4.6 Modeling
3.3.4.7 Quantitative models
3.3.4.8 Multiple linear regression
3.3.4.9 Principal component regression
3.3.4.10 Partial least squares regression
3.3.4.11 Classification or discrimination models
3.3.4.12 Principal component analysis
3.3.4.13 Soft independent modeling of class analogy
3.3.4.14 Robustness of models
3.4 Topical methods of extracting sugars and carbohydrates from sugar crops
3.5 Novel analytical technologies for determining the sugars and carbohydrates in secondary products
3.5.1 Determination of sugars or carbohydrates from secondary products
3.5.2 Determination of syrup adulteration
3.5.2.1 Adulteration of honey syrup
3.5.2.2 Adulteration of corn syrup
3.5.2.3 Adulteration of beet syrup
3.5.2.4 Adulteration of rice syrup
3.6 Research challenges in technologies applied for assessment of sugar contents in secondary products
3.7 Future perspectives and conclusions
References
4 Sample preparation methods
4.1 Introduction
4.2 Sample pretreatment techniques
4.3 Targeted sample pretreatment techniques with high specificity for target analytes
4.4 Quick, easy, cheap, effective, rugged, and safe methods
4.5 Conclusions
Abbreviations
References
5 Flow-based food analytical methods
5.1 Introduction
5.2 Flow-based methods of analysis
5.2.1 Modes of flow-based analysis
5.2.2 Sample pretreatment using flow-based methods and hyphenated flow methods
5.3 Representative applications of flow-based methods to food analysis
5.3.1 Nutrients and antinutrients
5.3.1.1 Sugars
5.3.1.2 Total phosphorus and nitrogen
5.3.1.3 Aminoacids
5.3.1.4 Vitamins
5.3.1.5 Antinutrients
5.3.2 Inorganic species
5.3.2.1 Cations
5.3.2.2 Anions
5.3.3 Additives, preservatives, and adulterants
5.3.4 Acidity
5.3.5 Antioxidant capacity
5.3.6 Pesticides
5.3.7 Pharmaceuticals
5.3.8 Miscellaneous
5.4 Conclusions
References
6 Categories of food additives and analytical techniques for their determination
6.1 Introduction
6.2 Food additives
6.2.1 Regulators of acidity
6.2.2 Anticaking agents
6.2.3 Antifoaming agents
6.2.4 Antioxidants
6.2.5 Bulking agents
6.2.6 Carbonation agent
6.2.7 Carrier
6.2.8 Appearance control and clarifying agents
6.2.9 Colorants
6.2.10 Color retention agents
6.2.11 Emulsifiers
6.2.12 Firming agents
6.2.13 Flavor enhancers
6.2.14 Bleaching and flour treatment agents
6.2.15 Foaming agents
6.2.16 Gelling agents
6.2.17 Glazing agents
6.2.18 Humectants
6.2.19 Preservatives and antimicrobial agents
6.2.20 Propellants
6.2.21 Raising agents
6.2.22 Sequestrant or chelating agents
6.2.23 Stabilizers
6.2.24 Sweetener
6.2.25 Thickener
6.3 Steps in the analysis of food additives
6.3.1 Sampling
6.3.2 Sample preparation
6.3.3 Sample pretreatments
6.3.4 Analytical techniques choices
6.3.5 Analytical parameters or validation parameters
6.4 Analytical techniques used in food additive analysis
6.4.1 Spectroscopic techniques
6.4.1.1 Ultraviolet/visible spectroscopy
6.4.1.2 Near-infrared spectroscopy
6.4.1.3 Fourier transform infrared spectroscopy
6.4.1.4 Raman spectroscopy
6.4.2 Chromatographic techniques
6.4.2.1 Gas chromatography
6.4.2.2 Liquid chromatography
6.4.3 Electroanalytical techniques
6.5 Conclusions
Acknowledgments
Dedication
References
7 Analysis of food Additives
7.1 Function of food additives
7.2 Classification of food additives
7.3 Examples of food additives
7.4 Regulation and measurements of food additives
7.5 Analysis of food additives
7.5.1 Analysis of food colorants
7.5.1.1 Amaranth
7.5.1.2 Tartrazine
7.5.1.3 Indigo carmine
7.5.1.4 Sunset yellow
7.5.1.5 Other colorants
7.5.2 Analysis of preservatives
7.5.2.1 Benzoic acid and its sodium salt
7.5.2.2 Parabens
7.5.2.3 Nitrite
7.5.2.4 Other preservatives
7.5.3 Analysis of sweeteners
7.5.3.1 Steviol glycosides
7.5.3.2 Sodium saccharin
7.5.3.3 Aspartame
7.5.3.4 Other sweeteners
7.5.4 Analysis of antioxidant
7.5.4.1 Butylated hydroxyanisole
7.5.4.2 Butylated hydroxytoluene
7.5.4.3 Propyl gallate
7.5.4.4 Tert-butyl hydroquinone
7.5.4.5 Other antioxidants
7.6 Analysis of other food additives
7.7 Prospects for the analysis of food additives
References
8 Innovations in analytical methods for food authenticity
8.1 Authentication of food products
8.1.1 Traceability for preventing adulteration
8.1.2 Labeling and compositional regulations
8.2 Revision of analytical methods for food authentication
8.2.1 Coffee
8.2.2 Honey
8.2.3 Milk and dairy products
8.2.4 Alcoholic beverages
8.2.5 Nuts
8.2.6 Fruit and vegetables
8.2.7 Fruit juices
8.2.8 Herb and spices
8.2.9 Meat
8.2.9.1 Indicators related with the animal of origin
8.2.9.2 Indicators related with meat processing
8.2.9.3 Indicators related to the final product
8.2.9.4 Current trends in meat authentication
8.2.10 Sea products
8.2.11 Edible vegetable oils
8.2.12 Cereals
8.2.12.1 Chemical composition
8.2.12.2 Cereal origin
8.2.12.3 Future trends in cereal authentication
8.2.12.4 Genetically modified organisms
8.2.13 Food supplements
8.3 Conclusions
References
9 Food traceability
9.1 Introduction
9.2 Traceability systems
9.2.1 Document-based systems
9.2.2 Information and communication technology
9.2.3 Alphanumerical codes
9.2.4 Barcodes
9.2.5 Holograms
9.2.6 Radio-frequency identification
9.2.7 Nanotechnology
9.2.8 Nuclear techniques
9.3 Traceability analysis
9.3.1 Immunoassays
9.3.2 DNA-polymerase chain reaction methods
9.3.3 Omics
9.3.4 Isotope ratio analysis
9.3.4.1 Meat
9.3.4.2 Cereals
9.3.4.3 Olive oil
9.3.4.4 Dairy products
9.3.4.5 Wine
9.4 Conclusions
References
10 Targeted and untargeted analytical techniques coupled with chemometric tools for the evaluation of the quality and authe...
10.1 Introduction
10.2 Rheological methods
10.3 Chromatographic techniques
10.3.1 High-performance liquid chromatography
10.3.2 Gas chromatography
10.4 Thermal analysis methods
10.5 Fluorescence spectroscopy
10.5.1 Milk and milk products
10.5.2 Meat and meat products
10.5.3 Fish and fish products
10.5.4 Edible oils
10.5.5 Cereals and cereal products
10.6 Mid-infrared spectroscopy
10.6.1 Dairy products
10.6.2 Meat and meat products
10.6.3 Cereals and cereal products
10.6.4 Edible oils
10.6.5 Sugar and honey
10.7 Visible and near-infrared
10.7.1 Milk and dairy products
10.7.2 Meat and meat products
10.7.3 Fish and fish products
10.8 Nuclear magnetic resonance
10.8.1 Milk and milk products
10.8.2 Meat and fish products
10.9 Microscopic methods
10.10 Conclusion
List of abbreviation
References
11 Food pathogens
11.1 Genome sequencing
11.1.1 Resequencing
11.1.2 De novo sequencing
11.2 RNA sequencing
11.3 Bioinformatics analysis
11.3.1 Bioinformatics analysis of genomic data
11.3.2 Bioinformatics analysis of RNA sequencing data
11.4 The application of innovative analysis on Enterobacter
11.4.1 Food pathogen Escherichia coli
11.4.2 Food pathogen Cronobacter sakazakii
11.4.3 Application of genome sequencing and bioinformatics analysis on Cronobacter sakazakii
11.4.4 Application of RNA sequencing and bioinformatics analysis on Cronobacter sakazakii
11.5 The application of innovative analysis on Staphylococcus aureus
11.5.1 Food pathogen Staphylococcus aureus
11.5.2 Application of RNA sequencing and bioinformatics analysis
11.6 The application of innovative analysis on Pseudomonas
11.6.1 Food pathogen Pseudomonas
11.6.2 Food spoilage bacteria Pseudomonas
11.6.3 Application of genome sequencing and bioinformatics analysis on Pseudomonas aeruginosa
11.6.4 Application of genome sequencing and bioinformatics analysis on Pseudomonas putida
11.7 The application of innovative analysis on Bacillus
11.7.1 Food spoilage bacteria Bacillus
11.7.2 Application of genome sequencing and bioinformatics analysis on Bacillus cereus
11.7.3 Application of genome sequencing and bioinformatics analysis on Bacillus thuringiensis
11.8 The application of innovative analysis on lactic acid bacteria
11.8.1 Food spoilage lactic acid bacteria
11.8.2 Application of genome sequencing and bioinformatics analysis on Lactobacillus acetotolerans
11.8.3 Application of genome sequencing and bioinformatics analysis on Lactobacillus casei
11.8.4 Application of genome sequencing and bioinformatics analysis on Lactobacillus harbinensis
11.8.5 Application of RNA sequencing and bioinformatics analysis on Lactobacillus acetotolerans
11.9 Analysis strategy for food pathogens
11.10 Conclusion
References
Further reading
12 Sensory analysis using electronic tongues
12.1 Electrochemical sensors in sensory analysis
12.2 Electrochemical devices
12.2.1 General principles
12.2.1.1 Potentiometry
12.2.1.2 Voltammetry and amperometry
12.2.1.3 Impedance spectroscopy
12.2.2 Electronic tongues and sensor arrays: design and development
12.2.2.1 Electronic tongue
12.2.2.2 BioE-tongues
12.2.2.3 Aptamers and aptasensors
12.2.3 Data processing—chemometric methods
12.2.4 Electrochemical sensor device applications
12.2.4.1 Pharmaceutical applications
12.2.4.2 Food industry application
12.2.4.3 Other applications
12.3 Conclusions and future perspectives
Acknowledgments
References
13 Hyperspectral imaging techniques for noncontact sensing of food quality
13.1 Introduction
13.2 Theory of near infrared-based techniques and fundamentals of hyperspectral imaging
13.2.1 Acquisition modes of hyperspectral images
13.2.2 Main components of the hyperspectral imaging system
13.2.3 Data handling
13.2.4 Hyperspectral image analysis
13.2.4.1 Image calibration and preprocessing
13.2.4.2 Image segmentation and extraction of useful information
13.2.4.3 Chemometrics and multivariate analysis
13.2.4.4 Spectral pretreatment
13.2.4.5 Evaluation of prediction or classification models
13.2.4.6 Application of calibrations: chemical images
13.2.5 Advantages and limitations of hyperspectral imaging
13.3 Applications of hyperspectral imaging for food quality assessment
13.3.1 Near infrared in agriculture and food research and origins of hyperspectral imaging applications
13.3.2 Applications of hyperspectral imaging for food quality assessment
13.3.3 Online applications of near infrared spectroscopy and hyperspectral imaging
13.3.4 Case studies of hyperspectral imaging applied to granular food commodities
13.3.4.1 Wheat quality inspection
13.3.4.2 Cocoa beans quality inspection
13.3.4.3 Coffee quality inspection
13.4 Future trends of hyperspectral imaging applications
13.5 Conclusions
Acknowledgments
References
Further reading
Index

Citation preview

Innovative Food Analysis

Innovative Food Analysis

Edited by Charis M. Galanakis Galanakis Laboratories, Chania, Greece King Saud University, Riyadh, Saudi Arabia Food Waste Recovery Group, Vienna, Austria

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2021 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www. elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-12-819493-5 For Information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Charlotte Cockle Acquisitions Editor: Patricia Osborn Editorial Project Manager: Lena Sparks Production Project Manager: Kumar Anbazhagan Cover Designer: Miles Hitchen Typeset by MPS Limited, Chennai, India

Contents List of contributors Preface

xi xiii

1. Compositional and nutritional analysis 1 Valentina Melini and Francesca Melini 1.1 Introduction 1 1.2 Carbohydrates 1 1.2.1 Definition of dietary carbohydrates and classification thereof 1 1.2.2 Labeling of carbohydrates in the EU 3 1.2.3 The importance of carbohydrate analysis 4 1.2.4 Traditional and emerging methods for sample preparation in carbohydrate analysis 4 1.2.5 Emerging technologies for carbohydrate analysis 6 1.2.6 Dietary fiber analysis 8 1.3 Fat and fatty acids 8 1.3.1 Definition of dietary fat and sources thereof 8 1.3.2 Labeling of fats in the EU 10 1.3.3 The importance of fat analysis 10 1.3.4 Traditional methods for fat analysis 11 1.3.5 Emerging methods for fat extraction and fractionation 12 1.3.6 Emerging technologies for fat analysis 13 1.3.7 Lipid oxidation and food analysis 15 1.4 Minerals 16 1.4.1 Definition of minerals and sources thereof 16 1.4.2 Labeling of minerals in the EU 17 1.4.3 Ash analysis 18 1.4.4 Analysis of minerals of nutritional interest 19 1.5 Proteins 21 1.5.1 Definition of dietary proteins and sources thereof 21 1.5.2 Labeling of proteins in the EU 22 1.5.3 The importance of protein analysis 22 1.5.4 Traditional methods for dietary protein analysis 22

1.5.5 New priorities in protein analysis 1.6 Water 1.6.1 Water content in foods 1.6.2 Water content determination 1.7 Conclusions References

2. Bioactive component analysis

26 30 30 30 31 31

41

Senem Kamiloglu, Merve Tomas, Tugba Ozdal, Perihan Yolci-Omeroglu and Esra Capanoglu 2.1 Introduction 41 2.2 Polyphenols 41 2.2.1 Sample pretreatment 42 2.2.2 Extraction of polyphenols 42 2.2.3 Isolation of polyphenols 43 2.2.4 Analysis of polyphenols 43 2.3 Carotenoids 46 2.3.1 Extraction and isolation of carotenoids 46 2.3.2 Analysis of carotenoids 47 2.4 Vitamins 47 2.4.1 Pretreatment, extraction, and isolation of samples 48 2.4.2 Analysis of vitamins 48 2.5 Omega-3 fatty acids 50 2.5.1 Pretreatment and extraction of samples 50 2.5.2 Analysis of omega-3 fatty acids 52 2.6 Organic acids 54 2.6.1 Extraction and analysis of organic acids 54 2.7 Nucleosides and nucleotides 55 2.7.1 Pretreatment and extraction of sample 55 2.7.2 Analysis of nucleosides and nucleotides 56 2.8 Phytosterols 56 2.8.1 Pretreatment, extraction, and analysis of phytosterols 57 2.9 Conclusions and future perspectives 57 References 58 v

vi

Contents

3. Analytical technologies in sugar and carbohydrate processing

67

K. Ncama and L.S. Magwaza 3.1 Introduction 3.2 Analytical techniques for analyzing sugars and carbohydrates in sugar crops 3.2.1 Sugar beet 3.2.2 Sugarcane 3.2.3 Fruits 3.2.4 Maple trees 3.2.5 Sweeteners (fruits, honey, cereals) 3.3 Application of spectroscopy and chemometric data analyses for assessment of quality parameters of sugar commodities 3.3.1 Statistical terms used to measure the accuracy of visible to near-infrared radiation spectroscopy regression models 3.3.2 Procedure and necessary treatment of spectroscopic data 3.3.3 Data collection 3.3.4 Data exploratory methods 3.4 Topical methods of extracting sugars and carbohydrates from sugar crops 3.5 Novel analytical technologies for determining the sugars and carbohydrates in secondary products 3.5.1 Determination of sugars or carbohydrates from secondary products 3.5.2 Determination of syrup adulteration 3.6 Research challenges in technologies applied for assessment of sugar contents in secondary products 3.7 Future perspectives and conclusions References

4. Sample preparation methods

67 67 67 68 69 69 69

70

70 71 72 72 78

78

78 78

79 80 80

85

Renata Raina-Fulton 4.1 Introduction 4.2 Sample pretreatment techniques 4.3 Targeted sample pretreatment techniques with high specificity for target analytes 4.4 Quick, easy, cheap, effective, rugged, and safe methods 4.5 Conclusions Abbreviations References

85 86

87 91 95 95 95

5. Flow-based food analytical methods 99 Anastasios Economou 5.1 Introduction 99 5.2 Flow-based methods of analysis 99 5.2.1 Modes of flow-based analysis 99 5.2.2 Sample pretreatment using flow-based methods and hyphenated flow methods 101 5.3 Representative applications of flow-based methods to food analysis 101 5.3.1 Nutrients and antinutrients 101 5.3.2 Inorganic species 103 5.3.3 Additives, preservatives, and adulterants 106 5.3.4 Acidity 108 5.3.5 Antioxidant capacity 108 5.3.6 Pesticides 110 5.3.7 Pharmaceuticals 111 5.3.8 Miscellaneous 111 5.4 Conclusions 113 References 113

6. Categories of food additives and analytical techniques for their determination

123

Fernanda C.O.L. Martins, Michelle A. Sentanin and Djenaine De Souza 6.1 Introduction 6.2 Food additives 6.2.1 Regulators of acidity 6.2.2 Anticaking agents 6.2.3 Antifoaming agents 6.2.4 Antioxidants 6.2.5 Bulking agents 6.2.6 Carbonation agent 6.2.7 Carrier 6.2.8 Appearance control and clarifying agents 6.2.9 Colorants 6.2.10 Color retention agents 6.2.11 Emulsifiers 6.2.12 Firming agents 6.2.13 Flavor enhancers 6.2.14 Bleaching and flour treatment agents 6.2.15 Foaming agents 6.2.16 Gelling agents 6.2.17 Glazing agents 6.2.18 Humectants 6.2.19 Preservatives and antimicrobial agents

123 123 128 128 128 129 129 129 129 129 130 130 130 131 131 131 131 131 132 132 132

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Contents

6.2.20 Propellants 6.2.21 Raising agents 6.2.22 Sequestrant or chelating agents 6.2.23 Stabilizers 6.2.24 Sweetener 6.2.25 Thickener 6.3 Steps in the analysis of food additives 6.3.1 Sampling 6.3.2 Sample preparation 6.3.3 Sample pretreatments 6.3.4 Analytical techniques choices 6.3.5 Analytical parameters or validation parameters 6.4 Analytical techniques used in food additive analysis 6.4.1 Spectroscopic techniques 6.4.2 Chromatographic techniques 6.4.3 Electroanalytical techniques 6.5 Conclusions Acknowledgments Dedication References

7. Analysis of food Additives

132 133 133 133 133 134 134 134 135 136 138 139 139 139 144 148 152 153 153 153

157

Long Wu 7.1 7.2 7.3 7.4

Function of food additives Classification of food additives Examples of food additives Regulation and measurements of food additives 7.5 Analysis of food additives 7.5.1 Analysis of food colorants 7.5.2 Analysis of preservatives 7.5.3 Analysis of sweeteners 7.5.4 Analysis of antioxidant 7.6 Analysis of other food additives 7.7 Prospects for the analysis of food additives References

8. Innovations in analytical methods for food authenticity

157 157 158 161 162 162 165 168 170 173 173 177

181

M. Esteki, M.J. Cardador, N. Jurado-Campos, A. Martı´n-Go´mez, L. Arce and J. Simal-Gandara 8.1 Authentication of food products 8.1.1 Traceability for preventing adulteration 8.1.2 Labeling and compositional regulations 8.2 Revision of analytical methods for food authentication

8.2.1 Coffee 8.2.2 Honey 8.2.3 Milk and dairy products 8.2.4 Alcoholic beverages 8.2.5 Nuts 8.2.6 Fruit and vegetables 8.2.7 Fruit juices 8.2.8 Herb and spices 8.2.9 Meat 8.2.10 Sea products 8.2.11 Edible vegetable oils 8.2.12 Cereals 8.2.13 Food supplements 8.3 Conclusions References

9. Food traceability

183 186 204 206 207 210 211 212 214 217 219 220 223 224 225

249

Burcu Guldiken, Simge Karliga, Esra Capanoglu, Perihan Yolci-Omeroglu and Senem Kamiloglu 9.1 Introduction 9.2 Traceability systems 9.2.1 Document-based systems 9.2.2 Information and communication technology 9.2.3 Alphanumerical codes 9.2.4 Barcodes 9.2.5 Holograms 9.2.6 Radio-frequency identification 9.2.7 Nanotechnology 9.2.8 Nuclear techniques 9.3 Traceability analysis 9.3.1 Immunoassays 9.3.2 DNA-polymerase chain reaction methods 9.3.3 Omics 9.3.4 Isotope ratio analysis 9.4 Conclusions References

249 250 250 251 252 253 254 255 256 257 258 258 258 260 261 263 263

10. Targeted and untargeted analytical techniques coupled with chemometric tools for the evaluation of the quality and authenticity of food products 269 Romdhane Karoui

181 181 182 183

10.1 Introduction 10.2 Rheological methods 10.3 Chromatographic techniques 10.3.1 High-performance liquid chromatography 10.3.2 Gas chromatography 10.4 Thermal analysis methods

269 270 271 271 272 273

viii

Contents

10.5 Fluorescence spectroscopy 10.5.1 Milk and milk products 10.5.2 Meat and meat products 10.5.3 Fish and fish products 10.5.4 Edible oils 10.5.5 Cereals and cereal products 10.6 Mid-infrared spectroscopy 10.6.1 Dairy products 10.6.2 Meat and meat products 10.6.3 Cereals and cereal products 10.6.4 Edible oils 10.6.5 Sugar and honey 10.7 Visible and near-infrared 10.7.1 Milk and dairy products 10.7.2 Meat and meat products 10.7.3 Fish and fish products 10.8 Nuclear magnetic resonance 10.8.1 Milk and milk products 10.8.2 Meat and fish products 10.9 Microscopic methods 10.10 Conclusion List of abbreviation References

11. Food pathogens

273 274 277 278 279 280 280 280 282 282 282 283 283 283 284 285 285 285 286 287 288 288 288

295

Junyan Liu, Yuting Luo, Zhenbo Xu and Birthe V. Kjellerup 11.1 Genome sequencing 295 11.1.1 Resequencing 295 11.1.2 De novo sequencing 295 11.2 RNA sequencing 296 11.3 Bioinformatics analysis 296 11.3.1 Bioinformatics analysis of genomic data 296 11.3.2 Bioinformatics analysis of RNA sequencing data 298 11.4 The application of innovative analysis on Enterobacter 298 11.4.1 Food pathogen Escherichia coli 298 11.4.2 Food pathogen Cronobacter sakazakii 298 11.4.3 Application of genome sequencing and bioinformatics analysis on Cronobacter sakazakii 299 11.4.4 Application of RNA sequencing and bioinformatics analysis on Cronobacter sakazakii 300 11.5 The application of innovative analysis on Staphylococcus aureus 301 11.5.1 Food pathogen Staphylococcus aureus 301

11.5.2 Application of RNA sequencing and bioinformatics analysis 301 11.6 The application of innovative analysis on Pseudomonas 302 11.6.1 Food pathogen Pseudomonas 302 11.6.2 Food spoilage bacteria Pseudomonas 304 11.6.3 Application of genome sequencing and bioinformatics analysis on Pseudomonas aeruginosa 304 11.6.4 Application of genome sequencing and bioinformatics analysis on Pseudomonas putida 306 11.7 The application of innovative analysis on Bacillus 306 11.7.1 Food spoilage bacteria Bacillus 306 11.7.2 Application of genome sequencing and bioinformatics analysis on Bacillus cereus 307 11.7.3 Application of genome sequencing and bioinformatics analysis on Bacillus thuringiensis 307 11.8 The application of innovative analysis on lactic acid bacteria 308 11.8.1 Food spoilage lactic acid bacteria 308 11.8.2 Application of genome sequencing and bioinformatics analysis on Lactobacillus acetotolerans 309 11.8.3 Application of genome sequencing and bioinformatics analysis on Lactobacillus casei 310 11.8.4 Application of genome sequencing and bioinformatics analysis on Lactobacillus harbinensis 311 11.8.5 Application of RNA sequencing and bioinformatics analysis on Lactobacillus acetotolerans 311 11.9 Analysis strategy for food pathogens 312 11.10 Conclusion 313 References 313 Further reading 320

12. Sensory analysis using electronic tongues

323

I´tala M.G. Marx, Ana C.A. Veloso, Susana Casal, Jose´ A. Pereira and Anto´nio M. Peres 12.1 Electrochemical sensors in sensory analysis 12.2 Electrochemical devices 12.2.1 General principles 12.2.2 Electronic tongues and sensor arrays: design and development

323 325 325 327

Contents

12.2.3 Data processing—chemometric methods 12.2.4 Electrochemical sensor device applications 12.3 Conclusions and future perspectives Acknowledgments References

13. Hyperspectral imaging techniques for noncontact sensing of food quality

330 330 335 336 336

345

Nicola Caporaso, Gamal ElMasry and Pere Gou 13.1 Introduction 345 13.2 Theory of near infrared-based techniques and fundamentals of hyperspectral imaging 346 13.2.1 Acquisition modes of hyperspectral images 347 13.2.2 Main components of the hyperspectral imaging system 348 13.2.3 Data handling 353 13.2.4 Hyperspectral image analysis 354 13.2.5 Advantages and limitations of hyperspectral imaging 358

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13.3 Applications of hyperspectral imaging for food quality assessment 359 13.3.1 Near infrared in agriculture and food research and origins of hyperspectral imaging applications 359 13.3.2 Applications of hyperspectral imaging for food quality assessment 360 13.3.3 Online applications of near infrared spectroscopy and hyperspectral imaging 361 13.3.4 Case studies of hyperspectral imaging applied to granular food commodities 364 13.4 Future trends of hyperspectral imaging applications 370 13.5 Conclusions 371 Acknowledgments 371 References 371 Further reading 379 Index

381

List of Contributors L. Arce Analytical Chemistry Department, Faculty of Science, University of Cordoba, Rabanales Campus, Cordoba, Spain

Senem Kamiloglu Science and Technology Application and Research Center (BITUAM), Bursa Uludag University, Gorukle, Bursa, Turkey

Esra Capanoglu Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, Istanbul, Turkey

Simge Karliga Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, Istanbul, Turkey

Nicola Caporaso School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, United Kingdom; Campden BRI, Chipping Campden, Gloucestershire, United Kingdom; Department of Agricultural Sciences, University of Naples “Federico II,” Naples (NA), Italy

Romdhane Karoui Universite´ d’Artois, UMR BIOECOAGRO 1158, Institut Re´gional en Agroalimentaire et Biotechnologie Charles Viollette, Faculte´ des Sciences Jean-Perrin, Lens, France

M.J. Cardador Analytical Chemistry Department, Faculty of Science, University of Cordoba, Rabanales Campus, Cordoba, Spain Susana Casal LAQV-REQUIMTE, Faculty of Pharmacy, University of Porto, Porto, Portugal Djenaine De Souza Laboratory of Electroanalytical Applied to Biotechnology and Food Engineering (LEABE), Multidisciplinary Research, Science and Technology Group (RMP-TC), Uberlaˆndia Federal University, Patos de Minas, Brazil Anastasios Economou Department of Chemistry, National and Kapodistrian University of Athens, Athens, Greece Gamal ElMasry Agricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia, Egypt; Institute of Agriculture and Food Research and Technology (IRTA), Monells, Spain M. Esteki Department of Chemistry, University of Zanjan, Zanjan, Iran Pere Gou Institute of Agriculture and Food Research and Technology (IRTA), Monells, Spain Burcu Guldiken Department of Food and Bioproduct Sciences, University of Saskatchewan, Saskatoon, SK, Canada N. Jurado-Campos Analytical Chemistry Department, Faculty of Science, University of Cordoba, Rabanales Campus, Cordoba, Spain

Birthe V. Kjellerup Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA Junyan Liu School of Food Science and Engineering, Guangdong Province Key Laboratory for Green Processing of Natural Products and Product Safety, South China University of Technology, Guangzhou, China; Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA Yuting Luo School of Food Science and Engineering, Guangdong Province Key Laboratory for Green Processing of Natural Products and Product Safety, South China University of Technology, Guangzhou, China L.S. Magwaza Discipline of Crop and Horticultural Science, University of KwaZulu-Natal, Scottsville, South Africa A. Martı´n-Go´mez Analytical Chemistry Department, Faculty of Science, University of Cordoba, Rabanales Campus, Cordoba, Spain Fernanda C.O.L. Martins Laboratory of Electroanalytical Applied to Biotechnology and Food Engineering (LEABE), Multidisciplinary Research, Science and Technology Group (RMP-TC), Uberlaˆndia Federal University, Patos de Minas, Brazil ´Itala M.G. Marx Centro de Investigac¸a˜o de Montanha (CIMO), Instituto Polite´cnico de Braganc¸a, Braganc¸a, Portugal; LAQV-REQUIMTE, Faculty of Pharmacy, University of Porto, Porto, Portugal xi

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List of Contributors

Francesca Melini CREA Research Centre for Food and Nutrition, Rome, Italy Valentina Melini CREA Research Centre for Food and Nutrition, Rome, Italy K. Ncama Department of Crop Science, North West University, Mmabatho, South Africa Tugba Ozdal Department of Food Engineering, Faculty of Engineering, Istanbul Okan University, Istanbul, Turkey Jose´ A. Pereira Centro de Investigac¸a˜o de Montanha (CIMO), Instituto Polite´cnico de Braganc¸a, Braganc¸a, Portugal Anto´nio M. Peres Centro de Investigac¸a˜o de Montanha (CIMO), Instituto Polite´cnico de Braganc¸a, Braganc¸a, Portugal Renata Raina-Fulton Department of Chemistry & Biochemistry, University of Regina, Regina, SK, Canada Michelle A. Sentanin Food Analysis and Chemistry Laboratory, Chemical Engineering Faculty, Uberlaˆndia Federal University, Patos de Minas Campus, Patos de Minas, Brazil

J. Simal-Gandara Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, Ourense, Spain Merve Tomas Department of Food Engineering, Faculty of Engineering and Natural Sciences, Istanbul Sabahattin Zaim University, Istanbul, Turkey Ana C.A. Veloso Instituto Polite´cnico de Coimbra, ISEC, DEQB, Coimbra, Portugal; CEB Centre of Biological Engineering, University of Minho, Braga, Portugal Long Wu College of Bioengineering and Food, Hubei University of Technology, Wuhan, Hubei, 430068, P. R. China Zhenbo Xu School of Food Science and Engineering, Guangdong Province Key Laboratory for Green Processing of Natural Products and Product Safety, South China University of Technology, Guangzhou, China; College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA Perihan Yolci-Omeroglu Department of Food Engineering, Faculty of Agriculture, Bursa Uludag University, Bursa, Turkey

Preface Nowadays, food analysis includes the development of sensitive, effective, and robust methodologies in order to ensure foods’ quality, traceability, and safety as well as to comply with legislation and meet the demands of consumers. The classic techniques of wet chemistry have in many cases been replaced with instrumental ones that are able to reduce the detection limits, improve precision and accuracy, and enhance sample throughput. It is thus important to understand deeper the recent advances in detecting and determining food components. Subsequently, food chemists and analysts need more insights on new techniques, their advantages and disadvantages, and, of course, assistance in practical issues. Food Waste Recovery Group has published numerous books that deal with sustainable food systems, food waste recovery technologies, bio-based products and industries, valorization of different food processing by-products (e.g., from olive, grape, cereals, coffee, and meat), saving food actions, innovation in traditional foods, nutraceuticals and nonthermal processing, and innovation strategies in the food and environmental science. The group has also provided insights into shelf life and food quality, nonalcoholic drinks, personalized nutrition, as well as detailed guides for food components such as carotenoids, polyphenols, lipids, glucosinolates, dietary fiber, and proteins. The current book covers new trends and analytical techniques in food analysis. It aims at supporting chemists, analysts, scientists, and professionals that develop new methodologies in the analytical laboratories. The book consists of 13 Chapters. Chapter 1 deals with compositional and nutritional analysis. In particular, conventional and emerging analytical methods for the determination of carbohydrates, dietary fiber, lipids and fatty acids, proteins and amino acids, minerals, and water content are described. The newly developed methods are fast, demand little or no sample preparation, are not destructive for the sample, generate no risks to the operator, and produce no toxic waste. In Chapter 2, spectrophotometric, fluorometric, chromatographic, enzymatic, and electrophoretic methods that are used to analyze bioactive compounds among others are presented along with the required pretreatments. In addition, the advantages and disadvantages of the existing analysis methods are highlighted. Chapter 3 provides more target information on common analytical methods for the determination of sugars and carbohydrates, their efficacy and limitations, extent of application, and future perspectives that need research attention to advance the industry. Chapter 4 reviews sample preparation methods used for foods from obtaining a subsample for subsequent analysis to extraction and cleanup of extracts for both multiresidue and targeted analysis. Modified QuEChERS continue to have wide use in food analysis with a large range of modifications including cryogenic processing, selection of salts, organic solvent, buffers, or other conditions selected for phase separation or to enhance recoveries. Chapter 5 presents an overview of flow-based methods for food analysis. It briefly describes the main operational modes of flow analysis and brings together several representative applications for the determination of nutrients (sugars, amino acids, and vitamins), antinutrients, inorganic species (cations and anions), additives, preservatives, adulterants, pesticides, acidity, antioxidant capacity, pharmaceuticals, and several other compounds in food samples. Chapter 6 deals with the different categories of food additives, indicating their respective functions, main compounds in each class, foodstuff applications, and possible adverse human health effects. In addition, it introduces analytical techniques for their determination. In Chapter 7, traditional and new rapid analytical methods for the analysis of food additives are discussed in detail, with an ultimate goal to assist readers to facilitate food safety and quality in compliance with legislation and consumers’ demands. Classification of different food products is based on authenticity indicators, providing insight into future developments. To this line, Chapter 8 explores current critical concepts of foods’ traceability and labeling, followed by a systematic discrimination of authentication on different food products. It also presents common analytical techniques used for authenticity assessment, including their operation, advantages, and drawbacks. Chapter 9 deals with food traceability techniques including document-based systems, information and communication technologies, alphanumerical codes, barcodes, holograms, radio-frequency identification (RFID), nuclear techniques, and nanotechnology. In addition, immunoassays, DNA-PCR methods, omics, and isotope ratio analysis are also highlighted.

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Chapter 10 explores targeted and untargeted analytical techniques coupled with chemometric tools for the evaluation of the quality and authenticity of food products. This chapter starts by presenting some traditional methods comprising textural, high-performance liquid chromatography and gas chromatography. The spectroscopic techniques determining the structure at the molecular level, namely, front face fluorescence, near-infrared, and mid-infrared spectroscopies and nuclear magnetic resonance and microscopic levels such as X-ray tomography, scanning electron microscopy, and transmission electron microscopy are presented. Chapter 11 describes the innovative analysis strategies for food pathogens and spoilage microorganisms, mainly including genome sequencing, RNA sequencing, and bioinformatics analysis. It describes the different steps during genome sequencing, RNA sequencing, and bioinformatics analysis prior to explaining their application on different food pathogens and spoilage microorganisms, including Enterobacter, Staphylococcus, Pseudomonas, Bacillus, and lactic acid bacteria. Chapter 12 discusses the main research advances reported in the last decade regarding the electronic tongues’ applications as taste sensors, being focused on the operating principles and types of devices. The main advantages and limitations of these fast, accurate, bioinspired potentiometric, voltammetric, and/or amperometric green sensor-based tools are addressed, aiming to make an overview of the recent and future challenges toward industrial and commercial applications. Finally, Chapter 13 describes hyperspectral imaging techniques for noncontact sensing of food quality. The theory, fundamentals, and principles of such a system and all accompanying methods associated with the development of robust image processing algorithms of hyperspectral images are explored and reported. In conclusion, this book assists food chemists, analysts, and scientists working with food analysis, quality assurance, and safety, as well as researchers, academics, food technologists, and new product developers working in the food and laboratory sector. It could be used for ancillary reading in undergraduates and postgraduate level multidiscipline courses dealing with food chemistry, foodomics, food analytics, food science and technology, and food and nutrition. I would like to acknowledge and thank one by one all authors for their fruitful collaboration. I highly appreciate their acceptance of my invitation as well as their harmonization with guidelines and timeline schedule. I am fortunate to have had the opportunity to work together with different experts from so many countries, namely, Brazil, Canada, China, Egypt, France, Greece, Italy, Iran, Portugal, South Africa, Spain, Turkey, and United Kingdom. I would also like to thank the acquisition editor Patricia Osborn, the book manager Laura Okidi, and Elsevier’s publication team for their help during production of this book. Last but not least, a message for every single reader. Such collaborative editing project contains hundreds and thousands of words and the final manuscript could contain some errors. Comments and suggestions are always welcome, so please do not hesitate to contact me to discuss relevant issues.

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Charis M. Galanakis1,2,3 Research & Innovation Department, Galanakis Laboratories, Chania, Greece, 2 College of Sciences, King Saud University, Riyadh, Saudi Arabia, 3 Food Waste Recovery Group, ISEKI Food Association, Vienna, Austria [email protected]

Chapter 1

Compositional and nutritional analysis Valentina Melini and Francesca Melini CREA Research Centre for Food and Nutrition, Rome, Italy

1.1

Introduction

Food composition and nutritional analysis aims at providing data on the content of macro- and micronutrients, and/or other components in food products. It is the cornerstone of food composition databases, nutrition science and labeling, public health policies, and food quality and safety assessment. Basically, food composition data are necessary for nutritional labeling of prepacked foods, and nutrition label is a tool that enables consumers to make informed and conscious choices when buying foods. Food composition data are also included in food composition databases, which are basic elements in assessing the nutritional adequacy of a diet and in evaluating population nutritional status. Hence, food composition data are essential tools in nutrition science and in the development of public health policies. Food composition analysis also contributes to evaluating the safety of a product. Some components are not admitted by law or can be present in amounts higher than safe thresholds, other components may also form during processing and storage. Detection of these compounds can thus be used as a tool to evaluate what processing the food underwent or the occurrence of degradation and spoilage. A new frontier of food analysis is guaranteeing that allergens occur at concentrations lower than those harmful for human health and established by law. Hence, the concept of food analysis has increasingly grown in complexity and has followed up on new public health issues, new international regulations and standards, emerging consumer demands, novel safety emergencies, and globalization of the food market. Methods based on wet-chemistry are currently outdated, and have been increasingly replaced by powerful instrumental techniques that enable significant enhancements in analytical accuracy, precision, and detection limits. The emerging technologies overcome the main disadvantages of conventional methods, such as laborious sample preparation, time-consuming analysis, and production of great amount of toxic wastes, so as to comply with the principles of green chemistry. In this chapter, an overview of the conventional and emerging methods mostly applied to the analysis of macronutrients and minerals is provided, with a focus on green techniques and some of the current issues that food analysis has to face.

1.2

Carbohydrates

1.2.1 Definition of dietary carbohydrates and classification thereof Carbohydrates are organic compounds chemically composed of carbon, hydrogen, and oxygen. They are produced by plants from carbon dioxide (CO2) and water, using energy harnessed from sunlight. Dietary carbohydrates are almost exclusively from plants. Cereals, legumes, potatoes, roots, fruit, and vegetables are the major sources of food carbohydrates. Milk is a valuable source, as well. Besides occurring as natural food components, carbohydrates can be added to food products to improve the texture and quality thereof. Carbohydrates provide foods with a number of peculiar properties. They are responsible for the sweet taste of foods and for the generation of flavors and aromas of bakery products; they provide stability to emulsions and foams, and to freezing and thawing; they have gelling properties and provide desirable textures, such as crispness or smooth. Moreover, thanks to their ability of lowering water activity, carbohydrates improve food shelf life. Dietary carbohydrates may have different physiological fates, depending on their chemical identity and/or their exposure to food processing. In human body, they take part in several physiological functions: Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00001-7 Copyright © 2021 Elsevier Inc. All rights reserved.

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provide energy; partake in the control of blood glucose and insulin metabolism; contribute to satiety and gastric emptying and participate in the metabolism of cholesterol and triglyceride (TG); and influence gastrointestinal processes, such as laxation and fermentation.

Carbohydrates can be primarily classified according to their chemical features (Gerschenson, Rojas, & Fissore, 2017). Based on their degree of polymerization (DP), they are categorized into three groups: sugars, oligosaccharides, and polysaccharides, as shown in Table 1.1. Sugars have a DP ranging from 1 to 2 and comprise: 1. monosaccharides, such as glucose, galactose, and fructose; 2. disaccharides, such as sucrose, lactose, maltose, and trehalose; and 3. polyols, such as sorbitol and mannitol. The DP of oligosaccharides ranges from 3 to 9. They include maltooligosaccharides, mainly obtained from starch hydrolysis, and other oligosaccharides such as raffinose, stachyose, and fructooligosaccharides (FOS). Polysaccharides have a DP higher than 9. They may be subgrouped into starch and nonstarch polysaccharides. The formers comprise amylose, amylopectin, and modified starches, while the latters include a varied group of compounds, such as cellulose, hemicellulose, pectins, and hydrocolloids that are not digested by human body. While the Joint FAO/WHO Expert Consultation Report (1998) includes polyols in the sugar group, Regulation (EU) No. 1169/2011 clearly states that polyols are excluded from “sugars”. Hence, the definition of sugar includes only monosaccharides and disaccharides. Food polyols may be naturally occurring or chemically synthesized. The latter are used as sweeteners, since they are not absorbed in the small intestine (Lunn & Buttriss, 2007). Pertaining to their physiological and nutritional role, carbohydrates can be classified into “available carbohydrates” and “unavailable carbohydrates”. Carbohydrates that are hydrolyzed to monosaccharides, are absorbed in the small intestine, and enter the pathways of carbohydrate metabolism, thanks to the activity of enzymes in the human gastrointestinal system, are referred to as “available carbohydrates”. They commonly comprise starch polymers. In contrast, carbohydrates that are not hydrolyzed by endogenous human enzymes and are fermented in the large intestine are referred to as “unavailable carbohydrates”. The fermentable short chain carbohydrates and polyols poorly absorbed by the small intestine are referred to as FODMAPs (Fermentable Oligosaccharides, Disaccharides, Monosaccharides and Polyols). In 2010 the European Food Safety Authority (EFSA) provided a scientific opinion on dietary reference values (DRVs) for carbohydrates and dietary fiber (DF; EFSA, 2010). In this document, carbohydrates have been classified as “glycemic carbohydrates” and “dietary fiber”. The formers refer to carbohydrates digested and absorbed in the human small intestine; monosaccharides, disaccharides, maltooligosaccharides, and starch are the main glycemic carbohydrates. The term “dietary fiber” is referred to as nondigestible carbohydrates passing to the large intestine. In the abovementioned opinion, the definitions of “sugars” and “added sugars” are also reported. The term “sugars” refers to monosaccharides and disaccharides, while “added sugars” comprise sucrose, fructose, glucose, starch hydrolysates such as glucose syrup and high-fructose syrup, and other sugar preparations used as such or added during food manufacturing (EFSA, 2010). The sum of naturally occurring sugars and added sugars is referred to as “total sugars”. According to the World Health Organization (WHO), sugars can be categorized as: (1) “intrinsic sugars”, that is, sugars naturally present in fruit, vegetables, and milk; and (2) “free sugars”, that is, mono- and disaccharides added by the manufacturer during food manufacturing or cooking, and sugars naturally present in syrups, honey, and fruit/vegetable juices that are in excess compared with the same volume of 100% fruit and vegetable juice of the same type (WHO, 2015). TABLE 1.1 Carbohydrate classification according to the degree of polymerization. Class (Degree of polymerization)

Subgroup

Sugars (1 2)

Monosaccharides

Glucose, galactose, fructose

Disaccharides

Sucrose, lactose, trehalose

Polyols

Sorbitol, mannitol

Oligosaccharides (3 9)

Polysaccharides ( . 9)

Components

Maltooligosaccharides

Maltodextrins

Other oligosaccharides

Raffinose, stachyose, fructooligosaccharides

Starch

Amylose, amylopectin, modified starches

Nonstarch polysaccharides

Cellulose, hemicellulose, pectins, hydrocolloids

Compositional and nutritional analysis Chapter | 1

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1.2.1.1 Dietary fiber So far, no single definition of DF has been agreed upon food scientists and international organizations. Many scientific communities and regulatory bodies have attempted to provide a clear definition of DF for scientific and legal purposes. Regulation (EU) No. 1169/2011 defines as “dietary fiber” carbohydrate polymers with three or more monomeric units, which are neither digested nor absorbed in the human small intestine and belong to the following: i. edible carbohydrate polymers naturally occurring in the food as consumed; ii. edible carbohydrate polymers that have been obtained from food raw material by physical, enzymatic, or chemical means and which have a beneficial physiological effect demonstrated by generally accepted scientific evidence; and iii. edible synthetic carbohydrate polymers that have a beneficial physiological effect demonstrated by generally accepted scientific evidence (EU, 2011). In 2010 in the advice on DRVs for carbohydrates and DF, EFSA defined DF as nondigestible carbohydrates and lignin, including: (1) nonstarch (polysaccharides), that is, cellulose, hemicelluloses, pectins, and hydrocolloids (i.e., gums, mucilages, and β-glucans); (2) resistant oligosaccharides, such as FOS, galactooligosaccharides, and other resistant oligosaccharides, (3) resistant starch (consisting of physically enclosed starch, some types of raw starch granules, retrograded amylose, and chemically and/or physically modified starches); and (4) lignin, associated with the DF polysaccharides (EFSA, 2010). According to Food and Drug Administration (FDA) definition, the term DF refers to “nondigestible soluble and insoluble carbohydrates, with three or more monomeric units, and lignin that are intrinsic and intact in plants; isolated or synthetic nondigestible carbohydrates, with three or more monomeric units, determined by FDA to have physiological effects that are beneficial to human health” (FDA, 2016). While the EU and FDA definitions include carbohydrate polymers of three or more monomeric units in DF, the Codex Alimentarius specifies that the number of monomers constituting the carbohydrate polymers is 10 or more. According to American Association for Clinical Chemistry (AACC) International, DF is the edible part of plants or analogous carbohydrates that are resistant to digestion and absorption in the small intestine with complete or partial fermentation in the large intestine. Polysaccharides, oligosaccharides, lignin, and associated plant substances are included in DF (AACCI, 2001). Beneficial physiological effects, such as laxation and/or blood cholesterol attenuation and/or blood glucose attenuation, are promoted by DF. This definition covers the origin, chemistry, and physiology aspects of DF. DF might be also referred to as nonstarch polysaccharides fiber or as Association of Official Agricultural Chemists (AOAC) fiber. Nonstarch polysaccharide fiber includes polysaccharides of the plant cell wall components typical of plant foods. AOAC fiber comprises nondigestible carbohydrates, such as lignin and resistant starch. In addition, the AOAC fiber includes nonstarch polysaccharide fiber and nondigestible carbohydrates that can be added as ingredients to foods. A conventional but rather outdated approach for DF classification is based on its solubility in water. Soluble fiber comprises noncellulosic polysaccharides, oligosaccharides, pectins, β-glucans, and gums. Insoluble fiber comprises cellulose, hemicellulose, and lignin (Li & Komarek, 2017). In an attempt to relate the chemical properties of fibers with their physiological effects, soluble fiber was associated with lowering and moderating cholesterol and postprandial blood glucose, while insoluble fiber was associated with improved laxation and increased fecal bulk (Slavin, 2013). However, this relationship is inconsistent: inulin and oligofructose are soluble fibers with demonstrated ability to increase fecal weight and do not appear to lower blood cholesterol (Slavin, 2013).

1.2.2 Labeling of carbohydrates in the EU 1.2.2.1 Labeling of sugars in the EU According to Regulation (EU) No. 1169/2011 of the European Parliament and of the Council on the provision of food information to consumers, the nutrition declaration must include the content of carbohydrates and sugars (EU, 2011). Nutritional claims on sugars are admitted by Regulation (EC) No. 1924/2006 (EC, 2006). The claim “Sugar-free” indicates that the product contains no more than 0.5 g of sugar per 100 g or 100 mL. The “no added sugars” claim designates products not containing any added mono- or disaccharides, nor other components used as sweetener. The claim “naturally occurring sugars” appears in products containing only naturally present sugars. When the product contains no more than 5 g of sugar per 100 g for solids or 2.5 g of sugar per 100 mL for liquid, the claim “low sugars” can be applied (EC, 2006).

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1.2.2.2 Labeling of dietary fiber in the EU In Regulation (EU) No. 1169/2006, the declaration of DF content on the nutrition label is voluntary (EU, 2011). Regulation (EU) No. 1924/2006 laid down two nutrition claims related to DF: (1) “source of fiber”; and (2) “high in fiber.” The former is applied when the product contains at least 3 g of fiber per 100 g or at least 1.5 g of fiber per 100 kcal. The latter is used when the product contains at least 6 g of fiber per 100 g or at least 3 g of fiber per 100 kcal (EC, 2006).

1.2.3 The importance of carbohydrate analysis Carbohydrates contribute to the sweetness, appearance, and textural characteristics of many foods. As far as their effects on human body are concerned, they are an important source of energy, can affect a number of physiological functions, such as blood glucose and cholesterol control, and may influence intestinal functionality. Hence, determining the concentration of carbohydrates and characterizing them are important for assessment of food quality, economic value of food products, compliance with nutritional labeling, adulteration, and efficiency of food processing.

1.2.4 Traditional and emerging methods for sample preparation in carbohydrate analysis Sample preparation depends on both the specific carbohydrate being determined and the food product being analyzed. Whichever carbohydrate is going to be determined and whichever matrix is going to be analyzed, the sample preparation in the analysis of carbohydrates is a critical step, as other nutrients, e.g., lipids and/or proteins, may interfere with their determination and quantification. Hence, the first step in carbohydrate analysis is drying to constant weight. Then, carbohydrates are separated from other food components. It is important to remove quantitatively lipids and lipidsoluble substances using a Soxhlet extractor or by extraction with petroleum ether or hexane, in order to enable a complete extraction of water-soluble carbohydrates (Nielsen, 2017). Fractionation might be required after extraction if the extracted sample is too complex to be analyzed. Several methods can be used for carbohydrate extraction and fractionation: liquid chromatography (LC) and gas chromatography (GC) are the most commonly used (Sanz & Martı´nez-Castro, 2007). These techniques are, however, time-consuming and labor-intensive, and require high volume of solvent. Currently, the new challenge in carbohydrate analysis is the use of green techniques and possibly of green solvents. An ideal green solvent should 1. 2. 3. 4.

be easily biodegradable in the environment; have low toxicity to humans and other organisms; be naturally occurring and/or produced from renewable sources; and not require traditional evaporation steps (Montan˜e´s & Tallon, 2018).

In the following paragraphs, traditional and emerging methods for sample preparation in carbohydrate analysis are presented and compared.

1.2.4.1 Carbohydrate extraction and fractionation Liquid liquid extraction and green solvents Liquid liquid extraction is a technique commonly used in carbohydrate analysis. It is based on the partition of analytes between two immiscible liquids. It requires the use of great amounts of organic solvents. Many of them are toxic, flammable, corrosive, and harmful to analysts, and also contribute to environmental pollution. Their recovery and reuse require energy-intensive distillation, as well. Green solvents, such as ionic liquids (ILs), supercritical fluids, and deep eutectic solvents, have been used in carbohydrate synthesis and in recovery thereof from biomasses and food waste (Farra´n et al., 2015). The use of ILs in carbohydrate chemistry has been reported by Prasad et al. (Prasad, Kale, Kumar, & Tiwari, 2010). Xu et al. investigated the use of ILs for the extraction of aminoglycosides from milk samples (Xu et al., 2013). As far as the application of green solvents in food analysis is regarded, few studies are reported. The IL 1-n-butyl-3-methylimidazolium chloride has been used to analyze variations in the carbohydrate composition of banana pulps in order to evaluate their ripening stage by

Compositional and nutritional analysis Chapter | 1

high-resolution 2006).

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C-nuclear magnetic resonance (NMR) spectroscopy (Fort, Swatloski, Moyna, Rogers, & Moyna,

Solid-phase extraction Solid-phase extraction (SPE) is a separation technique based on the partition of the targeted analyte between a solid phase and a liquid phase. The former is commonly a sorbent held in a column, while the latter is the sample matrix or a ¨ tles & Kartal, 2016). Appropriate SPE extraction sorbents must be selected depending on the solution of analytes (O understanding of interactions between sorbent and analyte of interest. Reverse-phase cartridges, such as octyl (C8) and octadecyl (C18) silica phases, are commonly used for carbohydrate purification: they have higher affinity for hydrophobic compounds and lower affinity for hydrophilic solutes, namely, oligosaccharides. C18 cartridges have been also ¨ tles, 2011). Ion-exchange SPE has been used for desalting oligosacapplied for the fractionation of (1 4)-α-glucans (O charide mixtures (Soria, Brok, Sanz, & Martı´nez-Castro, 2012). Robinson et al. purified milk oligosaccharides by graphitized carbon-SPE (Robinson, Colet, Tian, Poulsen, & Barile, 2018).

Supercritical fluid extraction Supercritical fluid extraction (SFE) is based on the use of fluids at pressure and temperature above their critical points, that is, at conditions where there is no distinction between the gas and liquid phases. A supercritical fluid has gas-like properties, such as diffusion, viscosity, and surface tension, and liquid-like properties, namely, density and solvation power. Thanks to that, SFE enables shorter time and higher extraction yields than conventional methods. CO2 is the solvent of choice for SFE, since its critical temperature is close to room temperature (  31 C) and its critical pressure allows operating at moderate pressures (Zhu et al., 2016). CO2 has additional advantages: it is odorless, tasteless, inert, and inexpensive. SFE by using CO2 as fluid has been widely used in the recovery of carbohydrates from food waste, despite the low polarity of this fluid (Herrero, Mendiola, Cifuentes, & Iba´n˜ez, 2010). In order to increase carbohydrate solubility, ethanol/water is used as cosolvent to obtain selective fractionations. This technique has been used to fractionate carbohydrate mixtures produced by enzymatic transglycosylation (Montan˜e´s et al., 2008; Montan˜e´s, Fornari, Olano, & Iba´n˜ez, 2012), to extract carbohydrates from barley hull (Sarkar, 2013), and to extract inulin from food plant material (Zhu et al., 2016).

Pressurized liquid extraction Pressurized liquid extraction (PLE) is a sample preparation technique based on the extraction of analytes from semisolid or solid matrices by using solvents under elevated temperature (50 C 200 C) and pressure (500 3000 psi) conditions for short time periods (5 10 min) (de la Guardia & Armenta, 2011). High temperatures and pressures increase, in fact, the extraction efficiency. In detail, high temperatures promote solubility of targeted analyte in the solvent, encourage the diffusion of the analyte to the matrix surface, and increase the diffusion rate of the analyte in the solvent. Hence, the solvation power of solvents is enhanced and the extraction rates increased. High pressures allow the solvent to remain under its boiling point and promote its penetration in the food matrix (de la Guardia & Armenta, 2011; Pico´, 2017). This technique combines the benefits of high throughput, automation, and low solvent consumption (de la Guardia & Armenta, 2011). Thanks to the small volume of organic solvent generated and the reduction of analysis time and cost, it is considered a green technique. Despite that, the presence of rather high percentages of water in the sample to be analyzed decreases the analyte extraction efficiency when using hydrophobic organic solvents, since water hampers the contact between the solvent and the analyte (de la Guardia & Armenta, 2011). PLE can be performed in both static and dynamic modes. It is also referred to as accelerated solvent extraction (ASE), pressurized solvent extraction, high-pressure solvent extraction, high-pressure high temperature solvent extraction, pressurized hot solvent extraction, and subcritical solvent extraction (Duarte, Justino, Gomes, Rocha-Santos, & Duarte, 2014). When water is used as solvent, in the condensed phase between 100 C and the critical point, the technique is referred to as subcritical water extraction, hot water extraction, pressurized hot water extraction, or high temperature water extraction (de la Guardia & Armenta, 2011; Pico´, 2017). PLE has been applied in the determination of carbohydrate content in the edible mushrooms Cordyceps (Guan, Yang, & Li, 2010). Alongside microwave extraction, PLE has been also used to isolate low-molecular-weight (LMW) carbohydrates, such as inositol, and inulin from artichoke internal bracts (Ruiz-Aceituno, Garcı´a-Sarrio´, AlonsoRodriguez, Ramos, & Sanz, 2016). It was also applied to the extraction of bioactive carbohydrates from mulberry (Morus alba) leaves (Rodrı´guez-Sa´nchez, Ruiz-Aceituno, Sanz, & Soria, 2013).

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Field flow fractionation Field flow fractionation (FFF) is a separation technique based on the interaction of the analyte with an externally generated field (possibly electric, thermal, magnetic, or gravitational), applied perpendicularly to the direction of the mobile phase flow. Basically, a laminar flow of mobile phase sweeps sample analytes within a capillary. The applied field drives the analytes into different laminar flows, depending on their size, density, and surface properties (Roda et al., 2009). In carbohydrate analysis, FFF enables to fractionate polysaccharides, such as cellulose, starch, and pullulan ¨ tles, 2011). In food analysis, FFF has been applied to determine the molecular size distribution of starches and modi(O fied celluloses, and to study protein aggregation during food processing. Qureshi and Kok reported on the application of FFF to the characterization of polysaccharides used as thickeners and dispersing agents (Qureshi & Kok, 2011).

Chromatography-based methods Chromatography-based methods are commonly applied to carbohydrate fractionation. They require the use of open columns with stationary phases, based on anion exchange, adsorption, or gel-filtration/permeation mechanisms. Few recent applications of chromatography-based methods in carbohydrate analysis are reported. Marin˜o et al. applied weak anionic-exchange chromatography in milk carbohydrate analysis (Marin˜o et al., 2011), while Jantscher-Krenn et al. used gel-filtration chromatography in the analysis of oligosaccharides and separation thereof from lactose and salts in human milk (Jantscher-Krenn, Lauwaet, et al., 2012; Jantscher-Krenn, Zherebtsov, et al., 2012). The use of gelfiltration matrices has some shortcomings: many of them, such as Sephadex and Sepharose, are themselves carbohydrates; hence, they shed carbohydrate polymers into the mobile phase. Moreover, nonspecific interactions with matrix materials can occur because of the amphipathic properties of sugars.

Membranes Ultrafiltration and nanofiltration have been applied to carbohydrate sample preparation. These techniques are based on the use of membranes selected upon the value of the molecular mass of the smallest compound retained to an extent larger than 90% (molecular weight cutoff). Recently, Mehra et al. prepared powders enriched in bovine milk oligosaccharides by membrane filtration technology (Mehra et al., 2014).

1.2.4.2 Acid hydrolysis and derivatization for traditional analysis of monosaccharides and oligosaccharides As aforesaid, high-performance liquid chromatography (HPLC) and GC are commonly used in the determination of monosaccharides. The use of these techniques allows a qualitative and quantitative characterization of samples. Compared with enzymatic methods, they enable the simultaneous separation and determination of carbohydrates in a single analysis. However, when determining the composition of polysaccharides by HPLC, a depolymerization step is required. It consists of treating samples with a strong acid and heat. In these conditions, the glycosidic bond between the monosaccharide residues is cleaved. Sulfuric acid and trifluoracetic acid are commonly used. The latter is preferred, since it can be easily removed prior to HPLC analysis. If GC is used for monosaccharide analysis, derivatization is necessary in order to make carbohydrates volatile. Neutral sugars are analyzed by GC as alditol acetates obtained by reduction and acetylation, while acidic sugars are determined by GC as trimethylsilyl or trifluoroacetyl ethers.

1.2.5 Emerging technologies for carbohydrate analysis Several analytical techniques have been applied to carbohydrate analysis: spectroscopic methods such as UV-Vis, fluorescence, infrared (IR), Raman, atomic absorption, atomic emission, NMR, and mass spectrometry (MS). Separation methods, such as LC and GC, have been used. Novel approaches such as biosensors, hyperspectral imaging (HSI), and hyphenated techniques will be discussed in this section.

1.2.5.1 Biosensors Biosensors are analytical devices consisting of a bioreceptor element, recognizing the targeted analyte and a transducer converting the biological response into a measurable electrical signal (Monosik, Stredansky, Tkac, & Sturdik, 2012). Enzymes, antibodies, DNA, and microorganisms can be used as bioreceptor elements. A broad classification categorizes biosensors into enzymatic and nonenzymatic (Hu, Sun, Pu, & Pan, 2016). Enzymatic biosensors have been used in the

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determination of sugars. Glucose oxidase (GOD) and d-fructose dehydrogenase are the most commonly used enzymes in glucose and fructose enzymatic biosensors. Invertase and mutarotase are used in sucrose enzymatic biosensors. Sucrose is hydrolyzed into fructose and glucose by an invertase, then α-d-glucose is converted into β-d-glucose by mutarotase, and finally, GOD is used (Monosik et al., 2012). Recently, the content of glucose in fruit homogenates has been detected by using an enzymatic biosensor based on GOD (Ang, Por, & Yam, 2015). Enzymatic biosensors have also been applied to the determination of glucose in raw fruits by using a continuous flow system (Vargas, Ruiz, Campuzano, Reviejo, & Pingarro´n, 2016). This method still has some drawbacks such as the need of pretreatments like homogenization, filtering, and dilution in order to obtain solutions as pure as possible. Nonenzymatic biosensors are based on synthetic biomimetic enzymes. A biosensor of a new polyvinyl acetate electrode reinforced by MnO2/CuO loaded on graphene oxide nanoparticles was developed for glucose determination (Farid, Goudini, Piri, Zamani, & Saadati, 2016).

1.2.5.2 Supercritical fluid chromatography and supercritical fluid chromatography-mass spectroscopy Supercritical fluid chromatography (SFC) is a separation technique based on the use of supercritical fluid, mostly CO2, in combination with one or more polar organic solvents, especially alcohols, used as mobile phase. SFC instrumentation is obtained adapting LC or GC systems. In SFC equipment similar to HPLC instruments, the mobile phase is a binary or ternary solution, with CO2 being the main component. The separation is commonly obtained as gradient elution. The most common detector used is the ultraviolet (UV). Compared with HPLC, SFC requires shorter time and more rapid equilibration; hence, it enables to analyze more samples in a day. In addition, lower amounts of solvents are consumed, and CO2 can be easily removed and lower energy is required in fractionation and evaporation. SFC thus meets the law requirements for environmental protection and has a reputation as “green technique.” Few shortcomings are reported: SFC pump system must have a chilled pump in order to have liquid CO2, and the system for UV detection must be under pressure (Taylor, 2010). Compared with GC, SFC requires no derivatization and enables to analyze thermally labile compounds and solutes of higher molecular weight (Montan˜e´s & Tallon, 2018). The use of mobile phases modified with a certain proportion of water allowed using SFC in more polar compounds such as carbohydrates and amino acids (Fornari & Stateva, 2015). The separation and/or analysis of carbohydrates by SFC have been reported in few studies. Lefler and Chen separated a mixture of fructose, glucose, sucrose, and neohesperidine dihydrochalcone by SFC (Lefler & Chen, 2008). Preparative scale SFC was also used to separate derivatized anomeric monosaccharides (Montan˜e´s, Rose, Tallon, & Shirazi, 2015). Coupling of SFC to MS has been applied in the determination of carbohydrates and also of oils and lipids (Kaklamanos, Aprea, & Theodoridis, 2012).

1.2.5.3 Liquid chromatography: high performance anion-exchange chromatography with pulsed amperometric detection LC methods have been extensively used in the determination of carbohydrates in foods (Costa & Conte-Junior, 2015; Weiß & Alt, 2017). Silica-based amino-bonded, polymer-based and cation-exchange columns with refractive index or low wavelength UV detection have been used. However, these methods preclude the use of gradients and require sample acid hydrolysis and stringent clean up prior to injection. Moreover, they have a low sensitivity. High-performance anion-exchange chromatography with pulsed amperometric detection (HPAE-PAD) allows overcoming some drawbacks of traditional LC methods used in carbohydrate analysis: it enables to quantify carbohydrates in their nonderivatized forms, at low picomole levels, with minimal sample preparation and clean up. It is extremely carbohydrate selective and specific: only carbohydrates containing functional groups that are oxidable at the targeted detection voltage are detected. Moreover, neutral or cationic sample components, other than carbohydrates, elute in or close to the void volume of the column; hence, there are no interferences with carbohydrates of interest. Since derivatization is unnecessary, sample preparation requires only the removal of interfering components such as lipids and proteins. In HPAE-PAD systems for carbohydrate analysis, columns are coated with exchange resins, and sodium hydroxide is used as eluant to separate mono- and disaccharides. Sodium hydroxide is inexpensive and relatively safe. In oligosaccharide analysis, sodium acetate is used alongside sodium hydroxide. The former increases ionic strength and ensures proper pushing off of oligosaccharides from the column. The separation is based on the ability of carbohydrates to ionize in a strong alkaline environment. Moreover, the use of PAD enables to achieve low detection limits and to use gradient elution. HPAE-PAD also allows the separation of neutral and acidic monosaccharides in one analytical run.

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Recently, HPAE-PAD has been applied to the determination of carbohydrates in microalgae and has been found effective in resolving 13 monosaccharides (Templeton, Quinn, Van Wychen, Hyman, & Laurens, 2012).

1.2.5.4 Hyperspectral imaging HSI is a noninvasive and reagent-free spectral imaging technique, providing the spatial distribution of different components in samples with very high spectral resolution (Wu & Sun, 2013). One of the peculiar properties of HSI is its ability to display the metabolite changes; hence, it can be applied in monitoring compositional changes in foods. The use of HSI systems as a scientific tool for quality assessment of fruit and vegetables has been reviewed by Lorente et al. (2012). More recently, Nogales-Bueno et al. described the application of near-infrared (NIR) HSI to assess the sugar content of red and white grapes (Nogales-Bueno, Herna´ndez-Hierro, Rodrı´guez-Pulido, & Heredia, 2014). Hu et al. developed a model based on HSI to assess blueberry postharvest quality (Hu, Dong, & Liu, 2016).

1.2.6 Dietary fiber analysis The analysis of DF is intimately related to its definition. So far, agreement on DF definition has not been achieved by scientific bodies and regulatory committees, and different components have been included in or excluded from DF. Hence, several official methods have been developed by the AOAC, today AOAC International, and by the AACC, each complying with a proper DF definition and periodically revised. They can be broadly categorized into: (1) enzymatic-gravimetric methods; and (2) enzymatic-chemical methods including the use of colorimetry, GC, and HPLC for quantification (Table 1.2). Enzymatic-gravimetric methods are commonly used for routine analyses, since they are simple, fast, and robust. Total, soluble and insoluble DFs can be determined. These methods involve sample treatment with a succession of enzymes to remove digestible material. Then, an ethanol precipitation enables to isolate nonstarch polysaccharides. Finally, ash and protein are determined and their content is subtracted from the dry residue weight to obtain total DF. The main shortcoming of enzymatic-gravimetric methods is the lack of information about the different DF components. In contrast, enzymatic-chemical assays, based on GC and HPLC, enable to determine all sorts of DF. Methods allowing the determination of compounds that behave physiologically as DF but are soluble in aqueous ethanol, such as fructans and polydextrose, have been selectively developed (AOAC Official method 997.08 and AOAC Official method 999.03 for fructans and AOAC Official method 2000.11). They are reported in Table 1.2 and enable a proper estimation of DF content. The most widely used method for DF analysis is the AOAC method 985.29. It requires sample treatment with 80% ethanol. As a consequence, digestible carbohydrates are solubilized in aqueous ethanol, while indigestible carbohydrates form a residue. Fructans are partially soluble in ethanol 80%; hence, they might be excluded from DF determination despite they can increase fecal bulk (Cui, 2005). Due to the partial solubility of fructans in ethanol, inulinase is used in order to completely exclude fructans and prevent them from being counted twice and overestimate DF content. Whichever official method is used, DF analysis is labor-intensive, is time-consuming, and requires skilled technicians, besides a large amount of laboratory space and glassware. Efforts for automation of this analysis have been done. Bolen et al. reported on the effectiveness of ANKOMTDF Dietary Fiber Analyzer to automate the AOAC Method 991.43 fiber analysis (Bolen, Patel, Mui, Kasturi, & Challa, 2018) and on the validation thereof. The AOAC Official Method 991.43 enables to determine total dietary fiber, insoluble dietary fiber, and soluble dietary fiber in food products. Despite being one of the most commonly used methods among the scientific community, it requires several steps, such as sample homogenization, desugaring, defatting, enzymatic digestion of starch and protein, and protein and ash measurements, and an analysis may take from 3 to 7 days to complete. Barrington Analytical Laboratory has partnered with ANKOM Technology (Macedon, NY, United States) for the automation of this complex analytical procedure and developed the ANKOMTDF analyzer. It enables to increase productivity and efficiency, to simplify sample handling, to lower glassware clean up, to simplify analyst training, and to increase laboratory safety.

1.3 Fat and fatty acids 1.3.1 Definition of dietary fat and sources thereof Dietary fats are food components playing several important roles in human body. They supply energy (about 9 kcal/g), act as structural components of cell membranes, are precursors of some hormones, bile acids, and bioactive compounds,

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TABLE 1.2 Methods for dietary fiber analysis. AOAC official method

AACC approved method

Method categorization

Method description

985.29

32-05.01

Enzymatic-gravimetric method

Determination of TDF in cereal grains and grain-based products

32-06.01

Gravimetric method

Determination of TDF

991.42, 992.16

Enzymatic-gravimetric method

Determination of IDF in fruits, vegetables, and cereal grains

993.19

Enzymatic-gravimetric method

Determination of SDF

991.43

32-07.01

Enzymatic-gravimetric method

Determination of TDF, IDF, and SDF in grain and cereal products, processed foods, fruits, and vegetables

2002.02

32-40.01

Enzymatic method

Determination of RS2 and RS3 in food products and plant materials

32-21.01

Enzymatic-gravimetric method

Determination of SDF and IDF in oats and oat products

32-32.01

Enzymaticspectrophotometric method

Determination of total fructan (inulin and FOS)

Enzymaticspectrophotometric method

Determination of fructan (inulin)

999.03 997.08

32-31.01

AE-HPLC method

Determination of fructan in foods and food products (applicable to the determination of added inulin in processed foods)

2000.11

32-28.02

AE-HPLC method

Determination of polydextrose in foods

32-22.01

Enzymatic method

Determination of β-glucan in oat fractions and unsweetened oat cereals

32-23.01

Enzymatic method

Determination of β-glucan content in oat and barley

2001.03

32-41.01

Enzymatic-gravimetric and HPLC method

Determination of DF containing added resistant maltodextrin

2001.02

32-33.01

HPLC method

Determination of TGOS. Applicable to added TGOS in foods

2009.01

32-45.01

Enzymatic-gravimetric and HPLC method

Determination of high-molecular-weight and low-molecular-weight soluble DFs

2011.25

32-50.01

Enzymatic-gravimetric and HPLC method

Determination of TDF, SDF, and IDF according to Codex Alimentarius definition

AACC, American Association for Clinical Chemistry; AOAC, Association of Official Agricultural Chemists; FOS, fructooligosaccharides; IDF, insoluble dietary fiber; RS2, type-2 resistant starch; RS3, type-3 resistant starch; SDF, soluble dietary fiber; TDF, total dietary fiber; TGOS, trans-galactooligosaccharides.

act as carriers for nutrients, such as fat-soluble vitamins, and take part in many vital processes. The influence of dietary fats on inflammatory gene expression has been also reported (Rocha et al., 2017). Dietary fats mainly consist of TGs (or triacylglycerols), mono- and diglycerides, phospholipids, and cholesterol (Erdman, MacDonald, & Zeisel, 2012). From a structural point of view, TGs are composed of three fatty acids (FAs) attached to a glycerol backbone. Based on the number of carbon atoms and number of double bonds they possess, TGs can be classified into: (1) saturated fatty acids (SFA); and (2) unsaturated FAs. The former can be further categorized into short, medium, long, and very long chain FAs, based on their chain length. Depending on the number of double bonds, unsaturated FAs can be subgrouped as: (1) monounsaturated FAs (MUFAs), which display only one double bond; and (2) polyunsaturated FAs (PUFAs), which have two or more double bonds. Some PUFA, such as alpha-linolenic acid (ALA) and LA, are considered “essential”, since they cannot be synthesized by human body and they must be thus supplied by the diet. Some long-chain PUFAs, such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are considered “conditionally essential”, since they can be synthesized by the human body contingent on having essential precursor FAs. Unsaturated FAs can be otherwise grouped as cis-isomers or trans-isomers, based on the geometric configuration of their double bonds. Trans-isomers are referred to as trans-FAs (TFA) and can be further divided into: (1) ruminant TFA; and (2) industrially produced TFA (iTFA). The formers are produced from unsaturated FAs in ruminant animals, due to the activity of rumen bacteria, while the latters are formed during the incomplete hydrogenation of oils, especially plant oils, into solid or semisolid fats (hydrogenated oils).

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The main sources of dietary fat are: (1) butter, margarine, and vegetable oils; (2) meat and poultry; (3) milk and dairy products; (4) egg yolk; (5) nuts; and (6) a variety of processed foods. Dietary fat intake mostly occurs in the form of TGs (IOM, 2005).

1.3.2 Labeling of fats in the EU In Europe, Regulation (EU) No. 1169/2011 on the provision of food information to consumers lays down that the nutritional label of prepacked foods mandatorily displays declaration for total and saturated fats (EU, 2011). The content must be reported as grams per 100 g of product. The presence of partially or fully hydrogenated oils must also be declared in the ingredient list, while the indication of the amounts of MUFA and PUFA is optional (EU, 2011). According to Regulation (EC) No. 1924/2006 (EC, 2006), the following fat-related nutrition claims can be reported on food packages: a. Fat free: The product contains no more than 0.5 g of fat per 100 g or 100 mL. b. Low fat: The product contains no more than 3 g of fat per 100 g for solids or 1.5 g of fat per 100 mL for liquids (1.8 g of fat per 100 mL for semiskimmed milk). c. Saturated fat free: The sum of saturated fat and TFAs does not exceed 0.1 g of saturated fat per 100 g or 100 mL. d. Low saturated fat: The sum of SFA and TFAs in the product does not exceed 1.5 g per 100 g for solids or 0.75 g/ 100 mL for liquids, and in either cases, the sum of SFA and trans-FAs must not provide more than 10% of energy. e. Source of omega-3 fatty acids: The product contains at least 0.3 g ALA per 100 g and per 100 kcal, or at least 40 mg of the sum of EPA and DHA per 100 g and per 100 kcal. f. High omega-3 fatty acids: The product contains at least 0.6 g ALA per 100 g and per 100 kcal, or at least 80 mg of the sum of EPA and DHA per 100 g and per 100 kcal. g. High unsaturated fat: At least 70% of the FAs present in the product is from unsaturated fat under the condition that unsaturated fat provides more than 20% of energy of the product. h. High monounsaturated fat: At least 45% of the FAs present in the product is from monounsaturated fat under the condition that monounsaturated fat provides more than 20% of energy of the product. i. High polyunsaturated fat: At least 45% of the FAs present in the product is from polyunsaturated fat under the condition that polyunsaturated fat provides more than 20% of energy of the product. The Report from the Commission to the European Parliament and the Council regarding trans fats in foods reports that no indication on the TFA content must be displayed (EC, 2015). However, Regulation (EU) No. 2019/649 of 24 April 2019, amending Annex III to Regulation (EU) No. 1925/2006 of the European Parliament and of the Council, harmonizes the limit for iTFA in food intended for the final consumer, across all the EU Member States. It lays down that the content of iTFA, in food intended for the final consumer, shall not exceed 2 g per 100 g of fat (EU, 2019). In fact, a high intake of iTFA has been found to increase seriously the risk of heart disease more than any other nutrient on a per calorie basis (EC, 2015).

1.3.3 The importance of fat analysis According to EFSA, the reference intake (RI) for fats should be lower than 35% (EFSA, 2017). The effect of fat intake and metabolites thereof on human health might be beneficial or detrimental depending not only on the total fat intake but also on the type of fat. Omega-3 FAs (i.e., ALA, EPA, and DHA) have beneficial effects, as they protect from the risk of Non Communicable Diseases (Abraham & Speth, 2019; Lau, Yu, & Xu, 2019). In contrast, SFA have been related to increased risk for cardiovascular disease; hence, their replacement has been recommended (Briggs, Petersen, & Kris-Etherton, 2017). Products from lipid oxidation, such as malondialdehyde or cholesterol oxides, have toxic properties on human health, as well (Jackson & Penumetcha, 2019; Vicente, Sampaio, Ferrari, & Torres, 2012). For these reasons, nutritional guidelines commonly encourage low consumption of SFA and avoidance of iTFA while promoting the consumption of omega-3 PUFA (Lawrence, 2013). As FAs are associated with different health effects, and individual FAs within each category have different biological properties, the aim of fat analysis is to determine the total content of fat in food and/or to characterize FAs, as well. The values of total content are commonly used to calculate the energy provided by a food product, while the determination of peculiar FAs enables to estimate the effect of a food product on human health. Before starting fat analysis, the analyst should choose the most suitable method. Definitely, fat analysis enables to assess the quality and safety of a product and the compliance thereof

Compositional and nutritional analysis Chapter | 1

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with legislation and consumer demands. Hence, an accurate and precise quantitative and qualitative analysis of fat in foods is crucial for nutrition labeling, for estimating fat intake, and for nutritional recommendations.

1.3.4 Traditional methods for fat analysis Fat analysis comprises both the determination of total fat content and the determination of lipid composition. Total fat content analysis aims at measuring the total amount of fats in a food product, while the characterization of fats provides information on the qualitative and quantitative composition of lipids in a food matrix.

1.3.4.1 Total fat content The estimation of total fat content is commonly accomplished by conventional analytical techniques such as Soxhlet method, Mojonnier method, or nonsolvent wet extraction methods, (e.g., Babcock and Gerber) that are specifically applied to some food matrices. These methods are based on the solubility of fats in organic solvents. The main drawbacks of these methods are laborious sample preparation, production of bulk of toxic wastes, and poorer safety for technicians. Total fat can also be determined by GC. The AOAC 996.06 method for the determination of total fat is based on a GC analysis of FA methyl esters (FAMEs). The sum of FAs is equal to fat content expressed as TG equivalents. This method prevents overestimating fat content, since lipidic compounds, other than fat, are not included in the calculation of fat content. However, GC is time-consuming and expensive; thus it is not feasible for the analysis of large number of samples.

1.3.4.2 Fat characterization Lipid components in foods include FAs, monoglycerides, diglycerides, TGs, phospholipids, sterols, and lipid-soluble pigments and vitamins. GC is the method of choice for the determination of lipid fractions. This technique requires several steps: (1) lipid extraction; (2) fractionation of lipid classes; (3) derivatization of lipid components in volatile molecules (when necessary); and (4) separation and identification. As far as the lipid extraction is concerned, the analyst may carry out an exhaustive extraction of all lipids and then separate the lipid class of interest. Otherwise, a selective extraction of lipid class of interest may be performed. Commonly, hexane, ethyl ether, and petroleum ether are used. They allow the extraction of neutral lipids, such as TG, free cholesterol (FC), and cholesterol esters, but are not suitable for phospholipids and free fatty acids (FFAs). A predrying step before lipid extraction is usually necessary for an effective extraction: in fact, ethyl ether cannot penetrate the moist food tissues due to its hydrophobicity, and petroleum ether saturates with water due to its hygroscopicity, thus resulting inefficient for lipid extraction. Moreover, sample predrying makes sample grinding easier, breaks fat-water emulsions, and contributes to increasing fat removal from tissues (Nielsen, 2017). When analyzing individual FFAs, it must be considered that FFAs differ in their solubility and volatility because of the different carbon chain length. Hence, for an accurate quantification, both water-soluble short chain FFAs and organic-soluble FFAs must be extracted. Methanol, ethanol, n-butanol, 2-butanol, isopropanol, chloroform, diethyl ether, hexane, n-eptane, and petroleum ether are used (Amores & Virto, 2019). After lipid extraction, fractionation should be performed in order to select the lipid class of interest. A total lipid extract may be fractionated in its classes by using solvents with different polarities, by thin layer chromatography, or by SPE. Silicas with different functional groups enable sophisticated lipid fractionation. Aminopropyl SPE columns are widely used for fractionation. Silver ion chromatography is an innovative technique for lipid fractionation. Basically, silver ions interact reversibly with the π electrons of double bonds. The stronger the complex is formed between the analyte and the column, the more the analyte is retained. Hence, FAs can be separated depending on the number, position, and geometry of its double bonds. The use of silver ion chromatography has been recently applied to the separation of cis/trans isomers from a Bulgarian bovine butter sample (Momchilova & Nikolova-Damyanova, 2012). After fractionation, the conversion of FAs into methyl esters, which are more volatile compounds than their counterparts, is necessary for GC analysis. Methylation of FAs to FAMEs can be obtained by both acid or basic catalysis. Acidcatalyzed methylation is performed by using boron trifluoride in methanol. The basic derivatization is carried out by sodium methoxide or potassium hydroxide in methanol (Amores & Virto, 2019). However, the latter method enables a selective conversion of acyl moieties to FAME, while other lipidic components such as FFAs are not methylated. The use of tetramethylammonium hydroxide enables the conversion of FFAs to FAME.

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Innovative Food Analysis

HPLC and capillary electrophoresis (CE) have been also used in FFA analysis. Octadecylsilyl columns are commonly used as stationary phase in FFA analysis, and acetonitrile or methanol in water is used as mobile phase (Amores & Virto, 2019). CE combines electrophoresis and chromatography. Compared with HPLC and GC, it has some advantages, such as high separation efficiency, low consumption of samples and reagents, short analysis time, rapid method development, and easy sample preparation without derivatization (Gabriel, Amelia, Cristina, & Aura, 2015). It has been used for the separation of dietary omega-3 and omega-6 FAs from flax seeds and beef muscles (Soliman et al., 2013).

1.3.5 Emerging methods for fat extraction and fractionation Several methods commonly used for fat extraction are laborious, time-consuming, and not environment friendly due to the large amount of solvents they require. ASE and SFE are greener alternatives to traditional methods.

1.3.5.1 Accelerated solvent extraction ASE is an extraction technique based on the use of high temperatures and pressures to promote the isolation of molecules from solid and semisolid matrices, such as some foods. Compared with common extraction techniques, it enables to reduce the amount of time and solvent required to achieve analyte isolation, thanks to the use of elevated temperature and pressure. The application of high temperatures increases the extraction efficiency, since at elevated temperature, solvent strength is higher, diffusion rates are faster, solvent viscosity is decreased, and the analytes are removed from the food matrix more readily, because the solute matrix interactions are disrupted more easily. However, the application of high temperature alone is not enough to obtain efficient extractions, since many of the organic solvents, commonly used in extractions, boil at relatively low temperatures. Hence, the application of high pressures enables to use extraction solvents even at temperature above their boiling point. Moreover, the application of high pressures forces the solvent into the pores of the sample matrix, promoting a close contact between the extracting solvent and the analytes to be extracted. ASE systems enable to eliminate many manual steps, thus accelerating the process and increasing the reproducibility of data. Briefly, the solid sample is loaded into a sample cell and the filled cells are loaded onto a cell tray. Each cell is transferred into the oven that is maintained at a selected operating temperature. Elevated pressures, about 1500 psi, are applied in order to maintain the solvents as liquid at temperatures above their boiling point. Single solvents or any combination thereof are pumped to the sample cells. The cell contents are heated by the oven to the operating temperature, and the extraction enters a static period lasting a time set by the user. After the static time, fresh solvent is pumped through the cell to remove the extracted analytes. The extraction usually takes less than 15 min and the amount of solvent necessary for the extraction is commonly 1.5 times the volume of the sample cell. Compared with microwave extraction, in ASE apparatus, the temperature and pressure of each cell are controlled independently regardless of the solvent used, the moisture or mineral content of the sample, or any matrix characteristic possibly affecting the actual extraction temperature. ASE has been applied to the extraction of fat from several food matrices, such as meat, dried milk products, dairy products, and chocolate. Ion exchange-based materials have also been used in ASE equipment cells to remove acid or base reagents without compromising the recovery of lipids (Ullah, Murphy, Dorich, Richter, & Srinivasan, 2011).

1.3.5.2 Supercritical fluid extraction SFE can be applied in sample preparation for the chemical analysis of fats and oils in food products. This method is based on the use of a fluid in its supercritical state. It means the fluid is used in a state above the critical temperature and critical pressure where gases and liquids can coexist. CO2 is the most popular solvent used in fat analysis. It has several advantages over other solvents. It is inexpensive, nontoxic, and nonflammable. Moreover, it has a low critical temperature; hence, it can be used to extract thermally liable molecules. CO2 can be retrieved from the environment, because it is ubiquitous and it returns clean after extraction. As a consequence, the technique matches the principles of green chemistry. Despite its several advantages, CO2 has limited solvating power and its strength is comparable with that of hexane. FFAs, cholesterol, TGs, and waxes are quite soluble in supercritical CO2, while the solubility of polar lipids, such as phospholipids, can be improved by adding small amounts of polar modifier solvents, such as ethanol, or a surfactant agent (Doane-Weideman & Liescheski, 2019; McHugh & Krukonis, 2013). From a technical point of view, the sample is placed in an extraction vessel, and the manipulation of the temperature and pressure of CO2 enables to solubilize the material of interest and to extract it selectively. Extracted analytes are then transferred to a fraction collector where the

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contents are depressurized. Hence, the CO2 loses its solvating power and causes the analyte to precipitate, while the condensed CO2 can be recycled.

1.3.6 Emerging technologies for fat analysis Conventional methods applied to fat analysis have some drawbacks, such as being time-consuming, laborious, and destructive to the analyzed samples. They require a bulk of solvents, hazardous for environment and technicians, and properly trained personnel. The need for fast and greener analysis boosts the development of innovative techniques. Currently, near-infrared spectroscopy (NIRS), Raman spectroscopy (RS), NMR, HSI, and X-ray microtomography (μCT) techniques are promising alternatives to traditional methods, due to their potential for real-time and online applications.

1.3.6.1 Infrared spectroscopy The application of the IR spectroscopy in fat analysis is based on the absorption of IR energy by fat. Generally speaking, wavelengths from 0.8 to 100 μm can be used for IR spectroscopy and three regions have been defined: the near-IR (0.8 2.5 μm; 12,500 4000 cm21), the mid-IR (2.5 15.4 μm; 4000 650 cm21), and the far-IR (15.4 100 μm; 650 100 cm21). Basically, the interaction between IR radiation and food component functional groups results in vibrations, providing a fingerprint of food components. IR spectra have bands that can be assigned to functional groups of the food components. Band positioning enables a structural characterization and its intensity correlates with the concentration. Hence, the IR spectroscopy can be used both in qualitative and quantitative applications. Transmission or reflectance spectra can be obtained. The formers are obtained when IR light passes through a sample, and the fraction absorbed by the sample is determined; the latter are based on the light reflected by the sample. Methods based on near-IR (NIR) and mid-IR have been applied in the determination of fat content and fat characterization in commodities, such as cereals, meats, and oilseeds. As far as grains and pulses are concerned, Pandey et al. developed a nondestructive method based on NIR and Fourier transform NIR for the determination of fat in wheat grains (Pandey, Mishra, & Mishra, 2018), Ferreira et al. developed partial least square (PLS) models for the quantification of lipids in quinoa seeds (Chenopodium quinoa Willd.) from NIR data (Ferreira, Pallone, & Poppi, 2015), and Hell et al. compared the performance of Fourier transformed NIR with Fourier transformed mid-IR spectroscopy for the determination of fat content in wheat bran (Hell et al., 2016). Regarding milk, both fat content and FA profile were determined by NIR spectroscopy. In detail, Revilla et al. reported on the use of NIR for predicting FA profile in ewe’s milk and showed the NIR performance was comparable with GC (Revilla, Escuredo, Gonza´lez-Martı´n, & Palacios, 2017), while Nu´n˜ez-Sa´nchez et al. applied NIR spectroscopy to the determination of FA composition in goat’s milk (Nu´n˜ez-Sa´nchez et al., 2016). Portable NIRS instruments have also been developed and their application for the in situ determination of milk composition was reported (de la RozaDelgado et al., 2017). Disagreement on the potentiality of NIR application in predicting FA composition in meat was observed. Prieto et al. reported excellent predictability of NIR data in the determination of SFA and MUFA in intact beef (Prieto et al., 2014), Mourot et al. observed great performance of NIR analysis in the determination of SFA and MUFA in homogenized beef (Mourot et al., 2015), and Su et al. found an outstanding ability of NIR spectroscopy in determining intramuscular fat content (Su et al., 2014). In contrast, Balage et al. reported a low predictability of this technique for the determination of intramuscular fat content in beef and pork (Balage, da Luz E Silva, Gomide, de Bonin, & Figueira, 2015). Online NIR equipment was also developed and applied to meat analysis (Gou et al., 2013; Pullanagari, Yule, & Agnew, 2015). Some official methods applied to food composition analysis are based on NIR. The AOAC Method 2007.04 for fat, moisture, and protein in meat and meat products is based on correlating NIR data with the values obtained from conventional methods, so as to predict the concentration of fat, moisture, and protein in a sample being tested. The AOAC Method 972.16 for the determination of milk fat content is based on mid-IR spectroscopy (AOAC Method 972.16). Compared with traditional methods, IR spectroscopy requires little or no sample preparation, no chemicals, nor consumables. Moreover, it is nondestructive, operator-friendly, fast (it takes 30 60 s), reliable, and precise.

1.3.6.2 Raman spectroscopy RS is a vibrational spectroscopic technique based on the scattering of monochromatic light. Generally speaking, sample molecules can be excited by the collision with a photon of light and reach an unstable virtual energy state. Most

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molecules relax back to their initial low energy level and the photon is scattered (elastic scattering or Rayleigh scattering). However, a few molecules relax to a higher vibrational state with a change in the vibrational and rotational energy of the molecule causing a shift in the wavelength of the scattered radiation (inelastic scattering or Raman scattering). In order to have Raman scattering, the polarizability of the molecule electron cloud must change, while there is no need to undergo a change in dipole moment. Hence, RS can observe symmetrical vibrations that IR spectroscopy cannot detect. For this reason, RS is considered complementary to IR spectroscopy. Alongside IR spectrum, the Raman spectrum represents the fingerprint of a molecule. However, IR peaks are broad and they might be affected by adjacent peaks. In contrast, Raman spectrum shows isolated bands with little interference. Each band corresponds to a vibration of a chemical bond and/or of a functional group. RS has several advantages over classical methods. It requires little or no sample preparation; hence, it is time-saving. Moreover, in contrast to IR, it can be applied to liquid foods, such as milk, juices, and alcoholic beverages, since water and alcohols are weak Raman scatterers (Tao & Ngadi, 2018). Alongside IR spectroscopy, RS methods involve the development of calibration equations or models, and other techniques are required in order to prepare them. RS has been applied to the determination of fat content and fat characterization in milk, meat, and fish or derivatives thereof. Regarding dairy products, Zhao et al. applied RS, alongside NIR and FT-mid IR, to the quantification of total TFA and iTFA in butter, Cheddar cheese, and dairy spreads (Zhao, Beattie, Fearon, O’Donnell, & Downey, 2015). ElAbassy et al. showed the capability of RS combined to PLS regression as a rapid technique for the determination of milk fat (El-Abassy, Eravuchira, Donfack, von der Kammer, & Materny, 2011). Stefanov et al. applied RS for direct semiroutine quantification of individual or grouped trans-MUFAs and conjugated linoleic acids (CLA) in milk fat (Stefanov et al., 2011). RS was also applied in online systems for monitoring of fat, protein, sucrose, and total lipids during yoghurt fermentation (Chen et al., 2019). Yazgan Karacaglar et al. used this techniques in combination with chemometrics in order to determine the adulteration of milk fat in dairy products, with cheaper nonmilk-based fats or oils (Yazgan Karacaglar, Bulat, Boyaci, & Topcu, 2019). As far as meat is concerned, RS was found effective in the analysis of fat in meat samples and in the discrimination of beef and horse meat (Boyacı et al., 2014). Lee et al. reported on the rapid detection of pork lard in beef tallow and duck oil samples by RS and on its application in assessing adulteration of lard with vegetable oils or cheaper animal fats (Lee et al., 2018). This technique was also applied to the differentiation of conventional and omega-3 FA-enriched eggs (de Oliveira Mendes, Porto, Almeida, Fantini, & Sena, 2019) and in the identification of FAs and carotenoids in goji berry oils and extracts (Pedro et al., 2019). RS can be also applied to the estimation of lipid damage during frozen hake storage (Sa´nchez-Alonso, Carmona, & Careche, 2012) and during deep frying, cooking, and baking of vegetable oils (Alvarenga, Xavier, Soares, & Carneiro, 2018).

1.3.6.3 Nuclear magnetic resonance While the fundamentals of NMR are rather complex, the application of NMR is relatively simple, thanks to the high degree of automation and computer control. Compared with other techniques, the application of NMR in food analysis provides several advantages. The measurement time is short, typically few seconds. Sample is analyzed in its natural states: it is loaded into an NMR tube and measured directly. Hence, no or minimal sample preparation is required and no solvents are used, in compliance with green chemistry principles. Compared with Sohxlet, it is faster and does not require solvents or skilled operator. In contrast to NIR, NMR is insensitive to sample granularity and it requires infrequent recalibration. Moreover, it is a nondestructive technique, and thus measurements can be repeated. The AOAC Method 2008.06 applies NMR to the determination of fat in meat and meat products. Several other applications have been reported in scientific literature. 1H NMR was used in the determination of CLA content in Canadian cheeses from conventional, organic, and grass-fed dairy sources (Prema et al., 2013). It was also used as part of metabolomic and lipidomic analysis (Li, Vosegaard, & Guo, 2017), despite that, compared with MS, it has some limitations: 1. It is less sensitive. 2. Crowded 1H spectra are obtained, hampering the discrimination of resonances from different components in complex mixtures. 3. Saturated fatty acyl residues are poorly differentiated. Nevertheless, Go´mez-Gallego et al. identified up to 68 metabolites in human breast milk (Go´mez-Gallego et al., 2018) by using NMR, and O’Callaghan et al. quantified 49 metabolites in cow’s milk samples, included short-chain FAs (O’Callaghan et al., 2018).

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1.3.6.4 X-ray microtomography X-ray μCT is an emerging 3-D imaging technique based on the same principles as computed tomography used in medical domain and showing higher resolution. It enables to obtain information on internal structures of several food matrices, such as grain kernels, biscuits, and bread. Compared with conventional imaging techniques such as scanning electron microscopy, it is nondestructive and does not require sample cutting to expose cross section to be analyzed. Xray μCT is based on the differences in X-ray attenuation resulting from differences in density within the food matrix. This technique has been applied to the determination of fat content in meat products. It is based on the different X-ray absorption between lean meat and fat. In detail, lean meat shows a higher X-ray absorption than fat (Nielsen, 2017). The application of this technique in fat analysis has been reported also by Laverse et al. who studied five types of Italian salami exhibiting different structures of fat (Laverse, Frisullo, Conte, & Del Nobile, 2012). The percentage of fat content was determined by using X-ray μCT and chemical analysis. No statistical differences among the results obtained by applying the methods under consideration were observed. Compared with NIR, X-ray μCT is faster. It can measure entire batches of meat by providing accurate determination of total fat content and has been also used in inline analysis of meats. X-ray-based instruments have also been developed for the rapid determination of fat content in meat products, such as the MeatMaster II fat analysis instrument (Foss, Eden Prairie, MN, United States).

1.3.6.5 Hyperspectral imaging Generally speaking, HSI is an emerging technology integrating spectroscopic and imaging techniques to obtain simultaneously spectral and spatial information on an object (Tao & Ngadi, 2018). Hyperspectral images, also known as hypercubes, are 3D images, with two spatial dimensions and one spectral dimension. Hence, spectral information at each pixel of the hyperspectral image is obtained as well as the image information at each wavelength. An HSI system commonly contains objective lens, spectrograph, camera, acquisition system, translation stage, illumination, and computer (Gowen, O’Donnell, Cullen, Downey, & Frias, 2007). Like other spectroscopy techniques, HSI can be carried out in reflectance, transmission, or fluorescence modes. Compared with traditional methods, HSI enables to characterize surface color food and texture and also the chemical composition. It is suitable for quick screening of large sample sets. HSI has been applied to the determination of fat content, FA characterization, and evaluation of marbling scores in red meats. In contrast to spectroscopic techniques such as NIRS, RS, and NMR, HSI enables to evaluate the spatial distribution of white flecks of fat. The application of NIR HSI for the prediction of chemical composition in lamb and pork meat has been reported by Kamruzzaman et al. and Barbin et al., respectively (Barbin, ElMasry, Sun, & Allen, 2013; Kamruzzaman, ElMasry, Sun, & Allen, 2012). HSI has been also used to determine, nondestructively, the intramuscular fat content in pork (Huang, Liu, Ngadi, & Garie´py, 2014; Liu & Ngadi, 2014). As far as the application of HSI in the determination of FA composition is concerned, few studies are available. Kobayashi et al. reported on the determination of FA profile of beef by NIR HSI (Kobayashi, Matsui, Maebuchi, Toyota, & Nakauchi, 2010).

1.3.7 Lipid oxidation and food analysis Edible oils and foods containing unsaturated lipids may undergo lipid oxidation, one of the major deteriorative reactions leading to the formation of rancid odors, off-flavors, and potentially toxic compounds, and to nutritional losses, as well. Monitoring lipid oxidation from food manufacturing to storage and use is important for the sensory quality of foods and also for evaluation of hazards for human consumption. Three different pathways for lipid oxidation have been reported: (1) the radical mechanism (or autoxidation); (2) the singlet oxygen-mediated mechanism (or photooxidation); and (3) the enzymatic oxidation, catalyzed by lipoxygenases (Barriuso, Astiasara´n, & Ansorena, 2013). Peroxides are the primary products of lipid oxidation. When they are exposed to extended oxidation conditions, secondary products, such as aldehydes, ketones, epoxides, hydroxycompounds, oligomers, and polymers, form. UV-Vis spectroscopic and chromatographic methods have been traditionally used for the determination of primary and secondary lipid oxidation products. However, in the last decades, chemiluminescence, fluorescence spectroscopy, IR, RS, NMR, and electron magnetic resonance have been reported as alternative methodologies in assessing and/or monitoring lipid oxidation. Chemiluminescence was used for the evaluation of the thermal oxidative stability of red pepper seed oil added with capsaicin or tocopherol as antioxidants (Yang et al., 2010). Guille´n and Goicoechea reported on the detection of primary and secondary oxidation products by both Fourier transform infrared spectroscopy and 1HNMR in sunflower oil during storage (Guille´n & Goicoechea, 2007). RS has been widely applied to the determination of lipid oxidation. Guzma´n et al. applied low-resolution RS for monitoring the oxidation status of four olive oil samples (Guzma´n, Baeten, Ferna´ndez Pierna, & Garcı´a-Mesa, 2011),

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while Zhang et al. detected the level of malondialdehyde, a lipid oxidation marker, with surface-enhanced RS (Zhang et al., 2010). The use of 1H and 13C NMR spectroscopy has been extensively proved as a valuable tool for quantifying lipid oxidation in foods (Alonso-Salces, Holland, & Guillou, 2011; Guille´n & Uriarte, 2012a, 2012b; Scano et al., 2011).

1.4 Minerals 1.4.1 Definition of minerals and sources thereof Minerals are, together with vitamins, micronutrients, that is, nutrients that the human body requires in relatively small quantities (milligram and microgram amounts). They are the constituents that remain under the form of ash after combustion of plant and animal tissues. They can be classified into: (1) main elements; (2) trace elements; and (3) ultratrace elements (Table 1.3). Minerals play key roles in human body. Some of them are found in the structure of teeth (Ca, P, and F) and bones (Ca, Mg, Mn, P, B, and F), and/or are involved in immune (Ca, Mg, Cu, Se, and Zn) and brain (Cr and Mn) system functionality. Macroelements (Ca, Mg, P, Na, and K) have more significant functions in nerve cells (transmission and signaling) than microelements. In addition, macrominerals (e.g., Ca and K) have a high potential to control blood pressure, while microminerals have a role in the formation of erythrocyte cells (Co, I, and Fe), regulation of the glucose levels (Cr), and their protection via activation of antioxidant enzymes (Mo). Most microelements (Cu, Fe, Mn, Mg, Se, and Zn) also play a major role as a structural part in many enzymes. Food is the primary source of minerals for humans, and different plant and animal sources should be consumed for a balanced intake. The content of minerals in food depends on a wide range of factors, such as type of food (animal or vegetable), genetic origin, agricultural procedures, and geographical location. Calcium is mainly found in milk and dairy products, dark green vegetables, legumes, nuts, and fish with soft bones (e.g., canned sardines), followed by fortified cereal products. Hard water also brings a contribution to calcium intake (EFSA, 2017). However, oxalates and phytates decrease calcium bioavailability by forming insoluble complexes with the mineral. Magnesium is present in the TABLE 1.3 Mineral classification. Main elementsa

Trace elementsb

Ultratrace elementsc

Calcium (Ca)

Chromium (Cr)

Aluminum (Al)

Chlorine (Cl)

Cobalt (Co)

Antimony (Sb)

Magnesium (Mg)

Copper (Cu)

Arsenic (As)

Phosphorus (P)

Fluorine (F)

Barium (Ba)

Potassium (K)

Iodine (I)

Bismuth (Bi)

Sodium (Na)

Iron (Fe)

Boron (B)

Sulfur (S)

Manganese (Mn)

Bromine (Br)

Molybdenum (Mo)

Cadmium (Cd)

Nickel (Ni)

Cesium (Cs)

Selenium (Se)

Germanium (Ge)

Zinc (Zn)

Lead (Pb) Lithium (Li) Mercury (Hg) Rubidium (Rb) Samarium (Sm) Silicon (Si) Strontium (Sr) Thallium (Tl) Tin (Sn) Titanium (Ti) Tungsten (W)

a

Essential for human beings in amounts .50 mg/day. Essential in concentrations of ,50 mg/day. Essentiality tested in animal experiments over several generations and deficiency symptoms found under these extreme conditions.

b c

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chloroplast of green plants; hence, the main sources of magnesium in the diet are cereals and green vegetables, such as spinach, legumes, nuts, seeds, and whole grains (Zand, Christides, & Loughrill, 2015). Meat and animal products are also rich in magnesium; however, its bioavailability is affected by the presence of calcium, phosphate, and protein in these food categories. The major dietary contributors to phosphorus intake are foods high in protein content, such as milk and milk products, meat, fish, grain products, and legumes (EFSA, 2017). Potassium is an essential mineral in the human diet. It is present in all natural foods and, in particular, in starchy roots or tubers, vegetables, fruits, whole grains, dairy products, and coffee (EFSA, 2017). Regarding sodium, all unprocessed foods contain sodium, but at low levels. Its content typically ranges between 30 and 150 mg (1.3 and 6.5 mmol)/100 g in unprocessed raw meat and fish, whereas fruits and vegetables generally contain less than 50 mg (2.2 mmol)/100 g (EFSA, 2017). Sodium is present in variable amounts also in water; this means that drinking water also contributes to the intake of dietary sodium. Sodium is added to food mostly as sodium chloride (NaCl), during processing. NaCl is the main constituent of table salt. One gram of sodium chloride provides 0.4 g of sodium and 0.6 g of chloride (17 mmol sodium and chloride). Sodium content of processed foods can vary substantially from food to food, but also from country to country, depending on taste preferences and dietary habits. The main contributors to sodium intake are bread, meat and meat products, cheese, and dairy products (Cappuccio, Beer, Strazzullo, & European Salt Action Network, 2018; Kloss, Meyer, Graeve, & Vetter, 2015). Depending on its origin and production method, salt may contain varying traces of other minerals. For public health reasons, salt may also be fortified with, for example, iodine, iron, or fluoride. As to microminerals, grains and grain-based products are the main food group contributing to copper intake of all population groups, except infants. Meat and meat products are important contributor to copper intake, as well (EFSA, 2017). Other sources are nuts, poppy and sunflower seeds, chickpeas, and liver. Dietary iron is provided by meat, fish, cereals, beans, nuts, egg yolks, dark green vegetables, and potatoes. Fortified foods have a relatively high iron content; fortified cereal products, such as bread and breakfast cereals, provide approximately 50% of iron intake in developed countries because of fortification and consumption frequency. Other dietary sources of nonheme iron comprise legumes, nuts, vegetables, and eggs; however, in general, its bioavailability is low, because many dietary factors (e.g., phytates and polyphenols) interact with the metal and render it unavailable for absorption. The main contributors to manganese intake in adults are cereal-based products, vegetables, fruits and fruit products, and beverages. The major sources of dietary selenium are bread, cereals, meat, fish, and poultry. Bioavailability and distribution of selenium within tissues depend on the form that selenium is ingested. However, high-protein diets and ascorbic acid enhance its bioavailability. Zinc content is low in food, even in those food categories that are classified as zinc-rich foods, namely, red meats (Zand et al., 2015). Zinc level is high in unrefined cereals, but it has a low bioavailability due to other components. Mineral content depends not only on food composition, but is also affected by processing. In general, cooking procedures produce mineral losses due to heating or to their solubility, and this is particularly high in vegetables (Zand et al., 2015). In addition, food processing and cooking procedures can affect food composition (vitamins, proteins, or FAs), which in turn changes mineral bioavailability (Barciela-Alonso & Bermejo-Barrera, 2015).

1.4.2 Labeling of minerals in the EU Regulation (EU) No. 1169/2011 lays down that information about any of the minerals listed in point 1 of Part A of Annex XIII and present in significant amounts as defined in point 2 of Part A of Annex XIII must be included in the nutrition declaration or nutrition labeling (EU, 2011). Part A of Annex XIII specifies the minerals and the related reference values that must be declared in the nutrition declaration or nutrition labeling (EU, 2011): chloride (800 mg), calcium (800 mg), phosphorus (700 mg), magnesium (375 mg), iron (14 mg), zinc (10 mg), copper (1 mg), manganese (2 mg), fluoride (3.5 mg), selenium (55 μg), chromium (40 μg), molybdenum (50 μg), and iodine (150 μg). The regulation states that nutrition labeling of products to which vitamins and minerals have been added is compulsory, as well. Nutrition claims applying to minerals are established by Regulation (EU) No. 1925/2006 on food information to consumers. The claim “Source of [name of vitamin/s] and/or [name of mineral/s]” applies to a food containing at least a significant amount as defined in the Annex to Directive 90/496/EEC (e.g., 800 mg calcium, 800 mg phosphorus, 14 mg iron, 300 mg magnesium, and 15 mg zinc) or an amount provided by derogations granted according to Article 6 of Regulation (EU) No. 1925/2006 of the European Parliament and of the Council of 20 December 2006 on the addition of vitamins and minerals and of certain other substances to foods. The claim “High [name of vitamin/s] and/or [name of mineral/s]” can be made for any food product containing at least twice the value applicable for the “Source of [name of vitamin/s] and/or [name of mineral/s]” claim. Regulation (EU) No. 1925/2006 also lays down specific nutrition claims for sodium/salt. The need of laying down a specific mention follows on the fact that sodium is an essential nutrient and is necessary for normal cell function and

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for neurotransmission (WHO, 2012). However, from a public health perspective, a number of national and international institutions have examined the detrimental impact of salt intake on health and, in particular, on blood pressure, cardiovascular disease, stomach cancer, and renal functions. For this reason, recommendations to limit salt intake to less than 5 or 6 g per day have been laid down. The mandatory nutrition declaration of the salt content in prepacked foods is explained by the necessity for consumers to be informed about the content of sodium/salt in food. The words salt and sodium are often used interchangeably (WHO, 2016). Most sodium is consumed in the form of sodium chloride and the public understands the term salt better than sodium. To ensure that the final customer easily understands the labeling, it is appropriate to use the term “salt” instead of the corresponding term of the nutrient “sodium”. Therein, salt content is calculated as salt equivalents from the sodium content of a product salt, according to the formula “salt 5 sodium 3 2.5”. Under Regulation (EC) No. 1924/2006 on nutrition and health claims made on foods, three salt-related nutrition claims are established (EC, 2006). The “Low sodium/salt” claim applies to any food product containing no more than 0.12 g of sodium, or the equivalent value for salt, per 100 g or per 100 mL. Special mention is made for waters, other than natural mineral waters falling within the scope of Directive 80/777/EEC. The set value should not exceed 2 mg of sodium per 100 mL. The “Very low sodium/salt” claim is applicable to food products containing no more than 0.04 g of sodium, or the equivalent value for salt, per 100 g or per 100 mL. This claim should not be used for natural mineral waters and other waters. The claim that a food is “Sodium-free or salt-free” can only be made for a product that contains no more than 0.005 g of sodium, or the equivalent value for salt, per 100 g.

1.4.3 Ash analysis Ash analysis comprises the determination of the total mineral content. It refers to the inorganic residue left after the combustion or the complete oxidation of food organic matter. The evaluation of ash content can be part of proximate analysis for the assessment of the nutritional value of foods or can be the preparatory step for specific elemental analysis. Samples are typically ashed, as the minerals must not be bound to the organic matter of the analyzed food. Two main types of ashing can be applied: dry-ashing and wet-ashing. Dry-ashing is performed at high temperatures (500 C 600 C), by conventional or microwave heating. Wet-ashing (acid-facilitated oxidation) is preferred to dryashing in the analysis of minerals that might be volatilized and lost during dry-ashing. It uses low temperatures and relies on strong acids and chemical oxidizers to rid the sample of organic matter. Hydrochloric acid, sulfuric acid, nitric acid (HNO3), and perchloric acid (HClO4) are commonly used. Microwave systems can also be used to speed the ashing process. While dry samples can be ashed as they are, matrices like fresh vegetables or high-moisture foods require a pre-drying step. High-fat products (e.g., meat products) must be dried and fat-extracted, so that smoke generation during the heating step is prevented. During the ashing step, contamination and loss of volatile elements must be hampered. Hence, the use of metallic instruments is to be avoided during comminution (i.e., grinding and chopping) and mixing. Nonmetallic instruments or instruments made of the minerals not to be determined have thus to be used. Glassware also requires attention: acid washes and triple rinsed in very pure water are necessary. Solvents themselves may contain significant quantities of minerals; as a consequence, the purest reagents available must be used. This requirement poses the challenge of the costing, and the use of ultrapure reagents might be a limiting aspect. As an alternative, a reagent blank, that is, a sample of the reagents used in the sample analysis, is added and then subtracted from the sample values. The use of low temperatures also allows wet-ashing to prevent mineral losses due to volatilization. The oxidation time is short. Among the drawbacks of this method, there is the limiting factor of handling only a small number of samples, and the need of a constant attention by the operator. The use of a mixture of acids, rather than of a single acid, is to prefer, as it guarantees a complete and rapid oxidation of organic material. In general, different combinations of acid solutions can be used: HNO3, HClO4, and sulfuric acid-hydrogen peroxide. However, some mixtures are more performing than others. For example, if HNO3 is combined with HClO4, the procedure is faster than that with the sulfuring-acid version. The use of HClO4 is included in the AOAC Method 975.03. It allows a better extraction; however, the other solutions are preferred, as they are less dangerous to work with. Microwave wet-ashing (acid digestion) is an alternative to wet-ashing. It can be performed safely in either an openor closed-vessel microwave system. The choice depends on the sample size and the required temperatures. Closed vessels allow a more complete dissolution of hard-to-digest substances, as acids can be used also by heating them past their boiling points. Nitric acid is the acid of choice. Moreover, closed-vessel microwave digestion systems allow processing of up to 40 samples at a time. In contrast, open-vessel digestion systems are rather used for samples of larger size or

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generating gas as they are digested. The number of samples processed at a time is lower (e.g., up to six samples). Acar et al. have recently compared wet and microwave digestion methods in Cu, Fe, and Zn determination in standard reference material (RM) and food samples before analysis with flame atomic absorption spectrometry (FAAS) and found that the microwave digestion methods is simpler, more effective, faster, and more accurate than the wet digestion method (Acar, Tunc¸eli, & Tu¨rker, 2016). They thus recommended microwave digestion for routine determinations in food samples. Microwave digestion is also the most used sample preparation procedure for the determination of mineral content in milk powder, where it is pivotal to have an effective digestion of the sample to obtain low LOD (Limit of Detection), and quantitative, accurate, and precise data (Anderson, Bu-Hamdi, & Al-Harbi, 2016; Herreros-Chavez, Morales-Rubio, & Cervera, 2019; Oreste et al., 2016; Sola-Larran˜aga & Navarro-Blasco, 2009; Zand, Chowdhry, Wray, Pullen, & Snowden, 2012; Zand et al., 2011a). Microwave and ultrasound treatments were also used for sample preparations in the determination of macronutrients, micronutrients, and nonessential elements in milk-based infant formulas by atomic absorption spectrometry (AAS) and inductively coupled plasma optical emission spectrometry (ICP-OES) (Ahmed et al., 2017). The proposed methods showed to be rapid and to meet green chemistry approach: it was thus suggested that they are suitable for being implemented in laboratories for routine analysis. The use of microwave energy to sample preparation is already consolidated and allows the decomposition of many matrices at high pressure and temperature with safety. The combination of inorganic acids and hydrogen peroxide is widely used and enables the decomposition of organic and inorganic materials.

1.4.4 Analysis of minerals of nutritional interest Generally speaking, the analysis of minerals in foods aims at identifying and quantitating macro- and microelements, thus providing information about the nutritional value, the geographical origin, and/or the safety (e.g., arsenic speciation of rice, nitrite, and nitrate anions in meat products, and others) of a product. The number of analytical techniques and tools currently available for mineral determination in food products is high. An evolution occurred from techniques based on single-element determination to techniques that detect multiple elements present in food samples at minor, trace, and ultratrace levels. Currently, novel analytical techniques came up besides traditional methods in mineral analysis.

1.4.4.1 Traditional methods Precipitation titrimetry, complexometric titration by the hexadentate ligand ethylenediaminetetraacetate (EDTA), colometric methods, and ion-selective electrodes (ISE) are some of the traditional methods used for mineral analysis. Generally speaking, these methods can be performed also in small laboratories, as the required chemicals and equipment are routinely available. Regarding the specific methods for elemental analysis, EDTA complexometric titration is mainly used to test calcium plus magnesium as an indicator of water hardness. It is also used to determine calcium in ashed fruit and vegetables (as established by AOAC Method 968.31) and other food matrices containing calcium without appreciable magnesium or phosphorus. Methods based on precipitation titration are used with foods high in chlorides, such as processed cheeses and meats. Chloride is detected and salt content is estimated by calculation. Colorimetric methods are applied for the determination of total phosphorus in milk. ISE have been used to measure salt and nitrate in processed meats, salt in butter and cheese, and calcium in milk. The AOAC International Methods 971.27, 976.18, and 986.26 are based on ISE.

1.4.4.2 New frontiers in analysis of minerals of nutritional interest AAS or atomic emission spectroscopy, FAAS and flame atomic emission spectroscopy, inductively coupled plasmaatomic emission spectrometry, ICP-OES, and sodium-selective electrodes are the recommended and most often used techniques for elemental analysis. Recently, X-ray fluorescence (XRF) techniques have also been reviewed for the remarkable possibilities as fast and greener alternative to the abovementioned methods (de la Guardia & Garrigues, 2015; Herreros-Chavez et al., 2019). NIR spectroscopy also offers interesting possibilities as a “green alternative” to wet chemistry-based atomic spectroscopy for food analysis (Schmitt, Garrigues, & de la Guardia, 2014). AAS is a relevant analytical tool for elemental analysis in foods, thanks to its low cost, versatility, and good performance (Lo´pez-Garcı´a & Herna´ndez-Co´rdoba, 2015). Generally speaking, the sample is vaporized and the element of interest is atomized at high temperatures. The element concentration is determined based on the attenuation or absorption by

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the analyte atoms, of a characteristic wavelength emitted from a light source. In the case of sodium analysis, AAS is to be preferred to other techniques due to its sensitivity (Moreno-Rojas, Ca´mara-Martos, & Amaro Lo´pez, 2016). According to the type of atomizer, different systems are available: flame (FAAS), cold vapor (CV-AAS), hydridegenerating (HG-AAS), graphite furnace, and electrothermal (ET-AAS). FAAS has become a well-established analytical tool for routine determination of a large number of elements. It allows sensitive measurements. CV-AAS and HG-AAS also offer extreme sensitivity at a relatively low cost with respect to other atomic or mass-based techniques; however, they have a restricted scope, as the number of elements that can be determined is low (Lo´pez-Garcı´a & Herna´ndezCo´rdoba, 2015). When ET-AAS is used, the performance of AAS is greatly improved. The technique shows a very high sensitivity; moreover, it allows the use of solid or undigested samples with a subsequent saving of time and reagents. FAAS has been applied to the determination of Cu, Fe, Mn, and Zn in samples of multimineral and multivitamin tablets (Soriano, Netto, & Cassella, 2007). Analytes were extracted with diluted hydrochloric acid solution, and results were compared with those obtained after total digestion of samples using a closed-vessel microwave oven device. The influence of some parameters — namely, acid extraction solution concentration and nature, mixing mode (ultrasonic or magnetic stirring), extraction time, and sample composition — on the extraction process was studied, and differences in extraction yield were observed from mineral to mineral. FAAS has the advantages of lower initial cost and low cost per analysis, and requires less operator training than many other trace elemental techniques. AAS techniques can be substituted with ICP-based equipment, as they allow simultaneous detection of elements and have very high sensitivity. However, the costs and maintenance of ICP-MS are much higher than AAS (Lo´pez-Garcı´a & Herna´ndez-Co´rdoba, 2015). ICP-MS has increasingly become a powerful analytical tool for determination of elements in a number of food categories. Generally speaking, this technique shows the drawback of unwanted spectral and/or nonspectral interferences, which might change with the elemental composition of the samples analyzed (Astolfi et al., 2018). Astolfi et al. state that application of ICP-MS to milk samples might require a collision reaction interface or dynamic reaction cells to reduce these interferences, or a sector field ICP-MS (Astolfi et al., 2018). ICP-MS has been used in the determination of sodium. Regarding sample preparation, either wet oxidation or dry-ashing can be used for samples with a high degree of variability and minimum interferences (Moreno-Rojas et al., 2016). However, ICP-MS can yield accurate results with food products having a low content of sodium. ICP-MS has been used, alongside ICP-OES, in the elemental characterization of milk products (Anderson et al., 2016; Ba˘gdat, Baran, & Tokay, 2014; Ðurovi´c et al., 2017; Oreste et al., 2016; Pereira et al., 2013; Zand et al., 2012; Zand et al., 2011b). One advantage of ICP-OES is the fact that single- and multielement measurements can be performed in up to 70 elements, thus reducing analysis time. Moreover, it is competitive with other inorganic techniques for the sample throughput and sensitivity (Martı´nez, Gil, Pacheco, & Cerutti, 2015). ICP-OES was used for mineral evaluation in multimineral tablets (Roseland et al., 2008). This technique was compared with conventional ones and AAS. It was found to be the most suitable technique for most minerals. Over the last years, XRF spectroscopy showed to be a promising analytical technique for determination of elemental composition as well as a greener alternative to other techniques. The XRF technique has characteristics that overcome the limitations of many spectrometric techniques, such as AAS, ICP-OES, or ICP-MS. It brings, for instance, important advantages to the traditional sample preparation, which generally requires the use of large amounts of oxidants and concentrated mineral acids under aggressive conditions; a time-consuming procedure; sample dilution and subsequent loss in sensitivity, which may imply the undetectability of the analyte; sample contamination; or analyte loss by volatilization. In the energy-dispersive X-ray fluorescence (ED-XRF) and wavelength-dispersive X-ray fluorescence (WD-XRF) methods, samples can be measured directly, with a minimum sample preparation; sample manipulation is negligible; the risk of analyte loss is minimized; moreover, the use of corrosive and toxic reagent is avoided. XRF also does not generate residues of any type, with relatively low energy consumption (de la Guardia & Garrigues, 2015). Moreover, XRF techniques are able to conduct simultaneous multielement measurements, are compatible with solid and liquid samples, and are nondestructive (Brito, Teixeira, & Korn, 2017). In the last few years, XRF has been applied to determine the mineral profile of infant milk powder, both in the WDXRF (Fernandes, Brito, & Gonc¸alves, 2015; Pashkova, 2009; Perring & Blanc, 2008a) and ED-XRF format (Gunicheva, 2010; Herreros-Chavez et al., 2019; Jolly et al., 2017; Perring & Blanc, 2008b). The work by HerrerosChavez et al. has shown that the use of ED-XRF with an external calibration allows a direct quantification of elements like Ca, K, Fe, Cu, and Zn in infant milk powder with relative standard deviation lower than 10%. The reliability of the technique was also confirmed by obtained results, which were in agreement with certified concentrations in nonfat milk powder standard RM, as well. Results obtained with ED-XRF showed the importance of this technique as a green method that can be used for quality control and routine analysis of milk powder samples.

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ED-XRF also provided an approach to determine successfully Ca, K, and Mg in macroalgae samples, as an alternative to AAS that has long been the most common technique used for elemental determination in algae (Brito et al., 2017). ED-XRF allowed avoiding drastic or time-consuming sample pretreatment. Seaweed samples were just freezedried under vacuum and ground in a ball mill; they were stored in plastic containers and maintained in a desiccator. Afterward, they were mashed to below 100 μm, and sample powder was pressed into uniform pellets with a hydraulic press machine. The method developed by Brito et al. used, as calibration standards, samples previously analyzed by ICP-OES (Brito et al., 2017). In a parallel procedure applied for comparison purposes, macroalgae samples were also analyzed by ICP-OES after an acid digestion procedure. Analysis of three certified RM from plants was performed to evaluate the accuracy of the method. NIR spectroscopy may be a “green alternative” to wet chemistry-based atomic spectroscopy in elemental analysis (Schmitt et al., 2014). The application of atomic spectroscopy involves, in fact, the use of long and tedious sample preparation, consumption of acids and reagents, and use of expensive instrumentation. Prediction of some major minerals by NIR analysis may be possible based on the use of chemometrics to model the NIR spectra of a food from a series of “known” samples (de la Guardia & Garrigues, 2015). Mir-Marque´s et al. successfully investigated the possibility to apply NIR and XRF spectroscopy to the determination of calcium, potassium, iron, magnesium, manganese, and zinc concentrations in artichoke samples (Mir-Marque´s, Martı´nez-Garcı´a, Garrigues, Cervera, & de la Guardia, 2016). As the main drawback of both NIR and XRF is the low sensitivity and high matrix effects, they used chemometric tools (i.e., PLS data treatment) to develop appropriate calibration models from the spectra of well-characterized samples in order to increase the prediction capability of measurements made. Based on the coefficients of determination, obtained for the regression between predicted values and reference ones for all the elements under analysis, XRF provided the best results to be used as a quantitative screening method. NIRS has been also used for sodium analysis, especially in meat products (Campos, Mussons, Antolin, Deba´n, & Pardo, 2017; Collell, Gou, Arnau, & Comaposada, 2011; De Marchi et al., 2017; Prevolnik et al., 2011). The NaCl prediction through NIRS is based on the change of water spectra and is not related to a specific absorption band. The results obtained by De Marchi et al. showed that near-infrared transmittance spectroscopy can predict sodium content in both intact nonground and ground samples of meat products. Prediction models were developed based on all samples spectra, and spectra were divided according to the manufacturing meat process. The prediction model for sodium content was more robust in case of ground than intact nonground samples because of a lower percentage of outliers (De Marchi et al., 2017). This technique thus confirmed again its reliability and robustness: it provides, in fact, a rapid, nondestructive, accurate analytical tool, and is greener than other techniques, as it requires no chemicals.

1.5 Proteins 1.5.1 Definition of dietary proteins and sources thereof Proteins are organic compounds composed of amino acids joined together by peptide bonds between the carboxyl and the amino (or imino in the case of proline) group of the next amino acid in line. The building blocks of proteins are 20 of the naturally occurring amino acids, so-called proteinogenic amino acids. Nine out of the twenty amino acids cannot be synthesized by the human body and have to be provided by the diet: phenylalanine, histidine, isoleucine, leucine, lysine, methionine, threonine, tryptophan, and valine. They are therefore defined as “essential” or “indispensable” amino acids. Proteins differ in their amino acid composition and essential amino acid content. Meat, fish, eggs, milk, and dairy products are dietary sources of animal origin with a high protein content. Most of them are high in essential amino acids. Protein content ranges from 2 to 6 g/100 g in dairy products, between 20 and 33 g/100 g in red meat, from 22 to 37 g/100 g in poultry, and between 27 and 34 g/100 g in hard cheese (EFSA, 2017). Bread and other grain-based products, such as leguminous vegetables and nuts, are plant-derived foods with a high protein content: from 6 to 13 g/ 100 g in bread, 4 to 14 g/100 g in legumes, and 8 to 29 g/100 g for nuts and seeds (EFSA, 2017). The content of indispensable amino acids in plant-origin proteins is usually lower than that in animal proteins (EFSA, 2017). The structure of a protein is dependent on the amino acid sequence (primary structure), which determines the molecular conformation (secondary and tertiary structures). Sometimes, proteins also occur as molecular aggregates that are arranged in an orderly geometric fashion (quaternary structure). Classification of proteins on the basis of their structure implies three groups: 1. simple proteins (e.g., albumins, globulins, glutelins, albuminoids, histones, and protamines), that is, proteins that, on hydrolysis, only yield amino acids and occasional small carbohydrate compounds;

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2. conjugated proteins that are simple proteins combined with some nonprotein material in the body (i.e., nucleoproteins, glycoproteins, phosphoproteins, hemoglobins, and lecithoproteins); and 3. derived proteins, originating from simple or conjugated proteins by physical or chemical means (e.g., denatured proteins and peptides). Proteins are also often divided into four main classes based on their solubility properties: albumins, globulins, glutelins, and prolamines. Albumins are water soluble, globulins are soluble in weak ionic solutions, glutelins are soluble in weak acid or basic solutions, and prolamines are soluble in 70% ethanol. Proteins have a major role in the growth and maintenance of the human body and are, along with carbohydrates and lipids, the nutrients giving energy in the diet. The nutritional energy value of proteins is as high as that of carbohydrates (17 kJ/g or 4 kcal/g). Moreover, they have other functions in the human body: they may have enzymatic activity, they may partake the transport of nutrients, and they can take part in cell signaling and in muscle work. They are also building blocks for several cellular structural elements. In order to maintain these important functions, it is essential to provide the body with good quality proteins through diet. Moreover, inadequate amounts of dietary proteins or essential amino acids lead to increased turnover of muscular proteins and, over time, to reduced growth and loss of muscle mass and subsequently reduced immunity and reduced hormonal and enzymatic activity. For these reasons, the intake of proteins trough the diet is of paramount importance. Not only the amount but also the quality of protein is important, that is, their ability to cover the requirements of essential amino acids, along with their absorption and utilization in the body. According to available dietary surveys, the average protein intake in European countries ranges from 67 to 114 g/die in male adults and from 59 to 102 g/die in female adults (EFSA, 2017).

1.5.2 Labeling of proteins in the EU Regulation (EU) No. 1169/2011 lays down that the protein content of a food is among the mandatory information to be specified in nutrition declaration or nutrition labeling (EU, 2011). For labeling purposes, an RI of 50 g/die was established for the average adult (with a diet of 2000 kcal) (EU, 2011). Nutrition claims on proteins are also regulated by Regulation (EC) No. 1924/2006. The claim “source of protein” is allowed when at least 12% of the energy values of the food is provided by protein; while the claim “high in protein” is applicable when at least 20% of the energy value of the food is provided by protein (EC, 2006). Some health claims are also allowed and they apply to food products whose protein content can contribute to growth and maintenance of muscle mass and to the capacity to maintain normal bones and normal growth and bone development in children (EC, 2006). Special attention was given to the dietary proteins that can trigger allergic reactions. Annex II of Regulation (EU) No. 1169/2011 lays down that it is mandatory to label 14 substances and/or food products with proven allergenicity: gluten-containing cereals, celery, crustaceans, eggs, fish, lupin, milk, mollusks, mustard, nuts, peanuts, sesame seeds, and sulfites (EU, 2011).

1.5.3 The importance of protein analysis Analysis of dietary proteins covers a wide range of purposes: nutrition declaration and labeling, allergen declaration, and declaration of protein for special dietary regimes (e.g., vegetarian, etc.), establishment of value of protein-based commodities for trading purposes, assessment of quality of protein-based food ingredients, assessment of food ingredient conformity to the regulatory framework, and establishment of the authenticity of protein-based food. This section will briefly report the methods traditionally used in analysis of dietary proteins, with emphasis on the advantages and disadvantages thereof; it will then focus on the main challenges of protein analysis in allergen detection.

1.5.4 Traditional methods for dietary protein analysis Food protein analysis is not a straightforward procedure, and this is mainly due to the heterogeneity of food matrices and to the fact that each food matrix comprises a range of other different macronutrients (e.g., lipids and carbohydrates) and micronutrients that also interact among them and thus reduce the protein accessibility. Throughout the years, a wide range of analytical methods for food protein analysis has been developed (Table 1.4). The Kjeldahl method is, together with the Dumas method, the primary chemical-based method for protein content determination (also known as combustion method). Annex I of Regulation (EU) No. 1169/2011 recognizes it as the method of choice and lays down that the protein content, which the law refers to, is calculated according to the formula

TABLE 1.4 Main protein analysis methods. Method

Indirect methods

Nitrogenbased methods (Kjeldhal)

Principle

Sample preparation

Applications

Advantages

Direct measurement of nitrogen content (only organic N 1 ammonia) through a four-step procedure (digestion, neutralization, distillation, and titration), followed by multiplication of N content by a conversion factor.

Little preparation

It is preferred to the Dumas method in high fat samples, as it allows avoiding that fat causes instrument fire during the incineration procedure of the Dumas method.

It is inexpensive.

Disadvantages

It measures total organic N and not only protein nitrogen. Risk of overestimating protein content. Corrosive reagents (concentrated sulfuric acid and a catalyst for digestion) are used. Lower precision than other methods.

Standards for total nitrogen and protein measurement

Codex Guidelines on nutrition labeling. CF: 6.25 (mixed foods). AACC Approved Methods 46-10, 4611A, 46-12, 46-13, and 46-16. AOAC 920.53 (Beer). CF: 6.25 AOAC 945.23 (brewing sugars and syrups). CF: 6.25 AOAC 920.103 (tea). CF: 6.25 AOAC 920.87 (grains). CF: 5.7 for wheat and its products, 5.18 for almonds, 5.46 for peanuts and Brazil nuts, 5.30 for tree nuts and coconut, and 6.38 for dairy products. AOAC 950.36 (bread). CF: 5.7 AOAC 981.10 (meat) CF: 6.25 AOAC 950.48 (nuts and nut products). CF: 5.18 (almonds), 5.46 (peanuts and Brazil nuts), and 5.30 (tree nuts and coconut) FAO Food and Nutrition Paper 14/7 (CF:s: 5.70 to 6.31 for cereals; 6.38 for milk and dairy (Continued )

TABLE 1.4 (Continued) Method

Principle

Sample preparation

Applications

Advantages

Disadvantages

Standards for total nitrogen and protein measurement

products; 5.18 5.71 for pulses, nuts and seeds, and 6.25 for other foods)

Direct methods

Nitrogenbased methods (Dumas)

Direct measurement of nitrogen content (total N, including inorganic fractions like nitrite and nitrate). Nitrogen is released in a gaseous form after combustion of a sample at very high temperature and is quantitated by gas chromatography with a thermal conductivity detector after removal of carbon dioxide and water aerosols.

Amino acid analysis

Proteins are broken into their constituent amino acids by hydrolysis of the peptide bonds. Liberated amino acid residues are then determined (often chromatographically). Protein content is calculated as the sum of individual amino acid residues after subtraction of the molecular mass of H2O.

Little preparation

It largely replaced the Kjeldhal method for nutrition labeling.

No need of toxic or harmful chemicals or catalysts. Rapid. Automation allows analyzing several samples. Faster than the Kjeldhal method (few minutes per measurement vs .1 h for Kjeldhal).

Expensive equipment

Direct measurement of amino acid residues.

Protein hydrolysis prior to analysis. Most peptide bonds are hydrolyzed, but some amino acids are reduced or even destroyed completely. Risk of underestimation of protein content.

ISO/TS 16634 2:2009 ISO 14891:2008 (IDF 185:2008) AACC Methods 46.30 ICC Standard No. 167 AOAC 990.03 AOAC 992.23 AOAC 997.09 OIV-MA-AS323-02A

FAO Recommendations, Rome (Italy) 2003.

Spectroscopic methods

IR

Method based on energy absorbed when a sample is subjected to a wavelength of infrared radiation specific for the peptide bond.

Little preparation

UV

Functional groups or regions within the protein (e.g., basic groups, aromatic groups, peptide bonds, etc.) absorb light in the ultraviolet or visible range of the electromagnetic spectrum. The absorbance is read and compared with known protein standards.

Protein extraction

After instrument calibration, it is the most rapid. High sensitivity, high sample throughput, and high precision. Nondestructive.

AACCI Method 3911.01. (Wheat Flour) AACC Approved Methods 39-25 (calibrated against either Kjeldhal and combustion nitrogen analysis results)

AACC, American Association of Cereal Chemists, St. Paul, MN, United States; AOAC, AOAC International, Washington, DC, United States; CF, conversion factor; ICC, International Association for Cereal Science and Technology, Vienna, Austria; IDF, International Dairy Federation, Brussels, Belgium; IR, infrared; ISO, International Organization for Standardization, Geneva, Switzerland; OIV, Organization of Vine and Wine, Paris, France; UV, ultraviolet.

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“Protein 5 Total Kjeldahl nitrogen 3 6.25” (EU, 2011). The Kjeldhal and Dumas methods are indirect methods, relying on total nitrogen determination as a marker to estimate protein content. The issue with these methods is the identification and choice of the nitrogen-to-protein conversion factor, which is usually 6.25, on the basis of the assumption that in food proteins, nitrogen content is generally 16% and that all nitrogen in foods is protein-bound. However, over the years, other conversion factors have been identified, as relative nitrogen content varies between amino acids, amino acid composition varies between food proteins, and a wide range of other compounds contain nitrogen, such as nitrate, ammonia, urea, nucleic acids, free amino acids, chlorophylls, and alkaloids (Lourenc¸o et al., 2002; Mæhre, Dalheim, Edvinsen, Elvevoll, & Jensen, 2018; Mariotti, Tome´, & Mirand, 2008). Direct methods, such as amino acid analysis, allow the determination of protein content based on the analysis of amino acid residues. However, one of the major drawbacks of direct protein determination lies in the fact that upon hydrolysis of peptide bonds, some amino acids are reduced or even destroyed completely; an underestimation of protein content may thus occur (Mæhre et al., 2018). Moreover, these methods are costly. Some scientists have recently examined and compared available protein analytical methods to provide an insight on the advantages and disadvantages thereof and to identify tentatively the most reliable. Mæhre et al. applied amino acid analysis and the Kjeldhal method on different matrices, that is, salmon (Salmo salar) loins, cod (Gadus morhua), peeled shrimp (Pandalus borealis), and wheat white and whole flours (Mæhre et al., 2018). Findings highlighted that nitrogen analysis can overestimate the protein content with respect to amino acid analysis, whether species-specific conversion factors are used or otherwise. In amino acids analysis, the hydrolysis step should be improved, as this method gives values lower than the Kjeldhal method (44% 71% higher), likely due to reduction of amino acids by the hydrolysis. However, in amino acid analysis, interfering substances do not affect the results (Mæhre et al., 2018). Results by Mæhre et al. showed that, when proteins are determined spectrophotometrically, the extraction step and the choice of the buffer are pivotal. Moreover, spectrophotometric methods can be influenced by interfering substances, and protein content might thus be overestimated, as well.

1.5.5 New priorities in protein analysis Dietary proteins usually induce immune tolerance, and yet some of them may trigger allergic reactions. Over the last few decades, new priorities have emerged in food protein analysis. Besides the evaluation of protein content and characterization of amino acids, the determination of proteins and peptides with allergenic properties emerged as a new priority and analytical challenge for food analysts. Regulation (EU) No. 1169/2011 laid down a list of 14 substances and/or food products for which mandatory declaration on labels of prepacked and nonprepacked foods is necessary, and legal threshold values were set worldwide for some of them (EU, 2011). In the following paragraphs, methods currently used for determination of allergenic proteins are described and discussed.

1.5.5.1 Enzyme-linked immunosorbent assays The immunoreactivity of some proteins is mainly determined by epitopes, that is, the part of a protein that is recognized by antibodies. Epitopes can be classified into linear (sequential), that is, continuous short peptide segments, and conformational (discontinuous), that is, three-dimensional motifs formed by spatially adjacent amino acids. T-cell-binding epitopes are exclusively linear. Some common sources of food allergens are, for example, ω-5 gliadin, γ-gliadin, and highmolecular weight and LMW glutenins in wheat; β-lactoalbumin, β-lactoglobulin, serum albumin, immunoglobulin, and caseins (i.e., αS1-Casein, αS2-Casein, β-Casein, and k-Casein) in milk; and defensin, cupin (7S vicilin like globulin), and glycinin in soybean (Chizoba Ekezie, Cheng, & Sun, 2018), among others. A good number of methods have been so far developed for detection of some allergens (Gomaa & Boye, 2015; Korte, Lepski, & Brockmeyer, 2016; Pilolli, De Angelis, & Monaci, 2018; Planque et al., 2019; Planque et al., 2017). However, enzyme-linked immunosorbent assays (ELISA), together with methods based on polymerase chain reaction, have shown to be the most commonly used techniques for the determination of allergens. ELISAs are currently the methods of choice in gluten detection. Generally speaking, they are analytical techniques based on the specific and high-affinity binding of antibodies with particular target antigens. They are thus based on detection of the antibodies, covalently linked to an enzyme, such as alkaline phosphatase and horseradish peroxidase, which generates a colored chemiluminescent or fluorescent product to measure (Melini & Melini, 2018). ELISAs can either use a specific monoclonal antibody (mAb) or polyclonal antibodies (pAbs) to detect the antigen. The market currently offers a good number of ELISA kits based on gluten- or gliadin-specific antibodies targeting different fragments

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of gluten proteins (Melini & Melini, 2018; Scherf & Poms, 2016). The Skerrit mAb (also known as mAb 401.21), the α-20 mAb, the G12, the R5 mAb, and other pAbs are the most commonly used antibodies. The available ELISAs, with a sandwich or competitive format, have the advantage of being commercially available in the format of test kits, at relatively low costs, and they are fast and easy to use, though the sandwich version is the most common (Baumert, 2014). They have, however, shown some challenges to be addressed. Sample preparation and extraction procedure are a critical point in gluten determination (Hochegger, Mayer, & Prochaska, 2015; Rossell, Barro, Sousa Martı´n, & Mena, 2014; Scherf & Poms, 2016). The most commonly used solvent in the extraction of the prolamin fraction from raw materials is aqueous alcohol (i.e., 60% ethanol and 50% propanol; Hochegger et al., 2015; Rossell et al., 2014; Scherf & Poms, 2016). Partially hydrolyzed gluten is soluble in aqueous alcohol solutions as well as in water and salt solutions. Native gluten proteins are insoluble in water or salt solution due to inter- and intramolecular disulfide bonds, and have a limited stability when solubilized. The critical point is thus represented by the fact that in heat-processed food products (i.e., bread) and extruded products (e.g., pasta), proteins are degraded and hydrolyzed, or protein aggregates may form. This implies that the aqueous alcohol solution is inadequate and the food toxicity can be underestimated (Hochegger et al., 2015; Melini & Melini, 2018; Rossell et al., 2014; Scherf & Poms, 2016). Gluten detection in thermally processed samples is thus achieved only after adding disaggregating and reducing agents (e.g., guanidine chloride and β-mercaptoethanol) to break the interchain S-S- bonds of proteins and to solubilize gluten. This extraction solution, also known as cocktail solution and recognized by the Codex Alimentarius Commission for the sandwich R5 ELISA, guarantees a limit of quantification (LOQ) of 1.56 ppm of gliadins (Rossell et al., 2014), but poses safety problems. Guanidine chloride is corrosive, and β-mercaptoethanol is harmful by inhalation, toxic and volatile, has an unpleasant odor, and is hazardous for the environment. Moreover, some immunoassays, especially in the competitive format, are also incompatible with this extraction solution, because the components can denature the protein receptor (Rossell et al., 2014). The use of cocktail solutions also permits the extraction of some glutenins, which can lead to an overestimation of the final gluten content if the antibody used binds to these fractions. Another shortcoming of cocktail extraction is that it is not compatible with competitive-type methods, as well. The Universal Prolamin and Glutelin Extractant Solution, containing the reducing agents Tris(2-carboxyethyl)-phosphine (TCEP) and N-lauroylsarcosine, is an alternative extraction procedure compatible with immunochemical methods and able to improve the extraction of native and denaturated gluten proteins. The TCEP breaks the disulfide bridges, and the N-lauroylsarcosine contributes to opening polypeptide chains, more than guanidine chloride (Rossell et al., 2014). Based on the shown promising results, it might soon be commercialized. The different kits currently available on the market also give inconsistent and noncomparable results, with a claimed 46% 124% lack of reproducibility and a 50% probability that a food deemed as having a gluten content ,20 ppm may actually contain up to 80 90 ppm of gluten (Rzychon et al., 2017). Regarding the type of antibody used, most official methods validated by AOAC and AACCI are based on the R5 mAb, which has shown some advantages. ELISAs based on R5 antibody have a high sensitivity and allow monitoring gluten levels as low as 2.5 ppm in commercial GF food products, raw products, wheat starches, and contaminated oator buckwheat-based foods. In contrast, ELISAs based on the Skerrit mAb have an LOQ of 5 mg gluten/kg (Scherf & Poms, 2016). The R5 Ab also has minor cultivar dependence with respect to other antibodies, such as the Skerrit mAb that is, in contrast, raised against ω-gliadins, whose content is strongly dependent on the wheat cultivar (Immer & Haas-Lauterbach, 2009). R5 is promising also to measure gluten in cooked foods, as long as heat treatment leaves unchanged the QQPFP epitope, against which R5 is raised. Moreover, as QQPFP epitope is not found in oat avenins, this implies that the R5 antibody reacts with rye and barley and no cross-reactivity to oats is observed. Another challenge in gluten analysis is represented by the RM currently used for calibration and validation. PWG, that is, a purified gliadin obtained from a mixture of 28 European wheat cultivars prepared by the Working Group on Prolamin Analysis and Toxicity is currently used. It guarantees a high degree of repeatability, reproducibility, and stability, but it has nevertheless shown some limits, as it actually represents the prolamin fraction of some cereals (Scherf & Poms, 2016). This implies that it is unsuitable for gluten detection in fermented wheat-, rye-, and barley-based food products. A total of 23 common bread wheat cultivars have been recently identified as suitable for production of RM, though further studies are necessary (Hajas et al., 2018). At the moment, it is also debated if RM should be based only on a single protein fraction or reference flour (Melini & Melini, 2018). Methods based on the sandwich version are not suitable for quantifying gluten in hydrolyzed samples, because the presence of protein fragments with more than one binding site is unlikely. Competitive assays are more suitable for quantifying gluten in hydrolyzed samples, as the target requires multiple epitopes to be quantified. Regarding ELISA kits for detection of other allergens in food, it was observed that in milk, the use of different ELISA formats or antibodies gives different results (Sharma, Khuda, Parker, Eischeid, & Pereira, 2017). When anti-

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β-lactoglobulin antibodies are used, sandwich format gives a lower detection limit; when β-lactoglobulin concentration is measured with a competitive format, results are 3 5 times higher (de Luis, Lavilla, Sa´nchez, Calvo, & Pe´rez, 2009). Quantitation of milk allergen residues is possible by employment of different extraction buffers and use of mono- or polyclonal antibodies mostly raised against casein or β-lactoglobulin. Proteins from egg white, namely, ovalbumin, ovotransferrin, ovomucoid, and lysozyme, are more allergic than proteins from egg yolk (Shoji, 2010). ELISA kits based on pAbs with specificity to a single egg protein (e.g., ovalbumin and ovomucoid) or to multiple egg proteins have been developed, so far. They have an LOQ lower than 1 ppm; however, their use is mainly ruled by antibody specificity. As a matter of fact, an ELISA targeting egg white protein may fail to detect egg yolk proteins (Sharma et al., 2017). Ovalbumin and ovomucoid content and related allergenicity thereof make them effective markers for detection of egg by ELISA (Sharma et al., 2017). Various allergens belonging to different protein families have been identified in peanut kernel; of them, Ara h 1 and Ara h 2 (i.e., 7S globulin and 2S albumin, respectively) can cause 95% of peanut allergy reaction in sensitive individuals. Peanut allergens vary in their protein conformation and are modified chemically by processing (e.g., denaturation or aggregation). This means that their extractability and determination in processed foods is challenging. So far, sandwich ELISAs based on pAbs are available (LOQ 5 0.3 2.5 ppm) and sensitivity varies according to the allergen (Sharma et al., 2017). ELISA methods have been also developed to trace allergens in tree nuts like almond, Brazil nut, hazelnut, pistachio, and walnut. 11S globulin is the major storage protein in almond and has been used as a marker protein for allergen detection in almond by ELISA (LOD 5 3 ng almond protein/mL). However, immunoreactivity varies significantly among almond varieties and depending on the used s-ELISA, for example, by using a rabbit antialmond polyclonal as the capture antibody and a mouse antiamandin monoclonal as detector antibody. Antibody cross-reactivity is, however, found among proteins from different tree nuts because of homologous amino acid sequences in tree nuts belonging to the same family, for example, walnut and pecan and cashew nut and pistachio (Sharma et al., 2017).

1.5.5.2 Immuno- and biosensors Besides ELISAs, immunosensors have been also developed with promising results, in terms of good LOD, costeffectiveness, rapidity, user-friendliness, and on-site analysis for the determination of gluten (Melini & Melini, 2018; Scherf & Poms, 2016). Combination of a cheap and robust piezoelectric transducer and the photonic immobilization technique has allowed obtaining a LOD of about 4 ppm and a sensitivity of about 7.5 15 ppm (Funari et al., 2017). Moreover, the experimental device showed to keep low the influence of false positives (Funari et al., 2017). A giant magnetoresistive sensor array (Ng, Nadeau, & Wang, 2016), an electrochemical label-free immunosensor for ultrasensitive and specific detection of gliadin (Chekin et al., 2016), an electrochemical assay combining differential pulse voltammetry, and disposable pencil graphite electrode (Eksin, Congur, & Erdem, 2015) have been also developed. Over the past decade, research has also focused on the innovation of biosensors and related techniques for the detection and characterization of new allergens (Neethirajan, Weng, Tah, Cordero, & Ragavan, 2018). An emerging technology, known as “point of care” (POC), has shown the advantage of requiring small sample volumes, rapid analysis, lower costs, and broader diagnostic accessibility. Moreover, most of POC technologies may generate either quantitative or qualitative results. POC technology, combined with label-free electrochemical immunosensors and graphene-based screen-printed electrodes, allows detection of different allergens. Regarding ovalbumin in egg whites, POC biosensors allow detecting concentrations from 1 pg/mL to 0.5 μg/mL (Neethirajan et al., 2018). Trace of ovalbumin in commercially available wine sample can be also detected by POC biosensors. Detected concentrations range from 0.03 to 0.2 μg/mL, with a lower limit of 0.25 mg/L of ovalbumin. Glycin, one common soybean allergen, can be also detected with POC technology combined with chromatographic lateral flow and gold-labeled antibody reaction (Neethirajan et al., 2018). Ara h 1 and Ara h 2 (target allergens for peanuts) are also detected by POC devices and results were confirmed by ELISAs, with a detection range of 1.0 mg/kg or 0.0001% of the allergen concentration with a limit of detection of Ara h1 and Ara h2 of 18.0 and 0.07 μg/mL, respectively. Nanomaterials (e.g., the metal oxides like silver, gold, titanium, carbon, and graphene nanotubes) have been also applied to POC biosensors because of their sensitivity, specificity, rapidity, low costs, and on-site detectability, as well as for their ability to immobilize bioaffinity agents. This is of paramount importance as randomized and unstable immobilization would otherwise lead to denaturation of enzymes and other bioaffinity agents (Neethirajan et al., 2018). Nanomaterial technology has shown to allow detection of peanut, gluten, fish, and sesame allergens (Neethirajan et al., 2018). Among fluorescent nanoparticles, quantum dots are the most popular alternative tool for food ˇ ´ et al., 2015; Neethirajan et al., 2018). allergen detection with sensitivity of nanogram levels in milliliters (Cadkova

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Optical, electromechanical and electrochemical biosensors are other examples of tools for allergen detection, for which a comprehensive analysis has been recently published by Neethirajan et al. (Neethirajan et al., 2018). However, the challenge remains, as these newly developed tools are not commercially available, and cannot be used by either food manufacturers, or food safety organizations, or individual consumers. Immunochromatographic assays are available in the format of dipsticks and lateral flow devices. They assure qualitative results with an indication of allergen presence or absence. They are considered very good candidates for allergen detection, as they are relatively cheap to produce, easy to use, and rapid, as they do not require special instrumentation. Moreover, little training is required to perform the analysis.

1.5.5.3 Mass spectrometry Regarding nonimmunological methods currently used in allergen detection and, in particular, in gluten detection, MS methods have shown a high sensitivity. They are also applied coupled with different methods of ionization, separation, and detection, such as matrix-assisted laser desorption/ionization, electrospray ionization, time-of-flight, and ion trap or triple quadrupole detection. LC-MS-based methods have drawn increasing attention, representing a sequence-specific, protein-based approach to allergen detection (Andjelkovi´c & Josi´c, 2018; Korte, Oberleitner, & Brockmeyer, 2019). A good number of methods have been so far developed with promising results in terms of sensitivity and high linearity (Boo, Parker, & Jackson, 2018; Korte & Brockmeyer, 2016; Korte et al., 2016; Monaci, Losito, De Angelis, Pilolli, & Visconti, 2013; New, Schreiber, Stahl-Zeng, & Liu, 2018; Pilolli et al., 2018). As regards the application of LC-MS/ MS to gluten detection, successful quantitative determination of wheat marker peptides has been obtained, with data comparable with those obtained by gel permeation-HPLC with fluorescence detector and R5 ELISA (Schalk, Koehler, & Scherf, 2018). Low sensitivity was observed in case of samples with a low content of gluten (,100 μg), though the use of a different MS instrument might help to overcome the challenge (Schalk et al., 2018). Identification and quantitation of gluten peptides by LC-MS/MS require, however, attention for the selection of the appropriate extraction procedure, the choice of a suitable enzyme (endoproteasis) for gluten digestion, the selection of a specific gluten marker peptide, and the calibration with a suitable RM for the final calculation of gluten content (Melini & Melini, 2018; Scherf & Poms, 2016). Research is thus required to tackle these issues. The high cost of equipment and the expertise of the operator to obtain accurate results are further disadvantages of application of this technique to allergen detection. A newly developed LC-MS/MS method showed it is possible to screen simultaneously or quantify the signature tryptic peptides of multiple allergen commodities (i.e., egg white, skim milk, peanut, soy, and tree nuts like almonds, Brazil nuts, cashew, hazelnut, pecan, pine nut, pistachio, and walnut) at a detection limit of 10 ppm in incurred bread and cookies (New et al., 2018). Quantitative analysis was performed also on whole-eggs, whole-milk, peanut butter, and hazelnut commodities, incurred or spiked into selected food matrixes, and excellent sensitivity was demonstrated, with a method quantitative limit of 3 ppm for whole eggs and 10 ppm for the remaining three allergen commodities (New et al., 2018). Multiallergen detection and quantitation of egg, milk, and peanut in cookies was performed with an LC-MS/MS multiple-reaction monitoring (MRM) method, and sample extraction, concentration, and digestion were optimized to guarantee the reliable detection and quantitation of egg, milk, and peanut allergens as low as 5 ppm per kg incurred sugar cookies (Boo et al., 2018). A method for detection of 10 allergens (i.e., egg, milk, soy, peanut, almond, hazelnut, walnut, pecan nuts, cashew, and pistachio) in eight different matrices high in fat, carbohydrates, proteins, tannins, or polyphenols in a single day has been also developed (Planque et al., 2019). Despite the complexity of some foods can lead to interferences that influence the sensitivity of the MRM signals, promising results were obtained (Planque et al., 2019). It has emerged the importance of setting signal-to-noise ratios above 10 and 3 for the first and second transitions, respectively, and a 2.5% retention time deviation according to guideline SANTE/11813/2017 (Planque et al., 2019). Moreover, the selection of marker peptides and the determination of the method sensitivity, in terms of LOQ and LOD, should be done with processed and incurred food products, as thermal processing has an impact on allergens (Planque et al., 2019). Food allergen quantification is mined also by food processing, which might affect protein structure and allergenicity through unfolding, aggregation, chemical modification, or cross-linking to matrix components (Rahaman, Vasiljevic, & Ramchandran, 2016). Regarding allergen detection in highly processed food products, ELISAs are not suitable because of protein modifications and interfering compounds, such as polyphenols, high fat content, etc. (Cho, Nowatzke, Oliver, & Garber, 2015; Korte et al., 2019; Planque et al., 2017; Platteau et al., 2011). Methods based on ultra-HPLC coupled to tandem MS (MS/MS) can be a promising alternative for the detection of allergens in products processed at high temperature (Gomaa & Boye, 2015; Planque et al., 2017).

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1.6 Water 1.6.1 Water content in foods Water is the predominant constituent in many foods, and it has several functions: it influences food sensory properties; it affects food preservation; it affects the product shelf life; and it influences the commercial value of products (Isengard, 2008). Differences exist between water (moisture) content and water activity. After moisture removal, what remains is the dry matter, which is commonly referred to as total solids. Legal limits are established by law on how much water must or can be present in some foods. Water activity (aw) is a measure of the availability of water in a food product; it describes the degree to which water is bound in a food system. It is actually a thermodynamic property of water in food, defined as the ratio of the fugacity of water in the food to the fugacity of pure water at the same temperature and pressure (Nielsen, 2017). It is aw that influences microbial growth, food physical properties, and chemical and enzymatic reactions in foods. In addition, the differences in aw drive moisture migration between the different parts of food components, or between a food and the environment.

1.6.2 Water content determination Determining water content and water activity of a food provides a complete moisture analysis. Regarding water/moisture content analysis, the choice of the method is affected by the moisture content of the food under investigation, as it varies greatly from food to food. Direct methods consist in removing water (separation step), followed by the quantitation step by either weight or volume. The separation step of moisture from the solids is traditionally performed by drying (forced draft oven, vacuum oven, microwave drying, IR drying, rapid moisture analyzer technology, and thermogravimetric analyzer). It is then followed by the quantitation step by either weight or volume: weighing, volumetry, or titration to determine moisture content. The most frequently used chemical method for the determination of water content is volumetric Karl Fischer (KF) titration. Dielectric methods, hydrometry, refractometry, microwave absorption, and IR analysis are the most commonly used physical methods. Indirect methods are based on the properties of the food related to the presence of water: density, refractive index, freezing point, specific gravity, capacitance, and electromagnetic absorption. Regarding aw determination, vapor pressures are directly measured, although indirect measurements are also used.

1.6.2.1 Traditional versus emerging methods Traditionally, a product is dried at a certain temperature for a certain time with a “classical” oven drying, or with vacuum drying, freeze drying, IR drying, or microwave drying (Marques et al., 2016). However, the application of these methods does not allow water distinction from other volatile substances; moreover, what is obtained is the mass loss and not water content (Marques et al., 2016). One more issue is represented by the fact that water in food is distributed in different bonding states: this means that both the dry matrix and its water content can affect the method performance. Recently, the routine moisture determination by loss on drying has been substituted by IR loss on drying (IRLOD) and microwave-assisted loss on drying (MALOD), which allow reducing the analysis time. In meat and meat products, for instance, Marques et al. observed that water removal by MALOD is 64-fold higher than that by loss on drying and provides similar results; moreover, there is a lower energy consumption and higher sample throughput (Marques et al., 2016). They also compared IRLOD with MALOD and observed that, however, for IRLOD, results were generally lower, whereas in meat products, MALOD showed to be a fast and accurate method, allowing to save time and energy expenditure in agreement with green analytical chemistry (Marques et al., 2016). Regarding the quantitation step, the most frequently used chemical method for the determination of water content is volumetric KF titration. It is often used as reference method in a wide range of organic and inorganic samples, together with the drying method within the constant weight (Adam, Dobia´sˇ, Bajerova´, & Ventura, 2009; Kestens, Conneely, & Bernreuther, 2008). KF titration is based on the reaction of water with iodine, performed in a methanolic solution. This method brings the advantage of a high selectivity to the water: with respect to the weight loss technique, only water content is determined, as iodine selectively reacts with water. Moreover, it is a fast analysis, with measurements of 1 2 min, and it is accurate and precise. This method has been mainly used in determination of water content in dried milk samples (Reh, Bhat, & Berrut, 2004). Over the last years, spectral techniques have been increasingly used in water content determination, with advantages over other physical methods, like dielectric techniques, hydrometry, refractometry, and microwave absorption. IR

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analysis brings the advantage that in both middle infrared and NIR regions, there exist, in fact, some absorption stretches of the characteristic vibrations, which are specific for water. NIR spectroscopy is the most used spectral technique, thanks to its speed, the minimal or lack of sample pretreatment, and the lack of chemicals. NIR has been applied to water determination in milk powder, which has become a widely accepted technique to monitor moisture in the dairy industry (Holroyd, 2013; Nagarajan, Singh, & Mehrotra, 2006). Cama-Moncunilla et al. developed a novel multiprobe NIR system, based on a Fabry Perot interferometer, combined with four fiber probes, to predict the moisture content of samples at varying moisture levels, and under static conditions or various levels of motion (Cama-Moncunilla et al., 2015). Partial least-squares regression was used to correlate the spectral response with the reference moisture values. They found that the method was suitable for in-line moisture analysis of powdered infant formula. NIR spectroscopy was also applied to moisture content determination in honey (RaJalakshmi, Gopal, Kumar, & Kumar, 2017). Yang et al. developed a rapid method for determination of moisture content in milk powder by microwave sensor. They observed that the microwave technique gave results comparable with those determined by standard weighing method. The maximal measurement deviation was 0.2%; this implies that microwave method can be considered an effective tool for determining the moisture content of milk powder in the dairy processing industry (Yang, Huang, Peng, & Shi, 2016).

1.7 Conclusions Over the last years, the scientific community has worked at the development of analytical procedures, alternative to classical techniques, to meet green chemistry principles. The newly developed methods have increasingly shown to be promising solutions to the demand of reducing or eliminating the use of solvents and reagents harmful to human health and to the environment, while improving the extraction of the products and the efficiency of the methods. These techniques also showed they can make a contribution to possibly minimizing energy requirements for the chemical process and limiting wastes. The new analytical methods (e.g., vibrational spectroscopy), associated with chemometrics, can be recommended as they are fast, demand little or no sample preparation, generate no risks to the operator, and produce no toxic waste. Their application remains, however, reliant on calibration with traditional methods. Some other techniques still require high acquisition and/or management costs.

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Chapter 2

Bioactive component analysis Senem Kamiloglu1, Merve Tomas2, Tugba Ozdal3, Perihan Yolci-Omeroglu4 and Esra Capanoglu5 1

Science and Technology Application and Research Center (BITUAM), Bursa Uludag University, Gorukle, Bursa, Turkey, 2Department of Food

Engineering, Faculty of Engineering and Natural Sciences, Istanbul Sabahattin Zaim University, Istanbul, Turkey, 3Department of Food Engineering, Faculty of Engineering, Istanbul Okan University, Istanbul, Turkey, 4Department of Food Engineering, Faculty of Agriculture, Bursa Uludag University, Bursa, Turkey, 5Department of Food Engineering, Faculty of Chemical and Metallurgical Engineering, Istanbul Technical University, Istanbul, Turkey

2.1

Introduction

In the 21st century, bioactive compounds have emerged as health-beneficial therapeutic agents, potentiating the design of novel supplements and functional food products. The interest in bioactive compounds continues to grow, supported by the development of new technologies and continuous research efforts to identify the properties and potential applications of these substances, together with public interest and consumer demands. Therefore food industry is interested in obtaining and characterizing bioactive compounds that can be used as functional food ingredients, supplements, or nutraceuticals (Ðorðevi´c et al., 2015). Prior to the analysis of bioactive compounds, plant materials need to be subjected to appropriate pretreatment, extraction, and cleanup procedures. For these treatments, along with conventional methods, numerous new methods have been established. However, until now, no single method is regarded as a standard one. The efficiencies of both conventional and nonconventional methods mostly depend on the critical input parameters, understanding the nature of plant matrix, chemistry of bioactive compounds, and scientific expertise (Azmir et al., 2013). Furthermore, the coupling of chromatographic methods and spectroscopic techniques, such as mass spectrometry (MS), nuclear magnetic resonance (NMR) spectroscopy, or infrared spectroscopy, can provide suitable structural information of the separated bioactive components (Huang et al., 2004). This chapter presents the different aspects of sample pretreatment, extraction, separation, identification, and quantification methods developed to determine bioactive compounds in food products, including polyphenols, carotenoids, vitamins, omega-3 fatty acids (FAs), organic acids, nucleosides and nucleotides, and phytosterols. In addition, the advantages and disadvantages of the existing analysis methods are highlighted.

2.2

Polyphenols

Polyphenols are divided into several classes according to the number of phenol rings that they contain and to the structural elements that bind these rings to each other. The main groups of polyphenols are flavonoids, phenolic acids, stilbenes, and lignans (D’Archivio et al., 2007). Flavonoids are low molecular weight compounds, comprising of 15 carbon atoms, arranged in a C6 2 C3 2 C6 configuration. The flavonoid structure comprised of two aromatic rings, joined by a 3-carbon bridge, usually in the form of a heterocyclic ring. Alterations in the substitution patterns of this heterocyclic ring result in six different subclasses, namely, flavonols, flavones, flavanones, flavanols, isoflavones, and anthocyanidins (Balasundram, Sundram, & Samman, 2006). Differences within each group originate from the variation in number and arrangement of the hydroxyl groups and their extent of alkylation and/or glycosylation (Grootaert, Kamiloglu, Capanoglu, & Van Camp, 2015; Pandey & Rizvi, 2009; Spencer, El Mohsen, Minihane, & Mathers, 2008) (Fig. 2.1). Major flavonols include quercetin, myricetin, and kaempferol. Flavones consist mainly of glycosides of apigenin and luteolin. The main flavanone aglycones are naringenin, hesperetin, and eriodictyol. Flavanols exist both in monomer (catechins) and polymer (proanthocyanidins) forms. Isoflavones contain three main molecules, namely, genistein, daidzein, and glycitein. The most widespread anthocyanidins are cyanidin, delphinidin, malvidin, pelargonidin, and peonidin (Manach, Scalbert, Morand, Re´me´sy, & Jime´nez, 2004). Phenolic acids consist of two subgroups of Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00002-9 Copyright © 2021 Elsevier Inc. All rights reserved.

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FIGURE 2.1 Classification of major classes of dietary polyphenols (Grootaert et al., 2015).

hydroxybenzoic and hydroxycinnamic acids. Hydroxybenzoic acids include gallic, p-hydroxybenzoic, protocatechuic, vanillic, and syringic acids having C6
C1 structure, whereas hydroxycinnamic acids are aromatic compounds with a three-carbon side chain (C6
C3), with caffeic, ferulic, p-coumaric, and sinapic acids being the most common ones (Balasundram et al., 2006). Stilbenes contain two phenyl moieties connected by a two-carbon methylene bridge and the main representative of stilbenes is resveratrol (Ignat, Volf, & Popa, 2011). Lignans are produced by oxidative dimerization of two phenylpropane units and secoisolariciresinol is the major compound present in dietary sources (D’Archivio et al., 2007; Manach et al., 2004). Lately, polyphenols have attracted great attention due to their role in prevention of several chronic diseases including cardiovascular diseases and certain types of cancer (Shahidi & Ambigaipalan, 2015). Accordingly, the methods that are used to analyze these compounds are also of interest. The preliminary steps before the analysis of polyphenols involve the sample pretreatment, extraction, and isolation.

2.2.1 Sample pretreatment Sample pretreatment involves procedures such as grinding, filtration, centrifugation, hydrolysis, or drying. In particular, liquid samples can be analyzed simply after filtration, centrifugation, and/or dilution. On the other hand, solid samples need to be ground after freezing with liquid nitrogen or may need to be dried. Grinding is performed to increase the contact surface by reducing the sample size, while drying is carried out in order to prevent undesirable chemical and enzymatic reactions that may occur in samples with high moisture content. Hot air-, microwave-, and freeze-drying are among the most common drying methods. Hot air- and microwave-drying are time-consuming and may cause degradation of polyphenols, whereas freeze-drying, although expensive, is more rapid and retains the polyphenols better (Kamiloglu et al., 2016). Nevertheless, it is important to keep in mind that extensive removal of moisture from the sample may cause the contraction of cells, entrapping the polyphenols, and, therefore, hampering their extraction. Hydrolysis can be carried out with acid, base, or enzymes, with acid hydrolysis being the most common one. However, it should be noted that changing the pH would break down the conjugated polyphenols into their aglycones. Overall, there is no standard sample pretreatment procedure as each sample matrix is complex and requires different pretreatment(s) according to its specific needs (Plaza, Domı´nguez-Rodrı´guez, Castro-Puyana, & Marina, 2018).

2.2.2 Extraction of polyphenols The extraction of polyphenols from samples is commonly applied using conventional methods such as solid
liquid or liquid
liquid extraction (LLE) techniques. In solid
liquid extraction, a solid sample is placed in contact with a solvent

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or solvent mixture, in which the polyphenols are dissolved accompanied by stirring and/or heating. The optimum extraction parameters rely on the solubility of polyphenol(s) in the extraction solvent. Studies in the literature indicated that the most common extraction parameters that are used to extract polyphenols from solid samples are as the following: from room temperature up to 90 C for 15 min 2 3 days, using water or aqueous methanol/ethanol as solvent (Gontijo et al., 2017; Mallek-Ayadi, Bahloul, & Kechaou, 2017; Sharma, Joshi, Kumar, Agrawal, & Prasad, 2017). On the other hand, LLE depends on the solubility of polyphenols between two immiscible liquids (Tasioula-Margari & Tsabolatidou, 2015), and for this technique, water or acidified water is the most common extraction solvent (IvanovaPetropulos et al., 2015; Rodrı´guez-Pe´rez & Quirantes-Pine´, 2015). As conventional methods have some disadvantages including the use of high amount of solvents, long procedure times, low reproducibility, and poor selectivity (Herrero, Castro-Puyana, Mendiola, & Iban˜ez, 2013), some new advanced extraction techniques including ultrasound-assisted extraction (UAE), microwave-assisted extraction (MAE), pressurized liquid extraction (PLE), supercritical fluid extraction (SFE), CO2-expanded liquid extraction, high hydrostatic pressure extraction, and nonthermal extraction techniques have been developed. Among these advanced extraction techniques, UAE is the most common one that disrupts the cell wall of the matrix due to bubble cavitation, and hence assists the penetration of extraction solvent to sample matrix to release polyphenols (Corbin et al., 2015). Power, frequency, time, temperature, and solvent composition are the important parameters to be considered. In general, the power and frequency used for polyphenol extraction are reported to be 50 2 400 W and 20 2 60 kHz, respectively (Plaza et al., 2018). Compared with conventional techniques, UAE requires less time and lower temperatures. In fact, extraction of polyphenols with ultrasound is commonly carried out at room temperature. Different solvents including water or methanol/ethanol or their mixtures can be used, and in order to prevent the degradation of polyphenols, occasionally formic acid or a similar kind is added to the extraction solvent (Kamiloglu, 2019a).

2.2.3 Isolation of polyphenols Polyphenols are often isolated using cleanup procedures such as LLE and solid-phase extraction (SPE) techniques. One of the most common LLE applications is the removal of undesired lipophilic compounds using hexane as a washing solvent. LLE has the disadvantages of high amount of solvent consumption and low selectivity, whereas SPE is cheaper, faster, and more straightforward (Lucci, Saurina, & Nu´n˜ez, 2017). In order to remove undesired substances from polyphenol rich extracts, different SPE cartridges can be used; among them, C18 and LH20 are the most common ones. SPE cartridges are preconditioned with water, methanol, or their mixtures. Afterward, the samples are loaded to activated cartridges, which are subsequently washed with water, methanol, or their mixtures. For C18 cartridges, polyphenols can be eluted with methanol (Kamiloglu et al., 2016) or with other solvents such as aqueous acetonitrile (Rodrı´guez-Pe´rez & Quirantes-Pine´, 2015), while aqueous acetone is used for LH20 cartridges (White, Howard, & Prior, 2010). On the other hand, the sorbents in these cartridges are not very sensitive for the recovery of some polar polyphenols including phenolic acids (Lucci et al., 2017). Besides SPE, some other new techniques are also developed for the isolation of polyphenols. Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS), dispersive-SPE, molecularly imprinted polymers, and high speed countercurrent chromatography are among these novel techniques, which are applied for the purification of polyphenols (Plaza et al., 2018).

2.2.4 Analysis of polyphenols 2.2.4.1 Spectrophotometric methods Spectrophotometric methods are convenient for the polyphenol analysis as UV-Vis spectrum is attributed to the electron transitions occurring within the hydroxyl groups, which is distinct for different classes of polyphenols (Sanna et al., 2014). For the quantification of polyphenols in food products, spectrophotometric methods are commonly applied to determine the total phenolic content, total flavonoid content, total anthocyanin content, total proanthocyanin content, and total hydrolysable tannin content. For the determination of total phenolic content, Folin
Ciocalteu is the most widely used method. Folin
Ciocalteu assay was initially designed for the analysis of proteins (Folin & Ciocalteu, 1927), which was later adopted to analyze phenolic compounds in wine (Singleton, Orthofer, & Lamuela-Ravento´s, 1999), after which it became a routine analysis for the assessment of total phenolic content in food products. This assay is based on the redox reaction of polyphenols with phosphomolybdic/phosphotungstic acid complexes in an alkaline condition to yield a blue-colored chromophore with maximum absorption at 765 nm (Magalha˜es, Segundo, Reis, & Lima, 2008). The molybdenum center in the complexes is considered as the reduction site, where the Mo61 ion is reduced to Mo51 by accepting an electron donated by

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the phenolic compound. Gallic acid is commonly used as a reference standard and results of total phenolic content are usually expressed as gallic acid equivalents (Shahidi & Zhong, 2015). Folin
Ciocalteu assay has the advantages of being simple, reproducible, and robust. On the other hand, there are also some drawbacks of this method. The major concern with this assay is the lack of specificity for phenolic compounds. Reducing agents such as ascorbic acid, citric acid, simple sugars, or certain amino acids can interfere with the analysis, and thus overestimate the total phenolic content. In addition, the assay is applied in aqueous medium, and thus the measurement of lipophilic phenolic compounds is limited (Capanoglu, Kamiloglu, Ozkan, & Apak, 2018). For the latter, it has been proposed that NaOH-containing isobutanol/water medium can be used for simultaneous measurement of hydrophilic and lipophilic phenolics (Berker, Ozdemir Olgun, Ozyurt, Demirata, & Apak, 2013). For the determination of total flavonoid content, chromogenic analysis involving aluminum chloride is the most commonly applied method. In the literature, there are two widely used assays based on the formation of aluminum chloride complex. In the first approach, aluminum chloride (2% 2 10%) is mixed with polyphenol extract and the absorbance of the mixture is measured at 240 2 500 nm and the results are expressed as quercetin equivalents (Mammen & Daniel, 2012). In the second one, sodium nitrite and aluminum chloride are mixed with polyphenol extract followed by the addition of sodium hydroxide. In this approach, the absorbance of the mixture is recorded at 510 nm and the results are commonly expressed as catechin or rutin equivalents. The first approach is selective for flavonols and flavones, whereas the second one is specific for catechins, rutin, luteolin, and phenolic acids (Pe˛kal & Pyrzynska, 2014). Therefore aluminum chloride assay has the disadvantage of being selective, which does not correspond to the total flavonoid content. Furthermore, many food products include glycosylated flavonoids, which hinder the chelation with aluminum chloride (Mammen & Daniel, 2012). Anthocyanins undergo structural rearrangements in response to changes in pH in four molecular structures: quinoidal base (blue), flavylium cation (red), carbinol (colorless), and chalcone (yellowish) forms. They are stable in acidic solutions (pH 1
3), where they exist primarily as flavylium cations. At pH . 4, anthocyanins adopt the forms of the carbinol and chalcone (Kamiloglu, Capanoglu, Grootaert, & Van Camp, 2015). Considering this behavior, the pH differential method is employed to determine the total anthocyanin content in food products. Hence, pH differential method is based on the formation of red flavylium cation at pH 1.0 and colorless hemiketal form at 4.5. The difference in the absorbance of the pigments at 520 nm is proportional to the pigment concentration. The absorbance at 700 nm is also measured to correct the haze. The molar absorption coefficient and the molecular of predominant anthocyanin in the analyzed food sample are used to calculate the total anthocyanin content. It is worth to mention that the pH differential method is applicable to monomeric anthocyanins, as degraded polymeric anthocyanins are resistant to color change regardless of pH (AOAC, 2006). Determination of total proanthocyanidin content is carried out using different assays including butanol-HCl, dimethylaminocinnamaldehyde (DMAC) and vanillin-HCl/H2SO4 assays. Butanol-HCl assay is based on oxidative depolymerization of proanthocyanidins in acidic and alcoholic medium provided by HCl and butanol, respectively, forming anthocyanidins. This method is later modified by suspending the test sample with an iron (II) solution to promote the conversion of proanthocyanidins into anthocyanidins. DMAC assay employs p-DMAC as chromogen, which reacts with proanthocyanins in one monomeric unit. This method is highly reproducible, that is, ,2% standard deviation, and selective, that is, proteins, ascorbic acid, and cysteine have a negligible interference on the results. On the other hand, vanillin assay is very specific for flavanols and dihydrochalcones that have a single bond at 2,3 position and free meta-oriented hydroxyl groups on the B ring. Experimental data have shown that flavones, flavonols, isoflavones, phenolic acids, and gallotannins do not react in the vanillin-HCl assay. However, when HCl is replaced by H2SO4, the results present more repeatability and higher sensitivity. For these three assay, results are measured at 500
640 nm, and in general expressed as catechin equivalents (Granato, Santos, Maciel, & Nunes, 2016). Total hydrolysable tannin content can be determined with different methods including potassium iodate, rhodanine, and sodium nitrite assays. In potassium iodate assay, potassium iodate and galloyl esters in hydrolysable tannins react in acetate or methanol and produce a coloration measured at 525 nm. This method is nonspecific, as variable reaction times are required to obtain an optimum absorbance and yellow products are formed when different polyphenols are present in the extract (Hartzfeld, Forkner, Hunter, & Hagerman, 2002). Rhodanine assay is based on the reaction of rhodamine with hydroxyl groups of gallic acid, which generate a coloration measured at 518 nm. This assay has the limitation of being specific to free gallic acid, thus determining the gallotannins that an acid hydrolysis pretreatment is necessary (Arapitsas, 2012). In sodium nitrite assay, sodium nitrite reacts with esters of ellagitannins under basic environment with pyridine and the chromophore formed with ellagic acid is measured at 538 nm. This assay is sensitive to oxygen and requires long times for hydrolysis and large volume of pyridine solution (Plaza et al., 2018).

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2.2.4.2 Chromatographic methods Chromatographic methods provide more specific and detailed information about the polyphenol content of food products than spectrophotometric methods. High-performance liquid chromatography (HPLC) and ultraperformance liquid chromatography (UPLC) are by far the most common techniques that are used for separation, identification, and quantification of polyphenols. Several factors including polarity, molecular weight, stereochemistry, and degree of polymerization of polyphenols may affect the chromatographic separation. The analysis of polyphenols is often performed in the reverse-phase mode using C18 or C8 bonded silica columns with 100
300 mm in length and 2
4.6 mm in diameter. The column temperature ranges from room temperature to up to 50 C (Plaza et al., 2018). Gradient elution is usually carried out with binary solvent system using acidified water as the polar solvent and methanol or acetonitrile as an organic solvent. The acidification of the mobile phase is carried out to depress the dissociation of phenolic hydroxyl groups to obtain sharp peaks with reduced tailing (Kuwahara, Hatate, Chikami, Murata, & Kijidani, 2010). The gradient is initiated with water followed by the increase in methanol/acetonitrile with a flow rate of 0.3
1.6 mL min21, and depending on the diameter of the column, injection volume ranges from 1
100 μL. Due to the use of high pressure pump and ,2 μm particle packed column, UPLC has the advantages of being more sensitive and rapid compared with HPLC (Plaza et al., 2018). In addition, hydrophilic interaction liquid chromatography (HILIC) is also used for the analysis of polyphenols. HILIC is based on a normal-phase chromatography of polar compounds with a mobile phase composed of organic solvents such as acetonitrile (Montero, Herrero, Iba´n˜ez, & Cifuentes, 2013). Diode array detection (DAD) is the most common detector that is coupled to HPLC to be used in polyphenol analysis. With this detector, the detection of various classes of polyphenols is carried out at different wavelengths, with phenolic acids being detected at 240
285 nm, flavones and flavonols at 350
365 nm, and anthocyanins at 460
560 nm (Lorrain, Ky, Pechamat, & Teissedre, 2013). For the identification of polyphenols, retention time and the spectral data are compared with the ones of commercial standards. However, the lack of some commercially available standards is one of the major challenges of polyphenol analysis in complex matrixes such as food products. Fluorescence detector is another type of detector that is used to analyze procyanidins (Wu et al., 2017). Furthermore, charge aerosol detector and electrochemical detection are also among the other detectors that are also used in polyphenol analysis (Bayram et al., 2012; Plaza, Kariuki, & Turner, 2014). When the concentration of polyphenols in the analyzed food sample is low, more sensitive and selective detectors such as MS are necessary. In MS, electrospray ionization (ESI) source is used for the ionization of polyphenolic compounds and the detection is most commonly done at negative mode, whereas positive mode is also used for the analysis of anthocyanins (Kamiloglu et al., 2016). Several MS detectors including triple quadrupole, ion trap, time of flight (TOF), quadrupole TOF, and orbitrap are used for the analysis of polyphenols. In fact, MS can also be used alone, without chromatographic separation, for the analysis of polyphenols. Besides ESI, there are also other ionization sources; among them, matrix-assisted laser desorption ionization (MALDI) is used to obtain structural information of polyphenols by direct flow injection analysis. MALDI is commonly used together with TOF, which has the advantage of having unlimited mass range (Ignat et al., 2011). In addition to the above, gas chromatography (GC) and supercritical fluid chromatography are also used for the analysis of polyphenols. However, these methods are rarely used compared with liquid chromatography. GC is laborintensive, needs derivatization step, and it is not suitable for the analysis of high molecular weight compounds (Kivilompolo, Ob˚urka, & Hyo¨tyla¨inen, 2007). In supercritical fluid chromatography, a supercritical fluid, commonly compressed carbon dioxide, is used as the mobile phase. This technique is more versatile than HPLC, more costefficient, user friendly, with higher output, better resolution, and faster analysis times than general LC methods (Ignat et al., 2011).

2.2.4.3 Other analysis methods Capillary electrophoresis and NMR are some other techniques that are used to analyze polyphenols. Capillary electrophoresis is a convenient technique for the separation and quantification of low to medium molecular weight polar and charged compounds, and has high separation efficiency, high-resolution power, short analysis time, and low sample and reagent consumption. On the other hand, this technique has the limitations of being less sensitive in terms of solute concentration, and less reproducible compared with chromatographic techniques. NMR, although has the advantages of being powerful in terms of structural elucidation and nontargeted analysis of metabolites, also has the disadvantages of being expensive and insensitive compared with chromatographic techniques (Ignat et al., 2011).

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2.3

Innovative Food Analysis

Carotenoids

Carotenoids, the most wide and significant group of fat-soluble pigments, provide the characteristic yellow, orange, and red colors of many fruits, vegetables, and plant life (Eggersdorfer & Wyss, 2018). It has been strongly suggested that consumption of a diet rich in carotenoids has been epidemiologically correlated with a lower risk for several chronic diseases including cancer, cardiovascular diseases, osteoporosis, and other human diseases (Rao & Rao, 2007). Carotenoids are tetraterpenoids and synthesized in plants, and other photosynthetic organisms as well as in some nonphotosynthetic bacteria, yeasts, and molds (Stahl & Sies, 2005). However, humans do not synthesize carotenoids; instead, they must be consumed through the diet or via supplementation (Eggersdorfer & Wyss, 2018). According to their chemical composition, they are classified into two main groups: xanthophylls and carotenes. Xanthophylls consist of a variety of derivatives frequently containing hydroxyl, epoxy, aldehyde, carboxylic acid, and keto groups. Furthermore, zeaxanthin, lutein, α- and β-cryptoxanthin, canthaxanthin, and astaxanthin are important members of the xanthophylls group. On the other hand, β-carotene, α-carotene, and lycopene are the main members of the carotene group, which are composed only of carbon and hydrogen atoms (Damodaran & Parkin, 2017). Due to the presence of the conjugated double bonds, carotenoids can undergo isomerization to cis-trans isomers (Rao & Rao, 2007). The alltrans form is thermodynamically the most stable and predominant in nature, whereas cis isomers of carotenoids are present in blood and tissues (Stahl & Sies, 2005). Lycopene, lutein, zeaxanthin, β-cryptoxanthin, α-carotene, and β-carotene are generally abundant in foods. Due to the unsaturated structure of the carotenoids, they tend to oxidize. Moreover, temperature, light, pH, and processing can also influence them and, hence, their nutritional value. The carotenoid content of foods is influenced by a number of factors such as stage of maturity, variety or cultivar, climate or season, part of the plant consumed, production practices, postharvest handling, processing, and storage of food (RodriguezAmaya, 2003).

2.3.1 Extraction and isolation of carotenoids Various extraction methods have been used for the recovery of carotenoids from food matrix. Selection of method for carotenoid extraction from food matrices is critical due to the presence of diverse carotenoids with varied levels of polarity, physical, and chemical barriers in the food matrices (Saini & Keum, 2018). In addition, solvent type, cost, and feasibility are some other factors that are need to be considered when choosing a method for carotenoid extraction. Both the carotenes and the xanthophylls are lipophilic compounds and soluble in oils and organic solvents. In general, nonpolar solvents, including hexane, petroleum ether, or tetrahydrofuran, are used for nonpolar carotenoids or esterified xanthophylls, whereas polar solvents, acetone, ethanol, and ethyl acetate, are suitable for polar carotenoids (Pe´rezGa´lvez & Roca, 2018). On the other hand, mixture of solvents, which provides a synergistic effect, is commonly used to optimize the extraction. In particular, acetone/ethanol/hexane mixture is found to be suitable for carotenoid extraction (Barba, Hurtado, Mata, Ruiz, & De Tejada, 2006). It is important to add antioxidants, such as butylated hydroxytoluene, tert-butlylhydroqinone, pyrogallol, or ascorbyl palmitat to the solvent mixture in order to prevent the degradation of carotenoids (Cernelic et al., 2013). In addition, sometimes saponification may be required during extraction, which eliminates undesirable lipids, FAs, chlorophylls, and carotenoid esters for chromatographic analysis (Singh, Ahmad, & Ahmad, 2015). In general, the most common method for the isolation of the carotenoids is the conventional solvent extraction, whereas new environmentally friendly nonconventional methods including MAE, UAE, PLE, and SFE are also becoming popular. Soxhlet extraction provides the highest recovery of carotenoids. However, this conventional method has some drawbacks such as long procedure times, exposure of the extracts to excessive heat, light, and oxygen, use of significant amounts of solvents, low yield, and thereby increase in the cost of extraction (Saini & Keum, 2018). MAE was proposed as an efficient and rapid processes to extract bioactive compounds, allowing reduced solvent consumption and shorter extraction times, with equivalent or higher extraction yields, whereas its disadvantage is inhomogeneous heating (Pasquet et al., 2011). UAE of carotenoids reduces extraction time, saves energy, increases yield, and lowers the temperature. Moreover, UAE is preferable to saponification, which breaks the cell wall through alkaline condition, due to no chemical involvement in the process (Lianfu & Zelong, 2008). Nevertheless, other nonconventional methods can be combined with UAE for higher efficiency of extraction yield (Azmir et al., 2013). The PLE is the application of high pressure to remain solvent liquid beyond their normal boiling point, which prevents degradation of thermolabile compounds (Singh et al., 2015). SFE is a superior green technique for the extraction of carotenoids. It reduces time of extraction and is the most important technique for thermolabile compounds such as carotenoids (Saini & Keum, 2018).

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2.3.2 Analysis of carotenoids Various analytical techniques have been used for the analysis of extracted carotenoids, including UV-Vis spectrophotometry, thin-layer chromatography (TLC), HPLC, MS/MS, NMR, and Fourier transform infrared spectroscopy (FTIR). UV-Vis spectrophotometry is inexpensive and easily performed. However, it has limitations due to exhaustive extraction methods, requirement of large amount of toxic solvents, and application to only specific food components (Biswas, Sahoo, & Chatli, 2011). Since carotenoids range in color from yellow to red, detection wavelengths for monitoring carotenoids typically range from approximately 400
480 nm (Damodaran & Parkin, 2017). Carotenoids have been also separated by TLC using 50% acetone in heptane (v/v) as mobile phase (Singh et al., 2015). Nevertheless, carotenoids are particularly prone to oxidation by air when adsorbed on TLC plates. Another disadvantage of this method is the difficulty in quantitative applications and the recovery of the separated carotenoids from the plate (Rodriguez-Amaya & Kimura, 2004). HPLC is the most widely used technique in carotenoid analysis, owing to its rapidity, nondestructiveness, preciseness, automation, and simplicity (Oliver & Palou, 2000). Reversed phase (RP)-HPLC on octyl (C8), octadecyl (C18), and triacontyl (C30) bonded phases columns are preferred for separating carotenoids for quantitative analysis. C18 and C8 are not resolved geometric isomers, whereas C30 columns give good resolution for carotenoids (Rodriguez-Amaya, 2010). The majority of carotenoid separation has been performed with 5-mm spherical particles packed in a 250 3 4.6 mm column (Rodriguez-Amaya & Kimura, 2004). Moreover, the photodiode array (PDA) detector has provided the visible absorption spectra of the separated carotenoids online range, using range of 350
550 nm. The column temperature ranges from room temperature to up to 40 C (Oliver & Palou, 2000). On the other hand, while the required run time for separation of carotenoids is about 10
30 min for C18 and C8 columns, C30 columns requires longer run times (60
100 min) (Pe´rez-Ga´lvez & Roca, 2018). The main mobile phase solvents are acetonitrile and methanol for carotenoid separation (Craft & Soares, 1992). Several ionization procedures have been carried out for the MS analysis of carotenoids: electron impact, atmospheric pressure solids analysis probe, atmospheric pressure chemical ionization, fast atom bombardment, electrospray, atmospheric pressure photoionization, and matrix-assisted laser desorption/ionization. Especially, electron impact is the most common ionization method due to the good molecular ions and many fragmentations diagnostic of particular structural features (Rodriguez-Amaya, 2016). On the other hand, MS/MS have been found to be efficient and advantageous for separation of carotenoids, as it reduces analysis time, distinguishes between co-eluting carotenoids, and provides information about structural isomers (Rivera, Christou, & Canela-Garayoa, 2014). NMR spectroscopy is a powerful technique for the identification of purified carotenoids. Tiziani, Schwartz, and Vodovotz (2006) reported that high-resolution multidimensional NMR experiments can be used as an excellent means to rapidly identify (all-E)-, (5Z)-, (9Z)-, and (13Z)-lycopene isomers and other carotenoids such as (all-E)-β-carotene and (15Z)-phytoene with minimal purification procedures. On the other hand, separation of carotenoids by FTIR spectroscopy has not yet been investigated much. Baranska, Schu¨tze, and Schulz (2006) carried out studies on the application of Fourier transform (FT)-Raman, attenuated total reflection infrared, and near infrared spectroscopy for fast and direct measurements of lycopene and β-carotene in tomato products. They reported that FTRaman spectroscopy can be successfully applied for the identification of carotenoids directly in the plant tissue and food products without any preliminary sample preparation step compared with others.

2.4

Vitamins

Vitamins are essential micronutrients that are needed in small amounts for metabolic and physiological functions taking place in the human body (Schmidt et al., 2019). Since humans cannot synthesize most of the vitamins, it is vital to obtain them from animal and plant sources, food products, and supplements (Pegg & Eitenmiller, 2017). There are 13 vitamins that play important role in human nutrition. They are categorized into two main groups based on their solubility properties. Fat-soluble group includes vitamin A (retinol), vitamin D (cholecalciferol), vitamin E (tocopherol), and vitamin K (phylloquinone). Water-soluble group comprises of vitamin C and vitamin B group including thiamine (vitamin B1), riboflavin (vitamin B2), niacin (vitamin B3), pantothenic acid (vitamin B5), pyridoxine (vitamin B6), biotin (vitamin B7), folic acid (vitamin B9), and cobalamins (vitamin B12) (Ball, 2004; Fatima et al., 2019). Inadequate intake of vitamins from diet results in diseases such as beriberi, anemia, neurological diseases, oral lesions, and pellagra. Therefore current trends in research and industry are to formulize functional foods or food supplements rich in vitamin content. This leads to the identification and determination of the vitamin content of natural sources correctly and the analysis of the food products and supplements to prove the content of vitamins indicated on the label in addition to the dietary risk assessments. Due to the variety of the food matrix composed of different types

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of additives, sugars, fats, proteins, and other compounds and due to the sensitivity of the vitamins to light, oxygen, temperature, and pH, accurate determination of vitamins is a challenging issue for an analyst (Blake, 2007; Katsa & Proestos, 2019). Therefore an analyst should select proper and reliable analytical methods for vitamin analysis and verify or validate the method performance parameters before adopting it to the routine analysis conditions (Fatima et al., ¨ rnemark, 2014; Pegg & Eitenmiller, 2017). 2019; Magnusson & O There are three main approaches for vitamin analysis: (1) bioassays methods; (2) microbiological methods; and (3) chemical methods. There are many official and other methods based on those three approaches. Those methods were excellently reviewed by Ball (Ball, 2005), Blake (Blake, 2007), Pegg and Eitenmiller (Pegg & Eitenmiller, 2017), Zhang et al. (2018), and Fatima et al. (2019), which were also briefly summarized below.

2.4.1 Pretreatment, extraction, and isolation of samples Similar to the other types of food analysis, sample size reduction should be conducted by a type of grinder, chopper, miller, or others to increase the contact surface area between the organic solvent and the sample. Before selection of a pretreatment process, the conditions that affect the stability of the targeted vitamins should be taken into account. If required, filtration, centrifugation, and dilution are performed in due course. For the separation and determination of vitamin content, proper extraction and cleanup steps should be selected (Katsa & Proestos, 2019). Selection of the sample treatments during extraction procedure depends on the solubility of the vitamins. For example, fat-soluble vitamins from animal sources are deposited in fatty tissues. Therefore the organic solvent used for extraction dissolves triglycerides, sterols, and phospholipids. Fat-soluble vitamins from plant sources are deposited in the thylakoid membranes of the chloroplasts. Therefore those coextracts should be removed with pretreatments including saponification (Blake, 2007), acid hydrolysis, enzymatic digestion, or further cleanup procedure (Fanali, D’Orazio, Fanali, & Gentili, 2017). Acid hydrolysis is one of the common extraction methods to release the bonded vitamins from the food matrix. On the other hand, it can cause the destruction of some B vitamins (especially vitamin B5) and nicotinamide in some foods (Caprioli, Sagratini, Vittori, & Torregiani, 2018; Fatima et al., 2019; Ha¨lvin, Paalme, & Nisamedtinov, 2013). The other options are to precipitate proteins to remove the links between the peptide and the vitamin (Fatima et al., 2019) and to treat the sample with enzymes. The enzymes used are α-amylase, papain, takadiastase, claradiastase, β-glucosidase, and acid phosphatase for water-soluble vitamins and lipases for fatsoluble vitamins (Fanali et al., 2017; Katsa & Proestos, 2019). Selection of the extraction solvent is based on the solubility and polarity of the vitamins. For instance, for vitamin A hexane, diethyl ether and CHCl3-acetone are used, and for vitamins D and E, petroleum ether, CH2Cl2, diethyl ether, ethanol, hexane, and mixtures of CHCl3 are generally preferred. If the sensitivity of the method is aimed to be increased, the extract can be evaporated to dryness with N2 and reconstituted with a proper organic solvent (Aubert & ˇ c et al., 2014). Chalot, 2018; Katsa & Proestos, 2019; Zili´ SPE methods based on different adsorbents are used as cleanup method for water-soluble vitamins (Fatima et al., 2019; Ha¨lvin et al., 2013; Xie et al., 2019). Since vitamins are polar compounds, SPE C18 cartridges are used to leave hydrophobic compounds on the adsorbent and enable passing of polar compounds dissolved in polar solvents through the cartridge (Katsa & Proestos, 2019). There are nonconventional extraction techniques used for extraction of the vitamins. Among them, PLE decreases extraction period due to the application of solvent at higher temperature and pressure. After PLE, SPE method can be applied as a cleanup step (Fanali et al., 2017). SFE method is rarely used for water-soluble vitamins to discard nonpolar interferences from the sample extract (Fatima et al., 2019; Zougagh & Rı´os, 2008).

2.4.2 Analysis of vitamins 2.4.2.1 Bioassay methods Bioassay methods are defined as “the method used for estimation of the potency of substances by observing their pharmacological effects on living animals (in vivo) or isolated tissues (in vitro) and comparing the effect of these substances of unknown potency to the effect of a standard.” A bioassay comprises a stimulus applied to a subject. Application of stimulus is followed by a change in some measurable characteristic of the subject, the magnitude of the change being dependent on the dose. The intensity of the stimulus is varied by using the various doses by the analyst (Panuganti, 2015). The application of bioassay principles to vitamin analysis is limited. There is only one standard method [Association of Official Analytical Chemists (AOAC) Method 936.14] defining analysis of Vitamin D in milk, vitamin

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preparations, and concentrates. For the sake of the analysis, rats are maintained on richetogenic diet and measurement is based on bone calcification in rats (Pegg & Eitenmiller, 2017).

2.4.2.2 Microbiological methods Microbiological methods are based on the comparison of growth of microorganism including bacteria, yeast, or protozoans in the extract of a vitamin-containing sample and in a media that contain the known quantities of that vitamin. The degree of growth that is an effect of vitamin is measured in terms of turbidity, acid production, or respiration (Pegg & Eitenmiller, 2017). The method dates back to 1947 when it was found that Lactobacillus lactis required a medium composing vitamin B12 to grow satisfactorily. The method includes four steps: (1) extraction of the vitamins from the matrix; (2) incubation of the extract at predetermined conditions with growth medium and the microbiological culture; (3) observing the growth of the microorganism, in general by reading the turbidity at spectrophotometer; and (4) quantifying the amount of the vitamin using a calibration curve (Blake, 2007). Tsukatani, Suenaga, Ishiyama, Ezoe, and Matsumoto (2011) developed a colorimetric microbial viability assay based on the reduction of water-soluble tetrazolium salts for water-soluble vitamins such as vitamin B6, biotin, niacin, folic acid, and pantothenic acid with an exception of vitamin B12. Moreover, recently, Martı´nez-Herna´ndez, Castillejo, Carrio´n-Monteagudo, Arte´s, and Arte´s-Herna´ndez (2018) determined the vitamin B12 content in algae powder using a commercial microbiological kit. According to the procedure, sample was dissolved in distilled water, vortexed, and incubated at 95 C, followed by centrifugation at low temperature after cooling. Then, the solution is filtered and mixed with assay medium on the wells of the microtiter plate coated with Lactobacillus delbrueckii ssp. lactis (leichmannii). After the incubation at 37 C for 46 h, the absorbance was measured at 620 nm using a plate reader. The quantification was based on reference standard supplied in the kit. It was indicated that the method provides rapid determination with less sample preparation, especially with the developed commercial test kits. On the other hand, the disadvantage of the method includes cost of these commercial test kits, possibility of positive reaction to B12 analogues, and requirement of experienced analyst for the interpretation of the results in case of the presence of interference.

2.4.2.3 Chemical methods Chemical methods including spectrophotometric, fluorometric, chromatographic, enzymatic, immunological, and radiometric methods are commonly used for analysis of vitamins. HPLC is a common method used for identifying and quantifying vitamins in different type of foods and food supplements with higher selectivity and sensitivity. HPLC is also identified as a separation technique for most of the official methods released by AOAC and European Committee for Standardization (Pegg & Eitenmiller, 2017; Wang, Li, Yao, Wang, & Van Schepdael, 2018). Analysis of vitamins with LC methods requires proper selection of detector, which is mainly based on the method performance requirements such as sensitivity at lower concentrations and the type of the targeted analyte (Bosco & Gentili, 2019). Depending on the type of the vitamin, HPLCs are equipped with different type of detectors for vitamin analysis (Pegg & Eitenmiller, 2017; Wang et al., 2018). For example, UV, PDA, and DAD can be used for watersoluble vitamins including vitamin C (Andre et al., 2015; Aubert & Chalot, 2018; Xie et al., 2019) and fluorescence detector can be equipped to HPLC for fat-soluble vitamins such as tocopherol (Aubert & Chalot, 2018). Modes of chromatography including normal-phase, RP, nonaqueous RP, hydrophilic interaction, supercritical fluid, and ion chromatography should be selected properly based on the requirements of the analysis (Bosco & Gentili, 2019). RP mode with nonpolar stationary phase and a polar mobile phase is generally preferred if complex separations are performed in gradient elution. C18 column with aqueous mobile phase can be used for RP mode (Xie et al., 2019). Normal-phase is used if there are homologues compounds, e.g., vitamin E, which are aimed to be separated in an isocratic mode. It is well known that RP has advantage of improving peak shape, faster equilibration, and good reproducibility for retention times and compatibility with MS detector (Bosco & Gentili, 2019; Fatima et al., 2019). In recent years, it is common to combine MS to LC to increase sensitivity and selectivity. ESI and triple quadrupole (LC-MS/ MS) is the common choices for that type of equipment (Pegg & Eitenmiller, 2017). It is very difficult to analyze more than one vitamin in a single run with LC equipped with a simple detector due to the different chemical and physical properties of water- and fat-soluble vitamins (Eiff, Monakhova, & Diehl, 2015). There are multianalyte methods that enable simultaneous analysis of several vitamins in addition to their metabolites with several chromatographic methods in a single run (Bosco & Gentili, 2019; Pegg & Eitenmiller, 2017). Lately, UPLC equipped with MS/MS or LC-MS systems has been used for multivitamin analysis such as the water-soluble vitamins (Go¨ncu¨o˘glu Ta¸s, & Go¨kmen, 2018; Katsa & Proestos, 2019). Moreover, Eiff et al. (2015) successfully developed

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and validated a multicomponent analysis based on an NMR method to analyze simultaneous qualitative measurement of water- and fat-soluble vitamins including B1, B3, B5, B6, and E. Capillary electrophoresis is a new method that can be an alternative to HPLC. It can be coupled to UV, fluorescence or laser-induced fluorescence, electrochemical, and MS detectors. Electromigration techniques including capillary electrophoresis can be used for water-soluble vitamins due to their special electrochemical behavior. Electrokinetic chromatography is the most common separation technique based on capillary electrophoresis for the analysis of water-soluble vitamins due to its improved selectivity and suitability (Fatima et al., 2019). There are also immunochemical methods including radioimmunoassay and enzyme-linked immunosorbent assay, which was reported as potential techniques for the analysis of B vitamins (Ball, 2005; Blake, 2007). In addition, spectrophotometric methods, based on the measurement of the absorbance of the sample extract and the reference standard, are still valid in official methods for the analysis of tocopherol (Otemuyiwa & Steve, 2013) and vitamin C (Schiassi, de Souza, Lago, Campos, & Queiroz, 2018).

2.5

Omega-3 fatty acids

FAs, a subgroup of lipids, are formed by aliphatic carbon chains with a methyl group at one end of the molecule and a carboxylic group located at the other end (Comunian & Favaro-Trindade, 2016). FAs are mainly grouped into three fractions: saturated FAs, cis-monounsaturated, and cis-polyunsaturated FAs (PUFAs) containing no double bonds, a single double bond, and two or more double bonds between carbon atoms within the FA chain, respectively (Schwingshackl & Hoffmann, 2012). Omega-3 FAs (ω-3, omega-3s, or n-3s) categorized under PUFAs contain the first double bond located at the third carbon from the methyl end of the chain. There are different forms of omega-3 FAs, and alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) constitute the main place for scientific studies. ALA, EPA, and DHA contain 18 carbon atoms (C18:3n3), 20 carbons (C20:5n3), and 22 carbons (C22:6n3), respectively (DeMan, Finley, Hurst, & Lee, 1999; Table 2.1). Omega-3 FAs are naturally found in animals including cold water fish (salmon, tuna, anchovy, and mackerel) and in plant seeds including soybeans, rapeseed, flaxseed, walnuts, chia, and perillaechium (Comunian & Favaro-Trindade, 2016; Hernandez, 2014). While omega-3 FAs from plant sources have shorter carbon chain (ALA), omega-3 FAs from animal sources have the longer chain (EPA and DHA) and with more bioactive properties. Furthermore, microalgae constitute as a common source for DHA (Hernandez, 2014). Since omega-3 FAs are not synthesized in human body, they are considered as essential FAs for healthy diet due to their benefits to human health such as prevention and reduction of cardiovascular diseases including coronary heart disease (Kralovec, Zhang, Zhang, & Barrow, 2012), regulating occurrence of anti-inflammatory diseases (Laye, Nadjar, Joffre, & Bazinet, 2018), prevention of inflammatory mediated disorders including allergy, diabetes, Alzheimer’s disease, and related neurodegenerative diseases (Lavie, Milani, Mehra, & Ventura, 2009), improving body composition, and counteracting obesity-related metabolic changes (AlbrachtSchulte et al., 2018). Therefore omega-3 FAs are extracted from their natural sources and have been successfully used in formulizing functional foods and food supplements generally in their triacylglycerol (TAG) form (Comunian & Favaro-Trindade, 2016; Curtis & Black, 2013; Karunathilaka et al., 2019). Accordingly, the methods that are used to analyze omega-3 FAs either in their natural sources or in functional foods including baby food, infant formula, chocolate, dairy products, eggs, and others are also of interest to define their content accurately. Methods commonly used for analysis of omega-3 FAs include GC equipped with flame ionization detector (GC-FID) (Hernandez, 2014). The preliminary steps before the chromatographic analysis involve the sample pretreatment and fat extraction. Below, detailed information about these steps in addition to alternative methods is briefly summarized.

2.5.1 Pretreatment and extraction of samples Based on the analysis method and the matrix, different types of sample pretreatment procedures including peeling, removing seeds, or shell, grinding or homogenization, filtration, centrifugation, drying, dilution, etc. can be selected (Curtis & Black, 2013; Da Silva et al., 2016; Rydlewski et al., 2017). Based on the matrix and the time elapsed between the sample preparation and further analysis, homogenized/ground sample needs to be lyophilized, vacuum packaged, and frozen after the pretreatment steps (Da Silva et al., 2016). Since omega-3 FAs can be surrounded in the food matrix with other food components including proteins, carbohydrates, and other fats (Curtis & Black, 2013; Hernandez, 2014), analytical chemists should select a proper extraction

TABLE 2.1 Chemical structures of major omega-3 fatty acids (Comunian & Favaro-Trindade, 2016). Common name

Scientific name

Molecular formula

Short description

Alpha-linolenic acid

9,12,15-Octadecatrienoic

C18H30O2

C18:3n3

Eicosapentaenoic acid

5,8,11,14,17Eicosapentaenoic

C20H30O2

C20:5n3

Docosahexaenoic acid

4,7,10,13,16,19Docosahexaenoic

C22H32O2

C22:6n3

Chemical sturucture

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method. There are conventional and nonconventional extraction methods to extract fat from the matrix. If the matrix is liquid such as oil, the chromatographic analysis is carried out without any extraction step. Conventional extraction of fats includes solid
liquid or liquid
liquid solvent extraction. Petroleum ether and hexane are the common solvents used in the fat extraction. If fat molecules are embedded in other compounds of food matrix or omega-3, FAs are protected by microencapsulation, or in a dairy emulsion, acid or alkaline hydrolysis/digestion should be carried out along with extraction (Curtis & Black, 2013; Hernandez, 2014). Soxhlet extractor is a common way to extract fat or oil from the matrix. Once the extraction is carried out with the proper solvent, a rotary evaporator can be used to evaporate excess amount of solvent (Fernandes et al., 2015). If the analytical result is expressed in g/100 g of fat or sample weight, then, fat content of the matrix should be determined properly. There are some official fat content analysis methods, such as the method released by Nordic Committee on Food Analysis (Nordic Committee on Food Analysis NMKL, 1998). This method states that in order to release bound fat and convert salts of FAs into free acids, total fat should be determined by an acid hydrolysis method with 2 M of 37% HCl solution and petroleum ether in an automated fat extractor. Once the fat content of the sample is determined, the extracted fat can also be used for the determination of omega-3 FA compositions by GC-FID. SFE is a nonconventional method separating fat from other compounds by using supercritical fluids such as CO2 at low temperatures. The main advantage of the SFE method compared with Soxhlet extraction includes the use of nonˇ toxic fluids (Ivanov, Colovi´ c, Bera, Levi´c, & Sredanovi´c, 2011). Ivanov et al. (2011) reported that FA compositions obtained by SFE were more representative due to the degradation of long-chain FAs during long processing time of Soxhlet extraction method. Da Silva et al. (2016) optimized SFE conditions for omega-3 and omega-6 FAs with propane at 40 C and 8 MPa. It was reported that the extraction yields showed no statistical difference between two methods (P , .05). Other nonconventional fat extraction method is MAE and UAE methods that need less amount of solvent than conventional extraction methods (Curtis & Black, 2013). Moreover, PLE was applied to extract fat from milk for further analysis of FA compositions (Castro-Go´mez et al., 2014).

2.5.2 Analysis of omega-3 fatty acids 2.5.2.1 GC analysis GC-FID has been used for analysis of FA compositions including omega-3 FAs for many years. The separation in GCFID is based on the molecular weight, chain length, number of double bonds, and geometric configurations of FAs. To get better separation of individual peaks, oven temperature, flow rate of carrier gas, and type and length of capillary GC column stationary phase are optimized (Mossoba, Karunathilaka, Chung, & Srigley, 2017). Fig. 2.2 shows a typical chromatogram of 37 FA methyl ester (FAME) standards including ALA, EPA, and DHA. FA peaks are identified by comparison of their retention times with the appropriate FAME standards. Accordingly, individual omega-3 FA concentrations are generally expressed as relative area percentages of the identified FAs (Uysal, Taykurt, Bulut, & Emiroglu, 2011). On the other hand, the area percentages of EPA and DHA can be converted into absolute weights per gram of sample for food labels of nutritional supplements. This conversion needs the application of internal or external standards, and correction factors of the FID response (Curtis & Black, 2013). Omega-3 FAs are generally found in food supplement or in food matrix in FA esters of glycerol form (TAGs) (Curtis & Black, 2013). Due to the high molecular mass of the TAGs and their consequent low volatility and higher boiling points, it is difficult to analyze them directly by GC-FID, which gives a low detector response (Zhang, Wang, & Liu, 2015). Therefore before chromatographic separation, they are converted into FAMEs. The formation of FAMEs is the most important step in the analysis. There are three main methyl ester formation procedures defined in an international standard; transesterification employed to form FAMEs from FA esters in fats (i.e. TAG); alkali- or acid-catalyzed transesterification procedures used to form FAMEs in a methanolic medium (transmethylation); and an acid-catalyzed esterification mechanism employed to form FAMEs from FAs (International Organization for Standardization ISO, 2014a; International Organization for Standardization ISO, 2014b; International Organization for Standardization ISO, 2014c; International Organization for Standardization ISO, 2014d). Zhang et al. (2015) developed a simple and derivatization-free method for the simultaneous analysis of oleic acid and related FAs including linoleic and arachidic acids in a single run. The optimization parameters included solvent, inlet, and column temperature program, stationary phase of the column, inlet type, and sample size and injection technique. Mismatched polarity between the sample solvent and the stationary phase of the column leaded to broader shape of chromatogram; therefore optimum shape was obtained with isopropanol. Moreover, it was observed that polarity of

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53

Norm. 12.5 12 11.5

1

2

3

11 10.5 10 9.5 9

10

15

20

25

30

35

40

45 min

FIGURE 2.2 Typical chromatogram of reference standard mixture of fatty acid methylesters including alpha-linolenic acid, eicosapentaenoic acid, and docosahexaenoic acid and their retention time in minutes. (1) C18:3n3 (33.537); (2) C20:3n3; (3) C22:6n3 (46.693) Modified from YolciOmeroglu, P., Ozdal, T., Fatty acid composition of sweet bakery goods and chocolate products and evaluation of overall nutritional quality in relation to the food label information, Journal of Food Composition and Analysis 88, 2020,103438.

the column stationary phase affected the separation of FAs. To increase peak resolution, the polarity of the column stationary phase was matched with the polarity of the FAs. Optimum separation was obtained with nitroterephthalic acid modified polyethylene glycol capillary column. Traditional GC-FID methods are based on comparison of retention times of peaks in the sample and the reference standard. Therefore they cannot identify actual structure of the FAs. On the other hand, GC coupled to MS has been used to provide retention times as well as data that can be related to the true identity of the compounds (Hernandez, 2014; Santurino, Calvo, Gomez-Candela, & Fontecha, 2017). Moreover, GC
MS has the ability to separate peaks from a noisy background or co-eluting peaks if unique ions are available. In addition, the sensitivity and selectivity of GCMS make it an advantageous method for quantification of all FAs, including omega-3 FAs (Dodds, McCoy, Rea, & Kennish, 2005).

2.5.2.2 Other analysis methods Since FA analysis methods based on GC-FID are costly and needs longer sample preparation and analysis time, a robust rapid classifying and quantifying method based on the chemometric tools including calibration between the Fourier transform near infrared spectroscopy (FT-NIR) and GC-FID data was developed by Azizian and Kramer (2005) for edible oils. Accordingly, the method was improved for the analysis of omega-3 FAs in fish oil or its concentrates (Azizian, Kramer, Ehler, & Curtis, 2010). It was indicated that the method can be used for plant matrix only if the contaminants are absent. Plans, Wenstrup, and Rodriguez-Saona (2015) applied the same approach to characterize omega-3 oil supplements by collecting infrared spectral data with portable mid-infrared FT equipment and application of principal components analysis and partial least square regression. Karunathilaka, Mossoba, Chung, Haile, and Srigley (2017) reported that this simple, nondestructive quantitative method can be regarded as a rapid screening tool and a time and costsaving alternative method providing reliable results less than 5 min within the spectral ranges of 650 2 1500 and 2800 2 3050 cm21. The authors generated a calibration library of 174 gravimetric mixtures using 33 marine oil dietary supplement products. Since validation of a calibration model needs large independent test sets, Karunathilaka et al. (2019) built more robust calibration models based on 95 different types of marine oil dietary supplements with application of attenuated total reflection FT infrared and benchtop FT-NIR. Moreover, a rapid method based on NIR in combination with chemometrics has been recently validated against GC method and found to be an alternative method to

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determine arachidonic acid, DHA, and EPA in fish by using spectral libraries (Van Der Merwe, Manley, & Wicht, 2018). NIR methods enable rapid analysis without the need of chemical solvents and reagents that results in an environmentally friendly methodology and are capable of handling large number of samples in small quantities (Cascant et al., 2018; Ribeiro & Peralta-Zamora, 2013). NMR using 1H, 13C, and 31P nuclei is an instrumental method in the determination of lipid classes (Curtis & Black, 2013). It was successfully applied to quantify omega-3 FAs in different matrices within 1 min (Castejo´n, MateosAparicio, Molero, Cambero, & Herrera, 2014; Tengku-Rozaina & Birch, 2014; Vicente, de Carvalho, & Garcia-Rojas, 2015). Application of NMR reduced the analytical steps by disregarding hydrophilic and lipophilic extractions and, therefore, decreasing the subsequent sample degradation (Nestor et al., 2010).

2.6

Organic acids

Organic acids have a vital role and function in foods and beverages, affecting the organoleptic properties, stability, and microbiological quality of the food products. Therefore they are well established as chemical markers of ripeness, quality, authenticity, and geographical origin of many food products (Gonza´lez & Gonza´lez, 2012). Organic acids are recognized as safe for most of the food products (Mani-Lo´pez, Garcı´a, & Lo´pez-Malo, 2012). In general, organic acids present in foods are originated from: (1) biochemical processes; (2) addition of preservatives, acidulants, and stabilizers; and (3) activity of some microorganisms such as bacteria and yeast (Gomis, 2000). Organic acids can, therefore, be either naturally present as constituents of the food or can be added directly or indirectly to the products (Theron & Lues, 2007). Unsubstituted monocarboxylic acids (formic acid, acetic acid, propionic acid, butyric acid, and isobutyric acid), substituted monocarboxylic acids (glycolic acid, glyoxylic acid, pyruvic acid, lactic acid, and glyceric acid), alicyclic monocarboxylic acids (quinic acid, shikimic acid, galacturonic acid, and glucuronic acid), dicarboxylic acids (oxalic acid, succinic acid, fumaric acid, maleic acid, malic acid, and tartaric acid), and tricarboxylic acids (citric acid, isocitric acid, and oxalacetic acid) are the organic acids that are present in foods (Gonza´lez & Gonza´lez, 2012). It has been shown that identifying and quantifying the organic acids are very important to monitor ripening and organoleptic properties and to detect adulteration of foods. For instance, some of the wine adulterations are related to the increase in the levels of acetic and lactic acids (Mato, Sua´rez-Luque, & Huidobro, 2005). In addition, the ratio of citric acid to isocitric acid is an indication of dilution with aqueous solution of citric acid (Belitz, Grosch, & Schieberle, 2004; Kamiloglu, 2019b).

2.6.1 Extraction and analysis of organic acids For the extraction of organic acids, UAE or solid
liquid extraction are commonly used. Studies in the literature showed that the most common extraction parameters that are used to extract organic acids are as follows: temperatures of 40 C
100 C, up to 1.5 h incubation using water, hydrochloric, orthophosphoric, metaphosphoric, or trifluroacetic acids. In addition, trichloroacetic, perchloric, or sulfuric acids can be used to clean up the sample when FA and protein contents are high in the food matrix (Kaminarides et al., 2007a; Kaminarides et al., 2007b). On the other hand, UAE is a good alternative compared with classical extraction methods, due to its high efficiency (Mato et al., 2005; Rodrı´guez Galdo´n, Rı´os Mesa, Rodrı´guez Rodrı´guez, & Dı´az Romero, 2010). The most commonly used analysis methods are chromatographic and electrophoretic methods. HPLC is an efficient method in the analysis of organic acids, due to its high sensitivity, selectivity, and expeditiousness. The choice of the stationary phase is critical to achieve a suitable separation in chromatography. In general, ion-exchange, ion-exclusion, ion-pair, hydrophilic interaction, and reverse-phase are widely used (De Quiro´s, Lage-Yusty, & Lopez-Hernandez, 2009). Recently, ion-exchange chromatography method has been commonly used, and the most frequently used column is the Aminex HPX-87H (300 mm 3 7.8 mm), providing a good separation of peaks (Ahmed et al., 2015; Pereira da Costa & Conte-Junior, 2015; Wang et al., 2013). C18 column is also often used in organic acid analysis. The detectors most frequently used in HPLC are conductivity, refractive index, UV (with detection at 200
215 nm), and the evaporative light scattering detector, as well as MS detectors. Regardless of its high sensitivity and selectivity, GC methods have not been preferred for the analysis of organic acids. Since majority of the organic acids are not volatile, it is necessary to perform additional procedures. In addition, it is costly and complex; hence, GC is scarcely used in organic acid analysis (Fu¨zfai, Katona, Kova´cs, & Molna´r-Perl, 2004). On the other hand, Yang and Choong (Yang & Choong, 2001) carried out studies on the analysis of C2
C12 volatile organic acids in aqueous samples such as vinegar, fruit juice, lactic acid drink, fermented milk, and soy sauce using GC. They have reported that 13 short-chain volatile organic acids in liquid foods including acetic, propionic,

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55

isobutyric, butyric, isovaleric, valeric, caproic, heptanoic, caprylic, capric, lauric, lactic, and levulinic acids can be simultaneously determined with detection limits of 0.025
1 ng. Capillary electrophoresis is also an efficient technique to analyze organic acids. It can provide more advantages than other classical methods, including minimum or no sample pretreatment, small sample volumes, high separation efficiency, low cost, high-resolution, simplicity, automation, short analysis times, flexibility, low consumption of reagents and samples, and minimum preparation of the sample even in complex matrices (Baena, Cifuentes, & Barbas, 2005; Klampfl, Buchberger, & Haddad, 2000; Mato et al., 2005; Soga & Imaizumi, 2001). In addition, capillary electrophoresis uses nonhazardous solvent, consumes low amount of solvent, and operates at near ambient temperature (Mato et al., 2005). For the determination of organic acids, injection mode, electrophoretic conditions, electrolyte composition, detection systems, and analysis time should be adjusted. Especially, the composition of electrolyte is extremely important to obtain a high capillary electrophoresis separation. In general, the conductivity and UV are used as detectors. The most frequently used detector is the UV, with detection at wavelengths of between 185 and 254 nm (Heiger, 1992). On the other hand, the main disadvantage of capillary electrophoresis is its lower reproducibility when compared with chromatographic methods (Mato et al., 2005).

2.7

Nucleosides and nucleotides

Nucleosides and nucleotides are intracellular compounds that are precursors of nucleic acids that compose DNA and RNA. Nucleoside comprises of a nitrogenized base (either puric or pyrimidinic) and a pentose (in RNA) or deoxyribose (in DNA) with a covalent bond. The pyrimidinic bases involve rings of six members including cytosine, thymine, and uracil. However, puric bases contain a second ring of five members with bases of adenine, guanine, hypoxanthine, and xanthine. A nucleotide is an organophosphate formed by combination of the carbon 5’ of the pentose of the nucleoside with a mono-, di-, or triphosphate group. They have important roles in regulatory and metabolic functions. They are vital in the storage, transfer, and expression of genetic information; operate in the transfer of chemical energy; participate in metabolic functions of biosynthetic routes; formation of a part of coenzymes; and work as biological regulators. Although all plant, mammal, and bacteria cells include nucleosides and nucleotides, foods of animal origin contain higher levels than plants, except for beans and other pulses. Human body can synthesize nucleosides and nucleotides endogenously, and indeed, they can absorb and use the ones taken from food materials exogenously (Domı´nguez´ lvarez et al., 2017). Under some emergency conditions in the human body, such as certain illnesses like intestinal A lesions, the need for nucleosides and nucleotides can increase and the synthesized endogenous matter may be insufficient. Therefore the need for taking them from food sources increases and becomes vital for maintaining the normal metabolism functions (Van Buren & Rudolph, 1997). Recent literature reveals that the presence of nucleosides and nucleotides in the nutrition has beneficial health effects on the immune response like the absorption of iron, lipid metabolism, gut microflora, and both intestinal and hepatic functions, among others (Hu & Yang, 2014). Therefore analysis of these compounds in food is of great significance to improve and ensure food quality.

2.7.1 Pretreatment and extraction of sample One of the widely used methods for sample pretreatment in the analysis of nucleosides and nucleotides is the addition of acids like trichloroacetic acid (Liao et al., 2011; Vin˜as et al., 2010), formic acid (Ren, Chen, Zhang, Li, & Song, 2011), and perchloric acid (Ishimaru, Haraoka, Hatate, & Tanaka, 2016; Mora, Herna´ndez-Ca´zares, Aristoy, & Toldra´, 2010; Zhou et al., 2012) to precipitate proteins and eliminate interference with the macromolecules present in the food matrix. After addition of acids, the sample media should be neutralized, as nucleosides are instable in acidic media (Mora et al., 2010; Zhou et al., 2012). Neutralization is not necessary for the samples that are not treated with acid but treated with boiling water and/or saline solutions, in which the recovery was found to be lower than the acid treated ´ lvarez, Garcı´a-Go´mez, & samples (Inoue, Obara, Hino, & Oka, 2010; Mateos-Vivas, Rodrı´guez-Gonzalo, Domı´nguez-A ´ lvarez, Mateos-Vivas, Garcı´a-Go´mez, & CarabiasCarabias-Martı´nez, 2016; Rodrı´guez-Gonzalo, Domı´nguez-A Martı´nez, 2014). In some conditions, preconcentration of samples might be necessary and the most used technique for cleaning and preconcentration of sample is SPE method (Gill, Indyk, Kumar, Sievwright, & Manley-Harris, 2010; Studzi´nska, Rola, & Buszewski, 2014). Moreover, the sorbent material can be added to the extract to separate the nucleosides from the food matrix and remove them from the extract by centrifugation using dispersive-SPE method (Cela-Pe´rez et al., 2015; Magdenoska, Martinussen, Thykaer, & Nielsen, 2013). The most widely preferred methods for extraction of nucleosides and nucleotides are solid
liquid or liquid
liquid extraction with water or other polar solvents, according to their polarity (Yang, Li, Feng, Hu, & Li, 2010). Extraction

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can be assisted with temperature (Phat, Moon, & Lee, 2016; Yang et al., 2010; Zhou et al., 2012), UAE (Chen, Shi, Wang, Li, & Wang, 2015; Liu et al., 2012; Ren et al., 2011; Wang et al., 2016), and PLE (Yang & Li, 2008). Various solvent mixtures have been used for extraction including water and ethanol (Seifar et al., 2009; Xue, Zhou, Wu, Fu, & Zhao, 2009), water and methanol (Chen et al., 2015; Liu et al., 2012; Wang et al., 2016), water, ethanol, and acetone (Wu et al., 2015), and methanol and chloroform (Magdenoska, Knudsen, Svenssen, & Nielsen, 2015). The quantity of extracted nucleosides was found to be higher when PLE and water extraction at high temperature are used (Yang & Li, 2008).

2.7.2 Analysis of nucleosides and nucleotides 2.7.2.1 Chromatographic analysis Nucleosides and nucleotides are highly polar and ionizable compounds; therefore it is not easy to analyze them using LC due to their limited use in C18 columns used in reverse phase liquid chromatography (RP-LC) unless a suitable mobile phase is selected (Phat et al., 2016; Zhou et al., 2012). An organic solvent gradient is able to separate late-eluting nucleosides (Gill et al., 2010; Ishimaru et al., 2016; Liu et al., 2012; Wu et al., 2015). Polar embedded and polar end-capped phases are used to enhance retention of nucleosides and nucleotides in stationary phases (Ren et al., 2011; Studzi´nska & Buszewski, 2013). Besides, recently, RP-LC had been widely used at appropriate pH conditions, as ionic natured organophosphates of the nucleotides interact with cationic reagents building ionic pairs (Magdenoska et al., 2013; Magdenoska et al., 2015; Seifar et al., 2009; Yang et al., 2010). Moreover, ion-exchange LC is another preferred method (Studzi´nska et al., 2014). Recently, HILIC method gained attention in the analysis of nucleosides and nucleotides because of the success of this method in separation of polar analytes (Inoue et al., 2010; Sfakianaki & Stalikas, 2015). HPLC is mostly used with UV-Vis and DAD (Gill et al., 2010; Ishimaru et al., 2016; Liao et al., 2011; Zhou et al., 2012). Together with MS analysis, it gives greater sensitivity and selectivity (Phat et al., 2016), but great attention should be given while using MS to obtain a successful separation of nucleosides and nucleotides, as the fragmentations may occur when the source interferes with the MS signals (Neubauer et al., 2012).

2.7.2.2 Capillary electrophoresis analysis Capillary electrophoresis is an efficient method for the analysis of nucleosides and nucleotides, as it is suitable for separation of highly polar compounds and work in a wide pH interval (Smyth & Rodriguez, 2007). For the determination of nucleosides and nucleotides, micellar electrokinetic chromatography (Iqbal & Mu¨ller, 2011; Shi et al., 2014) and mixed-mode hydrophilic/strong anion-exchange interactions in pressurized capillary electrochromatography (Wang et al., 2012) have been preferred. In food analysis, capillary zone electrophoresis is preferred for its flexibility in the selectivity of separation of nucleosides and nucleotides by changing the composition of the background electrolyte. The most frequently used detector is the spectrophotometric detector with DAD, which operates at 200
260 nm (Li et al., 2011), although coupling to MS could enhance the specificity of the analysis (Rodrı´guez-Gonzalo et al., 2014; Chen et al., 2015).

2.8

Phytosterols

Phytosterols are plant sterols that occur in almost all plant cell membranes, constituting a great variety of triterpenes. They are present in high amounts in vegetable oils and fats, fruits and berries, and cereals and cereal products (Piironen & Lampi, 2004). Recently, phytoserols in microalgae also gained interest as new natural sources of phytosterols (Francavilla et al., 2012). There are more than 200 types of phytosterols found in several plants (Lagarda, Garcı´aLlatas, & Farre´, 2006). They are bioactive components that are present in the cell membrane lipid bilayer (Schuler et al., 1991). Beta-sitosterol, campesterol, and stigmasterol are major plant sterols in the nature, whereas Δ5-avenasterol, sitostanol, and campestanol are minor phytosterols. They are present in the nature as free or conjugates of FA ester, glycosides, and acetylated glycosides (Moreau, Whitaker, & Hicks, 2002; Phillips, Ruggio, Toivo, Swank, & Simpkins, 2002). Phytosterols have many beneficial health effects including decreasing the level of serum cholesterol and related heart diseases (Hicks & Moreau, 2001; Jones, MacDougall, Ntanios, & Vanstone, 1997), anti-inflammatory, antibacterial, antiulcerative, and antitumor bioactive functions (Akihisa et al., 2000; Arisawa, Kinghorn, Cordell, Phoebe, & Farnsworth, 1985; Berger, Jones, & Abumweis, 2004), among others. The efficiency of the methods to analyze phytosterols can vary according to the method and solvents used in extraction. In this part, the extraction methods and various conventional and new analysis techniques of phytosterols are reviewed in detail.

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2.8.1 Pretreatment, extraction, and analysis of phytosterols There are some pretreatment methods used to increase the yield of extraction. One of the mostly used methods for pretreatment of phytoserols is the use of enzymes. Enzymes including α-amylase, pectinase, cellulase, and hemicellulose increase the extraction of compounds by breaking the cell wall and cleaving the structural polysaccharides and lipid bodies (Latif & Anwar, 2009). Novel technology methods such as pulsed electric field method may also be used before the conventional extraction method for pretreatment of samples to increase extraction yield (Guderjan, To¨pfl, Angersbach, & Knorr, 2005). Extraction of phytosterols is carried out to separate the soluble plant material in a selective solvent, and for this purpose, various conditions for each extraction technique are applied to have high extraction yield. The nature of the matrix and the form of phytosterols including free, glycosylated, and esterified forms affect the isolation techniques of phytosterols (Lagarda et al., 2006). Extraction depends on several factors including plant material, solvent, and extraction procedure, among others (Gupta, Naraniwal, & Kothari, 2012). Roiaini, Seyed, Jinap, and Norhayati (2016) studied the effect of several extraction techniques for phytosterols including Soxhlet, ultrasonic, supercritical carbon dioxide, and supercritical carbon dioxide with cosolvents on cocoa butter and they reported that the highest yield was gained using supercritical CO2 with a cosolvent (Roiaini et al., 2016). Conventional and nonconventional techniques are used to extract phytosterols. Conventional techniques can be listed as Soxhlet extraction, heating under reflux, maceration, percolation, and hydrodistillation. The most commonly used conventional techniques for phytosterols are Soxhlet extraction and maceration, and Soxhlet extraction technique is also used as a reference method to all nonconventional techniques (Orozco-Solano, Ruiz-Jime´nez, & De Castro, 2010; Vilela et al., 2013). Conventional methods have some limitations including long time of extraction, need for extra pure solvents, requirement of solvent evaporation, low selectivity of extraction, and degradation of bioactive compounds as a result of the high temperature conditions (De Castro & Garcia-Ayuso, 1998). To overcome these limitations, various nonconventional methods have been developed including MAE, enzyme-assisted extraction, UAE, hydrotropic extraction, pulsed electric field-assisted extraction, PLE, and SFE (Azmir et al., 2013; Brusotti, Cesari, Dentamaro, Caccialanza, & Massolini, 2014). An appropriate analytical method should be chosen, as phytosterols are found together with other nonsaponifiable components in plant lipids. Various chromatographic techniques to characterize and quantify the sterol compounds, including GC-MS (Eller, Moser, Kenar, & Taylor, 2010; Hrabovski, Sinadinovi´c-Fiˇser, Nikolovski, Sovilj, & Borota, 2012; Orozco-Solano et al., 2010; Pe´res et al., 2006), column chromatography, HPLC (Mustapa, Martin, Mato, & Cocero, 2015; Sajfrtova´, Liˇckova´, Wimmerova´, Sovova´, & Wimmer, 2010), and capillary electrochromatography (Rocco & Fanali, 2009), have been used to analyze the extracts obtained by various techniques. TLC has also been used for the preliminary evaluation of phytosterols both qualitatively and quantitatively (AOCS Official Method Ch 691, 2009).

2.9

Conclusions and future perspectives

In this chapter, an overview of the current analysis methods for bioactive compounds, including polyphenols, carotenoids, vitamins, omega-3 FAs, organic acids, nucleosides and nucleotides, and phytosterols, is presented. Overall, studies in the literature showed that there is no standard analysis procedure, as each sample matrix is complex and requires different treatments according to its specific needs. Indeed, each method may have several advantages and disadvantages, and therefore the most appropriate method should be selected considering these aspects together with the aim of the analysis performed. Below, a number of important points are highlighted that should be considered in future bioactive component analysis research: G

G

G

Advanced extraction techniques such as UAE, MAE, PLE, and SFE are more rapid, reproducible, selective, and greener than conventional solid
liquid or liquid
liquid extraction techniques and, hence, should be preferred in future research on bioactive components. Spectrophotometric methods used in the analysis of some bioactive compounds might be selective or lack of specificity due to interference with other compound in the sample. Therefore in order to obtain more reliable results, chromatographic methods should be preferred. In chromatographic analysis, lack of commercial standards of many bioactive compounds is one of the major challenges. In this case, in order to obtain more sensitive and selective data, MS detector should be preferred, which can also be used alone, without chromatographic separation. ESI and triple quadrupole (LC-MS/MS) are the common choices for that type of equipment.

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G

G

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Capillary electrophoresis, FTIR, or NMR can also be used as alternative methods for characterization of bioactive compounds, as they have several advantages compared with other classical methods, including minimum or no sample pretreatment, high-resolution, simplicity, automation, short analysis times, and low consumption of reagents and samples. For future studies, it is recommended to conduct research for developing standard analysis protocols using the above mentioned novel advanced analysis techniques.

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1047.

Chapter 3

Analytical technologies in sugar and carbohydrate processing K. Ncama1 and L.S. Magwaza2 1

Department of Crop Science, North West University, Mmabatho, South Africa, 2Discipline of Crop and Horticultural Science, University of KwaZulu-

Natal, Scottsville, South Africa

3.1

Introduction

Technology and innovation is developing at an alarming rate in food analytical methods (Ji et al., 2017; Magwaza & Opara, 2015; Panikuttira & Colm, 2018). The application of wet chemistry for analyzing concentrations of carbohydrates or sugars from food and beverage products is continually phasing out in the industry scale. The use of instrumental analysis replaces the wet chemistry because of its ease, accuracy, and time efficiency on the commercial line, where products come in bulk and are expected to be transferred to the market immediately. Sugars had previously been determined by different analytical methods such as electrochemical analysis (Gritzapis & Timotheou-Potamia, 1989), flow injection analysis (Peris-Tortajada, Puchades, & Maquieira, 1992), gas chromatography (Carlsson, Karlsson, & Sandberg, 1992), anion-exchange liquid chromatography (Goodall, Dennis, Parker, & Sharman, 1995; Swallow & Low, 1994), high-performance liquid chromatography (HPLC; Antoˇsova´, Polakoviˇc, & Ba´leˇs, 1999; Bugner & Feinberg, 1992), thin-layer chromatography (TLC; Pukl & Proˇsek, 1990; Reiffova´ & Nemcova´, 2006), and enzymatic methods (Verma, Barrow, & Puri, 2013). Most of the aforementioned techniques are destructive techniques where the product sample used for analysis cannot be returned into a batch taken to the market. Recent techniques based on the use of nondestructive procedures such as spectroscopy have dominated the research in determination of sugar concentration from sugar commodities (Cole, Eggleston, & Gaines, 2019). Research focusing on the development of rapid, accurate, and cheap techniques used for determining sugar contents of secondary food and beverage products is not significant. However, the research on the determination of sugar content from sugar crops and sweeteners has shown development over time (Bowman, 2017). Researchers with interest on analytical methods need information about new methods, efficacy, and limitations. The application of analytical methods in authentication and adulteration of honey was discussed in depth by Siddiqui, Musharraf, and Choudhary (2017) and Wu et al. (2017). This chapter reviews the techniques used to determine the sugar or carbohydrates concentrations in sugar crops and sweeteners, the novel techniques for extracting those sugars, and their analysis in secondary food and beverage products. The instrument that is mostly used to assess concentration of carbohydrates is the HPLC with alternative settings (Table 3.1). However, assessment of sugars in commodities is mainly based nondestructive methods such as spectroscopy.

3.2

Analytical techniques for analyzing sugars and carbohydrates in sugar crops

3.2.1 Sugar beet Seres et al. (2017) investigated the application of biocides (sodium hypochlorite, alkyl dimethylbenzyl ammonium chloride, chlorine dioxide) in the process of sucrose extraction from sugar beet. The authors found that the application of chlorine dioxide corresponded with the best pulp pressing characteristics with 5%
15% more efficient mechanical dewatering of the wet pulp compared to the other samples, significantly affecting the potential energy consumption. Mat, Rowshon, Guangnan, and Troy (2014) developed visible
shortwave near-infrared (Vis/SWNIR) Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00003-0 Copyright © 2021 Elsevier Inc. All rights reserved.

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TABLE 3.1 Common instruments used for assessing sugars and carbohydrates from different commodities (the table was self-developed). Assessment instrument

Assessed commodity or product

Assessed sugar or carbohydrate

Reference

1 H nuclear magnetic resonance spectroscopy

Honey

Fructose, α-glucose, and β-glucose

Del Campo et al., 2016

Capillary electrophoresis

Honey

Fructose, glucose, and sucrose

Dominguez et al., 2016

Capillary electrophoresis in combination with graphene
cobalt microsphere hybrid paste electrodes

Honey and milk

Fructose, glucose, lactose, mannitol, and sucrose

Liang et al., 2016

Capillary zone electrophoresis

Grape tissues

Fructose, glucose, and sucrose

Zhao et al., 2016

Fourier-transform infrared spectroscopy

Citrus juice

Brix

Clark, 2016

High-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD)

Onion, chestnuts, glutinous sorghum, green tea leaves, Black soybeans, and paddy rice

Fructose, glucose, galactose, lactose, raffinose, maltose, and sucrose

Shanmugavelan et al., 2013

High-performance thin-layer chromatography (HPTLC) combined with image analysis

Honey

Fructose, glucose, and sucrose

Puscas et al., 2013

High performance liquid chromatography method with refractive index detection (HPLC
RI)

Apple leaves and fruit peels

Fructose, glucose, sucrose, and sorbitol

Filip et al., 2016

HPLC-ELSD, UHPLC-MS/MS

Trichosanthes kirilowii Maxim

Fructose, glucose, stachyose, raffinose, and polysaccharide

Zhang et al., 2019

Near infrared spectroscopy

Sorghum juice

Fructose, glucose, and sucrose

Simeone et al., 2017

Nuclear magnetic resonance spectroscopy

Red wine

Fructose, α-glucose, and β-glucose

Forino et al., 2019

HPLC-ELSD, High-performance liquid chromatography coupled with evaporative light scattering detector; UHPLC-MS/MS, ultra-high-performance liquid chromatography coupled with tandem mass spectrometry.

spectroradiometer to assess values of Brix, fiber content, and moisture content of beet, which are the ideal parameters determining the amount of sugar extractable from sugarcane. Pan, Zhu, Lu, and McGrath (2015) successfully demonstrated efficacy of visible and near-infrared spectra in interactance mode to analyze sucrose content of intact and sliced beet samples, using two portable spectrometers for the spectral regions of 400
1100 and 900
1600 nm.

3.2.2 Sugarcane Sorol, Arancibia, Bortolato, and Olivieri (2010) demonstrated the application of visible to near-infrared spectrometer to assess Brix in samples of sugarcane juice. Nawi, Chen, Jensen, and Mehdizadeh (2013) demonstrated the potential application of a Vis/SWNIR spectroscopic technique, as a low-cost alternative to predict sugar content based on skin scanning was evaluated. The authors revealed the use of the Vis/SWNIR of sugarcane classification based on sugar content, which is the most useful result in the sugar industry where sugarcane need to be categorized for determining purchase prices and targeted markets. Optimal selection of varieties is also a very important physiological screening category as it determines the amount of sugar available from variety of different sugarcane crops (Benjamin, Garcı´aAparicio, & Go¨rgens, 2014). Phetpan, Udompetaikul, and Sirisomboon (2018) proposed a prototype online detection system based on the visible and near-infrared spectroscopic technique for the real-time evaluation of the soluble solids

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content (SSC) of sugarcane billets on an elevator conveyor. The system was demonstrated to be effective on elevator speed of 2 ms21 transfer of sugarcane billets. The sugar content of sugarcane has also been analyzed using gene or morphological markers as estimates. Singh, Sheoran, Sharma, and Chatrath (2011) used the microsatellites or simple sequence repeats markers to characterize genetic diversity and comparative linkage potential in high and low sugar bulk of two segregating progenies cultivated as high and low sugar cultivars. The use of transgene technology has been demonstrated as means for improvement of sugarcane cultivars by increasing expression of the respective sucrose phosphate synthase and sucrose synthase, which are enzymes responsible for the production of the sugar compound of sugarcane, sucrose (Verma et al., 2013). Chemical treatment of sugarcane products for obtaining an elevated amount of sugar has been demonstrated as well. Benjamin et al. (2014) used dilute acid pretreatment of bagasse from six varieties of sugarcane to enhance enzymatic hydrolysis for maximum combined sugar yield. The correlations of gene expressions, enzymes content, or concentration of acids to the concentration of sugar serve as alternative techniques for assessing the sugar contents of the sugarcane.

3.2.3 Fruits HPLC is the most common procedure in determining the carbohydrate concentration of fruit. Determination of galactose from pomelo (Citrus grandis) pectin by high-performance anion-exchange chromatography (HPAEC) with fluorescence detection was demonstrated by Burana-osot, Soonthornchareonnon, Chaidedgumjorn, Hosoyama, and Toida (2010). The authors enhanced the procedure by hydrolyzing the pectin with trifluoroacetic acid, which elevated the amount of extractable sugar compared with the ordinary use of HPAEC to assess untreated pectin. Filip, Vlassa, Coman, and Halmagyi (2016) used an HPLC method with refractive index detection (RID), for simultaneous determination of glucose, fructose, sucrose, and sorbitol in leaf and apple peel of nine apple (Malus domestica Borkh.) cultivars. Gomez-Gonzalez, Ruiz-Jime´nez, Priego-Capote, and Luque de Castro (2010) profiled the sugar contents in olive (Olea europaea) fruits, leaves, and stems using gas chromatography (GC)
tandem mass spectrometry after ultrasound-assisted leaching. One of topical procedures in estimating sugar contents of fresh fruit is the use of nondestructive technologies such as spectrometers. The interest in application of spectroscopy on fruit has greatly increased recently, with sugars analyzed from fruits such as Pyrus communis (Wang, Wang, Chen, & Han, 2017), Actinidia deliciosa (Yang et al., 2019), Mangifera indica (Polinar, Yaptenco, Peralta, & Agravante, 2019), and Prunus persica (Munera et al., 2017).

3.2.4 Maple trees Maple syrup is a natural sweetener obtained from the sap collected from sugar maple (Acer saccharum Marsh). This sweetener is mainly produced in North America. Duchesne, Houle, Coˆte´, and Logan (2009) estimated that 80% of its world production was from Quebec, Canada, where it is an important economic product for rural communities. Commercially, simple physicochemical tests such as inspectors tasting all batches are used for routine quality control to ensure authenticity (Cle´ment, Lagace´, & Panneton, 2010). However, analysis involving instruments are necessary for rapid and accurate quality control for maple syrup to engage in bigger markets. Cle´ment et al. (2010) demonstrated application of intrinsic fluorescence as a means to characterize the physicochemistry and typicity of maple syrup based on the varying availability of aromatic compounds in sap and syrup. The authors demonstrated that the intrinsic fluorescence could attain an accuracy (R2) of 0.91, which was associated to two major regions of fluorescence found at 320 and 275 nm, and the second one at 460 nm, excited at 360 (syrup) or 370 nm (sap). Mellado-Mojica and Lo´pez (2015) demonstrated the ability of applying principal component analysis (PCA) of Fourier transform (FT) infrared (FTIR) spectra for classifying maple syrup. The classification can be used for categorizing the syrup according to authentication, characterization, and even for detection of the syrup adulteration. MelladoMojica and Lo´pez (2015) quantified the concentration of glucose, fructose, and sucrose in maple syrup using HPAEC with pulsed amperometric detection (HPAEC-PAD). The authors found glucose to be the major carbohydrate in the maple syrup. Isselhardt, Perkins, Van den Berg, and Schaberg (2016) compared vacuum and gravity sap extraction methods of extracting carbohydrates. Using a digital refractometer (PA202X; Misco, Cleveland, OH) to assess the total soluble solutes in  Brix, the authors demonstrated vacuum sap extraction to result in higher sugar extraction compared to the gravity extraction method.

3.2.5 Sweeteners (fruits, honey, cereals) An important analysis in sweeteners is classification rather than quantification. It is commonly known that the quality of honey is mainly influenced by the plant providing the nectar, the pollinizer bee species, geographic area, and

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harvesting condition (Siddiqui et al., 2017). It is for this reason that most studies in technology used to measure sugar level in syrup are based on classification rather than quantification. Gan et al. (2016) demonstrated using sensor and spectral analysis to classify botanical origin and determine adulteration of raw honey. Most studies have focused on investigating the adulteration of pure honey syrup by syrup from other commodities (Naila, Flint, Sulaiman, Ajit, & Weeds, 2018; Siddiqui et al., 2017; Wu et al., 2017; Za´brodska´ & Vorlova´, 2015). However, some studies have developed methods of determining sugar levels in some sweeteners. Anjos, Campos, Ruiz, and Antunes (2015) demonstrated application of FTIR-attenuated total reflectance spectroscopy for assessing sugar concentration in honey. Evaluation of  Brix and sugar content in stem juice from sorghum varieties was analyzed by capillary electrophoresis by Kawahigashi, Kasuga, Okuizumi, Hiradate, and Yonemaru (2013). Determination of sugars from sweeteners such as honey and fruit has shown a transformation from destructive to nondestructive technologies with spectroscopy being the most common. Recent work such as J. Li et al. (2019) demonstrated development of models for determining sugar content in intact pear by means of visible to near-infrared spectroscopy. The success in application of spectroscopy is linked to appropriate chemometric data analytical methods, which take place after the spectral data acquisition. Visible to near-infrared radiation (NIR) spectroscopy (Vis-NIRS) assesses sugar concentrations in commodities by illuminating a sample with radiation and measuring radiation reflection, absorption, or transmission while it passes through or deflects from the commodity (Cozzolino et al., 2011). VisNIRS spectrometers detect overtone vibrations of fundamental stretching bands occurring in the NIR range. The spectrum of the diffused radiation changes its characteristics depending on chemical composition (absorption) and microstructures (scattering) that the radiation encountered while penetrating a sample (Rinnan, Van Den Berg, & Engelsen, 2009). This change is usually plotted and saved as either reflectance or absorbance (log of reflectance, 1/R) versus wavelength on the spectrometer (Lin & Ying, 2009). Glossiness and rough surfaces are associated with specular reflection (direct scattering) and external diffuse reflection, respectively. Those reflections only provide information about the surface of a sample. In plant commodities, the cell wall interfaces are the main elements of radiation scattering because they are linked with abrupt changes in refractive index (Mehinagic, Royer, Symoneaux, Bertrand, & Jourjon, 2004). In the analysis of sugar concentrations in plant commodities using spectroscopy, other internal particles such as starch granules, chloroplasts, and mitochondria may also cause particular scattering when their refractive index differs from that of their surroundings (Nicolai et al., 2007). Particles with diameters smaller than the wavelength of electromagnetic radiation (,nm/10) cause Rayleigh scattering, while those with bigger diameter cause Lorenz
Mie scattering (Rinnan et al., 2009). Therefore the scattering of spectra from plant commodities is very complex since plant samples are made up of tiny particles varying in size, number, and patterns in every sample of a given batch (Dham & Dham, 2001). However, awkward scattering becomes obvious since an abnormal spectrum from a batch of similar samples appears as an outlier. The size, shape, and homogeneousness are common factors of radiation scattering, but this may also be caused by interferences such as pores, openings, and capillaries randomly distributed within a sample (Magwaza et al., 2015).

3.3 Application of spectroscopy and chemometric data analyses for assessment of quality parameters of sugar commodities 3.3.1 Statistical terms used to measure the accuracy of visible to near-infrared radiation spectroscopy regression models The performance of Vis-NIRS models is typically measured by the value of the coefficient of determination (R2; Eq. 3.1), the root mean square error of calibration (RMSEC; Eq. 3.2), and the root mean square error of prediction (RMSEP; Eq. 3.3). An R2 value is the measure of correlation between values predicted by a Vis-NIRS model and values obtained using conventional laboratory methods. R2 values range from 0.00 to 1.00 based on the closeness of the predicted values to the reference values. A good model should have a high R2 value approaching 1.00. However, reaching 100% prediction accuracy (R2 5 1) is practically unacceptable because it may indicate improper analysis or inadequate number or variation of samples. Statistically, comparison of two analytical methods should have a 95% limit of agreement to be comparable (Bland & Altman, 1999). An RMSEC is the error of calibration during the correlation stage of a model, while RMSEP is the error of the developed model to predict parameter values of the validation set. A good model should have low RMSEC and RMSEP with a small difference between the two errors (Dos Santos, Lopo, Pa´scoa, & Lopes, 2013). This results in a low ratio of prediction deviation when predicting external samples.

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The residual predictive deviation (RPD; Eq. 3.4) is the value that measures the distribution of the reference data from the predicted data. The RPD value is a ratio of standard deviation of reference data to RMSEP (Naes, Isakson, Fearn, & Davies, 2002). It measures the model robustness based on three model reliability categories: excellent models, with RPD greater than 2.0; fair models, with RPD between 1.4 and 2.0; and nonreliable models with RPD less than 1.4 (Williams & Norris, 1987). However, statistical bases were not used in determining those thresholds, and some researchers may use different values. The interesting relationship between R2 and RPD is that they have an exponential relationship under normally distributed data variables (Bellon-Maurel, Fernandez-Ahumada, Palagos, Roger, & McBratney, 2010). There is an exponential relationship between RPD and R2. However, an R2 value ranges from 0 to 1, while the maximum value of RPD is infinite (Minasny & McBratney, 2013). That reduces the ability of setting real thresholds for RPD. Therefore the RPD values cannot be used as a relative classification that is appropriate for R2 values less than 0.8. In their critical review of chemometric indicators for assessing quality of the prediction of soil attributes by NIRS, Bellon-Maurel et al. (2010) recommended the use of ratio of performance to interquartile range as a better technique than the use of RPD. Their argument was that it is a better index than the RPD, because it is based on quartiles which better represents the spread of the population. Another statistical parameter that is used to measure the accuracy of a model is the biasness (Eq. 3.5). Bias is the average difference between predicted and measured values. Basically, it measures the average inequality in the contribution of each value toward the model development during calibration (Ncama, Opara, Tesfay, Fawole, & Magwaza, 2017). Bias should be low if a model has the potential of high precision when predicting samples with unknown reference biochemistry. ffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi P 2 y ð 2y Þ cal act R2 5 1 2 P (3.1) ðycal 2ymean Þ2 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi vP u u ðycal 2yact Þ2 RMSEC 5 t (3.2) n vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2ffi uP  u 2y y pred act RMSEP 5 t n SD RPD 5 RMSEP qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 1 X Bias 5 ypred 2yact n

(3.3)

(3.4) (3.5)

where n is the number of samples, yact is the actual value, ymean is the mean value, SD is standard deviation of reference data, ycal is the calculated value, and ypred is the predicted value of fruit attribute. Many other items can be stated including slope, intercept, and average of the neighborhood or global Mahalanobis distances in addition to the mentioned statistical terms when necessary and possible (Williams, Dardenne, & Flinn, 2017).

3.3.2 Procedure and necessary treatment of spectroscopic data Spectroscopy is arguably the most researched technique in assessment of sugars or carbohydrates available in a sugar commodity. This technique involves spectra collection, diagnosis, analysis, chemometric preparation, and interpretation of the collected spectral data. In spectra obtained from plant material, common absorption bands found in the nearinfrared region are overtone or combination bands of fundamental absorptions due to vibrational and rotational transitions (Nicolai et al., 2007). In Fig. 3.1, typical Vis-NIR reflectance spectra of “Nules Clementine” mandarin, “Marsh” grapefruit, “Hass” avocado, and “Valencia” oranges are shown. They all depict very similar characteristics with significant absorption bands at 490, 1000, 1234, 1470, and 1966 nm. Absorption bands normally found in spectra of organic samples are associated with O
H, N
H, and C
H bonds representing chemical properties of a sample. Water is also a main compound absorbed in the NIR regions of spectrum acquired from horticultural produce (Cozzolino, Cynkar, Shah, Dambergs, & Smith, 2009). Removal or awareness of spectral regions associated with water regions is necessary for assessing determinate portions of spectra and increase model’s accuracy if the interest is on investigating parameters

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Innovative Food Analysis

Mandarin

Absorbance (1/log R)

1.4

Grapefruit

1.2

Avocado

1

Orange

0.8

FIGURE 3.1 The typical reflectance spectra of “Nules Clementine” mandarin, “Marsh” grapefruit, “Hass” avocado, and “Valencia” oranges attained using bench-top monochromatic visible to nearinfrared radiation spectroscopy system (Model XDS spectrometer, FOSS NIR Systems Inc., MD, United States) (The figure was self-constructed).

0.6 0.4 0.2 0 –0.2 400

900

1400

1900

2400

Wavelength (nm)

such as structural carbohydrate without soluble sugars. However, spectra can also be complicated by wavelengthdependent scattering effects (Cozzolino et al., 2009; Magwaza et al., 2014). In addition to scattering effects and sample complexity, instrumental noise, instrument type, and matrix or environmental effects such as ambient temperature and light are other factors responsible for the complexity of spectra from plant commodities (Dos Santos et al., 2013). Therefore to fully explore the information in the spectra, spectral preprocessing and multivariate assessment techniques are key chemometric methods used for extracting and interpreting analytic information from raw spectra. As a general outline in application of spectroscopy to analyze sugars, carbohydrates, or any biochemical quality of sugar commodities, appropriate data collection, exploration of the collected data, data preprocessing, and developing an assessment model is necessary.

3.3.3 Data collection It is well known that no chemometric method can extract useful information from bad data. Therefore obtaining a good representative data is essential. Using samples with the widest variation of structural and physiological parameters is recommended during collection of data for modeling. The validity of calibration models for future predictions depends on how well the calibration set represents the composition of future samples (Wedding et al., 2013). The reference physical and chemical data from samples are normally acquired using standard techniques. It is in the collection of spectral data where a researcher needs to be more cautious. On the sample, the part where a spectrum is collected should be properly selected. In most cases, a researcher should obtain the average of spectra from different positions of a produce. Concentrations of biochemical compounds were shown to differ within a fruit (Guthrie, Liebenberg, & Walsh, 2006). The variation gets higher in big fruits such as papaya, melons, squash, and pineapples (Guthrie and Walsh, 1997). Acquisition of spectra at same spot (equatorial, toward pedicel end or toward calyx end) from all samples and clearly indicating that on the model information is feasible when scanning many spots in one fruit is not possible. Spectrometers have different designs, tolerances, probes, and spectral ranges. These factors can result in loss of correlation between spectra and biochemical property if not considered clearly. The light emitting and absorbing side of a probe should be fully covered by a sample to avoid interference from the surrounding environmental conditions such as light or deflected irradiation. Some spectrometers change their functioning accuracy with a change in temperature or relative humidity of the surrounding environment (Cozzolino et al., 2009). It is also beneficial that a researcher matches the probable wavelengths where the analyzed biochemical compound would be reflected. Carbohydrates, water, fats, and proteins are mostly absorbed in the NIR region because of their colorless nature (Williams & Norris, 1987). Therefore using spectrometers with relevant wavelengths could greatly increase the accuracy of acquired spectra for developing models of analyzing sugars or carbohydrates from sugar commodities.

3.3.4 Data exploratory methods After collection of spectral and chemical data from samples to be used for developing Vis-NIRS models, analysis begins by exploring whether data are associated and can build a useful model. Exploratory data analysis involves outlier detection, PCA, and data preprocessing.

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3.3.4.1 Outlier detection Outliers are samples that fall out of the expected range. They can be found from either or both spectral and biochemical data (Magwaza et al., 2012). Outliers can be caused by poor sampling, poor calibration of a spectrometer, typing errors, or laboratory errors. It is necessary for a researcher to explore the spectral data and the statistical distribution of chemical data prior chemometric analysis of it. Spectral data can be examined by plotting spectra using any graphics software and observe if all spectra have similar absorption peaks and fall within a probable range. The distribution of biochemical data can be explored by use of any statistical software to observe if the average, minimum, maximum, and standard deviation values are within a normal range. Chemical data should have a normal distribution with most values closer to the mean (Limpert et al., 2001). However, extreme pruning of outliers should be avoided since it would reduce the robustness of the developed model (Nicolai et al., 2007).

3.3.4.2 Principal component analysis There are mathematical methods of detecting outliers from a given set of samples. Such methods function with PCA of raw spectral or biochemical data before application of preprocessing transformations. PCA-based detection of outliers creates a sphere of similar samples, and the outlier would be recognized by not falling into the cluster. PCA inspects spectra based on a generated set of noncorrelated response variables called principal components (Naes et al., 2002). The commonly used PCA-based outlier detection technique is Hotelling T2, which detects outliers based on a set level of accuracy (Roussel, Preys, Chauchard, & Lallemand, 2014). This method and other PCA methods represent a spectrum using one point and form a sphere of points from samples with similar spectral characteristics. A sample that does not fall into the cluster is, therefore, an outlier. The Hotelling T2 technique was used in both spectra and chemical data by Magwaza et al. (2014) for developing a model for categorizing mandarin fruit based on their carbohydrates concentration.

3.3.4.3 Spectral data preprocessing techniques Preprocessing of spectral data is the basis of improving signal response during chemometric analysis. It enhances the ability to relate spectral data to chemical data. A variety of preprocessing techniques are normally applied to raw spectra for removing unexplainable or extracting analytical spectral information. Splitting the full Vis-NIR spectra into smaller ranges and applying preprocessing at different parts of a spectrum has been widely used in the horticultural industry. The technique is based on spectral range or wavelengths that are responsible for absorption of a specific biochemical compound (Merzlyak, Solovchenko, & Gitelson, 2003). However, biochemical compounds such as carbohydrates and water have been shown to have no specific absorption wavelengths (Polessello & Giangiacomo, 1981). Such compounds are most likely to be presented by the entire spectrum. In that case, only mathematical preprocessing methods can be executed to improve the process of obtaining information of the compounds from a spectrum. The commonly used mathematical preprocessing methods can be grouped into two major groups: scatter correction methods and spectral derivatives (Stevens & Ramirez
Lopez, 2014). Scatter correction methods are applied on spectral data for removing instrumental error by reducing spectral noise.

3.3.4.4 Spectral noise removal techniques Random or uncommon fluctuation or perturbation of spectrometer signal is called noise (Nicolai et al., 2007). This noise can be caused by spectra collection faults, instrumental faults, or conditions of a surrounding environment. The simplest method to nullify noise is the repetition of measurements to obtain an average spectrum from each sample. If pffiffiffi the repetition is n, the noise will be reduced by a factor n (Stevens & Ramirez
Lopez, 2014). However, repetition is not always possible when working on fresh produce because a large number of samples are required to increase model robustness while the period between the first measurement and the measurement of the last sample should be as short as possible to obtain spectra at the same maturity or ripening stages of all samples. If the repetition technique is not possible, mathematical smoothing approaches for spectral noise removal are commonly applied (Naes et al., 2002). However, multiple scans can increase the signal to noise ratio but not necessarily discard the use of smoothing. Those approaches include moving average filter, binning, and Savitzky
Golay filtering. Moving average filter is based on column operation that averages contiguous wavelengths in a given window size. Binning acts as a secondary averaging that can be applied after moving average filter. Savitzky
Golay filtering operates as reduction of spectral points by averaging of neighboring values of the original spectrum (Rinnan et al., 2009). Gaps between averaged spectral points can be manipulated to obtain a compressed spectrum with easy-to-handle size while optimally representing the original

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Innovative Food Analysis

spectrum. Smoothing techniques reduce baseline shift of all spectra by comparing all spectra from all samples in one batch (Magwaza et al., 2014). This is not equivalent to the technique of repeating spectra acquisition, but it alters all spectra based on common features of spectra from the majority of samples.

3.3.4.5 Spectral derivatives and transformations Spectral resolution is increased by using derivatives (usually first or second) or transformations (Naes et al., 2002). Savitzky
Golay derivatives are arguably the most used in the field of radiation application on fresh horticultural produce. Another common derivation procedure used is Norris
Williams (Norris & Williams, 1984). Both Savitzky
Golay and Norris
Williams derivation techniques are normally applied after smoothing to increase the ratio of the signal to noise in the corrected spectra. Basically, first derivatives calculate the difference between two points of the raw spectra alternated by a moving window (Rinnan et al., 2009). The second derivative calculates the difference of points from the first derivative and so on. The effect of derivation is illustrated in Fig. 3.2. Second derivatives are the most commonly applied derivatives because they correct both the additive (column-wise) and multiplicative (row-wise) scatter effects of spectra (Rinnan et al., 2009). Another spectral transformation used to reduce spectral noise is FT. The FT compresses the principal information from the dataset and increases the ratio of signal to noise in order to improve the signal quality (Ilari, Martens, & Isaksson, 1988). It takes only the principal components and discard the irresponsive part of a spectrum. The commonly employed FT is its wavelet form, which works similar to pure FT but uses bands instead of single points. The band size varies with frequency resulting in faster data decomposition represented by wavelet coefficients (Smith, 2011). The advantage of splitting a spectrum into multiple window bands is that those windows could be subjected to different transformations depending on their response to biochemistry of a sample. However, that would be tedious because software packages with various window-based commands are limited. Multiplicative signal correction (MSC), standard normal variate (SNV), and orthogonal signal correction (OSC) are other data preprocessing methods available and employed by many researchers of NIR application on fresh produce. MSC adjusts the baseline shifts of spectra from the same set of samples and linearize it to a generated average spectrum (Roussel et al., 2014). SNV normalizes each individual spectrum to a zero mean and unit variance. The OSC preprocessing technique is applied when there is little correlation between the spectral matrix and the concentration matrix of certain quality attributes due to model transfer or inadequate outlier detection (Wang et al., 2015). OSC was introduced by ¨ hman (1998) to increase the potential of calibration transfer between different instruments Wold, Antti, Lindgren, and O and software by keeping the orthogonal ratios of spectral variance from instrument A to instrument B. FIGURE 3.2 The effects of 7-point window smoothing and Norris-Williams derivation using a gap size of 3 on a raw spectrum (Rinnan et al., 2009).

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Lu et al. (2006) conducted a study investigating whether the use of MSC, SNV, or OSC technique yield models with different prediction results on assessing soluble solutes content, equivalent of sugar concentration in juice, of citrus fruits. The authors observed nonsignificant differences in statistics obtained from models developed using each of the three procedures. They concluded that selecting either of those preprocessing techniques have minor effects on the quality of the final prediction model, but they were all better than models developed without any preprocessing technique. Although those results cannot be generalized to all data, it is significant that there are lots of studies that used spectral preprocessing before calibration of Vis-NIRS models of assessing sugar contents in different fruit species. There are some authors who indicated that they obtained better results without applying any data preprocessing technique. Preprocessing includes removal or nullification of certain spectral information closely associated with noise or outliers. It is possible that useful information can be removed together with outliers or cutout region of the spectrum, which results in reduced performance of models developed with preprocessing compared to when preprocessing is not applied. Xudong, Hailiang, and Yande (2009) and Wang and Xie (2014) conducted studies investigating whether it is important to apply preprocessing on models for assessing soluble solutes contents of citrus fruits. The authors observed better resultant models from raw spectral data, which contradicted results of previous studies (Go´mez, He, & Pereira, 2006; Liu, Sun, Zhou, Zhang, & Yang, 2010). The inconsistency of preprocessing on the performance of Vis-NIRS models for determining parameters of plant commodities can cause mystification to an inexperienced researcher. It is, however, the choice of a researcher whether the data should be preprocessed or not. The selection of the appropriate preprocessing methods also depends on the researcher, and this is due to the fact that some techniques may be substituted by one another and yield similar results. Similar results from models developed from second spectral derivative (R2 5 0.87, SECV 5 0.60%) and extended MSC preprocessing (R2 5 0.88, SECV 5 0.59%) were obtained by Penchaiya, Bobelyn, Verlinden, Nicolaı¨, and Saeys (2009) on assessment of soluble solutes content of bell pepper. Their findings were consistent with a statement by Nicolai et al. (2007) who stated that choosing between MSC and Savitzky
Golay first derivative transformations is just a matter of favor because there is mathematically no additional effect if the techniques are applied in conjunction.

3.3.4.6 Modeling Modeling is the term used to refer to the process of developing assessment models. The modeling is divided into calibration (developing a model) and validation (testing the model). During calibration, a substitutive (leave-one-out) use of all samples for validation is normally used to test the correlation of the predicted values to original values. Another lot of samples with known chemistry are used as a test set validation to observe the model performance on external samples. In most cases, 60% of the total number of samples is used during calibration and the remaining 40% is assigned to validation set (Cle´ment, Dorais, & Vernon, 2008). However, increasing the ratio of calibration to validation samples is known to improve models accuracy at the prediction of external samples with unknown chemistry (Cen and He, 2007). The most used Vis-NIRS models can be categorized into quantitative and classification (discrimination) models.

3.3.4.7 Quantitative models Chemometrics is the term that refers to techniques of extracting, interpreting, and correlating useful information from raw spectra to biochemical data during Vis-NIRS regressions. In general, regression models are likely to obtain preference over classification models because they are quantitative; hence, they give an estimate of an assessed carbohydrate compound (Naes et al., 2002). However, the choice of a model is strictly aligned with a specific problem. The commonly used regressions are partial least square (PLS), principal component regression (PCR), and multiple linear regression (MLR). Regression Vis-NIRS models use spectra as a reference for assessment of biochemical parameters through multivariate analysis (Cozzolino et al., 2009). Capitals X and Y are used to refer to spectral and chemistry matrices, while x and y are assigned to individual spectrum and chemical compound, respectively (Roussel et al., 2014). Chemometric analysis for regression models involves preparation of raw spectral data into an informative form through preprocessing. The preprocessing stage is followed by correlation of descriptor X with response Y factor to formulate a linear model in a form of Eq. (3.6). y 5 mx 1 c

(3.6)

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where y is a biochemical concentration, m is a slope of a model’s line of best fit, x is a spectrum data, and c is the yaxis intercept. Once a linear model in a form of Eq. (3.6) is developed, spectra can be obtained from any samples resembling those used for model development and be substituted for X and the model will estimate Y.

3.3.4.8 Multiple linear regression MLR is one of the commonly used and is believed to be the oldest chemometric linear regression model (Norris & Hart, 1996). MLR estimates the response y using a linear combination of the spectral values at every single wavelength. The regression coefficient is increased by minimizing the error between predicted and prior-known values of the response based on least squares. The weakness of MLR is in the failure of removing the effect of colinearity of spectral variables, which causes model over-fitting and that results to low robustness (Pissard et al., 2012). Colinearity of MLR is caused by its assumption of a direct relationship between spectra and variables, which is a null assumption. There are specific portions of spectra responsible for reflecting overtones and combinations of vibrations mainly from C
H, O
H, and N
H bonds, which can be associated to absorption of certain chemical compounds (Magwaza et al., 2014).

3.3.4.9 Principal component regression PCR is a linear regression model based on PCA. The idea behind PCR is to split spectral matrix by PCA and fit an MLR model using fewer PCs to test a minimum number of PCs equivalent to original variables as forecasters (Magwaza et al., 2014; Polesello & Giangiacomo, 1981). The number of principal components must be at most equal to 10% of the number of samples. Otherwise, the model will be over-fitted or saturated (forced to be perfect) and lose accuracy on predicting external samples (Cozzolino et al., 2009; Dos Santos et al., 2013; Rinnan et al., 2009). The higher number of selected PCs results to higher exploration of spectral variance. Therefore the highest number of PCs is required to fully explain the response variable using a given spectrum. The weakness of PCR is that the order of PCs is based on a decreasing explanation of variance of the spectral matrix. However, the advantage of PCR compared to MLR is that the applied spectral PCs have no colinearity and the spectrum noise is filtered (Cozzolino, Cynkar, Shah, & Smith, 2011).

3.3.4.10 Partial least squares regression In PLS regression (PLSR) models, an orthogonal basis of latent variables is constructed one by one in such a way that they are oriented along directions of maximal covariance between the spectral matrix and the response value (Jamshidi, Minaei, Mohajerani, & Ghassemian, 2012). The technique is an improved modification of MLR that was introduced by Herman Wold in 1975 to overcome colinearity, a factor not considered in the MLR (Wold, Trygg, Berglund, & Antti, 2001). A unique feature of basic PLSR is its simplicity. The basic PLS method consists of a series of simple, least squares optimizations called nonlinear iterative PLS. The PLS technique also accounts for noisy and redundant spectral variables and can analyze more than one chemical variables at once (Lorente, Aleixos, Go´mez-Sanchis, Cubero, & Blasco, 2013). However, treating the responses together does not necessarily mean there are improved predictions although the technique can provide useful information from linear combinations of responses (Liu et al., 2010). The literature demonstrates that PLS is today probably the most applied regression method in chemometrics.

3.3.4.11 Classification or discrimination models Classification or discrimination of sugar or carbohydrate commodities is crucial for relevant quality management practices. In Vis-NIRS application, classification is the division of samples into groups of similar spectral characteristics based on pattern recognition technique (Wang and Xie, 2014). Classification or discrimination models represent the Vis-NIRS spectral data as clusters according to their similarities. Each spectrum is projected as a point in a scatter plot based on scores. Although the technique certainly loses fine details, it maximizes simplification. It assigns spectral data of a sample to associated clusters. Models to classify samples can be categorized into unsupervised or supervised models. Unsupervised models can classify samples without previous knowledge except spectral data, unlike supervised models that classify samples based on prestated categories of known biochemistry. Both categories are also classified into linear and nonlinear models (Roussel et al., 2014). Notably from the literature is that the PCA and soft independent modeling of class analogy (SIMCA) are arguably the most applied classification models in NIRS applications on horticultural produce.

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3.3.4.12 Principal component analysis PCA is arguably the simplest and the most common classification model from spectra of horticultural produce (Wold, Esbensen, & Geladi, 1987). It is commonly used as either exploratory technique prior development of linear regression models or simple nonlinear classification model (Mireei & Sadeghi, 2013). PCA is an unsupervised nonlinear model that cluster samples based on their spectra similarities. In Fig. 3.3, PCA-based clustering of grapefruit spectra was used to separate fruit from inside or outside canopy position by Ncama (2016). A clear separation of samples from inside or outside canopies was observed. Ncama (2016) stated that it is possible to develop similar models for discriminating commodities with different concentrations of carbohydrates or compounds generally. Spectra could be obtained at sorting lines, and PCA classification can be used to separate the fruit.

3.3.4.13 Soft independent modeling of class analogy SIMCA is a well-known method for multivariate classification. Wold et al. (1987) defined SIMCA as a disjoint PCA due to its resemblance to the PCA method on classification of samples. Its uniqueness is its ability to analyze the variance between the clusters and within the clusters at an extended number of classes. Basically, SIMCA functions as an aggregate model made up of smaller PCA models.

3.3.4.14 Robustness of models Model robustness is the ability of a model to accurately classify or predict parameters of external samples under different conditions (Swierenga et al., 2000). It can be lost when calibration models from an instrument are transferred to another instrument that responds differently compared with the first instrument. A model in an NIR instrument can lose its robustness when the response changes because of temperature fluctuations, electronic drift, and changes in wavelength or detector over time (Polesello & Giangiacomo, 1981). Samples belonging to different batches from different seasons or locations can also result to a poor model performance. Fruit from different trees, locations, seasons, or cultivars differ in their physicochemical attributes. The difference is likely to cause poor performance of an NIRS model developed from similar samples on another batch with distinct origin. However, few studies consider testing the robustness of calibration models on independent populations (McGlone and Kawano, 1998). In some studies, full cross validation is considered to be adequate to demonstrate that Vis-NIRS can be used to nondestructively evaluate a quality variable of interest. However, for practical purposes, the actual accuracy of the model must be estimated with an independent validation dataset. The stability of the model when tested using unknown samples and external factors demonstrates its robustness (Rinnan et al., 2009). McGlone and Kawano (1998) conducted a study to test model robustness on soluble solutes content of kiwifruit with different sizes, origins, and maturity stages. The author obtained higher validation errors when fruit were not categorized based on their differences. Similar results were also obtained on the SSC of apples and melons (Wedding et al., 2013). In order to increase model robustness, increasing the number of samples and involving samples with all possible variations during calibration of a prediction model are recommended. Although validating models with samples from different orchards or different seasons depict their robustness, it is essential that the performance of prediction models FIGURE 3.3 The principal components analysis-based clustering of spectra from fruit of outside or inside canopy position. PC, Principal component (Ncama, 2016).

Scores 80,000

Inside

60,000 PC-2 (1%)

40,000

Outside

20,000 0 –20,000 –40,000 –60,000 –80,000 –200,000 –100,000

0

100,000

200,000

PC-1 (99%)

300,000

400,000

500,000

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Innovative Food Analysis

is enhanced by adding few samples from a season or an orchard it will be applied to. This process reduces the error that may occur because of unknown biochemical changes as a result of alternate bearing, environmental changes, or other factors during the produce development. Magwaza et al. (2014) demonstrated that spiking calibration models with a few samples from the target prediction orchard improved models performance. The authors concluded that while the ultimate goal of any prediction technique is to be universal, it is important to stress that there is variation between growing locations and seasons, and it is advisable to recalibrate models for including the new population information.

3.4

Topical methods of extracting sugars and carbohydrates from sugar crops

Ordinarily, sugarcane stems are passed in electric milling and filtered to obtain the juice (Pereira et al., 2017). The pressing technique operates under two types of press, which are hydraulic and screw methods. In the hydraulic pressing method, the separation of the extract from the matrix is due to the application of a uniaxial force on the material. The pressure applied on the raw material differs based on the crop that is extracted and the expected extract. In general, the pressure required is 50
100 MPa for cocoa and up to 40 MPa for olive oil (Schwartzberg, 1997). In a screw pressing method, a helical screw in a barrel with restriction edges conveys the raw material from the inlet to the outlet of the press. Shear forces are developed along the screw, which allows compression and release of sweet solution contained in the crop part at the stage of the restriction area. The screw presses have eventually replaced hydraulic presses over the last decade (Chemat, Fabiano-Tixier, Vian, Allaf, & Vorobiev, 2015). Pressing for solventfree extraction is used in a great number of industrial applications in the food domain, for example, juice extraction from fruit such as grapes and tomatoes.

3.5 Novel analytical technologies for determining the sugars and carbohydrates in secondary products 3.5.1 Determination of sugars or carbohydrates from secondary products Organic acids and sugars are related to the chemical balance of wines and grape juices. The acids and sugars exert a strong influence on the taste balance and sensorial acceptance of wine by consumers (Coelho et al., 2018). Coelho et al. (2018) demonstrated simultaneous analysis of sugars and organic acids in wine and grape juices by HPLC coupled with RID and diode array detection (DAD). The authors demonstrated that the analysis was of high accuracy in comparison with the wet chemistry analysis. The authors found that the method provided values for linearity (R2 . 0.9982), precision (CV% , 1.4), recovery (76%
106%), and limits of detection (0.003
0.044 gL21) and quantification (0.008
0.199 gL21), which are acceptable for application in the characterization of liquor samples using the HPLC. Determination of sugar or carbohydrates from processed liquor products is normally not necessary because brewers or juice makers determine how much sugar they put during processing. However, some studies have demonstrated the ability of rapid technologies to assess the sugar content of some liquors such spectroscopy. Dos Santos et al. (2018) demonstrated the application of Raman spectroscopy for rapid and accurate determination of total sugars with R2 5 0.97 and RMSEP of 5.12%.

3.5.2 Determination of syrup adulteration Adulteration is the act of degrading or diluting the authentic quality of food presented to the market. The sweetness in food is either diluted or supplemented with low-grade material to increase the volume of food per volume of the sweetener. The imbalance and the limited availability of high-quality honey are a typical example where food is adulterated for increasing the available quantity without maintaining the quality of sweetness the honey contains. As such, there is a lot of research intended in the technology of determining the quality of honey or tracing its adulteration as a tool to regulate its quality that is provided to consumers (Siddiqui et al., 2017).

3.5.2.1 Adulteration of honey syrup HPAEC-PAD was used in a study of oligosaccharides for detecting honey adulteration by high-fructose corn syrups (HFCS; Morales, Corzo, & Sanz, 2008). The technique was able to detect honey adulterated with 5% corn syrup (CS). Another technique that was used by Ribeiro et al., 2014 to detect honey adulteration was the low-field nuclear magnetic resonance (LF-NMR) spectroscopy. The LF-NMR technique is based on proton relaxation time, which studies different water concentrations based on the longitudinal constant (T1) and transverse constant (T2) which detects the alteration of constants when the honey material changes. Ribeiro et al. (2014) detected the adulteration of honey syrup with CS using

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the LF-NMR. The qualitative and quantitative detection of honey adulterated with HFCS and maltose syrup was also successfully detected by using near-infrared spectroscopy (Li et al., 2017). Ruiz-Matute, Rodrı´guez-Sa´nchez, Sanz, and Martı´nez-Castro (2010) demonstrated detection of adulteration of honey with high-fructose syrup from inulin using GC. Wang et al. (2015) demonstrated detection of honey adulteration with starch syrup by HPLC. Sobrino-Gregorio, Bataller, Soto, and Escriche (2018) demonstrated monitoring honey adulteration with sugar syrups using an automatic pulse voltammetric electronic tongue. The TLC is one of the oldest methods for honey analysis. In this technique, the carbohydrates are separated to achieve the required degree of sensitivity and a charcoal-Celite column isolates oligoand polysaccharides fractions (Kushnir, 1979). The fraction is then examined by TLC on silica gel. Syrup from pure honey yield only one or two blue-gray or blue-brown spots at Rf values above 0.35. The adulterated samples show an additional series of spots or blue streaks (Kushnir, 1979). C4 sugar syrups (corn and cane sugars) change the δ13C ratio of honey when added, but beet sugar syrups do not affect the δ13C ratio when added to honey syrup. This change is used in techniques of C-isotope methods such as stable carbon isotope ratio analysis, which can differentiate adulterated honey from C3 plants (Tosun, 2013; Wu et al., 2017). Common analytical techniques for detecting honey syrup adulteration include HPAEC (Cordella, Militao, Clement, & Cabrol-Bass, 2003; Corradini, Cavazza, & Bignardi, 2012; Morales et al., 2008), GC (Doner, White, & Phillips, 1979), and HPLC (Yilmaz et al., 2014). Advanced techniques, including NIRS (Chen et al., 2011; Kelly, Petisco, & Downey, 2006), NMR, and Raman spectroscopy (Rios-Corripio, Rojas-Lopez, & Delgado-Macuil, 2012), which enhance the analysis process for larger numbers of samples, have also been demonstrated.

3.5.2.2 Adulteration of corn syrup FTIR spectroscopy was used to quantify CS, HFCS, and inverted sugar (IS) in honeys from four different locations of Mexico (Gallardo-Vela´zquez et al., 2009). The authors developed PLS models with high accuracy based on their standard error of prediction (SEP) that ranged from 1.5 to 2.1, 2.1 to 3.0, and 1.4 to 2.5 for CS, HFCS, and IS, respectively. Li et al. (2017) developed near-infrared spectroscopy models for detection of honey adulterated with HFCS and maltose syrup. The adulteration of CS is generally not vital as it is not mostly used pure but used to dilute honey syrup.

3.5.2.3 Adulteration of beet syrup An FTIR spectroscopy approach was used by Paradkar and Irudayaraj (2002) to anticipate the degree and the type of adulteration in honey. The authors used a PLS regression technique to develop a model for quantitative analysis of the adulteration level and used the PLS-DA and canonical variate analysis (CVA) to develop models to cluster adulterated syrup into groups of similarities. The FT-Raman spectroscopy was found to be an efficient technique with R2 value of 0.91 in predicting the quantities of beet and cane syrups in pure honey of clover, orange, and buckwheat. The authors reported 96% accuracy of the CVA model in clustering the adulterated honey in similar groups.

3.5.2.4 Adulteration of rice syrup The production of rice syrups involves hydrolysis of different polysaccharides and oligosaccharides. This results to difficult identification of the presence of adulteration in rice syrup by means of TLC and HPAEC-PAD (Siddiqui et al., 2017). However, Xue et al. (2013) was able to identify a characteristic compound, 2-acetylfuran-3-glucopyranoside, responsible for chromatography of rice syrup. The authors were able to detect the adulteration compounds in rice syrup adulterated to 10% using HPLC with DAD. However, the technique was unable to detect adulteration below 10%. Application of a three-dimensional fluorescence spectroscopy was also demonstrated as an efficient technique to detect different adulterations of rice syrup by Chen et al. (2014). The authors applied the back propagation artificial neural network (BP-ANN) and PLS algorithms as discrimination models. In that study, the BP-ANN was selected as the optimal model for detecting honey adulteration by rice syrup, which indicated the potential of the technique to assess adulteration of rice syrup.

3.6 Research challenges in technologies applied for assessment of sugar contents in secondary products The quality of secondary liquor products such as beverages and juices is determined by their concentration of different carbohydrates including the sugars and other types of polysaccharides. The development in quality of those products is also associated with the research focusing on determination of concentration of sugars or carbohydrates obtained from a

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commodity such as a sugar crop. After determination of sugar content from the sugar crop, the sugar is extracted and used to produce a secondary product. The concentration of the sugars in the secondary product is mainly controlled by how much has been added. As such, the innovation in research focusing on the assessment of sugar concentration or detection of adulteration in secondary products has not attracted major attention from researchers. It is, however, necessary to determine the level of honey adulteration because honey syrup is normally sold as an authentic product in the market. The research in determining honey syrup adulteration has shown a developing trend (Naila et al., 2018). The Raman spectroscopy (Oroian, Ropciuc, & Paduret, 2018), automatic pulse voltammetric electronic tongue (Sobrino-Gregorio et al., 2018), visible to near-infrared spectroscopy combined with chemometrics (Ferreiro-Gonza´lez et al., 2018), bioluminescent bacteria and chemometrics (Melucci et al., 2019), and untargeted headspace GC
ion mobility spectrometry (Arroyo-Manzanares et al., 2019) have been demonstrated as technologies for rapid detection of honey syrup adulteration.

3.7

Future perspectives and conclusions

Analysis of sugars or carbohydrates from sugar crops is mainly based on wet chemistry coupled with alterations of high performing liquid chromatography. Spectroscopy is also a common tool for nondestructive assessment of sugars and carbohydrates concentrations from intact crop commodities and honey. Detection of adulteration has received higher research interest in comparison with analyzing sugar or carbohydrates concentrations from secondary products. Currently, chromatography dominates the analytical technologies in sugar and carbohydrate processing. Rapid techniques for analyzing of sugar concentrations on beverages, liquors, or food products need specific attention because there is currently a few analytic technologies for analyzing their sugars and carbohydrates.

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102.

Chapter 4

Sample preparation methods Renata Raina-Fulton Department of Chemistry & Biochemistry, University of Regina, Regina, SK, Canada

4.1

Introduction

Sample preparation of food commodities is an important consideration prior to instrumental analysis. The techniques are designed to provide a representative sample with minimal influence of the sample matrix on separation or detection of the target analytes of interest. Other consideration often not discussed in sample preparation methodologies are the sample size used in collection and selection of appropriate size of subsample for sample pretreatment techniques. Most sample pretreatment methods such as those used for food matrices utilize typically 10 20 g subsamples including quick, easy, cheap, effective, rugged, and safe (QuEChERS) methods, which typically uses 10 15 g subsamples for samples of sufficient moisture content and 1 5 g for dry (low moisture content) samples (Alcaˆntara et al., 2019; Lawal, Wong, Tan, Abdulra’uf, & Alsharif, 2018; Musarurwa, Chimuka, Pakade, & Tavengwa, 2019; Peresterelo et al., 2019; Raina-Fulton, 2015; Santana-Mayor, Socas-Rodrı´guez, Herrera-Herrera, & Rodrı´guez-Delgado, 2019; Varela-Martı´nez et al., 2020; Vazquez et al., 2019; Zhang et al., 2019). If QuEChERS is used with low water content foods, then a wetting step is necessary either prior to or during the addition of acetonitrile to the sample matrix. Addition of water with the acetonitrile used in extraction is preferred when the target analytes are more prone to hydrolysis; however, some dry sample matrices such as powders from spices can take an hour to have sufficient time for swelling to occur (Raina-Fulton, 2015; Vazquez et al., 2019). With QuEChERS, there is for a pesticide analysis 20 6 18% difference in reanalysis if subsamples taken from B1 kg sample of different commodities of food stored are frozen until reanalysis (Lehotay & Cook, 2015). The particle size of the homogenized sample with blending can vary with the sample matrix and liquids are generally easier to obtain a representative sample than solids such as fruits and vegetables, while high fat content foods such as meat or fat tissues are often the most challenging. Chopping and blending can take place using a variety of food blenders from domestic to commercial blenders often not fully described in methods with only a note that samples were chopped and blended. When commercial blenders were used, models were more often identified. Approaches such as the Dutch two-step method use a probe blender at room temperature (e.g., Ultra-Turrax probe blender). More recent advances in homogenizing samples have used cryogenic procedures with addition of dry ice (278 C) or liquid nitrogen (2196 C) in a chopper (Anatassiades et al., 2019; Lehotay & Cook, 2015; Roussev et al., 2019). When cryogenic processing of samples is utilized, a more uniform particle size is obtained, which improves analyte accessibility. Cryogenic processing obtains a more homogeneous sample than that obtained with blending at room temperature and is recommended for fruits and vegetables in the QuPPe method (Anatassiades et al., 2019). There is minimal loss of more volatile or labile analytes particularly when liquid nitrogen is used. Cryomilled subsamples with dry ice has been shown for peaches, blueberries, grapes, and plums to be ,15% relative standard deviation when sample weights are taken while the sample is frozen (Lehotay & Cook, 2015; Roussev et al., 2019). Cryogenic processing is less prone to issues with loss of more volatile analytes when liquid nitrogen is used rather than dry ice, and the sample does not need to be prefrozen prior to cryogenic processes such as required when dry ice is used (Anatassiades et al., 2019; da Costa Morais, Collins, & Jardim, 2018; Lehotay & Cook, 2015; Roussev et al., 2019). Liquid nitrogen is inert such that the stability of labile pesticides is less of a concern, and powdered subsamples of more difficult samples such as dried fruit products can be obtained without addition of water (Roussev et al., 2019). Safety precautions with standard liquid nitrogen use should be followed. Liquid nitrogen is used with metal vessels that are prefrozen including metal blades and spatulas used. Some methods particularly for low water content samples such as nuts will grind samples to a fine powder using dry milling, and this could also be followed by passing through a mesh sieve (e.g., 24 mesh sieve) with analysis of only the Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00004-2 Copyright © 2021 Elsevier Inc. All rights reserved.

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smaller particles (Alca´ntara-Dura´n, Moreno-Gonza´lez, & Garcı´a-Reyes, 2019; Li et al., 2020a). Other dry powders such as spices and herbs are also more often extracted with conventional extraction methods to allow sufficient contract time for complete extraction of the target analytes.

4.2

Sample pretreatment techniques

In this chapter, sample preparation methods are reviewed for methods that aim to optimize the largest number of target analytes from varying chemical classes, the so-called MEGA methods, to achieve the best overall accuracy, precision, recoveries, and speed of analysis at low cost. Other methods that focus more on a smaller number of selected targeted analytes often aimed at improving recoveries in difficult sample matrices and reducing matrix effects will also be discussed. The matrix effects or matrix variability in chemical analysis over the long-term is still an area that needs further evaluation with these methods. Although selective methods are often more costly than approaches such as modified QuEChERS methods, they remain in use particularly where more challenging analytes have poor recoveries in MEGA methods or when more targeted analysis is desired. An important consideration when choosing a modified QuEChERS method or other extraction approaches is validation of the method for the food matrix and target analytes of interest with particular consideration to the sorbents or approaches used to remove matrix following extraction steps. Fruit and vegetables encompass a large portion of products tested in food analysis (Alcaˆntara et al., 2019; Lawal et al., 2018; Musarurwa et al., 2019; Peresterelo et al., 2019; Santana-Mayor et al., 2019; Varela-Martı´nez et al., 2020; Zhang et al., 2019). Fruits that represent moderate matrix issues and are among the most widely studied include apples, oranges, and grapes. Herein apples are taken as a model fruit of high water content that has been widely studied with different sample pretreatment methods that are applicable to apples, apple juices, apple puree such as used in baby foods, and apple vinegar. Fig. 4.1 shows the range of methods that have been used for the sample pretreatment of foods derived from apples for a range of analytes from pesticides including those more challenging, polyphenols, mycotoxins, and polycyclic aromatic hydrocarbons (Amiri, Tayebee, Abdar, & Sani, 2019; Bazmandegan-Shamili, Shabani, Dadfarnia, Moghadam, & Saeidi, 2017; Chen et al., 2017; Farooq et al., 2019; Gao, Chen, & Li, 2015; Hoisang et al., 2019; Jia, Su, Wu, & Sun, 2016; Jiang et al., 2014; Lucci et al., 2017; Majidi & Hadjohammadi, 2019; Nolvachai et al., 2014; Paris, Gaillard, & Ledauphin, 2019; Reddy et al., 2016; Sun et al., 2017; Yang et al., 2016; Zhang, Chen, Liu,

Blending/grinding, cryogenic processing, Wetting Step for matrices of low water content

Dilute and shoot Selective LCMS/MS methods

Buffer optimization, Solvent and salt for salt-out extraction Or freeze-out phase separation

Targeted Sorption of Anaytes with or ModifiedQuEChERS with clean-up for matrix interferences

dSPE for matrix remvoval – dSPE sorbent selection, and amount optimization

Lipid Removal

dSPE for selective adsorption of analytes

Centrifuge or in-vial filtration or magnetic nanoparticles Desorption of analytes From sorbent

Dry, solvent-exchange, Dilute as necessary

FIGURE 4.1 Sample pretreatment approaches and considerations when selection modifications or method selection.

Sample preparation methods Chapter | 4

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Chen, & Pan, 2012; Zhao et al., 2013; Zhou, Su, Deng, & Yang, 2017). The approach used can be divided into those that target sorption of the target analytes onto sorbent materials and those that target removal of matrix interferences from extracts leaving behind the target analytes (see Fig. 4.1). The challenge with all of these sample pretreatment methods is there must be selectivity differences between the target analyte and the matrix interferences in the solvents used. The method selection once a homogenized uniform subsample is obtained often relies on the subsample to have sufficient water content to provide conditions for extraction from the solvent. Fig. 4.1 shows the basic approaches used for most food sample matrices for pretreatment prior to instrumental analysis include those where the analytes are selectively sorbed onto solid materials from a solvent often an aqueous solution and those where the analyte is partitioned into an organic solvent such as with QuEChERS salt-out extraction. Both methods initially rely on a wetted sample. A significant portion of the interfering matrix in the QuEChERS extract (organic phase) is removed using dispersive solid phase extraction (dSPE), SPE, or other approaches to retain more strongly the matrix components than the target analytes and are discussed more in Section 4.4. The approaches of these methods differ in using sorbents to either selectively retain target analytes or remove matrix interferences such that the final solution for instrumental analyses contains predominately the target analytes of interest. In selection of the appropriate sample pretreatment method for a particular analyte and sample matrix, the lack of specificity of the sorbent or solvent used can often be the limiting factor.

4.3 Targeted sample pretreatment techniques with high specificity for target analytes Table 4.1 shows the approaches that largely rely on a selective interaction of the target analytes with a solid material including molecularly imprinted polymers (MIPs) that more commonly now have a magnetic nanoparticle or nanocomposites core (Amiri et al., 2019; Bazmandegan-Shamili et al., 2017; Chen et al., 2015, 2017; Farooq et al., 2019; Gao et al., 2015; Hengel et al., 2014; Hoisang et al., 2019; Jia et al., 2016; Jiang et al., 2014; Lucci et al., 2017; Majidi & Hadjohammadi, 2019; Nolvachai et al., 2014; Paris et al., 2019; Reddy et al., 2016; Sun et al., 2017; Yang et al., 2016; Zhang et al., 2012; Zhao et al., 2013; Zhou et al., 2017). This magnetic core allows the use of a simple magnet for removal of particles after sorption of target analytes from solution rather than more expensive centrifugation required in QuEChERS methods to remove the solid dSPE sorbent from QuEChERS extract (Amiri et al., 2019; BazmandeganShamili et al., 2017; Farooq et al., 2019; Gao et al., 2015; Majidi & Hadjohammadi, 2019; Sun et al., 2017; Yang et al., 2016; Zhou et al., 2017). MIPs have the advanced feature of being selectivity designed for usually a small group of target analytes by the selection of the template molecule in the molecular imprinting (Bazmandegan-Shamili et al., 2017; Farooq et al., 2019; Gao et al., 2015; Yang et al., 2016). This makes their application more limited often to within the chemical class of the template molecule. As shown in Table 4.1, this approach has been used to selectively retain target analytes from a variety of chemical classes including those most widely analyzed in pesticide analysis such as neonicotinoid insecticides and organophosphorus pesticides. In addition, the homogenized sample must contain water in the solvent that most MIPs or magnetic-MIPs (mMIPs) are used to ensure that the target analyte remains sorbed to the MIP during the initial contact with the sample. Other solvent choices have included water/ethanol mixture, water/acetonitrile mixture (acetic acid, or unbuffered) (Bazmandegan-Shamili et al., 2017; Yang et al., 2016). Few applications of MIPs or mMIPs are feasible for use in the selected sorption of target analytes in acetonitrile; however, mMIPs could be used if the core Fe3O4 surface was first modified with 3-aminopropyltriethoxysilane which introduced carbonyl and vinyl groups and then further modified with a functional monomer (methacryloyl chloride) (Farooq et al., 2019). mMIPs most often have a core shell of Fe3O4 or SiO2@Fe3O4 nanoparticles which have been used uncoated (morin analysis) or coated with an MIP (organophosphorus pesticides) from an aqueous phase (Bazmandegan-Shamili et al., 2017; Chen et al., 2012; Majidi & Hadjohammadi, 2019). Graphene oxide coated with ZnO and zeolitic imidazolate framework-8 magnetic nanoparticles can also be used for selective sorption of pesticides (Gao et al., 2015; Sun et al., 2017; Zhou et al., 2017). These nanoparticles selectively sorb the target analytes over the matrix components in a solvent most often with a high or 100% aqueous content. With all of these applications, advancements in the use of a core magnetic nanoparticle allow for the use of a magnetic to isolate easily the particles with sorbed target analytes from the solvent without the need to centrifuge, which is more costly and time consuming. These magnetic particles can also be washed to remove matrix prior to desorption of target analytes. Target analytes are desorbed with an organic solvent from the magnetic nanoparticles with common organic solvents used including methanol, ethanol, and acetone/hexane and generally do not require large volumes such that preconcentration of the organic solvent is not necessary prior to instrumental analysis (Bazmandegan-Shamili et al., 2017; Gao et al., 2015; Majidi & Hadjohammadi, 2019; Sun et al., 2017;

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TABLE 4.1 Comparison of sample pretreatment approaches used for the determination of analytes in food matrix such as apple or apple juice food matrix. Technique

Target analytes

Modification or advancement of method

Selective or multiresidue approach validated

Extraction

References

MIPs—magnetic nanoparticles

Imidacloprid

Core-shell magnetic (MIPs at Fe3O4) after QuEChERS extraction

Selective and compatible with use in ACN. Desorbed from mMIPs with MeOH:acetic acid (7:3 v/v)

Acetonitrile saltout extraction (20 g sample), 20 mL ACN; 6 g NaCl, and 8 g MgSO4

Farooq et al. (2019)

MIPs—magnetic nanoparticles

Chlorpyrifos-methyl, procymidone, and fenvalerate, dicofol

Core-shell magnetic (dual MMIPs at Fe3O4) or MNIPs after extraction

Selective

3-g sample diluted with 2-mL ethanol and 2-mL water

Yang et al. (2016)

SiO2 at Fe3O4 magnetic nanoparticles

Flavonoid: morin

Selective—pH 5 optimal as analyte is deprotonated and desorbed with 250 µL ethanol

DES as a carrier to improve extraction from aqueous sample

Majidi and Hadjohammadi (2019)

Reduced graphene oxide coated with ZnO nanocomposites

Organochlorine, for example, HCHs, DDT, DDE, and DDD

dSPE with rGO-ZnO

Selective—desorb dSPE sorbent with 0.4 mL acetone hexane (1:1 v/v) and repeat 3X, dry, and reconstitute in 50 µL MeOH

Adsorption of aqueous sample to dSPE sorbent of target analytes

Sun et al. (2017)

Magnetic MIPs CTABtemplate SiO2-coated Fe3O4

Diazinon and malathion

Sorbent: templated with surfactant to retain analytes

Selective pH 6.4, 150-mg sorbent; desorb from sorbent analytes with 183-µL MeOH under sonication

10-g sample, 15mL ACN with 1% acetic acid; 15-g MgSO4, 1-g NaAc; dried ACN layer, and reconstituted in 12-mL water

BazmandeganShamili et al. (2017)

Zinc-based metal-organic framework with histamine organic linker

Organophosphorus pesticides—diazinon, fenitrothion, fenthion, phosalone, and profenofos

Synthesized MOF used for dSPE for sorption of target OPs (8 mg) desorb with 200 µL ACN

20-mL aqueous sample

Amiri et al. (2019)

Zeolitic imidazolate framework-8 magnetic nanoparticle

Triazine herbicides— atrazine, ametryn, prometryn, and simazine

Sorbent negatively charged that selfassemble with sonication

Selective—25-mL aqueous sample with magnetic ZIF, desorbed with 2-mL MeOH with ultrasonication, and reconstituted to 200 µL

ZIP-8 [from Zn (NO3)2 6H2O with 2methylimidazole, samples diluted 20X with water]

Zhou et al. (2017)

Magnetic MIPs

Carbamates— propoxur, pirimicarb, and promecarb

SiO2 coating on the magnetic (with Fe3O4)-carbon nanotubes molecularly imprinted with propoxur as template

Selective—mMIPs used for sorption of carbamate— desorbed with 3 mL 3 2.0 mL ACN/acetic acid (95:5, v/v) under ultrasonic for 30 s

10-g ultrasonic extraction (with water content)

Gao et al. (2015)

(Continued )

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TABLE 4.1 (Continued) Technique

Target analytes

Modification or advancement of method

Selective or multiresidue approach validated

Extraction

References

MIPs

Flavonoids: catechin, morin, and quercetin

MIP-tip extraction kit—attached to disposable syringe or NIPs

Selective

Sample diluted or extracted into aqueous phase, 0.1-g mL21 MIP

Nolvachai et al. (2014)

MIP SPE

Patulin

MIP-SPE

Selective—wash step with 1-mL diethyl ether and elution with ethylacetate

Sample diluted with 1:1 with 2 % acetic acid

Lucci et al. (2017)

Binary-solventbased ionicliquid-assisted surfactantenhanced emulsification microextraction (BS-ILASEME)

Pyrimethanil, fludioxonil, cyprodynil, and pyraclostrobin

BS-ILASEME Miniaturized extraction method

Selective—sample liquid form with 0.06-g mL21 NaCl at pH 7, after extraction solvent diluted with MeOH

Extraction solvent—amyl acetate [HMIM] PF6 and emulsifier: Tween 20

Chen et al. (2017)

Ionic liquidbased vortexassisted dispersive liquid liquid microextraction

Isocarbophos, phtalofos, triazophos, fenthion, phoxim, profenofos

Vortex assisted DLLME

pH 7, diluted fruit juice 1:1 with water

10-mL diluted fruit juice, extraction solvent: 35 µL [C8MIM] [PF6]; dispersive solvent 1 mL MeOH

Zhang et al. (2012)

Effervescenceassisted DLLME

Pyrimethanil, fludioxonil, cyprodynil, and kresoxim-methyl

Effervescent powder—citric acid (0.42 g) and potassium carbonate (0.276 g)

pH range 3 7 compatible

10-mL juice sample, extraction solvent 120 µL chlorobenzene, dried and reconstituted in 100 µL ACN

Jiang et al. (2014)

Multiplug filtration cleanup with multiwalled carbon nanotubes (MWCNTs)

Range of pesticides including OPs and tebuconazole and other conazoles

m-PFC tip sandwich design consisting of 10-mg MWCNTs and 150-mg MgSO4 for 1g extract

Broader range of analytes—poor recoveries for carbendazim, fenoxaprop-P, prochloraz, quizalofop-P-ethyl, and thiabendazole suggested structural issue of strong sorption

10-g sample, 10mL ACN, 4-g MgSO4, and 1-g NaCl with ice bath cold-induced phase separation

Zhao et al. (2013)

Organic droplet microextraction

OPs and pyrethroids

Collect droplet for GC analysis

1% w/v NaCl optimized

10 mL of aqueous sample with 0.1 g of NaCl; extraction solvent 20-µL 1undecanol

Hoisang et al. (2019)

Selective pressurized solvent extraction (incell clean-up)

Atrazine, desethylatrazine, desisopropylatrazine, and hydroxyatrazine

In-cell clean-up sorbent

No additional cleanup; dry and reconstitute in 1 mL of MeOH/H2O

10 g of sample, 5 g of diatomaceous earth, 1 g of cleanest PEP, and methanol extraction solvent

Jia et al. (2016)

(Continued )

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Innovative Food Analysis

TABLE 4.1 (Continued) Technique

Target analytes

Modification or advancement of method

Selective or multiresidue approach validated

Extraction

References

Ultrasonicassisted extraction followed by microextraction

14 Polycyclic aromatic hydrocarbons

Microextraction with packed sorbent HyperSep retain PEP— PS/DVB modified with urea functional groups Desorption with 50 µL methylene chloride

Filtered prior to microextraction

Sonication at 30 C of 20 g sample with 100 µL methylene chloride and 25 mL ethanol at 30 C

Paris et al. (2019)

SPE

Chlorpyrifos, malathion, and diazinon

SPE Chromabond C18

Selective—SPE condition with MeOH, water; load sample; elute 3-mL MeOH, 3-mL dichloromethane/ ACN (1:1), dry, and reconstitute to 200 µL

15-g sample, 30mL acetone: MeOH (20:10 v/v) 1 1-mL pesticide standard, filtered, diluted filtrate to 500 mL with water

Reddy et al. (2016)

ACN extraction with optimized dSPE

Morpholine (present in wax coatings on fruits)

20-mg PCX for 2-mL extract—eluted with 2 mL of NH 4OH/ACN (3:97, v/v)

Selective

10-g sample, 10-g 1% formic acid in ACN/water (1:1)

Chen et al. (2015)

MeOH Extraction no dSPE

Morpholine

Dilute 1:5 with 20 mM NH 4formate in 60:40 water/ACN (pH 3.5) followed by filtering with PTFE syringe filter

Selective

15-g sample with 1.4 2.4-mL water, 15 mL of 1% acetic acid in MeOH

Hengel et al. (2014)

ACN, Acetonitrile; DDT, dichlorodiphenyltrichloroethane; DDE, dichlorodiphenyldichloroethylene; DES, deep eutectic solvent;DLLME, dispersive liquid liquid microextraction; dSPE, dispersive solid phase extraction; dSPE, dispersive solid phase extraction; GC, gas chromatography; GC-MS/MS, gas chromatography-tandem mass spectrometry; HCHs, hexachlorocylohexanes; HLB, hydrophilic-lipophilic balance; LC-qTOF, liquid chromatography-quad time-of-flight; m-PFC, multiplug filtration clean-up; MIPs, molecularly imprinted polymers; mMIPs, magnetic-molecularly imprinted polymers; MWCNT, multiwalled carbon nanotube; NIPs, nonimprinted polymers; OPs, organophosphorus pesticides; PCX, polymeric cation exchange sorbent; PEP, polarenhanced polymer; PS/DVB, styrene divinylbenzene copolymer; PTFE, polytetrafluoroethylene; QuEChERS, quick, easy, cheap, effective, rugged, and safe method; ZIF, nonimprinted polymers.

Zhou et al., 2017). In the case where the mMIP was compatible with the QuEChERS, extract desorption of imidacloprid occurred with acidified methanol (7:3 v/v MeOH:acetic acid; Farooq et al., 2019). MIPs have also been used in a tip or SPE format allowing for the selective retention of target analytes (Lucci et al., 2017; Nolvachai et al., 2014). MIPs have also been used to remove pigments in shellfish samples in a modified QuEChERS method using a more nonpolar organic solvent (9:1 v/v hexane/acetone) with target analytes (organochlorines and polychlorinated biphenyls) showing good recoveries (Li et al., 2020b). Other options for sorbents have also been utilized such as for a tea matrix (1 g) dissolved in 4:1 v/v ACN/water with 20 mg PCX (strong cation exchange sorbent) used to selectively sorb 68 pesticides including alkaline pesticides with desorption accomplished with 2 mL of 5% ammonium hydroxide in acetonitrile (Tuzimski & Rejczak, 2016). A miniSPE mixed cation exchange cartridge was also used off-line to clean-up extracts of infant milk-based samples for analysis of 5-nitroimidazole (Herna´ndez-Mesa et al., 2018). Carbon nanotubes have been used for both direct and reverse dSPE. When used for direct sorption of analytes from matrix, they were used as a supporting template for MIPs such as for carbamate analysis (Gao et al., 2015). However,

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carbon-based sorbents are more frequently used for removal of pigments from sample matrices (reverse dSPE). A sandwich tip design has also been used for multiwalled carbon nanotubes (MWCNTs) with MgSO4 to remove water. MWCNTs are commercially available as Cleanert NANO, with MWCNTs used to remove pigments, fatty acids, and other contaminant, and when carbon nanotubes are functionalized, they tend not to suffer from strong adsorption of planar pesticides or aromatics. In a dSPE format, MWCNTs are generally used at 5 15 mg mL21 extract for clean-up. Other extraction approaches have been used including more advanced microextraction methods such as effervescence-assisted dispersive liquid liquid microextraction (DLLME), organic droplet microextraction, and ultrasonic-assisted extraction followed by microextraction (Hoisang et al., 2019; Jiang et al., 2014; Paris et al., 2019). Few comparisons of pretreatment methods are available for the sample food matrices. A comparison of sample pretreatment methods for fruit-based baby food samples which have high water content showed that for pesticides analyzed by gas chromatography (GC), matrix effects were less when dSPE was used relative to DLLME, but lower detection limits could be obtained with DLLME (Petrarca et al., 2016). Traditional extraction methods that are still more widely used in environmental analysis include pressurized solvent extraction with in-cell clean-up and ultrasonic-assisted extraction (Jia et al., 2016; Paris et al., 2019). In-cell clean-up of extracts relies on the selection of a sorbent that can remove matrix coextracts and retain these coextracts in the extraction cell under the conditions (temperature and pressure and selected solvent for extraction of target analytes), which can be challenging. Ultrasonic-assisted extraction is generally used in combination with other approaches to remove matrix interferences from extracts obtained in food analysis (Paris et al., 2019).

4.4

Quick, easy, cheap, effective, rugged, and safe methods

Since 2003, there has been a wide application of QuEChERS and modified QuEChERS methods particularly for multiresidue pesticide analysis of foods. There have been numerous reviews for a large variety of food matrices including fruits, vegetables, and meat products (Alcaˆntara et al., 2019; Lawal et al., 2018; Musarurwa et al., 2019; Peresterelo et al., 2019; Raina-Fulton, 2015; Santana-Mayor et al., 2019; Varela-Martı´nez et al., 2020; Zhang et al., 2019). These assessments have consideration of target analytes, water and fat content of the subsample, and matrix interferences. Applications of modified QuEChERS methods have included pesticides, mycotoxins, and antibiotics analyses in food (Alcaˆntara et al., 2019; Lawal et al., 2018; Musarurwa et al., 2019; Peresterelo et al., 2019; Raina-Fulton, 2015; Santana-Mayor et al., 2019; Varela-Martı´nez et al., 2020; Vazquez et al., 2019; Zhang et al., 2019), and these methods have also been extended to environmental sample matrices where the range of analytes of interest may also include a larger range of pharmaceuticals (Santana-Mayor et al., 2019). Multiresidue analysis methods are validated for sample pretreatment techniques to ensure recoveries .70% 120%, that required limits of detection or limits of quantitation and achieved for desired sample matrix, and that relative standard deviation of analysis remains ,20%. The major modifications of QuEChERS methods in the last 5 years have focused on the use of different salts, the use of freezing in the salt-out phase separation, and modifications to the type of sorbents used in dSPE, SPE, or other approaches for clean-up of the extracts including incorporation of some of the sorbents discussed in Section 4.3. For pesticide analysis, QuEChERS methods are more widely used for the analysis of organophosphorus pesticides, carbamates, neonicotinoid insecticides, pyrethroids, and organochlorines (Alcaˆntara et al., 2019). The original QuEChERS method was first published in 2003 (Anatassides, Lehotay, Stajnbaher, & Schenck, 2005) and with further validation revisions to the methods included choice of salt with anhydrous MgSO4 as more efficient than Na2SO4 at removing water from the organic phase (AOAC Official Method, 2007; Lehotay et al., 2005). The original method included the most commonly chosen organic solvent, acetonitrile, which was preferred over acetone, ethylacetate, or other combinations for multiresidue analysis partially due to its compatibility with subsequent LC or GC methods and also the ease which by acetonitrile separates from water during the salt-out partitioning step. Further considerations were later made for the use of acetate buffer (AOAC official method 2007.01 of QuEChERS) or citrate buffer (EN QuEChERS, European Committee on Standardization) with different dSPE sorbent options including primary secondary amine (PSA; 50 mg mL21), C18, and graphitized carbon black (GCB) or PSA (25 mg) and GCB, respectively (European Committee for Standardization (CEN) Standard Method EN 15662, 2015). Buffers are used for acid and base sensitive analytes prone to degradation or those, such as pharmaceuticals, that can be partially ionized and require pH control for optimal extraction. The use of cold-induced two-phase (acetonitrile/water) extraction with dry ice or freezing at 220 C freezer followed by thawing to room temperature to initiate the phase separation rather than MgSO4 has shown improvement in recoveries for analytes such as conazole fungicides, while other pesticides such as acephate and methamidophos or

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more polar pesticides showed reduced recoveries ,70% (da Costa Morais et al., 2018; Wang et al., 2019). HCO2NH2 (0.5 g mL21 ACN used in QuEChERS) was also used more recently to induce phase separation (Han, Sapozhnikova, & Lehotay, 2014; Lehotay et al., 2016). With further modifications over time, there was a trend to have a wide scope of modified versions of the QuEChERS method available for use for multiresidue analysis or for those single analytes that require separate methods. These recent modifications or variations in approach have focused on utilizing a large scope of dSPE either commercially available or that can be synthesized easily in laboratories. As well, there is still need to use SPE format clean-up and newly developed sorbents materials that can reduce matrix that can interfere in the analysis of an increasingly larger range of target analytes requiring methods that can meet validation criteria for food matrices. Table 4.2 shows that even in recent years, numerous modifications of QuEChERS methods continue to be used even for a similar sample matrix (apples) or the same target analytes (Chen et al., 2012; Desmarchelier, Mujahid, Racault, perring, & Lancova, 2011; Hamdy Abdelwahed, Khorshid, El-Marsafy, & Souaya, 2019; Hu et al., 2018; Jiang et al., 2019; Lehotay et al., 2016; Li et al., 2012a; 2012b; Montiel-Leo´n et al., 2019; Oellig & Schmid, 2019; Petrarca et al., 2016; Tiryaki, 2016; Walravens et al., 2016; Wang & Leung, 2009; Xu et al., 2018). In the QuEChERS methods, dSPE is generally used in a “reverse” dSPE approach where the dispersive sorbent is used to sorb the interfering matrix components rather than the target analytes. Over all sample matrices, the modifications that have been made even for the same chemical class of target analytes or type of food matrix include the use of the original, citrate or acetate buffered QuEChERS, and other modifications for the clean-up of the QuEChERS extract by changing the dSPE sorbent or selecting other clean-up approach to minimize the need to centrifuge the extract, and all are still widely employed. Some chemical classes such as organophosphorus pesticides, carbamates, and pyrethroids more frequently employ an acetate or citrate buffer in the acetonitrile salt out extraction for food matrices (Alcaˆntara et al., 2019). PSA is the most widely used dSPE sorbent in combination with MgSO4 to remove residual water from the QuEChERS extract and is more frequently used in acetate or citrate-QuEChERS methods as the buffer reduces the capacity of the dSPE sorbents. PSA is efficient at removal of fatty acids and pigments and was widely used for clean-up of QuEChERS extracts of apple matrices (see Table 4.2; Desmarchelier et al., 2011; Jiang et al., 2019; Lehotay et al., 2016; Li et al., 2012a; 2012b; PeMontiel-Leo´n et al., 2019; Tiryaki, 2016; Wang & Leung, 2009). GCB was most often used with PSA and can also remove fatty acids and pigments but tends to strongly retain planar analytes including hexachlorobenzene, pyrimethanil, pentachlorothioanisole, and cyprodinil, so care should be taken in selecting the amount of GCB (Li et al., 2020; Raina-Fulton, 2015). Carbon-X could also be used and is less susceptible to strong sorption of planar analytes; however, studies with shrimp matrix showed strong sorption of several polycyclic aromatic hydrocarbons and polychlorinated biphenyls as well as pymetrozine, thifensulfuron-methyl, and spinosyns (Han et al., 2014; Lehotay et al., 2016). C18 is commonly used for sample matrices where removal of lipids is necessary particularly for foods of higher fat content and was also used for clean-up of QuEChERS extracts from apple matrices. Magnetic nanoparticles of Fe3O4 modified with 3-(N,N-diethylamino)propyltrimethoxysilane (Fe3O4-PSA) along with C18 were used to reduce matrix effects from fatty acids and nonpolar compounds in the QuEChERS extract of rice for the determination of pesticides and metabolites with reduction of time by 30% (Liu et al., 2017). Z-Sep or Z-Sep1 (zirconium-based sorbents) are used to remove phospholipids. Z-Sep has been used for clean-up of QuEChERS extracts from edible oils for pesticide analysis to obtain recoveries .70% with some pesticides such as terbutryn and bromopropylate having low recoveries (Tuzimski & Rejczak, 2016). It can also strongly retain selected organophosphorus pesticides, pymetrozine, thifensulfuron-methyl, and spinosyns (Han et al., 2014). Table 4.2 also shows that there has been a focus on potential replacements for more costly PSA with alternative dSPE sorbents including polyethyleneimine (PEI; Oellig & Schmid, 2019). In some cases, removing the need for dSPE either utilizing the QuEChERS extract with or without further dilution is more feasible when the analytes are analyzed by liquid chromatography (LC) high-resolution mass spectrometry (MS) methods (Hamdy Abdelwahed et al., 2019; Oellig & Schmid, 2019; Walravens et al., 2016) or nanoflow LC high-resolution MS (Alca´ntara-Dura´n et al., 2019). PEI is a weak anionic exchanger and can be used as a dSPE sorbent to replace more costly PSA and has high capacity for fatty acids and can be used to improve recoveries for base stable and labile pesticides (Oellig & Schmid, 2019). Aminopropyl (NH2) has also been used instead of PSA for extraction of base sensitive pesticides that are more prone to issues when PSA is used (Li et al., 2020a). For the extraction of polar pesticides that are analyzed by LC-MS/MS, it is proposed that the organic solvent in QuEChERS method should be replaced with methanol with 1% formic acid (QuPPe method, version 10) such that dSPE may not be required (Anatassiades et al., 2019). Comparison of the citrate buffer-QuEChERS (with and without dSPE) to ethylacetate method and NL-method (Dutch mini-Luke) shows for pesticides analyzed by GC that the percentage of interferences can be lower with ethylacetate but is matrix dependent, while

Sample preparation methods Chapter | 4

TABLE 4.2 QuEChERS based sample pretreatment techniques. Technique

Target analytes

Clean-up of QuEChERS extracts (noted volume of extraction and sorbents used)

Selective or MR approach validated

Salt-out extraction

References

Original QuEChERS

OPs, neonicotinoid insecticides, carbamates chlorpyrifos, dimethoate, indoxacard, imidacloprid

1-mL extract with 25-mg PSA, and150-mg MgSO4

MR

10-g sample 1 10-g ACN; 4-g MgSO4, and 1-g NaCl

Tiryaki (2016)

Citrate QuEChERS

20 pesticides including two plant growth regulators (1Naphthyl acetic acid, and 2(1-Naphthyl acetamide)

1 mL filtered with syringe PTFE filter dSPE

MR

10-g sample 1 10-g ACN; 4-g MgSO4, 1-g NaCl, 1-g Na3citrate dihydrate, and 0.5g Na2Hcitrate sesquihydrate

Hamdy Abdelwahed et al. (2019)

Modified QuEChERS

Famoxadone—chiral separation

dSPE for 1.5-mL extract,40-mg C18, 10mg GCB, and 150-mg MgSO4

Selective— chiral enantiomeric separation

10-g sample, 10-mL ACN, 4-g MgSO4, and 2-g NaCl

Xu et al. (2018)

Modified QuEChERS

Dinotefuran—enantiometric separation

SPE for 1-mL extract— ENVI-Carb (500 mg) condition with ACN and elute with ACN, reconstituted in ethanol

Selective

15-g sample, 15-mL ACN, (10-mL water for rice samples only); 2-g NaCl and 4-g MgSO4

Chen et al. (2012)

Modified QuEChERS

Indaziflam and its fivemetabolites

SPE clean-up of 1.5-mL extract with Oasis HLB (150 mg)

Selective

5-g sample, 10-mL ACN with 1% v NH4OH; 3-g MgSO4, and 2-g NaCl

Hu et al. (2018)

Modified QuEChERS

Tebuconazole

dSPE 40-mg PSA, 10-mg GCB, and 150-mg MgSO4

Selective— applicable to other matrices with adaption of dSPE

10-g sample, 5-mL water, and 10-mL ACN; 4-g MgSO4 and 1-g NaCl

Li et al. (2012b)

Modified QuEChERS

Cyflumetofen (acaricide)

dSPE with 50-mg PSA and 20-mg GCB

Selective— reconstitution in hexane for GC

10-g sample, 10-mL ACN, 2-g NaAc; 3-g NaCl, and 4-g MgSO4

Li et al. (2012a)

Modified QuEChERS

Improved recovery for base labile pesticides including captafol, folpet, acephate, phosmet, dichlofluanid, tolylfluanid, and pyridate

2-mL extract with dSPE,750-mg PEI (polytetrafluoroethylene)MgSO4 and 75-mg silica gel

MR—adapted to reduce bleed and issues from PSA and lower costs

Citrate protocol QuEChERS for labile pesticides under acidic or basic conditions

Oelligand Schmid (2019)

Modified QuEChERS

Neonicotinoid insecticides, triazines, phenylureas, and carbamate

4-mL extract with dSPE 600-mg MgSO4 and 200-mg PSA

MR

Citrate protocol with 5-g sample and 5-g ACN

Montiel-Leo´n et al. (2019)

Modified QuEChERS

Pyrimethanil, fludioxonil, cyprodinil, and kresoximmethyl

2-mL extract with dSPE and 30-mg PSA

Selective for difficult fungicides

5-g sample, 10-mL ACN, and 3-g NaCl

Jiang et al. (2019)

Modified QuEChERS

Patulin (metabolite of fungal species)

6-mL extract with dSPE—400-mg PSA, 400-mg C18, and 1200mg MgSO4

Selective

10-g sample (5-g apple flakes with 10mL water), 10-mL ACN, 1-g NaCl, and 4-g MgSO4

Desmarchelier et al. (2011)

no

(Continued )

93

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Innovative Food Analysis

TABLE 4.2 (Continued) Technique

Target analytes

Clean-up of QuEChERS extracts (noted volume of extraction and sorbents used)

Selective or MR approach validated

Salt-out extraction

References

Acetate QuEChERS

Four pesticides

dSPE with 50-mg C18, 50-mg PSA, 150-mg MgSO4 dispersive liquid liquid microextraction—1-mL extract amd75-µL CCl4

MR

15-g sample, 15-mL ACN with 1% HAc; 6-g MgSO4, and 1.5g NaAc

Petrarca et al. (2016)

Buffered QuEChERS

138 pesticides

6 8-mL extract with 400 mg of PSA and 1200 mg of MgSO4

Multiresidue LC-qTOF

10 mL of ACN/acetic acid (99 1 1, v/v), 1 g of NaAc, and 4 g MgSO4

Wang and Leung (2009)

Modified QuEChERS

54 pesticides, 14 flame retardants, 10 polycyclic aromatic hydrocarbons, and 12 polychlorinated biphenyls

Minicolumn SPE cleanup for 300 µL QuEChERS 45 mg of MgSO4/PSA/ C18/CarbonX 20/12/ 12/1 30-mgC18/Z-Sep/ CarbonX (20.7/8.3/1)

Multiresidue low-pressure GC-MS/MS

15-g sample, 7.5-g HCO2NH2, and 15mL ACN

Lehotay et al. (2016)

Salt-out ACN extraction

Mycotoxins—Alternaria toxins conjugates

Dried and reconstituted in injection solvent water/ACN

selective

2-g sample, (5-mL water for foods of lower water content), 10-mL ACN; 2-g MgSO4, and 0.5-g NaCl

Walravens et al. (2016)

dSPE, Dispersive solid phase extraction; GC, gas chromatography; GCB, graphitized carbon black; MgSO4, anhydrous MgSO4; MR, multiresidue; OPs, organophosphorus pesticides; PEI, polyethyleneimine; PSA, primary secondary amine; QuEChERS, Quick, easy, cheap, effective, rugged, and safe method.

the NL-method was prone to interferences in pesticide analyzed by both GC and LC mass spectrometric methods (Ucle´s et al., 2017). Other approaches have tried to minimize the need to use centrifugation after dSPE either with the use of in-vial filtration or use of mini-SPE column and automated sample injection (Han et al., 2014; Lehotay et al., 2016). Additionally as discussed in Section 4.3, magnetic dSPE particles could be used. For shrimp matrix, replacement of the centrifugation was replaced with dSPE with PSA and other typical dSPE sorbents for clean-up of the QuEChERS extract followed by in-vial filtration (Han et al., 2014). This in-vial filtration was identified, as faster and more convenient approach for clean-up of 0.5 mL extract that centrifugation which is required in standard QuEChERS methods. Invial dSPE of the QuEChERS extract and filtration has been shown to be suitable for a range of analytes including polycyclic aromatic hydrocarbons, polychlorinated biphenyl, and pesticides (Han et al., 2014). High fat content samples require special attention with an extra step to remove fats particularly for nuts, eggs, milk, meat, or fat tissue matrices. This clean-up is accomplished either through addition of C18, Z-Sep/C18, C18/PSA/ Florisil in dSPE or SPE format, freeze-out clean-up, or DLLME (Alca´ntara-Dura´n et al., 2019; Buah-Kwofie & Humpries, 2019; Hu et al., 2019; Raina-Fulton, 2015; Song et al., 2019). PSA and alumina have also been used for clean-up of QuEChERS extracts from milk samples for analysis of 13 steroid hormones (Tan et al., 2016). More recently, there has been a resurgence of methods that use SPE for sample clean-up which is more time-consuming than dSPE and can require preconcentration of extracts prior to final analysis. These SPE methods are generally utilized for more complex sample matrices including those of high fat or pigment content and have included the use of ENVI-Carb, Oasis PRiME HLB, Oasis HLB, or enhanced matrix removal (EMR)-lipid (Alca´ntara-Dura´n et al., 2019; Chen et al., 2012; Han, Matarrita, Sapozhnikova, & Lehotay, 2016; Hu et al., 2018; Qiao et al., 2018; Vazquez et al., 2019; Zhang et al., 2018). EMR-lipid has gained application to foods of high fat content due to the selectivity of this sorbent for

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removal of lipids (Alca´ntara-Dura´n et al., 2019; Chen et al., 2012; Hu et al., 2018; Vazquez et al., 2019). SPE using EMRlipid has been shown for a number of analytes to provide reduced matrix effects and improved recoveries for a range of pesticides and mycotoxins from peanut (Alca´ntara-Dura´n et al., 2019). It has also been used to remove matrix interferences in pesticide analysis of dry herbs and spices more than Z-Sep or Oasis PRiME HLB (Vazquez et al., 2019). Oasis PRiME HLB has been used for sample clean-up of QuEChERS extracts of meats for veterinary drug analysis (Zhang et al., 2018).

4.5

Conclusions

Sample pretreatment methods are designed to provide a homogenous sample such that the target analytes are accessible to subsequent instrumental analysis methods and instrument signal is not altered by matrix interferences. Sample matrix issues continue to present challenges in analysis of contaminants in foods. Approaches taken are multiresidue or targeted at single analyte or small groups of analytes often within the same chemical classes. Sorbents used are generally targeted to selectively interaction with the target analytes such as with MIPs, or mMIP, nanoparticles, and MWCNTs. These approaches are competitive with SPE, microextraction, and other classical extraction methods for targeted analysis. Molecularly imprinted nanoparticles, MWCNTs, have also been used in some applications for removal of matrix from extracts (e.g., QuEChERS) if compatible with organic solvents such as acetonitrile. Modified QuEChERS methods continue to be the most popular approach for sample pretreatment of foods. Modifications to QuEChERS methods are currently focusing on streamlining the need for centrifuging the extract after dSPE either with the use of in-vial filtration, magnetic dSPE sorbents, or reduction in need for clean-up for LC-highresolution MS. The challenge of selection of sorbents continues to be that sufficient selectivity difference must exist between the target analytes and interfering matric components and continues to drive the development of new sorbents. Other sorbents that are emerging, not discussed herein, are chitosan-based sorbents that can be functionalized and have also been developed in a magnetic nanoparticle design but yet to be evaluated in contaminant analysis. To improve the uniformity of sample particle size and to prevent loss of more volatile or labile analytes, cryogenic processing is increasingly used with either dry ice or liquid nitrogen. Removing the need for salt for phase separation in QuEChERS with freeze-out phase separation and changing the salts to more volatile ammonium salts has also recently been investigated to improve the compatibility with mass spectrometric methods. Regardless of the sample pretreatment technique selected, it is important to validate the method for providing desired recoveries, detection limits, reproducibility for the target analytes, and matrices under evaluation. There is still a need to understand more fully long-term trends of matrix effects for food samples.

Abbreviations dSPE dispersive solid phase extraction MIPs molecularly imprinted polymers mMIPs magnetic-molecularly imprinted polymers MS mass spectrometry MWCNTs multiwalled carbon nanotubes OPs organophosphorus pesticides PAHs polycyclic aromatic hydrocarbons QuEChERS quick, easy, cheap, effective, rugged, and safe SPE solid phase extraction; quick method for the analysis of numerous highly polar pesticides in foods of plant origin SPE solid phase extraction UAE ultrasound-assisted solvent extraction

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Sample preparation methods Chapter | 4

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Chapter 5

Flow-based food analytical methods Anastasios Economou Department of Chemistry, National and Kapodistrian University of Athens, Athens, Greece

5.1

Introduction

The analysis of food products is necessary for many reasons. Nowadays, the quality and safety of foods are issues of major importance directly impacting on public health, and therefore national and international authorities have imposed stricter legislation on food control. The quality, the nutritional value, and the shelf life of foods depend on their composition while the monitoring of additives and toxic compounds is necessary to ensure safety. Food analysis is also necessary for monitoring purposes during food production and storage as well as for detection of adulteration. Therefore novel sensitive and rapid analytical methods are necessary. The identification and quantification of chemical species in food samples are a challenging and complex interdisciplinary task requiring knowledge of biology, chemistry, and microbiology and involves different critical steps such as sampling, sample pretreatment, analysis, and data reporting. Nowadays, sophisticated sensitive instrumental analytical methods have largely replaced classical (titrimetric or gravimetric methods) providing both quantitative and qualitative information. Flow-based analytical methods constitute a viable and flexible approach for food monitoring purposes (Cerda`, Avivar, & Cerda`, 2014a; Christodouleas, Fotakis, Economou, Papadopoulos, & Timotheou-Potamia, 2011; PerezOlmos et al., 2005; Rocha, 2018; Ruiz-Capillas & Jimenez-Colmenero, 2008; Ruiz-Capillas & Nollet, 2015; RuizMedina, Llorent-Martı´nez, Ferna´ndez, Co´rdova, & Ortega-Barrales, 2017; Sasaki, Batista, Rocha, & Rocha, 2017; Trojanowicz & Kolacinska, 2016). Flow manifolds offer wide scope for flexible configuration by the user to enable chemical analysis, including sample pretreatment and detection (Cerda`, Avivar, & Cerda`, 2014a; Idris, 2010; Vakh et al., 2016). More importantly, all these steps can be performed using automated workflows so that the operators’ workload is reduced. Flow analysis is performed in enclosed manifolds, so that analyte losses and contamination are minimized and accuracy and precision are improved. In addition, flow systems make use of small volumes of reagents reducing the cost of the analysis and minimizing waste. This chapter presents an overview of flow-based methods for food analysis. It briefly describes the main operational modes of flow analysis and brings together several representative applications for the determination of nutrients (sugars, aminoacids, and vitamins), antinutrients, inorganic species (cations and anions), additives, preservatives and adulterants, pesticides, acidity, antioxidant capacity, pharmaceuticals, and several other compounds in food samples. Although the list of applications is not exhaustive, care was taken to cover the main classes of target compounds and the major detection techniques.

5.2

Flow-based methods of analysis

5.2.1 Modes of flow-based analysis The historical development of flow methods of analysis has been comprehensively described (Cerda`, Avivar, & Cerda`, 2014b). Initial applications of flow analytical systems were almost exclusively based on an online selective reaction of the target compound in the sample with a suitable reagent to form an optically or electrochemically active product. Therefore specificity was limited and multicomponent analysis was not possible. This limitation was later alleviated by the use of specific detectors (e.g., ion-selective electrodes), coupling to detectors with multicomponent capabilities (e.g., atomic spectroscopy) and hyphenation to separation techniques [liquid chromatography and capillary electrophoresis (CE)]. The versatility of flow systems has enabled the implementation of various online sample pretreatment Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00005-4 Copyright © 2021 Elsevier Inc. All rights reserved.

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operations (such as dissolution, digestion, liquid
liquid and solid-phase extraction (SPE), dialysis, gas diffusion, and pervaporation). Initially, flow systems, such as segmented flow analysis (SFA) and flow injection analysis (FIA), were operated manually. The widespread use of computers enabled the development of computerized controlled flow techniques such as sequential injection analysis (SIA), multisyringe FIA (MSFIA), and multipumping flow systems (MPFS), based on the concept of multicommutation. Lately, the requirements for miniaturization and integration have led to the introduction of miniaturized systems such as lab-on-valve (LOV), chip-on-valve, and lab-in-a syringe (LIS) manifolds. Flow-based analysis was introduced in the late 1950s with the technique of segmented flow analysis (SFA), which was widely employed in (mainly clinical) laboratories requiring a large sample throughput (Cerda` et al., 2014b). In SFA, samples are continuously aspirated and segmented by air bubbles that are removed before they reach the detector. Reagents are also continuously aspirated and mixed with the sample at appropriate confluence points, whereas a debubbler is used to remove air bubbles before detection. However, the presence of air bubbles has some disadvantages, which prevented further use of SFA. FIA was developed in the middle 1970s. An FIA manifold is based on a peristaltic pump transporting the sample and reagents through narrow tubes to the detector. In FIA, a zone of sample is inserted into a stream of liquid carrier via an injection valve. The sample is transported by the carrier to confluence point(s) where it is mixed with the reagent (s). The advantages of FIA over SFA are the reduction in sample and reagent consumption, the higher sample throughput, and the versatility (i.e., FIA enables the implementation of a host of analytical approaches such as kinetic analysis, stopped-flow methods, and online titrations). SIA was developed as an alternative to FIA (Cerda` et al., 2014b; Economou, 2015). A basic SIA system includes a bidirectional pump, a holding coil, a multiposition valve, a reaction coil, and a detector. The central port of multiport selection valve is permanently connected to a two-directional pump via the holding coil and also connected to one of the peripheral ports with sample and reagents as well as to a detector via plastic tubing. Sample and reagent zones are aspirated into a holding coil so that a stack of zones is formed is the holding coil. By means of a flow reversal, the sample and reagent zones penetrate mutually and the overlapped zones are transported to the detector. SIA supports multiparametric analysis but computer control is paramount to perform the necessary flow programming operations and the sample throughput is lower in FIA. Advantages of SIA over FIA are the reduction in the consumption of sample and reagents and the high versatility enabling stopped-flow methods, online sample pretreatment (metering, mixing, dilution, and incubation), and use of more than one detector. Bead injection analysis (BIA) is a variant of SIA, which is based on the aspiration of a defined volume of suspended beads (whose surface can be modified with immobilized reagents) and retention of the beads into a special cell, where they are put in contact with the sample solution (Cerda` et al., 2014b). Chemical reactions and biological or physical interactions take place at the bead surfaces, which can be monitored in real time, either in situ at the solid phase or in the eluting liquid phase. Finally, the beads can be discarded, collected, or transported to a detector. Multicommutation flow injection analysis (MCFIA) exploits fast-switching three-way solenoid valves that can be independently commutated (Cerda` et al., 2014b). The volume inserted in the flow path is proportional to the time that three-way solenoid valve remains to the ON position. MCFIA allows the insertion of alternate zones of sample and reagent(s) in the manifold that mutually disperse as the adjacent zones are transported toward the detector. The advantages of MCFIA are high sample throughput and reduced consumption of sample and reagents but computer control is necessary to accurately activate the solenoid valves. MSFIA relies on a multisyringe burette (a multichannel pump consisting of several syringes; Cerda` et al., 2014b). The pistons of all the syringes move simultaneously and unidirectionally for either liquid delivery or aspiration. MSFIA systems combine high sample throughput, robustness, and low sample/reagent consumption. MPFS make use of solenoid-operated piston micropumps (Cerda` et al., 2014b). The micropumps operate in the pulse mode with each pulse propelling unidirectionally a preset volume of fluid. Their main advantages are their simplicity, small size, and low cost, since the pumps also operate as solenoid valves (allowing or restricting the flow of liquids). The LOV concept significantly facilitates integration of various analytical units in the valve and improves the scope for miniaturization (Cerda` et al., 2014b; Luque de Castro, 2014; Miro & Hansen, 2012). The basic LOV system is a fluidic plastic device mounted atop a six-port selection valve. The operation of the LOV system is similar to SIA and this manifold can accommodate a wide variety of pretreatment operations (mixing and dilution) as well as detection. One of the LOV channels can accommodate a “jet-ring cell” for BIA, a concept widely exploited for separation and preconcentration of analytes in complex matrices. LOV has been employed as an effective front end to various separation techniques (Miro, Oliveira, & Segundo, 2011), atomic spectroscopy detection techniques such as inductively coupled plasmamass spectrometry (ICP-MS) and electrothermal atomic absorption spectrometry (ETAAS) (Yu, Jiang, Chen, & Wang, 2011), and electrochemical stripping analysis (Wang et al., 2009, 2010).

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The LIS concept is based on the integration of various analytical steps inside a syringe (Cerda` et al., 2014b). It has been mainly applied to automate liquid
liquid microextraction and its advantage is the simple instrumental setup required, which makes it very cost-effective. Batch injection analysis is a pseudoflow approach involving the injection of a plug of sample from a micropipette tip onto a detector immersed in a large-volume blank solution (Quintino & Angnes, 2004). The advantages of batch injection analysis systems are instrumental simplicity (since no pumps and valves are necessary), high sample throughput, low sample consumption, and small size.

5.2.2 Sample pretreatment using flow-based methods and hyphenated flow methods High selectivity and sensitivity are two of the most requirements in analytical determinations. The presence of interfering compounds at high concentrations together with the low concentrations of the target analytes usually necessitate some steps of sample pretreatment before the actual analytical measurement. Among the sample pretreatment approaches that can be readily adapted to flow manifolds are (Luque de Castro, 2008; Motomizu et al.; Cerda`, Avivar, & Cerda`, 2014c; Cerda`, Avivar, & Cerda`, 2014d) dilution and digestion, and extraction [liquid
liquid extraction (LLE) and SPE, and membrane-based separations (gas diffusion, dialysis)]. A major limitation of flow-based techniques is their limited scope for multicomponent analysis, which can be addressed by coupling to separation columns. One approach to address this problem is the coupling of high-pressure liquid chromatograph (HPLC) to low-pressure flow-based methods via the injection valve of the HPLC system that serves as an interface (Theodoridis, Zacharis, & Voulgaropoulos, 2007). The development of high-performance monolithic columns has facilitated the construction of separation systems operating at low pressures (,100 psi) or medium pressure (,1000 psi; Ferna´ndez, Forteza, & Cerda`, 2012). In low-pressure chromatography, a flow method (usually FIA, SIA, or MSFIA) is coupled as a front end to a low-pressure separation column, resulting in techniques such as flow injection chromatography (FIC), sequential injection chromatography (SIC), and multisyringe chromatography (MSC), respectively (Solich, 2008; Hartwell, Kehling, Lapanantnoppakhun, & Grudpan, 2013; Idris, 2014). Low-pressure separation systems are easy to assemble form existing laboratory components and involve low capital/running costs. Flow manifolds may also be employed to automate injection in CE using specially designed interfaces (Horstkotte & Cerda`, 2009; Kuban and Hauser, 2008). Finally, flow-based systems have been interfaced as front-end vehicles to various atomic spectroscopic techniques such as flame atomic absorption spectroscopy (FAAS), flow atomic emission spectroscopy, inductively coupled plasma-atomic emission spectroscopy (ICP-AES)/mass spectrometry (MS), atomic fluorescence spectroscopy (AFS), and ETAAS for simple sample introduction or, more commonly, in order to perform additional sample pretreatment (solvent extraction, SPE, cloud point extraction, and gas
liquid extraction after hydride generation can be implemented online before atomic spectroscopic detection) (Anthemidis & Miro, 2009; Wang, Chen, & Wang, 2007; Yu and Wang, 2013; Hansen and Miro, 2008; Cruz-Alonso, Sanz-Medel, & Pereiro, 2016). The LOV and BIA approaches have been shown to be particularly useful for these tasks (Albanese, Sannini, Malvano, Pilloton, & Di Matteo, 2014).

5.3

Representative applications of flow-based methods to food analysis

5.3.1 Nutrients and antinutrients 5.3.1.1 Sugars Flow-based methods have been widely employed for the quantification of sugars (such as glucose, fructose, and sucrose) using amperometric (bio)sensors modified with enzymes. Representative applications include the FIA enzymatic amperometric detection of glucose in wines, juices, and dried fruits (Albanese et al., 2014) and in honey and energy drinks (Amatatongchai, Sroysee, Chairam, & Nacapricha, 2017; Samphao et al., 2015). Multiplexed detection of more than one sugar has also been performed by flow-based methods using multiple enzyme-modified transducers; examples are the multiplexed FIA amperometric detection of five sugars in food samples (Maestre, Katakis, Narva´ez, & Domı´nguez, 2005) and the simultaneous monitoring of sucrose, fructose, and glucose with respective amperometric biosensors in a continuous flow system (Vargas et al., 2013; Fig. 5.1). A FIA amperometric sensor, coupled to a microdialysis unit, was used to determine glucose, galactose, and lactose in milk using soluble enzymes (Rajendran & Irudayaraj, 2002). A more recent research trend focuses on the use of nonenzymatic glucose electrochemical sensors as detectors in flow systems such as the use of a Ni/Cu-modified screen-printed electrode for the determination of glucose in beverages (Salazar, Rico, & Gonza´lez-Elipe, 2018). A FIA-CE system with end-column amperometric detection

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Flow cell for glucose and suxrose biosensors

Sample

Bi-channel amperometric detector

3-way connector

Carrier solution (pH = 6.0) 3-way connector valve

Amperometric detector

Peristaltic pump

Carrier solution (pH = 4.5)

Sample

Flow cell for fructose biosensor

FIGURE 5.1 Schematic diagram of the flow system developed for the multiplexed detection of glucose, fructose, and sucrose using enzymatic amperometric biosensors. Source: From, Vargas, E., Gamella, M., Campuzano, S., et al. (2013). Development of an integrated electrochemical biosensor for sucrose and its implementation in a continuous flow system for the simultaneous monitoring of sucrose, fructose and glucose. Talanta, 105, 93
100.

utilizing a novel H-shaped interface was developed by Fu and Fang for separating sucrose and glucose in approximately 1 min (Fu & Fang, 2000). Optical detection has also been used for the assay of sugars in food samples. Monitoring of glucose in foodstuff has been performed using SIA, FIA, or MSFIA by exploiting the enzymatic conversion of glucose into hydrogen peroxide using either soluble glucose oxidase (Economou, Panoutsou, & Themelis, 2006; Panoutsou & Economou, 2005; Piza`, Miro´, Estela, & Cerda`, 2004) or a minicolumn packed with the enzyme (Manera, Miro´, Estela, & Cerda`, 2004); the hydrogen peroxide was ultimately determined by the chemiluminescent (CL) reaction with luminol. A SIA system with attenuated total reflectance Fourier transform infrared spectroscopy detection has been developed for sugar and organic acid analysis in tomato samples (Vermeir et al., 2009). The simultaneous determination of sucrose and phosphate in cola drinks has been proposed using an SIA system with a low-cost light sensor, based on the Schlieren effect between the sucrose solution and water (Saetear, Khamtau, Ratanawimarnwong, Sereenonchai, & Nacapricha, 2013). Two MCFIA procedures for the spectrophotometric determination of reducing sugars in wine have been developed relying on selective reactions [with Cu21-neocuproine or potassium hexacyanoferrate(III) (Brasil & Reis, 2017; Da Silva, De Souza, Paim, & Lavorante, 2018)].

5.3.1.2 Total phosphorus and nitrogen A flow-based analytical approach with online extraction was developed for the estimation of inorganic phosphorus in food with quantification based on the spectrophotometric molybdenum blue method (Macedo, Mancini, Arakaki, & Rocha, 2018). A FIA conductometric manifold was developed for measuring the protein content in traditional Thai food (Yanu & Jakmunee, 2017); after Kjeldahl digestion, the sample was injected in an alkaline donor stream and nitrogen was converted into ammonia, which diffused through a membrane into an acceptor stream, thereby increasing its conductivity.

5.3.1.3 Aminoacids A spectrophotometric FIA system has been proposed for protein determination in dairy products in the presence of the brilliant blue G-250 Dye (Ali & Radhy, 2016). In addition, a low-pressure chromatography method utilizing a 10-mmlong monolithic coated with dodecyldimethylammonium bromide and amperometric detection has been applied to the

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determination of niacin in coffee (Santos & Rangel, 2015). A fluorimetric FIA procedure was proposed for the determination of tryptophan in soy sauce after derivatization of the target species with o-phthalaldehyde (Gao, Hsieh, Cheng, & Chen, 2015). Finally, Gao and Fan developed an SIA procedure with CL detection for the determination of tryptophan and tyrosine in beer, milk, and soybean milk (Gao & Fan, 2013).

5.3.1.4 Vitamins Ascorbic acid in fruit juices was determined with a flow-batch system by exploiting the quenching of the fluorescence of quantum dots by the analyte (Lima, Andrade, Barreto, & Arau´jo, 2014). Various SIA methods for ascorbic acid determination in juices with photometric detection were developed based on the reaction between Dawson-type molybdophosphate and ascorbic acid (Vishnikin, Sklena´ˇrova´, Solich, Petrushina, & Tsiganok, 2011) and reduction of the guanidinium salt of 11-molybdobismuthophosphate (Vishnikin et al., 2010). An automated flow system, featuring online SPE with a renewable molecularly imprinted material has been developed as front end to HPLC for riboflavin determination in pig liver extract, in infant milk formula, and in energy drinks (Oliveira, Segundo, Lima, Miro´, & Cerda`, 2010; Fig. 5.2). MSC was applied to the separation and determination of six water-soluble vitamins (thiamine, riboflavin, ascorbic acid, nicotinic acid, nicotinamide, and pyridoxine) in orange juice, strawberry milkshake, and malt, using a two-solvent step elution protocol (Fernandez, Forteza, & Cerda, 2012).

5.3.1.5 Antinutrients An MCFIA system has been proposed for the spectrophotometric determination of formaldehyde in mushrooms (Pinto, Rocha, Richter, Mun˜oz, & Silva, 2018). Biogenic amines are markers of food freshness and are formed during the bacterial degradation of aminoacids. Representative analytical methodologies for biogenic amines determination involve the determination of putrescine, cadaverine, and tyramine in chicken meat in an FIA system equipped with a biosensor (Telsnig et al., 2012), the flow-based quantitation of histamine in fish by fluorimetry (Tzanavaras, Deda, Karakosta, & Themelis, 2013) and in fish sauce with amperometry (Veseli et al., 2016), and the monitoring of hypoxanthine by voltammetry in fish and pork meat using an SIA-LOV system (Wang et al., 2012). An automated flow-through electrochemical immunosensor (involving magnetic beads modified with the analyte and an indirect competitive immunoassay) was developed for okadaic acid (a toxin) determination in mussel samples (Dominguez et al., 2012).

5.3.2 Inorganic species 5.3.2.1 Cations The majority of flow-based approaches for trace metal analysis in food samples is based on atomic spectroscopy detection after SPE separation/preconcentration. The BIA approach, in conjunction with an LOV manifold, has been used for solid-phase microextraction before detection by hydride generation AFS (HG-AFS) for the determination of Pd in fish (Beltran, Leal, Ferrer, & Cerda, 2015) and ICP-AES was applied to the determination of Cd and Pb in honey (Sixto et al., 2016; Fig. 5.3). As was determined in wines by ETAAS, after offline dispersive liquid
liquid microextraction using an ionic liquid as the extractor, which was retained on a FlorisilR-filled minicolumn placed in a flow system (Escudero, Martinis, Olsina, & Wuilloud, 2013). A FIA analyzer with FAAS detection was used to study the adsorption of Cd and Pb coffee (Marchioni, Oliveira, Magalhaes, & Luccas, 2015). A flow-batch extraction system was developed for the determination of metals in dried animal foods by ICP-AES (Marques & No´brega, 2018). An online SPE preconcentration method was proposed using a SiO2/Al2O3/SnO2 ternary oxide as extractor coupled to FAAS for Pb determination at trace levels in different kind of food samples (Tarley, Scheel, Zappielo, & Suquila, 2018). A MCFIA manifold, implementing inline sorption and elution, was developed for the determination of Cd, Ni, and Pb in foodstuffs by ICP-AES (Miranda, Reis, Baccan, Packer, & Gine´, 2002). Functionalized Amberlite XAD-4 was used for SPE of Cd, Cu, Mn, Co, Ni, and Pb (Karadas & Kara, 2013), whereas modified silica was applied for Cu preconcentration before detection by FAAS (Lima et al., 2012). An aminated Amberlite XAD-resin was assed as a SPE material for speciation of inorganic mercury and methylmercury in fish tissue (C¸aylak S¸ G, Ho¨l, Akdo˘gan, & Divrikli, 2019). Other functionalized silica gels were exploited for preconcentration of metal ions before detection by FAAS (Oliveira, Oliveira, Segatelli, & Tarley, 2013; Sivrikaya, Imamoglu, Yıldız, & Kara, 2016). The preconcentration of 15 rare earth elements was achieved with a minicolumn packed with walnut shell and detection by ICP-MS (Li, Yang, & Jiang, 2012). Online hydride generation has been implemented in MSFIA for the determination of Hg (Silva, Portugal, Serra, Ferreira, & Cerda, 2013) and Sb, As, and Se (Santana et al., 2016) by AFS for the analysis of rice and peanut samples.

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BS CS A

HPLC

W

LOV 6

λ

4

5

3

7 8

Sa

L1

2 1

MC

CC L2 HC

B

EL

V1

V2

V3

S1

S2

S3

IV P

FIGURE 5.2 Schematic representation of the manifold for determination of riboflavin in foodstuff. LOV, Lab-on-valve; MS, multisyringe; HPLC, high-performance liquid chromatography; S, syringe; V, three-way commutation valve (dashed line position off and solid line position on); A, air; CS, conditioning solvent (50% (v/v) MeOH/H2O); BS, bead suspension in conditioning solvent; C, carrier solution (H2O); D, diluent (H2O); W, waste; CC, central channel; EL, eluent (v/v) (50% (v/v) MeOH/H2O 1 1% CH3COOH); B, channel for bead discarding; Sa, sample/standard solution; HC, holding coil; L1, connection tubing (8 cm), L2, connection tubing (44 cm); P, chromatographic pump; IV, injection valve; MC, monolithic chromatographic column; λ diode array detector. Source: From, Oliveira, H.M., Segundo, M.A., Lima, J.L.F.C., Miro´, M., & Cerda`, V. (2010). Exploiting automatic online renewable molecularly imprinted solidphase extraction in lab-on-valve format as front end to liquid chromatography: application to the determination of riboflavin in foodstuffs. Analytical and Bioanalytical Chemistry, 397, 77
86.

MS

W

C

D

A similar methodology was extended as to perform sample pretreatment in an online fashion using a flow-batch analyzer for the determination of Hg in honey by GVG-AAS (Dominguez, Grunhut, Pistonesi, Di Nezio, & Centurion, 2012). Semenova et al. reported total As determination by HG-AFS using an MSFIA system (Semenova, Leal, Forteza, & Cerda`, 2002) while an MCFIA system was employed for the determination of Hg in fish by CVG-AAS (Silva, To´th, & Rangel, 2006). Flow systems have been reported for the determination of metals in herb, bovine liver, apple leaves, shrimp, and spinach samples by HG-ICP-MS (Marques, Wiltsche, Motter, Nobrega, & Knapp, 2015; Tai, Jiang, & Sahayam, 2016). A FIA method has been optimized for the determination of Se content in samples of dietary suppleˇ ´ , Rychlovsky´, & ments using photochemical vapor generation with ETAAS detection (Nova´kova´, Linhart, Cerveny Hranı´cˇ ek, 2017; Fig. 5.4). Escudero at al. developed a method to determine Se ions based on online coprecipitation with lanthanum hydroxide, retention of the precipitate at a column packed with polyvinyl chloride, and dissolution in acid for determination by HG-ICP-OES (Escudero, Pacheco, Gasquez, & Salonia, 2014). A headspace single-drop

Flow-based food analytical methods Chapter | 5

Air

Beads

LOV 1

Autosampler

6

C2

CC 3

HC

Eluent

5

2

4 Transfer line

W

2%(v/v) HNO3

1 6

2

5

3

PP

4 ICP-AES

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105

FIGURE 5.3 Schematic illustration of the lab-on-valve mesofluidic platform integrating Restricted Access-like Material-type bead injection micro Solid Phase Extraction for clean-up and preconcentration of Pd and Cd as contaminants in honey as a front end to inductively coupled plasma-atomic emission spectroscopy (HC, Holding coil; PP, peristaltic pump; IV, injection valve; LOV, lab-on-valve; W, waste; CC, communication channel; C2, mSPE column). The IV is activated in the figure to the load position. Source: From, Sixto A., Fiedoruk-Pogrebniak, M., Rosende, M., et al. (2016). A mesofluidic platform integrating restricted access-like sorptive microextraction as a front end to ICP-AES for the determination of trace level concentrations of lead and cadmium as contaminants in honey. Journal of Analytical Atomic Spectrometry, 31 (2), 473
48.

Ar 9 8 7

H2

6

5 2 3

10

4 FIGURE 5.4 Experimental setup for UV-Photochemical Vapour Generation of Se volatile species. 1—peristaltic pump; 2—low pressure six-port injection valve; 3—acetic acid; 4—photocatalyst suspension; 5—argon flow rate regulation; 6—UV-photochemical reactor; 7—gas
liquid separator with forced outlet; 8—hydrogen flow rate regulation; 9—externally heated quartz furnace atomizer; 10—removal of waste solution. Source: From, ˇ ´, V., Rychlovsky´, P., & Hranı´ˇcek, J. Flow injection determination of Se in dietary supplements using TiO2 mediated Nova´kova´, E., Linhart, O., Cerveny ultraviolet-photochemical volatile species generation, Spectrochimica Acta
Part B Atomic Spectroscopy, 134, 98
104.

microextraction system, based on a programmable LIS platform, was hyphenated to ETAAS for the determination of inorganic Hg in fish after in situ vapor generation (Mitani, Kotzamanidou, & Anthemidis, 2014). Molecular spectroscopy in conjunction with flow-based systems has been used for determination of Fe in edible oils (Barreto, Lima, Andrade, Araujo, & Almeida, 2013), Al in tea and fruit (Siriangkhawut et al., 2016; Tontrong, Khonyoung, & Jakmunee, 2012), and Fe in tofu, meats, and soybean (Pragourpun, Sakee, Fernandez, & Kruanetr, 2015) and in wine (Phansi, Danchana, Ferreira, & Cerda`, 2019), after complexation with various chromophores. An online cloud point extraction methodology was proposed for the determination of Fe in a diversity of food samples based on the retention of the surfactant phase in the flow cell of a spectrophotometer (Frizzarin & Rocha, 2014). An SIA-LOV optosensor was used for the determination of Fe in wine exploiting the BIA concept for SPE and spectrophotometric detection (Vidigal, To´th, & Rangel, 2011). A novel method was described for determination of total Fe in edible oils with inline single-phase extraction and detection based on the quenching of the fluorescence of CdTe quantum dots by the analyte (Lima, Andrade, Barreto, & Arau´jo, 2015). Alternative detection techniques include CL, such as the

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ultratrace determination of Cd in rice (Wang, 2014). A FIA-MS method was used to detect quaternary ammonium cations in food simulants (Yu, Malik, Duncan, & Jablonski, 2018). Flow potentiometric stripping analysis was evaluated for the determination of Cd and Pb in milk (Suturovi´c, Kravi´c, Stojanovi´c, Ðurovi´c, & Brezo-Borjan, 2019). An online preconcentration method (based on a resin-packed minicolumn) was developed for the simultaneous determination of Pb, Cd, and Zn in bottled water by anodic stripping voltammetry (Chuanuwatanakul, Punrat, Panchompoo, Chailapakul, & Motomizu, 2008).

5.3.2.2 Anions The photometric determination of iodate in salt was achieved with a flow analyzer controlled by solenoid micropumps (Lima, Barreto, Andrade, Almeida M, & Araujo, 2012). An FIA-CL system was proposed for the determination of iodide in food samples exploiting the CL from the reaction of iodide and KMnO4 enhanced by carbon nanodots in acidic medium (Han, Liu, Fan, Zhang, & Jiang, 2017). The determination of fluoride in salt and coffee with an ionselective electrode and FIA has also been reported (Galvis-Sanchez, Santos, & Rangel, 2015). The simultaneous determination of sulfite and phosphate in wine was proposed by Yao et al. using an FIA system coupled with enzymemodified electrodes (Yao, Satomura, & Nakahara, 1994); the enzymatically generated hydrogen peroxide was detected amperometrically. A FIA methodology was evaluated for the determination of sulfite, based on the reduction of MnO2 to Mn(II) by sulfite and determination of Mn(II) by AAS (Zare-Dorabei, Boroun, & Noroozifar, 2018). Chloride in milk was determined by pulsed amperometric detection in an FIA system (Chen et al., 2018). A multisyringe ion chromatography system with chemiluminescence detection was developed for oxalate determination in beer samples using a short surfactant-coated monolithic column (Maya, Estela, & Cerda`, 2011). A membraneless vaporization unit in a flow system was used for the simultaneous determination of ethanol and total sulfite in wine by spectrophotometry and conductimetry, respectively (Kraikaew et al., 2019).

5.3.3 Additives, preservatives, and adulterants Different SIA systems based on the Griess reaction have been developed for the determination of nitrate and nitrite in various types of food (Oliveira, Lopes, & Rangel, 2004; Oliveira, Lopes, & Rangel, 2007; Pisto´n, Mollo, & Knochen, 2011; Reis Lima, Fernandes, & Rangel, 2006); nitrate was determined in the same way after online reduction to nitrite by means of a cadmium column. A potentiometric SIA method was developed to quantify nitrite in cured meat products using a nitrite-selective electrode (Za´rate et al., 2009). A FIA-based method with spectrophotometric detection was developed for the simultaneous determination of nitrites and nitrates (Ahmed, Stalikas, Tzouwara-Karayanni, & Karayannis, 1996). A similar approach was used by Ferreira et al. for the assay of nitrite and nitrate in meat products (Ferreira, Lima, Montenegro, Olmos, & Rios, 1996) and by Pinho et al. for the evaluation of nitrite and nitrate contents in pate´ (Pinho, Ferreira, Oliveira, & Ferreira, 1998). A CL method for nitrite detection in beverage samples was developed on a microfluidic chip based on the carbon dot-NaNO2-acidified H2O2 system (Wu, Wang, Lin, Zheng, & Lin, 2016). A MCFIA system has been developed for the determination of food azo colorants (tartrazine, sunset yellow, and allura red) by square wave voltammetry (Silva, Garcia, Lima, & Barrado, 2006). FIA manifolds were proposed for the assay of indigo carmine and allura red in candies and the pairs tartrazine
sunset yellow/brilliant blue
sunset yellow in juices, gelatins, and sports drinks by multiple-pulse amperometry (Deroco, Medeiros, Rocha-Filho, & Fatibello-Filho, 2018; Medeiros, Louren, Rocha-Filho, & Fatibello-Filho, 2012). FIA has been used for the CL determination of chrysoidine in yellow croaker and yuba (Lu et al., 2012). Zhu et al. determined malachite green in fish with FIA and resonance Rayleigh scattering detection (Zhu et al., 2014). The xanthene dye Rhodamine B was determined in spices and rice by spectrofluorimetry (Acosta et al., 2014). A SIC manifold was used for development of a sub-1-min methodology for the quantitation of three polar water-soluble colorants (carmoisine, Ponceau 4R, and red 2G) in beverages using a ˇ ´nsky´, & Solich, 2017; Fig. 5.5). cyanomonolithic column (Chocholouˇs, Dˇedkova´, Boha´cˇ ova´, Satı A hybrid SIA
FIA spectrophotometric method with gas diffusion for the determination of sulfite in wines has been reported (Tzanavaras, Thiakouli, & Themelis, 2009). Another amperometric gas-diffusion SIA system was developed for the determination of sulfite in wine (Chinvongamorn, Pinwattana, Praphairaksit, Imato, & Chailapakul, 2008). Cardwell and Christophersen reported a dual channel FIA system with amperometric detection for the determination of ascorbic acid and sulfur dioxide in wines and fruit juices (Cardwell & Christophersen, 2000). An SIC methodology has been developed for the fast separation (,3 min) of benzoic acid, sorbic acid, and salicylic acid in fruit juice, syrup, and soft drink samples (Jangbai, Wongwilai, Grudpan, & Lapanantnoppakhun, 2012). The FIA determination of

Flow-based food analytical methods Chapter | 5

(A) MP1

SP

W

MP2

D

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HC

PR W

M

SV S

W

(B)

0.10

Mixture standard solution CAR 1.35

0.08

Absorbance

R2G 1.12

P4R 0.85

0.06

500 nm

0.04

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0.00 0.0 −0.02

0.5

1.0

1.5

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FIGURE 5.5 (A) Scheme of SIChrom manifold for the separation and determination of carmoisine, Ponceau 4R and red 2G: D, Vis detector; HC, holding coil; M, manometer with pressure release 750 psi; MC, chromatographic column; PR, pressure release 20 psi; SP, syringe pump; SV, selection valve; MP1, mobile phase 1; MP2, mobile phase 2; S, sample; W, waste. (B) Chromatogram of mixture standard solution (10.00 mg L21 each standard), Chromolith SpeedROD CN (50 mm 3 4.6 mm), gradient elution [0.05% (v/v) ammonium phosphate at pH 6.0, 1.5 mL 1 0.005% (v/v) ammonium phosphate at pH 6.0, 0.55 mL], flow rate 1.20 mL min21, and sample injection volume 10 μL. Source: From, Chocholouˇs, P., ˇ ˇ ´, L., Boha´ˇcova´, T., Satı´nsky ´, Dedkova D., & Solich, P. (2017). Fast separation of red colorants in beverages using cyano monolithic column in sequential injection chromatography. Microchemical Journal, 130, 384
389.

Time (min)

acesulfame-K, cyclamate, and saccharin in wines, yogurts, diet soft drinks, and sweetener tablets has been reported at filter-supported bilayer membranes (Nikolelis & Pantoulias, 2001). Aspartame, cyclamate, saccharin, and acesulfame K were determined in food samples by CE-SIA with contactless conductivity detection (Stojkovic, Mai, & Hauser, 2007). An FIC method, exploiting a short monolithic column, has been reported for the simultaneous analysis of eight analytes (including sweeteners (aspartame, acesulfame-K, and saccharin), preservatives, and antioxidants in various foodstuffs (Garcı´a-Jime´nez, Valencia, & Capita´n-Vallvey, 2007). The same authors also reported an FIC manifold for the determination of three antioxidants (propylgallate, butylhydroxyanisole, and butylhydroxytoluene (Garcı´aJime´nez, Valencia, & Capita´n-Vallvey, 2009) and four preservatives (methylparaben, ethylparaben, propylparaben, and butylparaben) in commercial food samples (Garcı´a-Jime´nez, Valencia, & Capita´n-Vallvey, 2010). A SIA system was used for the determination of aspartame in commercial sweetener tablets by means of an enzymatic spectrophotometric method (Pe., Lima, & Saraiva, 2004). A FIA method coupled to dialysis was developed as a front end to HPLC for the simultaneous determination of five food additives (acesulfame-K, saccharin, caffeine, benzoic acid, and sorbic acid) in soft drinks (Kritsunankul & Jakmunee, 2011). A spectrophotometric SIA method was developed to determine saccharin in foods (Wibowotomo, Eun, & Rhee, 2017). A simple FIA method, utilizing an amperometric enzymatic aspartame biosensor, was developed (Radulescu, Bucur, Bucur, & Radu, 2014). A microfluidic BIA system has been developed for detection of L-glutamate in food based on substrate recycling fluorescence detection (Laiwattanapaisal et al., 2009). The illegal food additive rhodamine B was determined in soft drinks using an LIS setup that combined liquid
liquid microextraction and spectrophotometric detection (Maya, Horstkotte, Estela, & Cerda, 2012). Tartrazine and sodium benzoate were determined in juices by an FIA method using UV detection (Al Sultani, Al-Rashidy, & Al-Samrrai, 2019). A new potentiometric sensor was fabricated and applied to 2,4-dichlorophenol quantification in fish samples using an FIA system (El-Shalakany, Hamza, & Kamel, 2018). Souza et al. have developed an MCFIA system for determination of adulterants (dichromate, starch, salicylic acid, and hydrogen peroxide) in milk (Souza, Silva, Leoterio, Paim, & Lavorante, 2014; Fig. 5.6). Flow-based methods have been reported for the determination of other organic compounds such as the CL determination of dibutyl phthalate in wine (Guo, Luo, Chen, Tan, & Song, 2013), the fluorimetric determination of bisphenol A in milk (Molina-Garcia, Cordova, & RuizMedina, 2012), and the photometric assay of thiourea in orange juice and peel (Chamjangali, Goudarzi, Moghadam, & Amin, 2015).

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V6 V5 V1

R4 X

S

P1

R3 B

V2

D

w

z

y P2 V4

V3 C

W

R1

R2

FIGURE 5.6 Diagram of the flow system for multiparametric analysis of dichromate, starch, salicylic acid, and hydrogen peroxide in milk. V1, V2, V3, V4, V5 and V6, Pinch solenoid valves normally closed and P1and P2, solenoid micropumps; S, sample or standard solutions; R1, Fe (III)solution; R2, 1,5-diphenylcarbazide; R3, V2O5 solution; R4, iodine solution; C, carrier solution, water; x, y, z e w, joint device; B1, reaction coil (120 cm); D, detector; W, waste. Source: From, Souza, G.C.S., Silva, P.A.B., Leoterio, D.M.S., Paim, A.P.S., & Lavorante, A.F. (2014). A multicommuted flow system for fast screening/sequential spectrophotometric determination of dichromate, salicylic acid, hydrogen peroxide and starch in milk samples. Food Control, 46, 127
135.

Hydrogen peroxide in foods has been determined on different modified electrodes using a batch injection analysis manifold (Reanpang, Themsirimongkon, Saipanya, Chailapakul, & Jakmunee, 2015; Silva, Montes, Richter, & Munoz, 2012). Flow analytical approaches have also been proposed for the determination of formaldehyde in milk by spectrophotometry (Nascimento et al., 2015), tofu with an optical biosensor (Hidayat, Christiana, & Kuswandi, 2013), and mushrooms/Chinese herbs with a microfluidic device and colorimetric detection (Liu, Wang, Fu, & Chieh, 2016).

5.3.4 Acidity A SIA titration process analyzer for the determination of acetic acid in vinegar has been proposed (van Staden, Mashamba, & Stefan, 2002). Titration was achieved by aspirating the sample between two zones of base and detection at a pH electrode. SIA titration systems based on the same principle and spectrophotometric detection (using indicators) have been developed for the assay of acetic acid content in vinegar (Lenghor et al., 2002), of acidity in fruit juices (Jackmunee, Rujiralai, & Grudpan, 2006), and of acetic, citric, and phosphoric acids in vinegars and various soft drinks (Kozak, Wo´jtowicz, Gawenda, & Ko´scielniak, 2011). Another titration approach using an LOV system has been proposed for the assay of acidity in fruit juices (Jakmunee, Pathimapornlert, Hartwell, & Grudpan, 2005). A MCFIA system, designed to implement online titration in vinegar and lemon, orange, pineapple, maracock, and acajou juices, has been reported (da Silva, Crispino, & Reis, 2010). Finally, the acidity of vinegar samples was evaluated using a tracermonitored titration approach (Sasaki, Rocha, Rocha, & Zagatto, 2016).

5.3.5 Antioxidant capacity FIA systems have been used for determination of the total antioxidant capacity (TAC) of wines and indigenous Thai plants using spectrophotometric detection of 2,2-diphenyl-1-picrylhydrazyl (DPPH) (Bukman, Martins, Barizao, Visentainer, & Almeida, 2013; Mrazek, Watla-iad, Deachathai, & Suteerapataranon, 2012); teas based on Fluorescence Recovery after Photobleaching (FRAP) assay (Martins et al., 2013), and green tea and coffee based on the Oxygen Radical Absorbance Capacity (ORAC) method (Ramos et al., 2016). Other FIA methods have been reported that make use of CL detection (Pulgarin, Bermejo, & Duran, 2012), amperometric detection (Amatatongchai, Laosing, Chailapakul, & Nacapricha, 2012; Arribas et al., 2013), and reagentless electro-CL detection (Liu, Wei, & Tu, 2013). A SIA method, based on the 2,20 -Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) methodology, was developed to measure the TAC of several beverages and foods (Reis Lima, To´th, & Rangel, 2005). An MSFIA system was developed for the determination of TAC in several food products using the DPPH assay (Magalhaes, Segundo, Reis, & Lima, 2006) and, recently, a FIA method was developed for TAC determination of fruit extracts, based the scavenging of a imipramine-derived radical by antioxidants in the sample (Panich & Amatatongchai, 2019). A indirect SIA method

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with CL detection was developed for the rapid assay of the TAC in wines (Fassoula, Economou, & Calokerinos, 2011). A microfluidic platform with CL detection has been reported for the determination of total phenolic content in fruit juices using the reaction of nanocolloidal Mn(IV) with phenolic compounds to induce CL (Al Mughairy, Al-Lawati, & Suliman, 2018). Similar versions of this lab-on-a-chip system with CL detection were used for the TAC assay of commercial fruit juices (Al Haddabi, Al Lawati, & Suliman, 2017), honey (Al Lawati, Al Haddabi, & Suliman, 2014; Al Lawati, Al Mughairy, Al Lawati, & Suliman, 2018), and tea and honey (Al Haddabi, Al Lawati, & Suliman, 2016) using various CL systems (Fig. 5.7). MPFSs with CL detection have been reported for the determination of the total polyphenol index in several plant food samples (Nalewajko-Sieliwoniuk, Iwanowicz, Kalinowski, & Kojło, 2016) and of the TAC in fruit juices (Rodrigues et al., 2015). A SIA method for the evaluation of the TAC in commercial instant ginger infusion beverages with amperometric detection has been reported (Chan-Eam, Teerasong, Damwand, Nacapricha, & Chaisuksant, 2011). Medeiros et al. reported a FIA system with multiple pulsed amperometric detection for the simultaneous determination of the antioxidants butylated hydroxyanisole and butylated hydroxytoluene (Medeiros, Louren, Rocha-Filho, & Fatibello-Filho, 2010). A programmable flow system was applied to the rapid electrochemical assessment of TAC in orange and pomegranate juices (Veenuttranon and Nguyen, 2018). An HPLC system coupled online with colorimetric detection of

Set 1 (Chip A) A: R B (or R6G) B: PC C: Ce (IV)

Set 2 (Chip A) A: R B (or R6G) B: Ce (IV) C: PC

Chip A

Set 3 (Chip A) A: PC B: Ce (IV) C: R B (or R6G)

Detection window

A

B Detection window Chip B A

B

C

C Chip C

A

B

Detection window

FIGURE 5.7 Different chip geometries for the total antioxidant capacity assay by chemiluminescent. Source: From, Al Haddabi, B., Al Lawati, H.A. J., & Suliman, F.O. (2016). An enhanced cerium(IV)-rhodamine 6G chemiluminescence system using guest-host interactions in a lab-on-achip platform for estimating the total phenolic content in food samples, Talanta, 150, 399
406.

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antioxidant activity in an FIA system was developed in order to separate antioxidants and to determine their activities in a single step (Nuengchamnong, Hermans Lokkerbol, & Ingkaninan, 2004). The determination of TAC in olive oils was performed by spectrophotometry in a MCFIA system (Lara-Ortega, Sainz-Gonzalo, Gilbert-Lopez, Garcia-Reyes, & Molina-Diaz, 2016). The TAC of several plant-derived oils was also determined by using a FIA system with CL detection (Christodouleas, Giokas, Garyfali, Papadopoulos, & Calokerinos, 2015). Other examples are the determination of total phenols in olive oil using a FIA system with differential pulse voltammetry (Bavol, Dejmkova, Scampicchio, Zima, & Barek, 2017) and of the TAC of olive oils by FIA-amperometry (Benedetti, Cosio, Scampicchio, & Mannino, 2012).

5.3.6 Pesticides An optical fiber biosensor was described for the determination of the propoxur and carbaryl based on the inhibition of acetylcholinesterase (AChE) by these analytes and using chlorophenol red as pH indicator (Xavier et al., 2000). Another biosensor-based FIA system using an enzymatic reactor has been developed to determine carbamate pesticides in water (Suwansa-ard et al., 2005). Wei et al. described the application of a biosensor based on photoelectrosynergistic catalysis together with FIA for the detection of organophosphorus pesticides (Wei et al., 2009). Another AChE-based biosensor was adapted to a flow system for determination of organophosphate pesticides in milk (Mishra, Dominguez, Bhand, Mun˜oz, & Marty, 2012; Mishra, Alonso, Istamboulie, Bhand, & Marty, 2015). A flow-based immunosensing system was reported for the determination of streptomycin in milk (Mishra, Sharma, & Bhand, 2015). An automated system has been developed for analytical flow-through microarrays, based on multiplexed immunoassays (Fig. 5.8; Kloth, Niessner, & Seidel, 2009); the microarray chip enables the CL assay of up to 13 different antibiotics in milk. Wutz et al. developed a similar multianalyte immunoassay for identification and quantification of four antibiotics (enrofloxacin, sulfadiazine, sulfamethazine, and streptomycin) in honey samples using horseradish peroxidase-labeled antibodies and CL readout (Wutz, Niessner, & Seidel, 2011). A FIA-CL system was described for the determination of paraquat in vegetable samples based on the enhancement of the CL of Ag(III)-luminol by the analyte in alkaline solution (Liu, Shi, Xu, Kang, & Li, 2011). A FIA-CL immunoassay was developed for atrazine detection by immobilizing atrazine antibody on a protein-A
Sepharose matrix packed in a minicolumn (Chouhan, Rana, Suri, Thampi, & Thakur, 2010). The photolytic degradation of organophosphorus pesticides in the presence of light has been widely utilized for screening food samples for the presence of these analytes (Ruiz-Medina, Llorent-Martı´nez, Ferna´ndez-de Co´rdova, & Ortega-Barrales, 2017). Waseem et al. have developed a FIA method for the determination of dithiocarbamate fungicides (maneb, nabam, and thiram) involving the photodegradation of the fungicides by means of UV irradiation and CL detection of the photoproducts after reaction with luminol (Waseem, Yaqoob, & Nabi, 2009). FIA combined with CL has been also used for detection of carbofuran and promecarb (Pe´rez-Ruiz, Martı´nez-Lozano, Toma´s, & Martin, 2002) and triazines (Beale, Porter, & Roddick, 2009) by making use of the property of the pesticides to be converted into methylamine upon exposure to UV radiation. A MCFIA system, featuring a flow-through optosensor, has been developed for the quantitation of chlorpyrifos in chili peppers (Ruiz-Medina et al., 2019); the fluorescent photoproduct of chlorpyrifos was retained on Sephadex QAE A-25 inside a flow-cell. Another MCFIA manifold, combined with a flow-through optosensor and photochemically-induced fluorescence, was developed for the determination of azoxystrobin in grapes, must, and wine (Flores, Dı´az, & Ferna´ndez de Co´rdova, 2007). A SIA-BIA-LOV system, exploiting LiChroprep RP-18 beads as renewable sorbent packing, was Enclosed flow-through microarray chip Multianalyte sample

Waste CL-reagent

Antibodies

Incubation loop

Affinity reaction

CL-reagent

Shielding layer a

b

c

d

Rinsing

Substrate

Cleaning

CL-signal transduction Pumps and valves

2-D-readout

FIGURE 5.8 Schematic setup of the analytical chemiluminescent flowthrough microarray platform for the multiplexed quantification of analytes with indirect (a and b), direct (c), and sandwich (d) assay formats. Source: From, Kloth, K., Niessner, R., & Seidel, M. (2009). Development of an open stand-alone platform for regenerable automated microarrays. Biosensors and Bioelectronics, 24, 2106
2112.

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hyphenated to HPLC for online micro-SPE of carbamate insecticides (isoprocarb, methomyl, carbaryl, carbofuran, methiocarb, promecarb, and propoxur; Vichapong, Burakham, Srijaranai, & Grudpan, 2011). Nanita et al. developed a FIA approach for high-throughput pesticide residue quantitation with MS/MS (Nanita, Pentz, & Bramble, 2009); the method enabled the fast quantitation of agrochemicals (sulfonylureas and carbamates) in food and water samples without chromatographic separation. Automated SIA methods using fluorescence detection were developed for the determination of thiabendazole in mushrooms (Llorent-Martı´nez, Ferna´ndez-De Co´rdova, Ruiz-Medina, & Ortega-Barrales, 2012) and carbendazim in capsicum powder and tomatoes (Llorent-Martı´nez, Alca´ntara-Dura´n, Ruiz-Medina, & Ortega-Barrales, 2013); detection was performed at a flow-through optosensor using C18 silica gel as solid support, which was placed in the flow-cell. The same concept was extended to the simultaneous determination of carbendazim and o-phenylphenol in fruits (Fig. 5.9; Llorent-Martı´nez, Delgado-Blanca, Ruiz-Medina, & Ortega-Barrales, 2013); the pesticides were separated online on the solid C18 support due to their different retention kinetics. A FIA method for pyridaben residues in fruits was developed based on ultrasound-enhanced CL detection (Zhang, Wei, Song, Gong, & Yang, 2017). CL detection was also used for the determination of carbofuran and omethoate in fruits and vegetables (Ge, Zhao, Yan, Zang, & Yu, 2012) and isocarbophos in citrus fruits (Chen, Song, & Lv, 2012). A flow system was used as a front end for online sample preparation before the determination of organophosphorus pesticides in vegetables by gas chromatography-mass spectrometry (Wu et al., 2016).

5.3.7 Pharmaceuticals A FIA system has been proposed for the photometric determination of sulfaguanidine in food samples (Frugeri, Marchioni, do Lago, Wisniewski, & Luccas, 2018). An automatic fluorometric MPFS system has been developed by Ribeiro et al. for glibenclamide determination in alcoholic beverages (Ribeiro, Prior, Taveira, Mendes, & Santos, 2011). Gonzalez-San Miguel et al. have described an MSC system using a monolithic column that was applied to the separation and determination of amoxicillin, ampicillin, and cephalexin. Five sulfonamide drugs were determined amperometrically in beef, pork, chicken, and fish samples after electrokinetic separation in a microfluidic system (Won, Chandra, Hee, & Shim, 2013). Three sulfonamide drugs (sulfathiazole, sulfamethazine, and sulfadimethoxine) were determined in milk (Fernandes, Silva, Rufino, Pezza, & Pezza, 2015) and honey (Catelani, Toth, Lima, Pezza, & Pezza, 2014) using a merging zones FIA approach with spectrophotometric detection. FIA systems with photometric detection have also been applied to the determination of some tetracyclines (Rodriguez, Silva, Pezza, & Pezza, 2016) and betalactam antibiotics (Abdel Azeem, Kuss, & El-Shahat, 2012) in milk. A flow-based wetting-film liquid
liquid microextraction procedure is proposed for preconcentration, separation, and fluorometric detection of bisphenol A in tap water samples (Nascimento & Rocha, 2018).

5.3.8 Miscellaneous Differential pulse voltammetry at a glassy carbon working electrode in combination with an FIA system was developed to determine capsacinoids in chili pepper (Bavol et al., 2017). A FIA method with a dual carbon screen-printed electrode was developed to determine ethoxyquin (an antioxidant widely used in the food industry) in salmon samples (Vandeput et al., 2018). Flow-through chronopotentiometry has been developed for identification and quantification of ˇ chlorogenic acids in coffees (Tomac, Seruga, & Beinrohr, 2017). Recently, melamine (a triazine added illegally to milk to increase its nitrogen content) was determined by micellar chromatography implemented in an SIC system, using a C18 column and aqueous sodium dodecyl sulfate:propanol as mobile phase (Batista, Nascimento, Melchert, & Rocha, 2014). Lactic acid enantiomers were monitored in beer samples with FIA using a double-path flow cell featuring a home-made D-lactic acid biosensor in one path and a commercially available L-lactic acid biosensor in the other (Ruiz et al., 2016). An Arduino-controlled MPFS for the determination of β-galactosidase activity has been proposed (Skoczek, Pokrzywnicka, Kubacka, & Koncki, 2019). A 10-mm-long monolithic column has been implemented in an FIC system for the analysis of methylxanthines (theobromine, theophylline, and caffeine) in coffee samples (Rodrigo Santos & Rangel, 2012). An improved SIC method, exploiting linear gradient elution, was further developed for the determination of theobromine, theophylline, and caffeine in coffee, tea, and cocoa (Fig. 5.10; Chorti, Ntousikou, & Economou, 2019). Caffeine in coffee beverages was determined using an LIS after online LLE (Frizzarin, Maya, Estela, & Cerda, 2016). Another LIS approach headspace single-drop microextraction was reported for the determination of ethanol in ˇ ´ mkova´, Horstkotte, Solich, & Sklena´ˇrova´, 2014). A MPFS system was developed for the spectrophotometric wine (Sra determination of phytic acid in foodstuffs (Carneiro, et al.). A spectrophotometric MPFS system, consisting of four

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(A) FiaLab 3500

Spectrofluorimeter

Multiposition valve

Holding coil

Flow-cell

Syringe pump

Additional solid phase (Separation zone)

Waste

Sample

MeOH Eluting solution (50% MeOH)

Carrier (25% MeOH)

Detection area

(B) Standard solution

Sample

OPP 600

Intensity (a.u.)

CBZ

MeOH Carrier

400

200

10

20

Time (min) FIGURE 5.9 (A) Sequential injection analysis manifold for the simultaneous determination of carbendazim and o-phenylphenol. (B) Sequential injection analysis profile from a standard solution and from a pineapple sample, both with 8 and 10 mg kg21 of carbendazim and o-phenylphenol, respectively. Source: From, Llorent-Martı´nez, E.J., Jime´nez-Lo´pez, J., Ferna´ndez-de Co´rdova, M.L., Ortega-Barrales, P., & Ruiz-Medina, A. (2013). Quantitation of hydroxytyrosol in food products using a sequential injection analysis fluorescence optosensor. Journal of Food Composition and Analysis, 32(1), 99
104.

Flow-based food analytical methods Chapter | 5

FIGURE 5.10 Schematic diagram of the gradient elution sequential injection chromatography setup for the determination of theobromine, theophylline, and caffeine (S1 to S6: standards or samples). Source: From, Chorti, P., Ntousikou, M., & Economou, A. (2019). A lineargradient sequential-injection chromatography method exploiting programmable fluidics for the determination of three methylxanthines, Talanta, 202, 514
519.

PC On/off flow rate Direction

Position S5

Absorbance

S6 S4

Selection valve

S3

Solvent 1

Pump 2

Holding coil

S2

S1

Cleaning solvent

Mixer

113

Monolithic column

Pressue valve Detector

Waste

Solvent 2 Pump 1

solenoid micropumps and a three-way solenoid valve, was proposed for determination of tannins in beverages (Infante, Soares, Korn, & Rocha, 2008). An SIA optosensor was developed for the fluorimetric determination of hydroxytyrosol in several foods (Llorent-Martı´nez, Jime´nez-Lo´pez, Ferna´ndez-de Co´rdova, & Ortega-Barrales, 2013). A miniaturized FIA CL method with gas diffusion has been proposed for determination of phenols in smoked food samples (Vakh & Evdokimova, 2017). A chemically modified electrode was fabricated and used for the simultaneous determination of guanine and adenine in food samples in an SIA-LOV manifold (Wang et al., 2014). A high-throughput method for multidetection of genetically modified organisms has been described using a microfluidic dynamic array (Brod et al., 2014). Analysis of genetically modified crops by DNA-based methods was undertaken in a Fluidigm system from BioMark consisting of a nanoscale network of fluid lines, valves, and chambers under pressure control (Spurgeon, Jones, & Ramakrishnan, 2008). Several flow-based methods have been reported for ethanol determination in distilled spirits including an FIA thermal infrared enthalpimetry method (Santos et al., 2019) and spectrophotometric methods using a membraneless vaporization unit (Ratanawimarnwong, Pluangklang, Chysiri, & Nacapricha, 2013) and a commutated flow procedure (Santos & Reis, 2013).

5.4 Conclusions This review has demonstrated that flow-based methods are particularly useful for the analysis of various food samples. Representative applications are presented for the determination of nutrients (sugars, aminoacids, and vitamins), antinutrients, inorganic species (cations and anions), additives preservatives and adulterants, acidity, antioxidant capacity, pesticides, pharmaceuticals, and other compounds in samples as diverse as meat, fruits, honey, wine and beverages, spirits, milk, etc. The advantages of flow-based analytical methods can be summarized as follows: vast array of operational modes and detection techniques, wide applicability to many sample types and target compounds, scope for sample pretreatment, easy hyphenation to separation systems and multielement detectors, low cost, rapidity, and versatility. The most noteworthy developments in the field include sample pretreatment approaches, multiplex assays, miniaturized systems (microfluidic devices and LOV manifolds), hyphenated systems, and low-pressure separation systems (SIC and FIC).

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Acosta, G., Talio, M.C., Luconi, M.O., Hinze, W.L., & Fernandez, L.P. (2014). Fluorescence method using on-line sodium cholate coacervate surfactant mediated extraction for the flow injections analysis of Rhodamine B. Talanta, 129, 516
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7249. Al Lawati, H. A. J., Al Mughairy, B., Al Lawati, I., & Suliman, F. O. (2018). Enhancing the chemiluminescence intensity of a KMnO4 formaldehyde system for estimating the total phenolic content in honey samples using a novel nanodroplet mixing approach in a microfluidics platform. Luminescence, 33(5), 863
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427. Anthemidis, A. N., & Miro, M. (2009). Recent developments in flow injection/sequential injection liquid-liquid extraction for atomic spectrometric determination of metals and metalloids. Applied Spectroscopy Reviews, 44, 140
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1192. Barreto, I. S., Lima, M. B., Andrade, S. I. E., Araujo, M. C. U., & Almeida, F. (2013). Using a flow-batch analyzer for photometric determination of Fe(III) in edible and lubricating oils without external pretreatment. Analytical Methods, 5, 1040
1045. Batista, A. D., Nascimento, C. F., Melchert, W. R., & Rocha, F. R. P. (2014). Expanding the separation capability of sequential injection chromatography: Determination of melamine in milk exploiting micellar medium and on-line sample preparation. Microchemical Journal, 117, 106
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347. Beltran, B., Leal, L. O., Ferrer, L., & Cerda, V. (2015). Determination of lead by atomic fluorescence spectrometry using an automated extraction/preconcentration flow system. Journal of Analytical Atomic Spectrometry, 30, 1072
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1410. Bukman, L., Martins, A. C., Barizao, E. O., Visentainer, J. V., & Almeida, V. C. (2013). DPPH assay adapted to the FIA system for the determination of the antioxidant capacity of wines: Optimization of the conditions using the response surface methodology. Food Analytical Methods, 6, 1424
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95.

Chapter 6

Categories of food additives and analytical techniques for their determination Fernanda C.O.L. Martins1, Michelle A. Sentanin2 and Djenaine De Souza1 1

Laboratory of Electroanalytical Applied to Biotechnology and Food Engineering (LEABE), Multidisciplinary Research, Science and Technology

Group (RMP-TC), Uberlaˆndia Federal University, Patos de Minas, Brazil, 2Food Analysis and Chemistry Laboratory, Chemical Engineering Faculty, Uberlaˆndia Federal University, Patos de Minas Campus, Patos de Minas, Brazil

6.1

Introduction

The last decades have observed a considerable change in the lifestyle of consumers (including women, men, young, children, and seniors), forcing the food industries to provide adequate food product compositions that extend the shelf life of the food and allow consumers to choose healthier and more nutritious food products. The main alterations in foodstuff production are related to the components added to ensure that the basic requirements of consumers, such as color, flavor, and taste, supply the needs according to the essential nutrients, as well as provide foodstuffs with adequate rheological and microbiological properties. These added components are called additives and can be divided into two groups: food additives and nutritional additives. The food additive group accounts for 85% of the total components added, as shown in Fig. 6.1 (Damodaran & Parkin, 2017; Msagati, 2012). Vitamins, amino acids, minerals, and other nutrients (rutin, linoleic acid, inositol, betaine, and choline) can be found in the composition of the raw material. These compounds play a key role in humans by supporting some metabolic reactions, aiding in nutrient absorption and acting as precursors in the biochemical formation of other important substances. However, during the processing, storage, and commercialization steps, their nutritional value decreases due to undesirable degradation reactions (C¸ehreli, 2018; Kathleen Mahan & Raymond, 2016). Consequently, health organizations, such as the United States Food and Drug Administration and the European Food Safety Authority (ESFA), have recommended the addition of these substances in foodstuffs, promoting the restoration, fortification, or enrichment of the nutritional properties of foodstuffs (EU, 2008; FDA, 2014). The consumption of foodstuffs fortified by nutritional additives does not always promote deleterious health effects, and so this type of additive has not aroused researchers’ interest in the development of analytical methodologies for their determination. There are about 230 different compounds permitted for use as food additives, which are broadly divided into 25 classes, as shown in Fig. 6.1, where each class corresponds to a specific function. For instance, food additives have been used to maintain and/or improve the microbiological, rheological, physicochemical, and sensorial properties of foodstuffs. The adequate quantities of each food additive and their specific uses are determined according to the legislation of each country, as well as food safety politics, which are based on the Codex Alimentarius (Food and Agriculture Organization of the United Nations World Health Organization, 2016). Monitoring the level of food additives is necessary to avoid toxicological and reactive effects in consumers and restrict modifications of food properties. The present chapter describes the categories of food additives and highlights the main analytical techniques employed for their identification and quantification.

6.2

Food additives

Food additives are a chemical class of compounds that are not present in the raw materials’ composition but are intentionally added by food industries during the food processing step. The purpose of their addition to foodstuffs is to Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00006-6 Copyright © 2021 Elsevier Inc. All rights reserved.

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Other nutrients (betaine, choloine, inositol, linoleic, and rutin) Vitamin

Nutritional additives Amino acids

Minerals

15.13% 84.87%

Thickener

Acidity regulator Anticaking agent

Sweetener

Antifoaming agent

Stabilizer

Sequestrant

Food additives

Raising agent Propellant Preservative Humectant Glazing agent Gelling agent Foaming agent

Antioxidant Bulking agent Carbonating agent Carrier Color Color retention agent Emulsifier

Emulsifying salt Firming agent Flavor enhancer Flour treatment and bleaching agent

FIGURE 6.1 Percentage relation of the nutritional and food additives used in food industries.

improve the safety, taste, texture, appearance, or freshness of foodstuffs. The use of food additives must be technologically justified, meaning that no other viable method exists to produce the desired result (Belitz, Groch, & Schieberle, 2009). The concentration, type of additive, and its specific application in different foodstuffs should comply with national legislation. The Food and Agricultural Organization of the United States and the ESFA are the two main governing bodies. These agencies follow the Codex Alimentarius recommendations to assure consumer safety and serve as references for other food legislative authorities worldwide (FAO/WHO/UNU, 2007). Therefore the regulatory agencies and food industries demand rigorous quality control to identify and quantify the 230 compounds employed as food additives. All these compounds, shown in Fig. 6.1, are used with technological functions that include the control of pH, viscosity, stability, and homogeneity, and prevent or delay food spoilage due to the growth of microorganisms. Consequently, the use of food additives can increase the shelf life. Furthermore, the color, smell, and flavor, main sensorial functions, can be controlled, stabilized, and modified by food additives, conferring the product more attractive to consumers. These food additive functions can be employed in the production of the derivatives of milk, vegetables, fruits, cereals, meat, eggs, and other food products (Belitz et al., 2009). Besides, the simultaneous use of different additives can sometimes promote a synergistic effect due to the chemical interactions between the food additives and/or other food components, improving the technological, microbiological, and sensorial properties (Damodaran & Parkin, 2017). Owing to the vast number of compounds used worldwide by food industries, the Codex Committee on Food Additives and Contaminants recommends the classification of food additives according to the international numbering system (INS), which facilitates the identification of food additives in the composition of foodstuffs, replacing the chemical nomenclature. The INS comprises three or four digits, sometimes followed by an alphabetical suffix to characterize individual additives. The existence of the INS is only to identify the type of food additive used, and not present information about their toxicity and permitted levels. Table 6.1 presents the most common additives employed in the food industries, including the INS, the specific functions, the food types in which the additives are used, and possible adverse human health effects associated with their use (Belitz et al., 2009). The food additives in Table 6.1 ensure food safety to consumers, because the levels permitted are based on toxicity studies specifically performed to verify the toxicity mechanisms and any adverse consumer health effects. It is,

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TABLE 6.1 Main type of food additives, including its international numbering system for food additives, classification, main applications in foodstuffs, and possible adverse effects in human health. Type of food additives

Compound/INS

Classes of food additives

Possible adverse effects

50 -Nucleotides

Guanylic acid, 5’ (626), disodium 5’ribonucleotides (635), disodium 5’isosinate (631), disodium 5’- guanylate (627), dipotassium 5’- guanylate (628), calcium 5’- inosinate (633), calcium 5’ribonucleotides (634), calcium 5’guanylate (629), inosinic acid, 5’- (630), potassium 5’- inosinates (632), and disodium 5’-ribonucleotides (635)

Flavor enhancer

Depression, chronic convulsion, and dyspnea

Acetic acid and its salts

Acetic acid, glacial (260), calcium acetate (263), and ethylene diamine tetraacetates (385, 386)

Acidity regulator, antioxidant, preservative, color retention agent, stabilizer, and sequestrant

Modification in the clotting efficiencies, reduction in the body weight, decrease of appetite, ulcer generation in the gastric mucosa, renal failure, and acidosis

Adipates

Adipic acid (355)

Acidity regulator

Hemorrhaging in the small intestine, stomach, and lungs

Ascorbic acid and erythorbic acid (iso-ascorbic acid)

Ascorbic acid (300) and erythorbic acid (315)

Acidity regulator, antioxidant, flour treatment agent, and sequestrant

Decrease in the concentration in the blood uric acid, growth in uric acid in the urine, and disposition of red cells to hemolysis

Aspartame

Aspartame (951)

Sweeteners

Endocrine modification, cancers, mental retardation, and other brain damage

Benzoates

Benzoic acid (210), sodium benzoate (211), potassium benzoate (212), and calcium benzoate (213)

Preservative

Reduction in their creatine output, reduced body weights, and food intake

Brilliant black BN*

NR

Color

Without teratogenic effect

Butylated hydroxyanisole

Butylated hydroxyanisole (320)

Antioxidant

Cancer in the no glandular stomach (forestomach), high incidence of papilloma, squamous cell carcinoma, neoplastic lesions, and hyperplasia

Butylated hydroxytoluene

Butylated hydroxytoluene (321)

Antioxidant

Dose associated toxic nephrosis, reduced weight gain, and increased weights of the liver, brain, and other organs

Caramel

Caramel III—ammonia caramel (150c) and caramel IV—sulfite ammonia caramel (150d)

Color

Without teratogenic effect

Carmoisine

Carmoisine (122)

Color

Increase in organ/body weight and alter biochemical markers in vital organs

Citric acid and its salts

Citric acid (330), ferric ammonium citrate (381), potassium dihydrogen citrate [332(i)], sodium dihydrogen citrate [331(i)], stearyl citrate (484), tricalcium citrate [333(iii)], triethyl citrate (1505), tripotassium citrate [332 (ii)], trisodium citrate 331(iii), triammonium citrate (380), and isopropyl citrates (384)

Acidity regulator, antioxidant, anticaking agent, carrier, color retention agent, emulsifier, firming agent, sequestrant, and stabilizer

Change in the absorption of calcium, modified in the blood chemistry, spleen, and thymus atrophy

Cyclamate

Cyclamic acid [952(i)], calcium cyclamate [952(ii)], and sodium cyclamate [952(iv)]

Sweeteners

Papillary carcinomas of the bladder and kidney tumors

Diacetyl*

NR

Flavor enhancer

Necrotizing rhinitis, necrotizing laryngitis and bronchitis, and peribranchial and peribronchiolar lymphocytic inflammation,

(Continued )

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TABLE 6.1 (Continued) Type of food additives

Compound/INS

Classes of food additives

Possible adverse effects

Erythrosine

Erythrosine (127)

Color

Decreases the uptake of several neurotransmitters and attenuates the suppressive effect of the monitoring of the electric shocks number

Fumaric acid and its salts

Fumaric acid (297), sodium fumarates (365)

Acidity regulator

Without teratogenic effect

Gallates*

NR

Antioxidant

Prostate gland inflammation, anemia, reduction in weight gain, patchy hyperplasia of stomach, retarded growth, anemia, hyperplasia in the outer kidney medulla, and an increment in the cytoplasmic activity, microsomal hepatic drug-metabolizing enzymes, and growth hepatic vacuolization incidence

Glutamic acid and its salts

Glutamic acid (620), monosodium Lglutamate (621), monopotassium Lglutamate (622), calcium di-Lglutamate (623), monoammonium Lglutamate (624), and magnesium di-Lglutamate (625)

Flavor enhancer

Common heartburn in hypersensitive individuals

Lactic acid and its derivatives

Lactic acid (270), potassium lactate (326), sodium lactate (325), calcium lactate (327), ferrous (585), and magnesium lactate (329)

Acidity regulator, antioxidant, bulking agent, color retention agent, emulsifier, firming agent, flour treatment agent, humectant, and thickener

Without teratogenic effect

Malic acid and its salts

Malic acid (296), sodium hydrogen DL-malate [350(i)], sodium DLmalate [350(ii)], calcium malate, and DL-[352(ii)]

Acidity regulator and humectant

Growth retardation, glucosuria, phosphaturia, aminoaciduria, and alteration in the renal proximal convoluted tubes

Phosphoric acid and phosphates

Phosphoric acid (338), sodium dihydrogen phosphate [339(i)], disodium hydrogen phosphate [339 (ii)], trisodium phosphate [339(iii)], potassium dihydrogen phosphate [340 (i)], dipotassium hydrogen phosphate [340(ii)], tripotassium phosphate [340 (iii)], calcium dihydrogen phosphate [341(i)], calcium hydrogen phosphate [341(ii)], tricalcium phosphate [341 (iii)], ammonium dihydrogen phosphate [342 (i)], diammonium hydrogen phosphate [342(ii)], magnesium dihydrogen phosphate [343(i)], magnesium hydrogen phosphate [343(ii)], trimagnesium phosphate [343(iii)], disodium diphosphate [450(i)], trisodium diphosphate [450(ii)], tetrasodium diphosphate [450(iii)], magnesium dihydrogen diphosphate [450(ix)], tetrapotassium diphosphate [450(v)], dicalcium diphosphate [450(vi)], calcium dihydrogen diphosphate [450 (vii)], pentasodium triphosphate [451 (i)], pentapotassium triphosphate [451 (ii)], sodium polyphosphate [452(i)],

Acidity regulator, anticaking agent, emulsifying salt, firming agent, flour treatment agent, humectant, raising agent, sequestrant, stabilizer, and thickener

Change in the osmotic pressure of body fluids due to disturbance in the mineral balance in the body

(Continued )

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TABLE 6.1 (Continued) Type of food additives

Compound/INS

Classes of food additives

Possible adverse effects

potassium polyphosphate [452(i)], sodium calcium polyphosphate [452 (iii)], calcium polyphosphate [452 (iv)], ammonium polyphosphate [452 (v)], and bone phosphate (542) Polysorbates

Polyoxyethylene (20) sorbitan monolaurate (432), polyoxyethylene (20) sorbitan monooleate (433), polyoxyethylene (20) sorbitan monopalmitate (434), polyoxyethylene (20) sorbitan monostearate (435), and polyoxyethylene (20) sorbitan tristearate (436)

Emulsifier and stabilizer

Depression of the central nervous system with a marked reduction in locomotors activity and rectal temperature

Propionate and thio-dipropionates

Sodium propionate (281), calcium propionate (282), thiodipropionic acid (388), and dilauryl thiodipropionate (389)

Preservative and antioxidant

Antihistaminic activity

Saccharins

Saccharin [954(i)], calcium saccharin [954(ii)], potassium saccharin [954(ii)], and sodium saccharin [954(iv)]

Sweeteners

Tumors and carcinogen

Sorbates

Sorbic acid (200), sodium sorbate (201), potassium sorbate (202), and calcium sorbate (203)

Preservative

Increased liver

Sorbitol

Sorbitol [420(i)] and sorbitol syrup [420(i)]

Bulking agent, humectant, sequestrant, stabilizer, sweetener, and thickener

Without teratogenic effect

Sulfites

Sulfur dioxide (220), sodium sulfite (221), sodium hydrogen sulfite (222), sodium metabisulfite (223), potassium metabisulfite (224), potassium sulfite (225), and sodium thiosulfate (539)

Antioxidant, bleaching agent, flour treatment agent, preservative, and sequestrant

Atrophy of bone marrow and visceral organ, asthma attacks, slow growth, other acute allergenic, renal tubular caste, and spectacle eyes

Sunset Yellow FCF

Sunset Yellow FCF (110)

Color

Allergic reactions and induces urticarial

Tartaric acid and its salts

Tartaric acid (334), sodium tartrate [335(ii)], and potassium sodium L (1)-tartrate (337)

Acidity regulator, antioxidant, emulsifier, flavor enhancer, sequestrant, and stabilizer

Without teratogenic effect

Tartrazine

Tartrazine (102)

Color

Allergic reactions, asthma, noted no immunological reactions, inhibit the action of cyclooxygenase, and have no effect on prostaglandin formation

Tertiary butylhydroquinone

Tertiary butylhydroquinone (319)

Antioxidant

Alteration in the growth rate or mortality rate, or histopathological features and organ weight

INS, international numbering system; NR, nanorod. *No considerate by Codex Alimentarius.

therefore, critical to control the type and quantity of food additive employed, regarding the limit dose permitted by legislation (Reserved, 2011). If used in suitable concentrations in the industrial process, these substances can afford consumer benefits, but if used in inadequate concentrations, adverse health effects can occur, such as allergic and idiosyncratic reactions, and compromise the quality of the foodstuff. These effects can occur immediately or slowly after the consumption, provoking local or systemic reactions. The combination of food additives requires extreme caution by industries and consumers, because, in some cases, after the ingestion of foodstuffs containing food additives,

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interactions can occur between these additives, which promote synergistic, additive, and sensitization effects (Klaassen & Watkins, 2015). Some examples of adverse health effects from the misuse or abuse of food additives are high growth and increased liver mass associated with sorbates. In general, however, sorbates are regarded as effective food preservatives due to their antimicrobial activity when used under sanitary conditions and in accordance with good manufacturing practices. The synthetic antioxidant, butylated hydroxyanisole, can cause fetal malformations, chronic allergic reactions, and damage to the metabolic system. The sweeteners, mainly saccharin and sodium cyclamate, have been studied intensively because of suspicions of yielding tumors in the bladder. Many synthetic colors are toxic and present carcinogenic effects, but natural colors do not always guarantee safety, such as curcumin that is 15 times more toxic than tartrazine (Shibamoto & Bjeldanes, 2004). The concentration of the additive and the type of foodstuffs in which it is incorporated determine not only its specific function but also the interactions that occur between additives when used in combination. This way, a food additive can present more than one function and, in turn, exist in more than one class (Watson, 2010). Hereafter, the 25 classes of food additives are defined, along with their specific functions and the main compounds present in each class.

6.2.1 Regulators of acidity Acidity regulators are used for altering the pH of foodstuffs and slow down or inhibit oxidation, reduction, and enzymatic reactions that contribute to decreasing the products’ shelf life. These additives can also restrict microbiological growth, minimizing alterations in the smell, flavor, viscosity, and texture of the foodstuffs. Common acidity regulators are inorganic and organic acids and some salts and bases, which can be used singly or in a mixture. These compounds act in the pH stabilization, improving the color retention, and promote improvements in the sensorial properties due to complexation of metal ions (Damodaran & Parkin, 2017). Some compounds used are phosphates, lactates, and citrates salts, but other buffer system can aid in the protein stabilization, without alterations in the smell and flavor. Other regulators of acidity acids are malic, lactic, fumaric, glacial acetic, citric, and ascorbic acids. The most commons salts used in pH stabilization are the adipates, lactates, phosphates, potassium, carbonates, fumarates, tartrates, and citrates, among others (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.2 Anticaking agents Anticaking agents are compounds used in powdered foods to inhibit the production of agglomerates by preventing the formation of lumps, which change some sensorial-related proprieties due to affecting the foodstuff solubility. The use of these additives is necessary because of the hygroscopic nature of some foodstuffs, which prevents these foodstuffs from flowing when poured and can promote alterations in the properties of the foodstuff during processing and storage. This class of food additives is employed in the processing of whey cheese products, salts, and powered juices and milk, and confectionery sugar, among other foodstuffs. Anticaking agents should not interfere with the final appearance of foodstuffs, and so their choice should be carefully evaluated (Belitz et al., 2009). The most commons compounds used for this goal are mannitol, phosphates, palmitic and stearic acids, silicon dioxide, ferrocyanides, ferric ammonium citrate, cellulose, carbonates, sesquicarbonates, salts of oleic acid, polydimethylsiloxane, calcium silicate, and talc (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.3 Antifoaming agents Antifoaming agents are used for decreasing foam production and rupturing the air bubble covered by a thin layer liquid, thereby promoting the foam instability and consequently the air bubble removal. In practice, fermentative processes produce air bubbles that are stabilized by the addition of surfactants, creating a barrier that blocks oxygen transfer from the air, preventing microbial respiration during aerobic fermentation. The addition of antifoaming agents prevents these problems by promoting a decrease in the integrity of the surfactant barrier (Damodaran & Parkin, 2017). Most common antifoaming agents used in the production of foodstuffs and beverages are silicone-based oils/emulsions, fatty acid esters, and polyesters. However, the excessive use of these substances decreases the shelf life of the foodstuffs, and therefore must be used with caution. Other compounds are silicon dioxides, polysorbates, polyethylene glycol, polydimethylsiloxane, mono- and diglycerides of fatty acids, microcrystalline wax, and calcium alginate (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

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6.2.4 Antioxidants Antioxidants act to decrease or avoid oxidation of the components with oxygen or free radicals, leading to degradation processes. Oxidation occurs due to the presence of molecular oxygen, which removes electrons from components that contain oxygen, nitrogen, or sulpfur elements in their structure, yielding highly reactive free radicals and promoting chain reactions that reduce the nutritional value and provoke undesirable flavor and smell in the foodstuffs. Some raw materials used in the food industries contain natural antioxidants, such as tocopherols and carotenes, which can confer consumer health benefits (Belitz et al., 2009). On the contrary, the high intake of some antioxidants can provoke some diseases, such as degenerative disorders and mutations problems, as well as undesirable changes in the foodstuffs sensorial properties. In addition, the addition of some synthetic antioxidants that have similar chemical structures to some natural antioxidants can exhibit toxicological concerns and, therefore, should be evaluated and controlled by industries and regulatory agencies. Usually, the industrial process exploits the synergism between antioxidants with different mechanisms of action that can increase the shelf life of the foodstuffs (Damodaran & Parkin, 2017). The antioxidants used are butylated hydroxytoluene, butylated hydroxyanisole, tertiary butylhydroquinone, ethylene diamine tetraacetates (EDTA), ascorbic and iso-ascorbic acid, ascorbates and iso-ascorbate, ascorbyl esters, citric acid, citrates, stearyl citrate, citric and fatty acid esters of glycerol, guaiacresin, phosphoric acid, lactates, propyl gallate, stannous chloride, sulphites, tartrates, thio-dipropionates, nitrous oxide, and tocopherols (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.5 Bulking agents Bulking agents can be employed to contribute to the bulk of the foodstuffs, thereby influencing the texture, which is a fundamental sensorial attribute. These low-calorie compounds are added to maintain the volume during the production of foodstuffs, resulting in a reduction in the sugar and fat quantities. Besides, in some solid and semiliquid foodstuffs, the bulking agent increases the bulk of the final product, without modifying the taste or contributing to the energy value (Martins, Sentanin, & De Souza, 2019). Therefore are commonly used processed Euchema seaweed (PES), sodium alginate, mannitol, methylcellulose, propylene glycol alginate, microcrystalline cellulose, hydroxypropyl methylcellulose, sodium lactate, and polydextrose (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.6 Carbonation agent Carbon dioxide (CO2) is a carbonation agent added to confer a tangy taste, effervescence, provide slight acidity and improve the tactual perception of carbonated beers, fruit juices, wines and soft drinks. It is added to improve the sensorial characteristics of such beverages, according to specific legislation and the processing steps. An increase in the pressure and a decrease in the temperature are required to improve the rate of CO2 solubility and conversion into carbonic acid when the container is opened (Belitz et al., 2009). In the production of carbonated beverages, the concentration of CO2 is one of the most determinative parameters contributing to its flavor and the inadequate quantities of CO2 liberated are indicative of a low-quality beverage (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.7 Carrier Carriers are compounds incorporated into the foodstuff formulations to help in the dilution, dispersion, dissolution, or physical alteration of other food additives previously added that could present low solubility and, ultimately, compromise the quality and safety of the foodstuff. These substances are employed only to facilitate the application and handling of other food additives (Martins et al., 2019). The carriers employed are castor oil, cyclodextrins, gum Arabic, magnesium hydroxide carbonate, polyethylene glycol, PES, and silicon dioxide (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.8 Appearance control and clarifying agents Clarifying additives act in the removal of particulate materials and in the prevention of oxidative reactions that occur during long-term storage. The use of appearance control agents hinders solid sedimentation, as well as viscosity and

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density changes, and dispersion of the flavoring in oil compounds. Both classes are usually incorporated into fruit juices, beers, and wines, as they directly influence the turbidity and color. Neither class exists in the Codex Alimentarius, despite their widespread usage in foodstuff production (Belitz et al., 2009). The inadequate choice of clarifiers can unintentionally modify the content of polyphenolic substances and produce undesirable effects during handling of the foodstuff. The main clarifying agents used in the wine industries are the bentonite and montmorillonite clays, which selectively prevent protein precipitation. In beer production, polyvinyl pyrrolidine and polyamides act to remove some substances responsible for undesirable flavor. In the beverage industries, some types of gelatin are used as clarifiers in the removal of tannins, proanthocyanins, and other polyphenols (Belitz et al., 2009).

6.2.9 Colorants The color is the first characteristic considered by consumers in evaluating a product. It influences the flavor and sweetness perception and is, thus, considered an important foodstuff quality factor. Nonetheless, the handling and storage of food products can promote undesirable color modifications due to the water activity, presence of metal ions, light, pH, and oxygen (Msagati, 2012). Some natural pigments derived from the tissues of plants (carotenoids, carotenes, riboflavins, chlorophylls, and betalains, among others) and animal cells can impart color to foodstuffs and, if added in adequate concentrations, do not produce residual taste or decrease the health benefits. In the industrial processing of foodstuffs, it is common to use artificial colors due to their stability, low cost, and facilitation in the processing steps. However, some colors can promote cumulative health effects, and therefore their use is banned in some countries (Belitz et al., 2009). Tartrazine, riboflavins, zeaxanthin, iron oxides, lutein from targets erect, caramel III (ammonia caramel), caramel IV (sulphite ammonia caramel), caramel I (plain caramel), caramel II (sulphite caramel), chlorophylls and chlorophyllins, carotenoids, indigotine (indigo carmine), sunset yellow, bixin-based, brilliant black, erythrosine, carmine, ponceau 4R, allura red, canthaxanthin, amaranth, beta-vegetable, norbixin-based, annato extracts, fast green, azorubine (carmoisine), grape skin extract, copper complexes, brown, curcumin, brilliant blue, and carotene are the colors employed in food industrial process (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.10 Color retention agents Color retention agents stabilize, preserve, or intensify the original color of foods, during the processing and storage steps. These compounds are widely used to produce the feeling of the foodstuff coolness and security and assist consumers to decide by foodstuffs purchase (Damodaran & Parkin, 2017). The compounds used with color retention function are aluminum ammonium sulfate, EDTA, stannous chloride, magnesium hydroxide, magnesium carbonate, nitrites, carbonate, ferrous lactate, magnesium chloride, ferrous gluconate, and magnesium hydroxide (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.11 Emulsifiers Emulsifiers are added to ensure the sensorial, physicochemical, and rheological properties of the foodstuff throughout its shelf life and promote stability in heterogeneous systems. These substances are responsible for the uniform dissipation of the scattered phase (droplets) and uninterrupted phase (bulk) in a water and oil mixture, improving the acceptation of the foodstuffs by consumers. Besides, emulsifiers yield stability against phase separation, increase shelf life, control texture, rancidity reactions, and viscosity, retain the properties of fat structures, and promote the solubilization of flavored substances (Msagati, 2012). Mono- and diacylglycerides and their derivatives are the emulsifiers most employed in food industries worldwide, but citric acid, tartaric acid, ethylene oxide, and succinic anhydride afford reactions with soluble fatty acids, producing different emulsifiers with distinct properties. Besides this, propylene glycol esters of fatty acids, agar, beeswax, castor oil, dextrin, guar and Arabic gum, and hydroxypropyl cellulose, sucrose esters of fatty acids, hydroxypropyl starch, lecithin, alginic acid, methyl and ethyl cellulose, monostarch phosphate, pectin, trisodium phosphate, ammonium alginate, polyethylene glycol, polyoxyethylene stearates, polysorbates, carrageenan, stearyl citrate, and trisodium citrate among others are used as emulsifiers in many food industries (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

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6.2.12 Firming agents Firming agents are additives that aid in the firmness and texture of foodstuffs, acting to stabilize the raw materials from vegetables and fruits. In addition, their use contributes to maintaining the rheological properties throughout the processing steps, even when temperature variation occurs, besides inhibiting the bitter flavor formation. The combined use of firming agents with calcium and aluminum salts during the industrial process retains the shape of pectin derivatives during the heating step. The action of firming agents is strongly influenced by the pH of the medium, which is maintained by lactic and acetic acid rises (Belitz et al., 2009). Other firming agents used are calcium chloride, aluminum ammonium sulfate, calcium sulfate, curdlan, phosphates, calcium lactate, potassium chloride, magnesium sulfate, and tricalcium citrate and calcium hydroxide (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.13 Flavor enhancers Flavor enhancers increase the flavor perception by consumers due to their action at the molecular receptor level, promoting alterations in the sensorial functions or neurological sensations. The use of flavor enhancers is necessary, because the acceptance of a foodstuff depends on its flavor. The addition of these substances can occur throughout the processing steps, contributing to improving the palatability, indicated by differences in the food flavor, body, complexity, fullness, continuity, and thickness, considered as sensorial properties. Moreover, flavor enhancers can aid in the consumers’ perception by influencing the smell, softness, creaminess, and succulence of the foodstuff (Msagati, 2012). Glutamic acid, sulfites, neotame, dipotassium 5’-guanylate, calcium 5’-guanylate, maltol, disodium 5’-inosinate, ethyl maltol, tartrates, calcium 5’-ribonucleotides, guanylin acid, calcium 5’-inosinate, disodium 5’-ribonucleotides, calcium di-L-glutamate, disodium 5’-guanylate, and benzoyl peroxide among others are the most commons flavor enhancer used in food industrial process (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.14 Bleaching and flour treatment agents Bleaching and flour treatment agents accelerate the ageing and maturation of flour, promoting a better appearance in the flour products and fermentation process. These substances are also used to promote the conversion of the carotenoid pigments to free radicals during the processing steps. The enzymatic inhibition and pH control afforded by bleaching and flour treatment agents facilitate the manipulation of the flour of pasta and improve their color and texture (Damodaran & Parkin, 2017). In the industrial process to foodstuff production, the bleaching and flour treatment agents used are citric and fatty acid esters of glycerol, benzoyl peroxide, bromelain, azodicarbonamide, polysorbates, alpha amylases, calcium sulfate, calcium oxide, calcium lactate, chlorine, and carbohydrase (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.15 Foaming agents Foaming agents promote the dispersion of molecules and improve the sensorial characteristics and, consequently, the consumers’ acceptance. These compounds are employed to stabilize foams intentionally generated during the processing steps as an essential component of the foodstuff. Foaming agents are employed as a prerequisite in the guarantee of the adequate sensorial properties of foodstuffs, because they promote chemical interactions with other components of the food, increasing the preservation and maintenance of its structure (Belitz et al., 2009). The foaming agents normally employed are polysorbates, sucrose esters of fatty acids, microcrystalline cellulose, calcium alginate, nitrogen, carbon dioxide, nitrous oxide, ammonium alginate, xanthan gum, and methyl ethyl cellulose (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.16 Gelling agents Gelling agents are used for gel formation, furthering alteration in the viscoelasticity property of viscous liquid and solid elastic products. These compounds provoke the formation of three-dimensional structures, providing suitable homogeneity and stability in the foodstuffs. Furthermore, they can enhance the viscosity, which is often a quality indicator of the foodstuff (Msagati, 2012).

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The physical characteristics of a food gel are influenced by the processing temperature, interactions between the ingredients, texture, the quantity of gelling agents, interest rheologic, pH, and functional characteristics. Furthermore, these compounds show other functions, such as flocculating, bulking, encapsulating, binding, bodying, whipping, clarifying, swelling, clouding, coating, and film-forming, and also act as crystallization and syneresis inhibitors, fat mimetics, and suspension, emulsion, and foam stabilizers (Belitz et al., 2009). Some hydrocolloids operate as structuring/gelling agents by interactions particularly interchain combinations in conformationally ordered junction zone. Others gelling agents employed are Tara gum, potassium alginate, konjac flour, calcium alginate, curdlan, PES, sodium carboxymethyl cellulose, pectin, alginic acid, sodium alginate, and ammonium alginate (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.17 Glazing agents Glazing agents are used throughout the manufacturing process to protect the external surface of the foodstuff, improving the shelf life of fruits and vegetables. In addition, glazing agents impart a brighter exterior to fruit candies, increasing their consumer appeal (Martins et al., 2019). Some compounds used as glazing agents are sucrose esters of fatty acids, konjac flour, mineral oil, polyvinyl alcohol, gum Arabic, polyvinylpyrrolidone, beeswax, carrageenan, carnauba wax, sucrose oligoesthers, candelilla wax, talc, castor oil, propylene glycol, microcrystalline wax, polyethylene glycol, hydrogenated poly-1-decenes, and PES (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.18 Humectants Humectants are additives that protect the foodstuffs from moisture loss during the transport and storage steps, which can change the sensorial perception and final texture due to unpleasant dryness or hardening of foodstuffs (Martins et al., 2019). The use of humectants in the formulation of foodstuffs susceptible to dryness contributes to increasing the shelf life without adversely affecting the final appearance of the product. They are used as humectants in foodstuffs production the glycerol, mannitol, phosphates, polydextroses, PES, propylene glycol, sodium malate, and sodium lactate (Damodaran & Parkin, 2017).

6.2.19 Preservatives and antimicrobial agents Preservatives/antimicrobial agents are compounds that if used in appropriate concentration can prevent the deterioration of foodstuff and guarantee its safety. Antimicrobial agents increase the shelf life by controlling the microbiological growth and preventing against enzymatic and nonenzymatic reactions that promote degradation of foodstuffs. Preservatives can restrict enzyme-catalyzed reactions, nonenzymatic browning, and the reversible disruption of proteins, producing derivatives from the sulfur dioxide and sulfites. The efficacy of preservatives/antimicrobial agents depends on their solubility and the pH of the surrounding medium. If used at elevated concentrations, these additives can provoke changes in viscosity, the formation of intense odors, decrease the solubility and color retention, and cause an undesirable taste of foodstuffs (Msagati, 2012). EDTA, nitrites, dimethyl decarbonate, lauric alginate ethyl ester, natamycin, nisin, benzoates and hydroxybenzoates, lysozyme, orthophenyl phenols, isopropyl citrates, potassium acetate, sulfites, calcium acetate, trisodium phosphate, sodium diacetate, calcium propionate, carbon dioxide, hexamethylene tetraamine, sorbates, propionic acid, and sodium acetate are the preservatives/antimicrobial agents used in industrial process of foodstuffs (Damodaran & Parkin, 2017).

6.2.20 Propellants Propellants are additives that are in intimate contact with the foodstuffs and are used to induce the expansion and stabilization of some sprays, foams, and liquids forms. These substances can exist as liquids and gasses, and their choice depends on the desired physicochemical properties of the foodstuff (Damodaran & Parkin, 2017). These propellants should be nonflammable, nontoxic, and low cost, and not impart any undesirable color and flavor to foodstuffs. Only CO2, nitrogen, and nitrous oxide present these properties and are used in foodstuff production (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

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6.2.21 Raising agents Chemical leavening or raising agents are substances added to improve the volume and texture of doughs or batters. Some raising agents act in concert with certain natural compounds and proteins present in the food formulation, strengthening the dough structure, and elasticity, which, consequently, enhances the overall quality and texture of baked products. A chemical leavening system that releases CO2 and/or triggers the production of gas via specific enzyme actions improves the volume and texture of bakery products. The concentration of the raising agent and temperature influence the foodstuff’s characteristics directly, inhibiting the development of unpleasant flavors and assisting with pH stabilization in the final products (Belitz et al., 2009). Therefore the raising agents used in foodstuff production are phosphates, sodium dihydrogen phosphate, gluconic delta-lactone, ammonium hydrogen carbonate, sodium hydrogen carbonate, sodium carbonate, sodium sesquicarbonate, ammonium carbonate, and sodium aluminum phosphates (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.22 Sequestrant or chelating agents The presence of some metal ions in the raw materials can accelerate the degradation reactions of foodstuffs. Compounds that react with these metal ions by complexation reactions are called sequestrates or chelating agents and are responsible for slowing the alterations that occur in the flavor, aroma, and colors of foodstuffs. The compounds used as sequestrates or chelating agents in the industrial processing of foodstuffs present an unshared pair of electrons in their chemical structure (H2PO3, -COOH, -OH, -S-, -NR2, -SH, -O-, and C 5 O) that can react with metal ions, producing a metallic complex, whose stability is influenced by the pH of the medium (Damodaran & Parkin, 2017). EDTA, tartrates, isopropyl citrates, sodium gluconate, diacetyl tartaric and fatty acid esters of glycerol, calcium sulfate, gluconic delta-lactone, acetates, phosphates, citrates, lactic and fatty acid esters of glycerol, citric acid, diacetates, and citric and fatty acid esters of glycerol are the sequestrates or chelating agents used in foodstuff production (Food and Agriculture Organization of the United Nations World Health Organization, 2016). The use of this class of food additives without control and/or in inadequate concentrations can provoke serious risks to consumers’ health due to that these compounds also can produce metals complexation from human body (Reserved, 2011).

6.2.23 Stabilizers Stabilizers are compounds added during the industrial process to inhibit the formation of crystals, increase the viscosity, and avoid losses of texture quality, in turn, enhancing the final product appearance. Thickening, gelling, suspending, and glazing agents are all examples of stabilizers, incorporated to ensure the homogeneity and prevent the phase separation of foodstuffs. The proprieties of a stabilizer depend on their molecular weight and concentration, as well as the temperature, pH, and ionic forces of the medium (Msagati, 2012). The stabilizers are the food additive class containing the highest number of compounds authorized for use in foodstuff production. Some compounds are citric and diacetyl tartaric fatty acid esters of glycerol, polyvinylpyrrolidone, salts of myristic, starches, agar, palmitic and stearic acids with ammonia, carob bean gum, alginic acid, aluminum ammonium sulfate, alginates, beeswax, EDTA, dextrin, PES, gum Arabic, celluloses, phosphates, magnesium chloride, mannitol; pectin, polydextroses, polysorbates, calcium, potassium, sodium, tartrates, citrates, and xanthan gum, among others (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.24 Sweetener Sweeteners are artificial or nonnutritive compounds added in the industrial processing of foodstuffs to increase the perception of the sweet taste. These compounds are zero- or low-calorie sugar alternatives that, if used in proper concentrations, exhibit food safety and low residual taste. Nutritive sweeteners are constituted by diverse types of carbohydrates that have less energy (kilojoule or calorie) than sugar but are not calorie-free. Sweeteners are chemically classified as polyols, chlorosaccharides, low-calorie sweeteners, sulfonamides, and peptides, which are much sweeter than sucrose, so only low amounts are needed. High concentrations can cause residual taste and flavor alterations (Belitz et al., 2009). Chlorosaccharides show great crystallinity, and stability and solubility at different temperatures, without bitterness. Polyols can handle the viscosity, crystallization, and texture, and aid to decrease the water activity, consequently improving the water rehydration properties of foodstuffs. However, in high concentrations, the sweeteners can provoke diseases and allergies, according to their toxicological level and consumers’ susceptibility (Msagati, 2012).

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Sweeteners most employed are steviol glycosides, saccharins, mannitol, acesulfame potassium, aspartameacesulfame salt, aspartame, cyclamates, alitame, and sucralose (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.2.25 Thickener Thickeners are compounds used in the food processing steps to assist in the foodstuff texture and viscosity, in turn, improving the perception of the food quality by consumers. These hydrophilic additives are used to stabilize and/or prevent sedimentation, and disperse compounds in suspensions of the raw materials and final foodstuffs. The most common thickeners are gums, starches, and pectin (Martins et al., 2019). Salts of the alginic acid, starches, alginates, cyclodextrin, phosphates, glycerol, celluloses, mannitol, glycol lactates, acetates, and succinates are the most commons compounds used as thickeners, jointly with PES, agar, and the gum Tara and xanthan (Food and Agriculture Organization of the United Nations World Health Organization, 2016).

6.3

Steps in the analysis of food additives

In recent years, some researchers have demonstrated that the cumulative consumption of food additives sometimes leads to obesity, allergies, diabetes, metabolic disorders, and several other adverse effects, as shown in Table 6.1 (Shibamoto & Bjeldanes, 2004). Ensuring the safe use of food additives requires a strict food safety policy. Every country has legislations that cover standards that ensure adequate quality control in industrialized foods, including the identification and quantification of permitted food additives used during the handling steps. Besides, the strict control in the quality of foodstuffs enables the identification and quantification of any illegal compounds employed by every industry to hide food degradation or inappropriate procedures performed during the industrial process. The food additives present in raw materials and intermediate compounds generated from industrial processes and foodstuffs need to be carefully identified and quantified. For this, environmentally friendly analytical techniques are employed that present adequate quickness, operational security, selectivity, sensitivity, reliability, and, mainly, low cost (Wrolstad et al., 2005). There are three broad classes of instrumental techniques used in food additives analysis: spectroscopic, chromatographic, and electroanalytical techniques. Additionally, volumetric titration, a classical technique, can also be used in food quality control. In all instrumental techniques, it is necessary to evaluate adequately some of the steps involved, which are very well defined and encompass the sampling, sample preparation, pretreatments, and the analysis and interpretation of the results, verified by analytical parameters or validation parameters, as indicated in the flowchart shown in Fig. 6.2, which determine the success of food additive analysis (Mitra, 2004). Some steps are operationally easy and follow specific protocols, while other steps are complex and need more qualified professionals. The following text will describe the basic steps related to food additives analysis of various foodstuffs, including the sampling, sample preparation, choice of technique, and evaluation of the obtained data.

6.3.1 Sampling In analyzing food additives, the sample may be solid or liquid, wet or dry, and homogeneous or heterogeneous in composition. The sample integrity, homogeneity, and representativeness are vital for providing meaningful and reliable results. The sampling procedure choice should separate a sample quantity that represents every inherent characteristic present in the raw materials and foodstuff (Nollet & Toldra´, 2017). The analyst can collect and blend several small increments of a foodstuff sample to obtain a representative of the entire foodstuff. However, this sampling procedure is time-consuming and does not permit the obtention of a statistically representative sample. A more practical, accurate, and reproducible approach is to collect a portion of a foodstuff sample of adequate size, which has a composition that represents the entire foodstuff. This representative portion is then reduced to a fine mix for analysis (Curren, King, & Barcelo´, 2002). The sampling steps are performed using a detailed protocol or sampling plan, previously optimized by statistical techniques in order to guarantee a samples’ representativeness, producing analytical results with suitable reliability. These sampling plans depend on the sample size and number, the sampling frequency and procedure, the personnel undertaking the sampling, the analysis that will be performed, the components’ variability, and the cost of the analysis of each foodstuff. Occasionally, other considerations are the specific legislations that regulate the use and analysis of

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Continuous Sampling Manual Homogenization Sample preparation

Size reduction Prevention of the composition changes Liqid–liquid extraction

Dilution Simple solvent extraction

Solid-phase extraction Sample pretreatments

Enzymatic extraction Cloud point extraction

pH adjustment

Supercritical-fluid extraction

Analytical techniques Sensitivity

Linearity

Selectivity

Range concentration Analytical parameters

Accuracy

Detection limit Precision Quantification limit

Robustness

FIGURE 6.2 Flowchart indicating the main steps in food additives analysis.

the food additives, showing the sampling plan and the quantity of sample necessary to realize the chemical analysis (AOAC, 2019; Nielsen, 2014). Preliminarily, the sampling plan indicates that the required sample size depends on the source of the food additive, indicated by the quantity of the foodstuff sample maintained during the processing steps. A sufficiently sizeable foodstuff sample should be collected and analyzed to enable measuring small quantities of the target food additive and suit the sensitivity of the chosen analytical method. The sampling steps correspond to the largest relative errors in the chemical analysis, so careful attention is required by the analyst. Sampling can be realized during or after the industrial process, in the storage and transportation steps of the foodstuff. The sampling methods may be manual or continuous. A receptacle, such as a probe or a tube, can be used to capture the foodstuff sample. Openings placed at intervals in the tube allow for simultaneous sampling at different depths of the foodstuff. Vast quantities of the foodstuff sample can be reduced using a riffle cutter, which is a device with equally spaced dividers to divide the foodstuff sample flow. Other proportional dividers include the straight-line sampler and the spinning riffle. Drill-type devices can be used to collect small quantities of a solid foodstuff sample. For liquid foodstuffs, the sample is removed using a syringe-type sampler or by submerging a receptacle under the liquid’s surface (Pomeranz, 1994).

6.3.2 Sample preparation After the sampling step, the samples must be homogenized before analysis to ensure insignificant differences among the samples. This approach, shown in the flowchart of Fig. 6.2, is defined by the Association of Official Analytical

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Chemists according to the foodstuff characteristics and the assay to be realized, but typically involves homogenization, size reduction, and preventing compositional changes (AOAC, 2019). Homogenization is achieved using blenders, mixers, slicers, and grinders, or by applying enzymatic methods (lipases, cellulases and proteases) or chemical methods (detergents and strong acids or bases). The physical state of the foodstuff, such as liquid, semisolid, and solid, defines the homogenization and size reduction modes. For solid or semisolid foodstuff samples, the size reduction methods include mechanical mixing, stirring, macerating, agitating, crushing, pulverizing, mincing, grinding, pressing, chopping, rolling, or other adequate procedures of sample comminution. A mortar and pestle, tissue grinder, meat mincer, bowl cutter, or blender is used for moist samples, while mortars and pestles or mills are employed for dry samples. For liquid samples, the size reduction is performed by mixing in magnetic stirrers or sonic oscillators. The analyst must ensure that no significant changes occur in the sample composition, from the moment of sampling until the time of the chemical analysis. Changes in sample composition can be promoted by enzymatic, chemical, microbial, or physical activities, and must be controlled to ensure the representativeness of the foodstuff sample. This control is effectuated by enzymatic inactivation, lipid protection, and preservation against microbial growth and contamination and physical changes. Some foodstuffs contain enzymes, which promote degradation of the food additives that will be analyzed. To eliminate or control these reactions, it is necessary to inactivate the enzyme by pH control or salting-out effects, heat denaturation, frozen storage, or the addition of reducing reactants. The lipids present in foodstuff samples must be safeguarded against oxidation by storage in liquid nitrogen or vacuum or by incorporating antioxidants that do not interfere with the food additive analysis. Microbial growth and contamination, which arise from the use of nonsterilized materials or the nonhygienic handling of foodstuffs, can modify the composition of the samples. Drying, freezing, and chemical preservatives are powerful methods of preserving foodstuff samples. The approach depends on the probability of contamination, the storage requirements, shelf life, and the analysis to be realized.

6.3.3 Sample pretreatments All foodstuffs exhibit complex compositions of carbohydrates, lipids, proteins, salts, and food additives, which can complicate the food additive analysis. Therefore the determination of food additives often requires pretreatment of the foodstuff sample to remove possible interfering compounds present in the sample composition. Ideal pretreatments are quick, simple, inexpensive, and environmentally friendly (Wrolstad et al., 2005). However, it is not always possible to apply a pretreatment mode with all these features because of the complexity of the foodstuff sample. Liquid samples are often free of matrix interferences, and so the pretreatment tends to be simplified. Solid foodstuffs usually require more than one pretreatment step to remove all interfering compounds. The simplest foodstuff sample pretreatment includes dilution, pH adjustment, and simple solvent extraction. The dilution is necessary for foodstuff samples where the food additive is present in a concentration beyond the linear detection range of the chosen analytical technique. The pH adjustment can provide optimized ionization of the analytes for solidphase extraction (SPE), including neutral species for reversed-phase chromatographic separation and ionic species for ionexchange chromatographic separation. The pH adjustment also contributes to stabilizing pH-labile compounds, reduces matrix interferences, and eliminates protein binding by inducing protein precipitation. Simple solvent extraction relies on the solubility differences between the food additive and other foodstuff components in a solvent or solvent mixture. The more complex pretreatment steps are related to the use of liquidliquid extraction (LLE) and SPE. LLE, also known as solvent extraction or partition extraction, partitions the food additive between two immiscible solvents with different polarities (Christian, Sandy Dasgupta, & Schug, 2014), typically water or an aqueous mixture and a nonpolar organic solvent. It is often conducted at a pH where the food additive is nonionized to facilitate its transfer into the organic phase. The sample is maintained in contact (mixed) with the solvent for a certain time, followed by a phase separation step. The extraction solvent and extraction conditions are chosen to maximize the difference in solubility of the food additive, which depends on its chemical affinity or distribution coefficient (Taylor, 2013). In food analysis, LLE is frequently used to separate hydrophilic and lipophilic food additives and is accompanied by a protein denaturation step performed using acid or organic solvent. These steps promote an acidbase partition where the acid solution obtained is directly extracted using an organic solvent, such as ethyl acetate, diethyl ether, or dichloromethane, in order to remove neutral or acidic compounds that can interfere with the analytical results. If the sample is homogenized in an organic solvent, the food additives are extracted with hydrochloric acid.

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One of the drawbacks of LLE is the extensive organic solvent consumption, conferring high cost of the analysis, and a large volume of residues. For this reason, LLE has been largely replaced by liquid phase-based microextraction, resulting in the reduced consumption of organic solvents, simplified and rapid operation, and high enrichment factor (Campillo, Lo´pez-Garcı´a, Herna´ndez-Co´rdoba, & Vin˜as, 2018). SPE also minimizes the use of organic solvents. In SPE, a food additive or other compound of interest is separated or removed from a mixture of compounds by selective partitioning of the target compound between a solid phase (sorbent) and liquid phase (solvent) (Majewska, Krosowiak, Raj, & Smigielski, 2008). In practice, in SPE, the solvent extract or liquid sample is passed through the sorbent that is loaded in a separate cartridge and selectively retains the target analyte. The solid phase is next washed by another solvent, and then, a stronger solvent that will remove the target analytes. The conditioning is fundamental to the effectiveness of the surface area and the minimization of interfering compounds to avoid the interactions between solid-phase material and food additives. The sorbent and solvent are selected based on the foodstuff components and food additive polarity, which determine the SPE separation mechanism, which generally includes normal-phase, reverse-phase, ion-exchange, and sizeexclusion separations (Taylor, 2013). The normal-phase separation is useful for water-sensitive compounds. The food additive and the sorbent are polar, and the solvent is nonpolar. Retention of the food additive occurs as a function of the interactions between the polar functional groups in the food additive and polar groups in the sorbent used. The reverse-phase mechanism involves a polar or moderately polar food additive, dissolved in a polar solvent, and a nonpolar sorbent. Ion-exchange separates charged (negative or positive) food additives in solution. It is principally based on the electrostatic attraction between the charged functional groups present in the food additive structure with the charged groups linked to the sorbent material, usually a silica surface. The pH of the foodstuff sample, the ionic strength, the use of organic solvents, and the flow rate of the solvent determine the efficiency of ion-exchange separations. Finally, size-exclusion separates molecules based on molecular size, where the sorbent material is a hydrated carbohydrate polymer that significantly retards the molecules with low molecular weight. Desalting and buffer exchange are the more common applications of size-exclusion. These applications separate substances with very large size differences, such as proteins from salts (Taylor, 2013). In comparison with LLE, SPE requires less operation time and cost, and enables an automated process, thereby eliminating errors associated with inaccurate measurements. Moreover, some materials employed in SPE can improve the analytical selectivity and sorptive capacity and enhance physicochemical and mechanical stability. To facilitate and guarantee the efficiency in the pretreatment steps, commercial cartridges are employed, which are chosen based on the physicochemical properties of the food additive and the foodstuff sample, as described above (Taylor, 2013). Other more sophisticated techniques, including immunoaffinity extraction, molecularly imprinted polymers, and solid-phase microextraction, have also been widely researched as methods of isolation, purification, and preconcentration of food additives (Samaddar & Sen, 2014). Alternative sophisticated pretreatments include enzymatic-assisted extraction (EAE), cloud-point extraction (CPE), and supercritical-fluid extraction. EAE requires little or no organic solvent and is employed to improve the yield and quality of the extracts in complex samples, with a high extraction efficiency of bioactive components, such as polyphenols, pigments, and peptides in foodstuffs. Before EAE, tissue maceration and disintegration of the cells and internal structure of the sample are necessary to facilitate the liberation of the target compounds. The enzyme incubation time, particle size of the sample, and the pH and temperature should be controlled to obtain the best pretreatment efficiency. EAE permits a rapid extraction, high recovery, reduced or no solvent use, and low energy consumption, which makes it an eco-friendly pretreatment. Moreover, it offers the possibility of high specificity in the analysis of food additives (Nielsen, 2014; Pietri, Gualla, Rastelli, & Bertuzzi, 2011). CPE is a highly efficient pretreatment technique used in the separation and preconcentration of food additives as an alternative to conventional LLE and SPE extractions. It is simple, provides both high recovery and enrichment (preconcentration) factor, is low cost, and, due to the substitution of solvents by surfactants, reduces the use of organic solvents. CPE is based on the cloud point, that is, the solution temperature at which two immiscible phases form, which can be promoted by the use of surfactants and a salt solution (Samaddar & Sen, 2014). Micelles are produced, resulting in a viscoelastic solution (Samaddar & Sen, 2014). Food additives interacting with micellar systems can thus be extracted or preconcentrated. The CPE efficiency depends on the anionic, cationic, or neutral surfactant employed and is mainly used for food colors. Supercritical-fluid extraction is a pretreatment step that separates components using a fluid as the extracting solvent, under temperature and pressure conditions above the critical point of the fluid (i.e., the equilibrium between gaseous and liquid phases). The low viscosity solvent and high diffusivity of supercritical fluids confer decreased analysis time and superior resolution over LLE (Anklam, Berg, Mathiasson, Sharman, & Ulberth, 1998).

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The sample, in the solid or liquid state, is mixed with the solvent and placed in specific equipment where the temperature and pressure are controlled in order to obtain a supercritical fluid. The instrumentation used promotes a considerable decrease in the analysis error and the suitable manipulation of the temperature and pressure of the solvent, besides eliminating the residues after the preparation steps. The most common extractor solvent used in food additive analysis is CO2 (Wrolstad et al., 2005). Each pretreatment technique described above presents a set of unique characteristics, allowing to separate and preconcentrate the target food additive, with advantages and drawbacks that depend on the procedure employed. The choice of pretreatment depends directly on the analytical technique that will be used to in identification and quantification of the target compound, as will be discussed below.

6.3.4 Analytical techniques choices Analytical techniques measure a physical or chemical property, including the volume, mass, radiant energy, radioactivity, heat, retention time, and electrical properties, related to the compound of interest. Several classical gravimetric and volumetric techniques exist, and instrumental techniques, such as spectroscopy, chromatography, and electroanalytical, permit identifying and quantifying organic and inorganic compounds, including food additives (Christian et al., 2014). Each technique presents specific applications, with advantages and disadvantages, as will be described later in this section. The classical techniques are common in food analyses. Although these techniques are simple, rapid, low cost, and do not need sophisticated instrumentation, for suitable accuracy, precision, sensitivity, and selectivity, instrumental techniques are preferred (Skoog, West, Holler, & Crouch, 2014). Many instrumental analytical techniques are available that can be used in the determination of food additives. The choice of technique is based on the chemical structure of the food additive of interest, the physical state and quantity of the sample, the quantity of the food additive that is expected to be detected, the instrumentation and reagents available, skilled labor, the analysis time, and the quantity and type of interferences in the composition of the foodstuff (Nielsen, 2014; Wrolstad et al., 2005). Spectroscopic techniques, based on electromagnetic radiation measurements, are sensitive, present suitable accuracy and precision, produce low detection and quantification limits, robustness, and a high linear range, and permit automation that decreases the systematic errors and the analysis time. However, these techniques demand skilled labor due to the high risk associated with their operation in the sample preparation steps, have poor selectivity, and require sample preparation steps to minimize the interferences. Besides this, spectroscopy requires food additives that absorb or emit radiation in a certain region of the electromagnetic spectrum, which is then measured by specific spectroscopic techniques, as described below (Leszczynski, Kolezynski, & Kro´l, 2019). Chromatographic techniques are based on the displacement of the food additives between a mobile and stationary phase. The time retention and signal intensity measurements are used in the identification and quantification, respectively. These techniques present adequate selectivity due to the preseparation steps of the food additive, and consequently, the results present high resolution and are free of interferences in the analytical signals (Lundanes, Reubsaet, & Greibrokk, 2014). Automated chromatographic techniques permit the acquisition of excellent accuracy and precision of analysis. Nonetheless, highly qualified professionals are required to operate the equipment, and the equipment maintenance and data acquisition are costly when compared with spectroscopic and electroanalytical techniques. Chromatographic analysis requires sample preparation and pretreatment steps that increase the time of the analysis and the volume of reactants used. The selectivity is related to the detector employed. The choice of the detector type, and the mobile and solid phases depend on the physical chemistry properties of the food additive (Lundanes et al., 2014), which will be explained below. Electroanalytical techniques measure the electrical properties of the food additive, such as its electric potential, current, conductivity, and resistance, which are associated with the food additive identification and quantification. These techniques enable the analysis of samples without preparation steps, are low cost and rapid, with effortless operation, and permit simultaneous analyses. The selectivity and sensitivity are related to the appropriate choice of the working electrode (WE) materials and the mode of the electric potential or current applications, as will be described below (Wang, 2001). Regardless of the analytical technique chosen, all analytical data can be validated via appropriate statistical methods, to ensure reliability. Validation methods are completed to comply with the specifications in the food safety legislations established to ensure the analytical methodology is accurate, specific, reproducible, and rugged. If the analytical method meets the preacceptance criteria and is capable of identifying and quantifying the food additive, with reliability in a suitable range of concentration adequate for foodstuff analysis, it can be considered valid (Wrolstad et al., 2005).

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6.3.5 Analytical parameters or validation parameters The effectiveness of identifying and quantifying food additives is highly dependent on the quality and performance of the analytical techniques used. Method validation and verification provide objective evidence that a method is fit for purpose. For this reason, the performance and limitations of the method, as well as any external interference, must be ascertained prior to its use. The analytical validation parameters include the linearity and concentration range, accuracy, precision, sensitivity, detection and quantification limits, and robustness. The flowchart for validating the analytical data is shown in Fig. 6.2 (Wrolstad et al., 2005). The linearity is the ability of the method to provide analytical responses/signals that are linearly proportional to the concentration of the food additive. It can be evaluated by analyzing five concentration levels of the food additive in at least triplicate experiments. Classical linearity acceptance criteria require the linear regression line to have a coefficient of determination (R2) . 0.998 or correlation coefficient (r) . 0.999. The range of concentration is the interval where the signals are linearly proportional to the concentration of the food additive, for which suitable precision and accuracy of quantitation have been demonstrated. The value is influenced by the nature of the food additive, pretreatment steps, and analytical technique employed (Skoog et al., 2014). The sensitivity is the magnitude of the response caused by a certain amount of analyte. It is related to the slope of the analytical curve, which is used in the calculations of the detection and quantification limits. The higher the slope of the analytical curve, the greater the sensitivity of the analytical method, which includes the pretreatment and chemical analysis. Selectivity establishes that the analytical signal is only measuring what it is intended to measure (Harris, 2009). The methodology detection limit is the smallest amount or concentration of an analyte that can be confidently determined from the background (i.e., instrumental noise). It is calculated as three times the standard deviation of the blank or three times the standard deviation of the analytical curve intercept. The quantification limit is the lowest amount or concentration of analyte measured in the foodstuff sample, which can be quantitatively determined with an adequate level of precision and accuracy. Its values calculated as 10 times the standard deviation of the blank or 10 times the standard deviation of the analytical curve intercept (Christian et al., 2014). The accuracy indicates the resemblance between the true value and the measured value, and can be calculated from experiments using reference materials, comparison with results from other analytical methods and using the recovery curves, prepared using standard addition methods, where the analytical signals are measured in the blank matrix of the samples. The precision is characterized by the repeatability and the internal reproducibility, where the repeatability is determined by carrying out 10 test measurements of the same solution on the same day by the same analytical procedure and the same analyst, and this can be considered as an interlaboratory precision. An interlaboratory precision is 10 repetitions of the experiment on different days with different solutions, but using the same analytical procedure, or different analyst (Christian et al., 2014; Skoog et al., 2014). Robustness and/or ruggedness are related to the reliability of the method, which assists in the verification of factors that contribute with alterations in the results and should not be changed. This parameter monitors the stability of the reagents, samples, and standards, and is a measure of the method to remain unaffected by small deliberate or uncontrolled changes in the experimental conditions, such as the pH, temperature, concentration of the compound, and reaction time (Harris, 2009).

6.4

Analytical techniques used in food additive analysis

The choice of the analytical instrumental techniques in food additives analysis is directly related to the chemical structure of the food additive, the sample matrix, the analytical parameters required, the instrumentation available, and the consumption of reactants (Christian et al., 2014). Other considerations are the analytical frequency, the complexity of the pretreatment steps, and the analysis and operational costs, which will depend on the analysis time and the analyst’s experience. In Fig. 6.3, a set of instrumental techniques commonly used in food additive analysis is shown, which will be described next. Table 6.2 compares the main analytical parameters among different analytical techniques, which will be detailed below.

6.4.1 Spectroscopic techniques The most popular spectroscopic techniques in food additive analysis involve measurements of the intensities of the electromagnetic radiation in the range of the ultraviolet (UV), visible (Vis), and infrared (IR) radiation (Leszczynski et al., 2019; Skoog et al., 2014). Each technique presents specific parameters that should be evaluated in order to obtain selective and sensitive analytical signals for each food additive, as shown in the flowchart presented in Fig. 6.4, and described in detail below.

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FIGURE 6.3 Squematic view of the main instrumental techniques employed in food additives analysis. (a) Spectroscopic techniques. (b) High-performance liquid chromatographic techniques. (c) Gas chromatographic techniques. (d) Electroanalytical techniques.

Ul tr

et / Visible iol av

an

In

fra

red

(A)

Ra

m

Signal registration

n mechanism lum Co

Detector

(B)

Column

Pump

Solvent

n mechanism lum Co

Injector Flux control

(C) Signal registration Column

Detector

Carrier gas Potentiostat

Electrochemical cell

(D) Signal registration

Solid electrode

TABLE 6.2 Comparison of the different analytical parameters or validation parameters from various analytical techniques employed in food additives analysis. Parameters analytical

Spectroscopy

Chromatography

Electroanalytical

Raman

NIR

FLU

UV/Vis

HPLC

UPLC

GC

DPV

SWV

Precision

Poor

Poor

Good

Moderate

Good

Excellent

Excellent

Good

Excellent

Accuracy

Poor

Poor

Good

Poor

Good

Excellent

Excellent

Good

Excellent

0.101

0.101

0.0011

1.0100

0.1010

0.0101

0.00101

1.0  10

0.00010  10

Sensitivity (μmolL )

0.10

0.10

0.001

1.0

0.10

0.010

0.0010

1.0

0.00010

Selectivity

Good

Good

Good

Poor

Good

Good

Excellent

Poor

Good

Robustness

Good

Good

Moderate

Poor

Good

Good

Excellent

Poor

Good

Analytical frequency

Fast

Fast

Fast

Fast

Slow

Slow

Slow

Moderate

Moderate

Preparations of sample

Without

Without

Little

Little

Enough

Enough

Enough

Little

Little

Cost

Moderated

Moderated

Moderated

Moderated

Highest

Highest

Highest

Low

Low

Operationally

Moderated

Moderated

Easy

Easy

Difficult

Difficult

Difficult

Easy

Easy

21

Linear range (μmolL ) 21

DPV, differential pulse voltammetry; FLU, fluorescence; GC, gas chromatography; HPLC, high-performance liquid chromatography; NIR, near-IR; SWV, square-wave voltammetry; UHPLC, ultra performance liquid chromatography; UV/Vis, ultraviolet/visible.

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FIGURE 6.4 Flowchart indicating the main steps in the spectroscopic techniques.

6.4.1.1 Ultraviolet/visible spectroscopy Each technique presents specific parameters that should be evaluated in order to obtain selective and sensitive analytical signals for each food additive, as shown in the flowchart presented in Fig. 6.4 and described in detail below (Hoffman, 2005). The source of the electromagnetic radiation, which can be heat, electric discharge, chemical reaction, or light, produces energy in the form of photons that can be absorbed by the compound of interest. However, a portion of radiation across the recipient containing the solution of analysis, and is measured in a light detector, that transform light transmitted in light absorbed, or absorbance, where their intensity is directly related to the quantity of the target compound, and wavelength where the occurs the absorption maximum is used in the identification (Harris, 2009; Leszczynski et al., 2019). The instrumentation is of low cost and very simple, with straightforward operation, finding wide use in food quality control. As radiation sources, lamps with a wavelength in the range from 180 nm to 2.5 μm permit analysis in the nearIR (NIR), as shown in the flowchart of Fig. 6.4 (Colthup, Daly, & Wiberley, 1990; Leszczynski et al., 2019). The solvent can absorb light in the same wavelength range of the food additive, in turn, influencing the absorptivity measured, so the solvent choice is crucial. The most common solvents are water, ethanol, and acetonitrile. It is essential to analyze the blank solution, with only the solvent present, in order to observe the light loss at the interfaces by reflection or scattering (Skoog et al., 2014). A monochromator, consisting of a prism, diffraction grating, or optical filter, is inserted between the light source and the sample to select the wavelength at which the UV/Vis radiation absorption measurement is performed. The sample is placed in a cell, called a cuvette, which usually has a pathlength, that is, an internal distance between the parallel walls, of 1 cm. This parameter is used in the BeerLambert law, which relates the light absorbed to the sample concentration of the food additive (Skoog et al., 2014). These cells are constructed from a specific material, usually quartz or glass, which must be transparent in the wavelength region being measured (Leszczynski et al., 2019). The detector converts the light intensities transmitted into electrical signals. Common detectors include phototubes, a photomultiplier tube, and a photodiode arrangement or diode array. Photomultiplier tubes are very sensitive, and the diode array is the most selective detector (Harris, 2009). UV/Vis spectroscopy is a rapid, nondestructive, and environmentally friendly technique that can be used to analyze systems in flux, such as titration and chromatographic separations. Nonetheless, as all food additives present similar chemical groups that absorb magnetic radiation, UV/Vis produces analytical results with low selectivity, necessitating that the sample undergoes some preparation steps for enhancing the selectivity (Christian et al., 2014).

6.4.1.2 Near-infrared spectroscopy Functional group presents in the food additive structure can absorb IR light, resulting in different vibration and rotation frequencies in the chemical bonds, which can be changed as a function of the vicinity of each chemical bond, producing signals that can be used in the identification of the food additives in solid, liquid, and gaseous samples (Colthup et al., 1990). NIR wavelength ranges from 800 nm to 2.5 μm and allows direct measurement without destroying the sample (Harris, 2009). NIR instrumentation employs hot wires, light bulbs, or glowing ceramics as the IR light source. Sample cells are made from glass or halide salts (KCl, KBr, or NaCl), and the detectors are photoconductive and thermal (Skoog et al., 2014). In photodiode detectors (PbS, PbSe, and PbSnTe photodiodes that respond to wavelength ranges of 13.6,

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1.55.8, and 314 μm, respectively), the electrical conductivity is increased when they receive IR radiation. A thermal detector operates by detecting the changes in temperature of an absorbing foodstuff. Thermocouples prepared from two pieces of bismuth wire fused to either end of a piece of antimony are the most used, where the IR radiation promotes a temperature variation, and a potential difference is measured and related to the concentration of the compounds that absorbed the IR radiation (Skoog et al., 2014). Other IR detector is the bolometers that are measured alterations in temperature in a time that is proportional to the ratio of the heat capacity of the absorptive element to the thermal conductance. Bolometers are prepared from a thin layer of an absorber that acts as a resistance thermometer and is connected to a large-capacity thermal reservoir at a constant temperature, which is altered by radiation. Alternatively, thermistors that are constituted by a Wheatstone bridge electrical circuit emit IR radiation that promotes alterations in the electrical resistance proportional to the concentration of the absorbent species (Christian et al., 2014). NIR analysis produces transmissions or diffuses reflection measurements, which, allied with multivariate statistical techniques and calibrations steps, enables measuring the amounts of various food additives in a variety of foodstuffs, feedstocks, and final products. Solid foodstuffs, such as cookies, granola bars, ready-to-eat breakfast, processed meat products, butter, and margarine, can be analyzed without the use of hazardous reagents and generation of chemical waste. Despite this, it is necessary to perform specific calibrations for each food additive measured, which, along with the high initial cost of the instrumentation, are the main disadvantages of NIR spectroscopy in food analysis (Hoffman, 2005).

6.4.1.3 Fourier transform infrared spectroscopy The instrumentation used in the Fourier transform infrared (FTIR) spectroscopy employs an interferometer to prevent the dispersion of the radiation, assuring that all wavelengths reach at the detector simultaneously. In the interferometers, the IR radiation is emitted from a Nernst Glower or Globar source to a beam splitter, which divides the radiation beam into two paths: one path going to a fixed position mirror and the other to a moving mirror. Both beams meet, and the radiation beams are recombined, producing an interference pattern, which includes all wavelengths present in the wavelength beam, before passing through the sample that receives all beams simultaneously (Leszczynski et al., 2019). As the IR light produced in the interferometer changes with time, the resultant signals and, in turn, the absorption intensities are a function of the time, which are converted in a typical IR spectrum using the Fourier transformations as mathematical procedure, giving absorbance vs frequency of the radiation. The use of a variety of software and algorithms produces FTIR signals, and the rapid and accurate data acquisition means FTIR finds increasing practical use in the food industries (Harris, 2009). The obtained FTIR signals have resolution relative higher than obtained by NIR and are quickly obtained, and present high quality and facility in the interpretation of the analytical results, permitting their application in the food control quality (Christian et al., 2014). Nonetheless, the high initial cost of the instrumentation is the main drawback of FTIR (Amir et al., 2013; Babushkin, Spiridonov, & Kozhukhar, 2016; van de Voort, 1992).

6.4.1.4 Raman spectroscopy In Raman spectroscopy, the sample is irradiated by monochromatic radiation with wavelengths in the range from the UV/Vis to NIR, emitted from laser sources focused on the sample using 180 backscatter geometry. The wavelength and power of the radiation beam are previously selected. This irradiation beam involves two photons, where one photon interacts with the food additive molecule and the other photon is scattered. The difference between the frequency, where the photon interacts with the molecule under study, and the frequency of the scattering photon are related to the vibrational energy levels, yielding information about the molecular conformation, chemical structures, intermolecular interactions, and chemical bonding, producing very selective signals (Colthup et al., 1990). The commercial development of lasers permitted their use as a suitable monochromatic electromagnetic radiation source, improving the applicability of Raman spectroscopy in the food industries for analysis of solids or powders or aqueous solutions of food additive samples. Laser sources with a wavelength range from 400 to 1100 nm are commercially available, although the most common wavelengths used in food control quality analysis are 473, 532, and 660 nm. The power required depends largely on the target food additive and the complexity of the sample, necessitating the evaluation of the power and spectral bandwidth of the radiation beam by an analyst (Li & Church, 2014). Charge-coupled devices, like those employed in photographer cameras, can be used as a light detector, but the photomultiplier tube and photodiode array are present in some commercial equipment. Raman analysis is quick and cheap due to simple or no sample preparation steps. In addition, the power of the laser that emits radiation in the

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UV/Vis and NIR wavelength range, and the use of optical fibers as a tool in the measurement of the scattered light allow the applicability of Raman spectroscopy in liquid and solid foodstuffs (Christian et al., 2014). The analytical techniques described above can also be used to complement the data from other spectroscopic techniques in the analytical determination of food additives. For instance, chemiluminescence and fluorescence are based on the light emission by compounds with chemical structures containing at least one conjugated ring but are not typically present in food additives. Table 6.3 lists some applications of the spectroscopic techniques in food additive analysis, considering several different foodstuffs.

6.4.2 Chromatographic techniques In chromatographic analysis, the solution containing the pretreated sample, as described in Section 6.3.3, is introduced via a specific device, called the injector, across a stationary phase. As the mobile phase moves, it produces a series of equilibria between each compound under study and both phases. The mix next reaches the detector, a device that converts the flow of the mobile phase into an electrical signal, in a specific time, called the retention time. The retention time and the intensity of the electrical signals are used to identify and quantify the compounds of interest, respectively (Harris, 2009; Skoog et al., 2014). The separation between different components present in the samples is achieved due to a series of equilibrium stages, called theoretical plates, occurring between the species of interest and the stationary phase inside a chromatographic column, which involves adsorption, partition, ion-exchange, size-exclusion, or a mixture of these mechanisms (Christian et al., 2014). In the column, a mass transfer of the analyte occurs across the phases, which is influenced by factors, such as the chemical structure of the compounds under study, the temperature, chemical composition of both phases, and, mainly, the mobile phase flux velocity, which will determine the quality of the analytical results (Skoog et al., 2014). All these factors should be previously evaluated in order to obtain reliable and suitable analytical results, which are characterized by short retention times and adequate resolution in the chromatographic peaks. The flowchart in Fig. 6.5 indicates the main parameters that can be evaluated in gas chromatography (GC), high-performance liquid chromatography (HPLC), ultrahigh-performance liquid chromatography (UPLC), and multidimensional separations, which employ multiple stationary and mobile phases simultaneously (Skoog et al., 2014).

6.4.2.1 Gas chromatography GC permits the separation, identification, and quantification of compounds that are volatile and thermally stable, where the mobile phase is constituted by an inert gas, called the carrier gas. The stationary phase can be either an immobilized liquid (partition mechanism or gasliquid chromatography) or a solid (adsorption mechanism or gassolid chromatography) packed in a column constituted by a closed tube, which features very low diameters, when compared with columns used in liquid chromatography, necessitating a higher pressure to maintain the carrier gas flow (Lundanes et al., 2014). The adequate choice of the mobile phase is made according to its compatibility with the detector. Nitrogen, helium, and argon are the most common carrier gasses. The pressure of the mobile phase directly influences the velocity of the carrier gas flow, and their values can change from 1 to 25 and 25 to 150 mL/min, in the capillary and packed columns, respectively. The mobile phase does not interact with the stationary phase and the mixture of components. Instead, it only functions to carry the components across the column, which must present high purity and a constant flow velocity (Harvey, 2000). The stationary phase should present low volatility and high purity, and be chemically inert and thermally stable. In choosing the stationary phase, the polarity of the compounds to be separated is considered. Existing stationary phases encompass a wide range of polarities. The most nonpolar is prepared from polymethyl siloxane, in which all the radicals are methyl groups (-CH3). The other polarities are obtained by substitution of the -CH3 groups with phenyl groups (-C6H5), or trifluoro propyl (-C3H6CF3) or cyanopropyl (-C3H6CN) groups, or using polyethene glycol (Fifield & Kealey, 2000). GC analysis requires rigorous temperature control in the injector, column, and detector so that the liquid mixture is converted into steam. The compounds are separated by their boiling temperature and remain as separate components in the steam state. The temperature of the injector should be kept at around 50 C above the highest boiling temperature of the components to assure that all components in the mixture are volatilized. The column temperature can remain at a constant value (isocratic elution) or varied during the analysis (gradient elution) to promote suitable separation between components with different boiling temperatures (Christian et al., 2014).

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TABLE 6.3 Spectroscopy techniques used in some food additives analysis. Classes

Food additives

Technique

Conditions

Samples

Ref.

Preservative and color

Nitrite

CL

Acid medium, CuNCsDPA-FA, samples were ground, and then for nitrate separation and solid phase, extraction was performed using hot water and borax solution (5%) potassium ferrocyanide with solution of zinc acetate solution to precipitate the protein

Water, pickled vegetable, and sausage

(Han & Chen, 2019)

Colors

Carotenoids

Raman

Without sample preparation

B. glandulifera pulp (“falso guarana”)

(Carvalho, Sebben, De Moura, Trierweiler, & Espindola, 2019)

Color

Riboflavin

FLU

Carbon dots were synthesized from onion and lemon, and analytical curves were made in the pH 6.90 phosphate buffer solution

Multivitamin/mineral supplements

(Montefilho, Andrade, Lima, & Araujo, 2019)

Colors

Tartrazine

FLU

NiNCs solution with phosphate buffer (66.7 mM, pH 5 10.00) and water were employed in the determination

Drink

(Wang, Mu, Hu, Zhuang, & Ni, 2019)

Colors

Curcumin

FTIR

Sample was diluted in alcohol and posteriorly realized a distillation

Rhizomes

(Thangavel & Dhivya, 2019)

Acidity regulator, anticaking agent, emulsifying salt, firming agent, flour treatment agent, humectant, raising agent, stabilizer, and thickener

Phosphate

Colorimetric

Compact hydrodynamic sequential injection system

Water

(Khongpet, Pencharee, Puangpila, & Jakmunee, 2019)

Flour treatment agent, preservative, bleaching agent, and antioxidant

Sulfite and ascorbic acid

Colorimetric

Distillation to concentration of substances

Wine

(Chi, Zhu, Jing, Wang, & Lu, 2019)

Antioxidant and acidity regulator

Ascorbic acid

Colorimetric

M-CQDs were synthetized employing mustard seeds. The samples were centrifuged and filtrated to removal of impurities

Juice of grapes, apple, orange, mango, emblic, banana, guava, pineapple, papaya, and lemon.

(Chandra et al., 2019)

Preservative and color

Nitrite

FLU

Samples were ground, homogenized, and then liquid solid was extracted, utilizing saturated borax solution and water

Sausage

(Li, Li, & Gao, 2019)

(Continued )

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TABLE 6.3 (Continued) Classes

Food additives

Technique

Conditions

Samples

Ref.

Acidity regulator and antioxidant

Ascorbic acid

Colorimetric

Solution of acetate buffer in the pH 4.70. The samples were diluted using the buffer

Bacuri, cupuac¸u, muruci, yellow mombin, mango, orange, and passion fruit

(dos Santos, da Silva, de Oliveira, & Suarez, 2019)

Colors

Carotenoids

Raman

Extraction utilizing nhexane, acetone, ethanol, and BHT in acetone.

Tomato and pepper

(Kirkhus et al., 2019)

Preservative

Sulfur dioxide

UV/Vis

PET/Paper chip. Samples were homogenized and underwent microaeration distillation

Candied wax gourd, candied melon, dried daylily, dried guava, clove flavored olive, dried kumquat, dried kiwifruit, candied pineapple, dried pineapple core, dried mango, candied pineapple, desiccated coconut, dried salted plum, black fungus, dried fig, dried pineapple, succade, olive slices, dried radish, dried shrimp, dried sweet plum, red bean pills, white fungus, sago, and lotus seed

(Fu et al., 2019)

Ref., Reference; CL, chemiluminescence; CuNCs-DPA-FA, copper nanoclusters diperiodatoargentate—folic acid; FLU, fluorescence; NiNCs, nickel nanoclusters; FTIR, Fourier transformed infrared; M-CQDs, carbon quantum dots; BHT, butyl hydroxytoluene; UV/Vis, ultraviolet/visible; PET, polyethylene terephthalate.

Chromatography

Mobile phase

Flow velocity

Nonpolar

Mobile phase

Polar

Stationary phase Stationary phase

Injector Temperature

CG

Thermal conductivity Detector

Reverse

Column Detector

Normal

Syringe HPLC/ UPLC

Column diameter

Flux velocity

Pump

Diode array

Detector spectroscopic Detector

Pressure pneumatic

Ultraviolet/Visible Refraction index

FIGURE 6.5 Flowchart indicating the main steps in the chromatographic techniques.

Reciprocal

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A wide variety of commercial detectors exist, including flame ionization, thermal conductivity, electron capture, mass spectrometry (MS), thermionic or phosphorous nitrogen, electrolytic conductivity, photoionization, and FTIR detectors. However, in food additive analysis, MS and thermal conductivity detectors, which are considered as the most selective and the universal detector, respectively, and FTIR, which was previously described in Section 6.4.1.3, are the most common. In the thermal conductivity detector, the flow of the mobile phase containing the separated components passes over a tungstenrhenium filament, with a known and constant conductivity, which is changed according to the thermal conductivity of each component in the mixture. For this detector, the mobile phase is typically helium. The MS detector is a highly suitable, sensitive, and selective approach to identify different chemical species present in mixtures of volatile organic samples. The complete system, including the GC and MS detector (GCMS), is the largest and most expensive equipment ever developed. However, due to its excellent applicability, some manufacturers have developed relatively inexpensive compact bench-top systems that can be used in industries for food quality control, including food additive analysis (Fifield & Kealey, 2000). In all GC analyses, the eluted compounds must be volatile and stable from 50 C to 300 C, which is the temperature range employed in commercial instrumentation, including the injector, column, and detector. Nonetheless, if the target compound does not have these characteristics, some derivation reactions can be performed to confer volatility and thermal stability, but derivatization is not always possible and efficient (Skoog et al., 2014). Besides, the high cost in the initial acquisition and operation of the equipment, and the necessity of a highly skilled analyst mean the GC analysis requires exhaustive and rigorous preparation steps for foodstuff samples.

6.4.2.2 Liquid chromatography In the HPLC analysis, after the sample preparation and pretreatment steps, the samples are dissolved in a suitable solvent and injected into the HPLC system, composed of pumps (to maintain a constant mobile phase flow), injector (to mix all the component samples with the mobile phase and assure the quantity of the samples injected in the column is reproducible), columns (that can be wrapped in a furnace with temperature control to increase the solvent viscosity and improve the separation), and detector (that converts the eluate flow into the electrical signal, which is related to the identity and quantity of the compounds under study) (Naushad & Khan, 2014). The mobile phase is chosen based on the polarity differences between the compounds in the sample and the mobile and stationary phases, which are related to the chemical properties of the food additive to be separated, such as solubility, polarity, charge, and equilibrium constant. The mobile phase should be of high purity and not react with the stationary phase or components in the sample or those in the equipment. In the HPLC separations, the mobile and stationary phase should have opposite polarities to provoke different equilibrium stages and, consequently, the chromatographic separation. Normal-phase chromatography utilizes nonpolar solvents, such as hexane and tetrahydrofuran, and a stationary phase with high polarity. In reversed-phase chromatography, the mobile phase is composed of polar solvents (methanol and acetonitrile or the mix with buffer solutions), and the stationary phase is comparatively less polar (Nova´kova´, Svoboda, & Pavlı´k, 2017). The stationary phase is composed of silica particles with diameters lower than 5 μm and should present high purity. The silica particles contain silanol groups that provide polar interaction to separate polar compounds, and these sites can be functionalized by modifying the polarity of the stationary phase, according to the polarity of the compounds to be separated. In order to increase the column lifetime, an auxiliary column, called a guard column, can be used, which has the same chemical composition of the main column that retains possible interferents present in the samples (Christian et al., 2014; Lundanes et al., 2014). The mobile phase must be pumped at a constant flux through the column, achieved by pumps of syringe, reciprocal, or pneumatic types, which are differentiated by the mobile phase capacity. Pulsed flow can lead to undesirable disturbances in the analytical signals or chromatograms and possibly the gradient or isocratic elution. After the separation, each component is identified and quantified using a specific detector, which can be a UV/Vis, fluorescence, refractive index, electrochemical, or MS detector. The UV/Vis and fluorescence detectors measure the absorption of radiation by chromophore-containing compounds present in the eluate and the intensity of light emitted by the eluate, respectively. The UV/Vis detector presents broad applicability in food additive analysis, because all compounds contain chemical groups (double and triple bonds and the presence of a heteroatom) within their structure that absorb radiation at a specific wavelength. This information is used in the identification of the food additive, while the light intensity is used to measure the food additive concentration. The diode array is a specific light detector used in HPLC analysis, as it permits simultaneous monitoring of the different wavelengths and choosing the most suitable wavelength in the selectivity of the analysis. The refractive index

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detector, which measures the change in the refraction index of the mobile phase due to the dissolved compound of interest present in the samples, permits the quantification of all chemical classes of the compounds. The electrochemical detectors, which are based either on electrochemical oxidationreduction of the target compound, are least employed in the food analysis. MS detector, described above, separates and detects ions from the column, and is considered as the most sensitive and selective detector (Nova´kova´ et al., 2017). The advantages of HPLC include the great diversity of stationary phases, high sensitivity due to different detector types, and its quickness and ease of identification and quantification of multiple components in various sample types with a good resolution. Nonetheless, some of its drawbacks are the high costs of the acquisition and operation of the equipment, the large volume of toxic residues due to the great quantity of the organic solvents employed, and the need for a highly specialized analyst. In this context, UPLC can be an appropriate alternative to increase the speed of elution, in turn, reducing the volume of solvents employed and increasing the resolution and sensitivity in the food analysis. Its separation principle is like that of the HPLC systems, but the unique UPLC feature is the column length and the small particle size (less than 2 μm), which provide a better resolution and separation in the component samples when compared with traditional HPLC. Furthermore, the increase in the pressure when UPLC columns are used begets considerable temperature alterations, in turn, reducing the mobile phase viscosity and minimizing the back-pressure. In addition, the use of UPLC decreases the quantity of the injected sample (Naushad & Khan, 2014). The multidimensional techniques, which employ a series of columns with different polarities, are employed to improve the analytical signals obtained, which provide an increase in the resolution and the selectivity and a decrease in the retention time. Theoretical, the use of multidimensional techniques in food control quality analysis could increase the analytical frequency and the column half-life. Capillary electrophoresis is used in the separation of the charged molecules, which are led when an electrical field is applied, and the movement of the molecules will depend of their charge-to-size ratio, of the molecular weight, the three-dimensional structure, and the degree of solvation. This technique has high speed in the separation, high efficiency, ultrasmall sample volume, low consumption of solvent, simplicity, selectivity, large separation capacity, and relatively low cost, allowing its use in food control quality (Skoog et al., 2014). Table 6.4 provides some recent applications of chromatographic techniques used in food additive analysis, considering several different foodstuff samples.

6.4.3 Electroanalytical techniques Electroanalytical techniques measure the electrical properties, such as the current, electric potential, charge, resistance, conductance, impedance, and conductivity when two separate electrical conductors, called electrodes, are maintained in contact with a solution containing the compounds of interest (Skoog et al., 2014). In food additive analysis, potentiometric techniques present low applicability, because the diverse chemical structures of food additives do not permit the development of specific indicators or selective membrane electrodes. Amperometry, a common transducer technique in biosensors or flow-injection analysis, is little employed in food analysis. The electrochemical impedance spectroscopy is, likewise, of little practical use in food analysis, because of the complexity in the interpretation of the produced signals (Dahmen, 1986; Scholz, 2010). In food analysis, the electroanalytical techniques most employed are the voltammetric techniques, which involve the application of an electrical potential difference to promote the reaction of electron transfer, called redox reaction. The intensities and position values of the generated current are used in the quantification and identification, respectively. Various modes can be employed, which will determine the profile of the analytical signal, known as voltammogram. The modes most frequently used in food analysis are cyclic voltammetry (CV), differential pulse voltammetry (DPV), and square-wave voltammetry (SWV) (Martins et al., 2019; Mirceski, Komorsky-Lovric, & Lovric, 2007; Wang, 2001). The flowchart in Fig. 6.6 describes the main parameters that need to be evaluated for CV, DPV, and SWV use in food additive analysis, considering the raw materials and different types of foodstuff samples. The application of CV, DPV, and SWV analyses requires the use of a conventional electrochemical cell, composed by an ionic conductor and electrical conductors, plus the samples containing the target compounds. The electrochemical cell can be constructed using a variety of materials that are easily processable, completely inert to the electrochemical reactions, and present low cost. Glass, Teflon, and nylon are the main materials used in the construction of the electrochemical cell, considering different size and configurations, according to the analysis’ necessity. The ionic conductor is composed of a solvent and supporting electrolyte. The solvent is needed to promote the dissolution and dissociation of the salts to obtain a solution with high ionic conductivity, promote the solvation of the

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TABLE 6.4 Chromatography techniques used in some food additives analysis. Classes

Food additives

Techniques/ detector

Mobile/stationary phase

Samples

Ref.

Color

Sudan (I, II, III, and IV), erythrosine, carmoisine, red AC, allura, ponceau 3R, ponceau SX, ponceau 4R, amaranth, and carmine

HPLC-UV/ Vis

Acetonitrile and solution of buffer acetate in pH 7.00/ C18 RP

Meat products

(Iammarino et al., 2019)

Color

Azorubine, sunset yellow FCF, quinoline yellow, ponceau 4R, allura red AC, and tartrazine

UHPLCHRMS

10-mM ammonium acetate buffer with methanol/C18 with endcapped polar and ether-linked phenyl phase

Spices and spice blends

(Pe´riat, Bieri, & Mottier, 2019)

Color

Erythrosine, azorubine, allura red ponceau 4R, and amaranth

HPLC-UV/ Vis

Ammonium acetate buffer/C8

Water, beverage, jelly, fruity pastille, smarties, candy, soft drinks, sugarand gelatin-based confectionery, chocolate, fizzy drink, fruit juices, black tea, fishery, meat, vegetable, diet supplements, beverage, dragee, and bakery

(Faraji, 2019)

Acidity regulator

Tartaric acid, succinic acid, oxaloacetic acid, malic acid, lactic acid, citric acid, and acetic acid

CE-biosensor SOM

Phosphate pH 7.40 containing KCl

Grape wine

(Milovanovic, Obo, Pelcova´, Lacina, & Cakar, 2019)

Acidity regulator and antioxidant

Tocopherol, fumaric acid, citric acid, succinic acid, shikimic acid, malic acid, quinic acid, and oxalic acid

HPLC-FLU and UFLCUV/Vis

Acetonitrile and deionized water/ reverse phase C18

Bean, carrot, carob fruit, and rice extruted rice

(Arribas et al., 2019)

Colors

Carotenoids

HPLC-UV/ Vis and Raman

Methanol, ammonium acetate (pH 7.20); acetonitrile, water, and ethyl acetate/reversephase SB-C18

Snow algae

(Osterrothova´ et al., 2019)

Antioxidant and colors

Carotenoids and tocopherol

HPLC-UV/ Vis

Methanol, methyl tert-butyl ether and water/C30

Fruit juice

(Stinco et al., 2019)

Antioxidant and color

Tocopherols and carotenoids

LC-MS/MS

Formic acid in methanol and acetonitrile/C18

Camellia oil and olive oil

(Zhang et al., 2019)

Color

Tartrazine, brilliant blue, fast green and indico carmine, and sunset yellow

HPLC-UV/ Vis

Ammonium acetate buffer at pH 7.40 (pH was adjusted with acetic acid) and acetonitrile with methanol/C18

Like soft candy, hard candy, and jellybeans

(Mathiyalagan, Mandal, & Ling, 2018)

Acidity regulators

Lactic acid

GCMS

p-toluenesulfonic acid in distilled water and p-toluenesulfonic acid with bis-Tris, and EDTA-2Na in distilled water/LC10A

Water, wheat flours, rye flour with yeast and lactic acid, bacteria

(Fujimoto, Ito, Narushima, & Miyamoto, 2018)

(Continued )

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TABLE 6.4 (Continued) Classes

Food additives

Techniques/ detector

Mobile/stationary phase

Samples

Ref.

Anticaking agent

Ferrocyanides

HPLC-UV/ Vis

NaClO 4 e NaOH/IC SI90 4E and AS11HC

Different types of salts

(Soo Lim et al., 2018)

Acidity regulator

Malic acid and gallic acid

UHPLC/MS

Formic acid in water and methanol/C18

Chaenomeles speciosa

(Yang et al., 2019)

Antioxidant, bleaching agent, flour treatment agent, and preservative

Sulfites

LC-MS/MS

NR

Apricot, coconut, ginger, mango, papaya, pineapple, potato, and tomato

(Carlos, Treblin, & De Jager, 2019)

Preservative

Benzoates, sorbates, and parabens

HPLC-UV/ Vis

Acetonitrile and acetate buffer pH 4.20 (B)/CLC-ODS

French fries, olives, pickles, cheese, and carbonated drinks

(Lucas et al., 2019)

Preservative and colors

Nitrate and nitrite

HPLC-UV/ Vis

0.01 M n-octylamine in methanol/ACE C18

Meat

(Chetty, Prasad, Pinho, & Morais, 2018)

CE, capillary electrophoresis; FLU, fluorescence; GC, gas chromatography, HPLC, high-performance liquid chromatography; HRMS, high resolution mass spectrometry; LC, liquid chromatography; LD, Limit of detection; MS, mass spectroscopy; Ref., reference; SOM, self-organized maps; UFLC, ultra flash liquid chromatography; UHPLC, ultra performance liquid chromatography; UV/Vis, ultraviolet/visible.

CV

Scan rate Pulse amplitude

DPV Voltammetric parameters

Time Pulse frequency Scan increment

SWV

Pulse amplitude

Electroanalytical Solvent Electrochemical cell

Ionic strength Supporting electrolyte

pH

Mercury-based Carbon-based Electrodes

Noble metals Chemically modified

FIGURE 6.6 Flowchart indicating the main steps in the electroanalytical techniques.

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reactants and products, and inhibit any electrochemical reactions from occurring in any electrical conductors used. Water, methanol, and ethanol are common polar solvents used, and acetonitrile and dimethyl sulfoxide are common nonpolar media (Wang, 2001). The supporting electrolyte is necessary to minimize or eliminate the electric field effects from electrode polarization on the movement of the electroactive molecules, which are the compounds that will be reduced and/or oxidized. Besides, it is important to provide ionic mobility in the electrochemical cell, allowing electrode potential control and or measurements. As the supporting electrolyte, inorganic salts (chloride, nitrate, sulfates, and the perchlorate salts of potassium and sodium), mineral acids (hydrochloric, perchloric, sulfuric, and nitric acids), hydroxides of sodium and potassium, and some buffer systems, such as phosphates, acetates, ammonium, borates, and oxalates, which cover the usual range of pH from 0 to 12, are used in an aqueous medium. However, electroanalysis in organic media employs tetraalkylammonium salts, such as tetraethylammonium, tetrabutylammonium, and tetraphenylphosphonium, as the supporting electrolyte (Wang, 2001). The supporting electrolyte is also responsible for controlling the ionic strength and pH value, which affect the position and the intensities of the obtained signals. Additionally, the pH of the medium will define the potential range of the WE and can promote dislodgment of the potential redox values where the electron transfer occurs, in turn, decreasing the energy involved in the electron transfer (Wang, 2001). The electrical conductors used in the electrochemical cell are the WE, which is where the reaction of interest occurs, the reference electrode (RE), which oversees the potential at the WE by means of a potentiostat, and the counter electrode (CE), which is used as the current-carrier and, for this reason, is also known as the auxiliary electrode. The RE indicates an electrical potential with steady and reproducible values that are independent of the sample composition and against which the WE potential is compared. The RE can be commercially obtained or laboratory-made and is constituted by a robust body with adequate top seal, junction, and an active component that defines the reference potential. The silversilver chloride (Ag/AgCl) and the saturated calomel reference (Hg/Hg2Cl2), both with different concentrations of chloride, which provide the known potential values, are the most common RE. The CE is prepared from an inert conductor material, such as platinum wire or graphite rod. The WE is a key factor in the use of electroanalytical techniques, and the choice of the WE materials will influence the position and intensities of the analytical signals. Another important consideration is the electric potential range where the redox reaction of the target compound occurs and the properties related to the WE surface (electrical conductivity, reproducibility in its surface, mechanical stability, low cost of manufacturing, easy availability of the material, and without toxicity). The WE of choice is dependent on the redox behavior of the target compound and the background current over the potential region used in the analysis. Hence, there are varied materials available for specific use in food analysis, such as mercury-based, carbon-based, noble metals, and chemically modified electrodes. The appropriate choice depends mainly on the application of interest, such as the chemical group that reacts, the medium (aqueous or organic solvents), potential range, and the pH of the medium (De Souza, Mascaro, & Fatibello-Filho, 2011; Martins et al., 2019). Instrumentation used, called as potentiostat/galvanostat, is composed of two inexpensive integrated circuits, where the first is a polarizing circuit responsible by potential application between the WE and RE, and one is responsible by a measuring circuit that indicates the current measure the currents between the RE and WE. This instrumentation presents relatively very low costs and operational ease, when compared with chromatographic and, for this, can be used in food quality control, including food additive analysis (Wang, 2001). Table 6.5 shows recent research that developed and applied electroanalytical techniques in the analysis of food additives belonging to different classes in various sample types of foods.

TABLE 6.5 Some electroanalytical application in food additives analysis. Classes

Food additives

Working electrode

Conditions

Samples

Ref.

Color

Riboflavin

DPV/N-CQD/SnO2

Samples were diluted in deionized water, sonicated, and then filtered. Phosphatebuffered (pH 7.00)

Tablets and milk powder

(Muthusankar et al., 2019)

Preservative and color

Nitrite

DPV/e-PCMA/CNT/Au

Without sample preparation. Phosphate-buffered (pH 4.00)

Water

(Xu et al., 2019)

(Continued )

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TABLE 6.5 (Continued) Classes

Food additives

Working electrode

Conditions

Samples

Ref.

Antioxidant and preservative

Ascorbic acid

CV/MWCNTs

Samples were grinded, dissolved, filtered, centrifuged, and then diluted. Phosphatebuffered (pH 7.00)

Tablets

(Huang et al., 2019)

Acidity regulator

Lactic acid

Potentiometric/ polymer/MWCNTs/ PVC

Without any pretreatment. Lactic acid (pH 6.00)

Milk and yoghurt

(Alizadeh, Nayeri, & Mirzaee, 2019)

Color

Amaranth, tartrazine, and quinoline yellow

DPV/GCE

Samples were filtered and diluted. Phosphate-buffered (pH 7.00)

Red grape beverage and sunich orange nectar

(Ghanbari, Roushani, Farzadfar, Goicoechea, & Jalalvand, 2019)

Color

Sunset yellow

DPV/PDDA-dispersed graphene

Phosphate-buffered (pH 3.00)

Soft drink

(Li, Zheng, Guo, Qu, & Yu, 2019)

Acidity regulator

Citric acid

CV/g-C3N4/Fe3O4/ BiOI-carbon paste electrode

Samples were centrifuged and the supernatants obtained were diluted with acetate buffer. Acetate buffer (pH 6.00)

Fruit juice

(Alizadeh, Nayeri, & Habibiyangjeh, 2018)

Preservative

PG

Photoelectrochemical sensor/SPPC

The samples were sonicated, centrifugation, and filtered. Phosphate buffer (pH 7.00)

Broth

(Kelly et al., 2019)

Antioxidant

TBHQ

DPV/GCE

Samples were centrifuged with ethanol (Φ 5 95%). BrittonRobinson buffer (pH 2.00)

Edible oil

(Yue, Luo, Zhou, & Bai, 2019)

Sweetener

Aspartame

DPV/SPCE- CAF/ASP

The samples were degassed in an ultrasonic, diluted in water, and filtered. Phosphate buffer (pH 7.00)

Soft drink

(Le, Su, & Cheng, 2019)

Antioxidant, bleaching agent, flour treatment agent, and preservative

Sulfite

DPV/screen-printed electrode modified with La31-doped Co3O4 nanocubes

Samples were filtered. Phosphate buffer (pH 7.00)

Water

(Beitollahi, Mahmoudimoghaddam, Tajik, & Jahani, 2019)

CNT, Carbon nanotubules; CV, cyclic voltammetry; DPV, differential pulse voltammetry; e-PCMA, poly(VCz-co-VMc-co-AA); GCE, glassy carbon electrode; MSAgNPls 5 2, mercaptoethanesulfonate-modified silver nanoplates; MWCNTs, multiwalled carbon nanotubes covalently bonded (moleculary imprited poly); N-CQD, nitrogen-doped carbon quantum dots; NRs, nanorods; PDDA, poly(diallyldimethylammonium chloride); PG, propyl gallate; PVC, polyvinyl chloride; Ref., reference; SPCE, preanodized screen-printed carbon electrodes; SPPC, self-powered photoelectrochemical system; TBHQ, tertiary butylhydroquinone.

6.5

Conclusions

This chapter presented a brief discussion about the 230 food additives that are employed in the food industry to enhance the shelf life and improve the rheological, sensory, and microbiological properties of foodstuffs. These compounds are classified, according to the guidelines of the Codex Alimentarius, into 25 classes based on their specific functions. Each food additive class was described, indicating their functions, specific compounds, main foodstuffs employed, and some adverse effects when consumed in excess. The relevance of food additives in the food industries was emphasized in the context of control of the food characteristics during processing, transportation, storage, and commercialization of foodstuffs. Additionally, the importance of quality control of type and quantity of these compounds in foodstuff samples were described, due to the possible harm to consumers’ health, if ingested inappropriately. Moreover, this chapter described the main steps in food additive analysis, with the use of flowcharts depicting the sampling, preparation, pretreatment, qualitative and quantitative analysis, evaluation of the analytical data, and the all analytical techniques employed in food additives determination. These steps can be employed to improve the analytical

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parameters or validation parameters of the analytical techniques of the food additives determination. These parameters allow a comparison of the different analytical parameters for various analytical techniques in food additive analysis. Furthermore, the classical, spectroscopic, chromatographic, and electroanalytical techniques were discussed, presenting their optimization parameters in flowcharts, which are explained in detail for those frequently employed in the food industry. Master advantages and drawbacks of all analytical technique used were described, and recent research of food additives determination was highlighted.

Acknowledgments The authors thank FAPEMIG (Minas Gerais State Research Support Foundation) and PROPP-UFU (Dean of Research and Graduate of the Federal University of Uberlaˆndia) by financially supporting in the development of the all studies.

Dedication This chapter is dedicated to all young researchers that worked in the Laboratory of Electroanalytical Applied to Biotechnology and Food Engineering (LEABE), Campus of Patos de Minas, Federal Uberlaˆndia University, since its foundation in 2012 until 2019.

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van de Voort, F. R. (1992). Fourier transform infrared spectroscopy applied to food analysis. Food Research International., 25, 397403. Available from https://doi.org/10.1016/0963-9969(92)90115-L. Wang, J. (2001). Analytical Elecctrochemistry (2nd ed.). New York: Wily-VCH. Wang, Y., Mu, Y., Hu, J., Zhuang, Q., & Ni, Y. (2019). Rapid, one-pot, protein-mediated green synthesis ofwater-soluble fluorescent nickel nanoclusters for sensitive and selective detection of tartrazine. Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, 214, 445450. Available from https://doi.org/10.1016/j.saa.2019.02.055. Watson, D. H. (Ed.), (2010). Food Chemical Safety. Boston: Press, CRC. Available from http://dx.doi.org/10.1201/9781439832813. Wrolstad, R. E., Acree, T. E., Decker, E. A., Penner, M. H., Reid, D. S., Schwartz, S. J., & . . . Sporns, P. (Eds.), (2005). Handbook of Food Analytical Chemistry Pigments, Colorants, Flavors, Texture and Bioactive Food Components. New Jersey: Wiley-Interscience. Xu, M., Zhu, X., Hong, H., Hu, K., Yang, R., Wang, X., . . . Liu, X. (2019). An electro-active amphiphilic copolymer to functionalize carbon nanotubes for highly sensitive determination of nitrite in water. Colloids Surfaces A Physicochemical and Engineering Aspects. Available from https:// doi.org/10.1016/j.colsurfa.2019.05.014. Yang, J., Dong, X., Hu, Y.-H., Wang, Q.-Y., Wang, S.-L., Cao, J., & Zhang, H.-H. (2019). Calixarene and ionic liquid assisted matrix solid-phase dispersion microextraction of organic acids from fruit. Journal of Chromatography A.. Available from https://doi.org/10.1016/j.chroma.2019.03.055. Yue, X., Luo, X., Zhou, Z., & Bai, Y. (2019). Selective electrochemical determination of tertiary butylhydroquinone in edible oils based on an in-situ assembly molecularly imprinted polymer sensor. Food Chemistry., 289, 8494. Available from https://doi.org/10.1016/j.foodchem.2019.03.044. Zhang, L., Wang, S., Yang, R., Mao, J., Jiang, J., Wang, X., . . . Li, P. (2019). Simultaneous determination of tocopherols, carotenoids and phytosterols in edible vegetable oil by ultrasound-assisted saponification, LLE and LC-MS/MS Author links open overlay. Food Chemistry., 289, 313319. Available from https://doi.org/10.1016/j.foodchem.2019.03.067.

Chapter 7

Analysis of food Additives Long Wu College of Bioengineering and Food, Hubei University of Technology, Wuhan, Hubei, 430068, P.R. China

7.1

Function of food additives

Food additives are a kind of raw material added in food or in the process of food production, which aim to improve food edibility as well as the taste and aesthetic feeling of food (Ishidate et al., 1984). It is believed that there is no packaged food that does not include food additives. By using food additives correctly, it can effectively preserve the flavor of food, improve the nutrition of food, increase the convenience of processing, prevent food spoilage, extend the shelf life of food or enhance other qualities. For example, the preservatives introduced during food processing can prevent food from gong bad and extend the shelf life of food by inhibiting the growth of microorganisms (Schnuch, Geier, Uter, & Frosch, 1998). Antioxidants can prevent or delay food oxidation deterioration by reducing oxygen or free radical level around food, and thus improve the stability and storage resistance. These antioxidants include natural like ascorbic acid and tocopherols, as well as synthetic ones of propyl gallate, tertiary butylhydroquinone, butylated hydroxyanisole and butylated hydroxytoluene (Ni, Wang, & Kokot, 2000). It is known that food additives play an important role in modern food industry, which have given a pushing effect on the development of modern food. For example, it is inevitable that a certain loss of nutrients can be caused in food processing. If proper food nutrition enhancers were added during processing, they can greatly improve the nutrition of food, which is effective in preventing malnutrition and nutrient deficiency and promoting nutrition balance (Teucher and Cori, 2004). What’s more, the use of food additives such as defoaming agents, stabilizers and coagulants in food is conducive to the operations during food processing. For example, when gluconate lactone as tofu coagulant is used, the mechanization and automation of tofu production can be easily realized. Although certain levels of food additives are required in the food industry, there is still considerable debate on whether foods or supplements with additives have positive effects on people’s health (Cheftel, 2005). Moreover, if they are actually beneficial, it is not entirely sure which food additives are health-promoting and in what amounts beyond typical dietary intake. However, what we certainly know is that the excessive use of additives or illegal use of non-food additives will cause a series of food safety problems.

7.2

Classification of food additives

According to their composition to be distinguished, two major categories of food additives including natural additives and synthetic additives are classified (Devcich, Pedersen, & Petrie, 2007). Wherein, natural food additives are mainly produced by purifying the ingredients from plant or animal sources. While chemically synthesized additives are based on chemical raw materials, from which organic or inorganic matter can be extracted and purified. Based on their functions, food additives can also be divided into different groups such as antioxidants, bleach, sweeteners, preservatives, colorants, thickeners and so on. In 1990, "The classification and numbering of food additives" (GB 12493-1990) promulgated by China lists 23 classes for food additives (Fig. 7.1) (Roberts and Rogerson, 2008). In 1992, FAO/WHO Joint Expert Committee on Food Additives divided food additives into 23 classes according to their main functions. The European Union has 24 classes for food additives and the United States falls into 32 classes. Nowadays, many more additives have been introduced with the advent of processed foods, of both natural and artificial origin. Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00007-8 Copyright © 2021 Elsevier Inc. All rights reserved.

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FIGURE 7.1 The classification and numbering of food additives (People’s Republic of China, GB 12493-1990).

7.3

Examples of food additives

At present, there are more than 25,000 compounds of food additives being used around the world (Wu, Zhang, Shan, & Chen, 2013). Different countries have different regulations and standards towards food additives. For example, under the Canadian Food and Drug Regulations, food additives do not include: G G G G G G

Food ingredients such as salt, sugar, starch; vitamins, minerals, amino acids; Spices, seasonings, flavouring preparations; Agricultural chemicals; Veterinary drugs; or Food packaging materials.

And because of that, the Joint FAO/WHO Expert Committee on Food Additives (JECFA) has been meeting annually since 1956 to update and revise its related standards (Anderson, 1986). In this chapter, several widely used food additives include colorants, preservatives, antioxidants, sweetening agent, stabilizers and emulsifiers as well as their usage, dosage were introduced (Table 7.1). Food color plays a role in making food more palatable because color stimulates the eyes and enhances perception of flavor. Colorants are a kind of additives that added to food to remedy colors lost during preparation or to make food look more attractive. Colorants present in both natural and synthetic forms, which are mainly used in foods and drinks (Wissgott and Bortlik, 1996). For example, carotene, red amaranth and anthocyanin are a few of the natural colorants currently in use. These water-soluble colorants make them a good choice in sodas, teas and drinks. Preservatives prevent or inhibit spoilage of food due to fungi, bacteria and other microorganisms. Often mold spores appear on bread or pantry moths attack other dried goods. Preservatives can reduce the risk of foodborne infections, decrease microbial spoilage, and preserve fresh attributes and nutritional quality. Commonly used preservatives include calcium propionate, sodium nitrate, sulfites, nitrites and nitrates (Abdulmumeen, Risikat, & Sururah, 2012). There is some overlap between preservatives and antioxidants because they exert the similar effect on the food preservation. Antioxidants mainly prevent or inhibit the oxidation process to keep the food from spoilage (Aruoma, 1994). The most common natural antioxidant additives are ascorbic acid and ascorbates. Thus, antioxidants are commonly added to oils, cheese, and chips. Other artificial antioxidants include the phenol derivatives of butyl hydroxyanisole

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TABLE 7.1 Typical representatives of four food additives (not given: —). Category

Colorants

Common substance

Mainly used

Maximum dosage range (g/kg)

Natural carotene

Dairy products, frozen drinks, processed fruits, dried vegetables and soybean products, etc.

—

Natural amaranthus red

Candied fruit, candy carbonated beverage, jelly, etc.

0.25

β-carotene

Fermented milk, cheese, candy and baked goods

0.015B0.018

Erythrosine (Cherry red) and Aluminum Lake

Cold fruits, meat enema and sauces, condiments, etc.

0.015B0.05

Tomato red

Dairy products and candy, baked food, ready-to-eat food, solid soup

0.015B0.39

Sunset Yellow and Its Aluminum Lake

Jam, dairy products, cocoa products, starch desserts, compound condiments, beverage products

0.025B0.5

Amaranth, Carmine, Allura Red, Lemon Yellow, Brilliant Blue, Indigo Blue

Sodas, teas and drinks

—

Benzoic acid and Sodium benzoate

Condiments, pickled products, beverage products, fruit wine

0.2B2.0

Sorbic acid and its salts

Dairy products, soybean products, processed vegetables, cooked meat products, aquatic products, etc.

0.075B2.0

Propionic acid and its salts

Soybean products, wet flour products, bread, pastry, vinegar and soy sauce, etc.

0.25B2.5

Ester preservative

P-hydroxybenzoates

Jam and sauce products, carbonated drinks, etc

0.012B0.5

Others

Sodium diacetate

Soybean products, meat products, aquatic products, pastry and puffed foods, etc.

1.0B10

Natural sweetener

Xylitol

Dairy products, tea products, alcoholic drinks, seasonings, starch products, processed fruits and vegetables, etc.

—

Sorbitol

Dairy products, jams, wet flour products, baked products, beverages and soybean products, etc.

0.5B30

Mannitol

Candy

—

Lactitol

Dairy products, Spices

—

Maltitol

Processing fruits, frozen surimi products, soybean products, etc.

—

Stevioside

Flavor fermented milk, candy, condiments, canned fruits, flavored syrup and tea products, etc.

0.17B10.0

Ammonium Glycyrrhizinate and Potassium salt

Candied fruits, candy, biscuits, canned meat, etc.

—

Natural pigments

Artificial synthetic pigments

Preservative

Sweeteners

Acid preservative

(Continued )

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TABLE 7.1 (Continued) Category

Artificial sweetener

Antioxidant

Emulsifiers

Common substance

Mainly used

Maximum dosage range (g/kg)

Saccharin sodium

Frozen drinks, dehydrated mango, dried figs, cold fruits, cooked beans and dried fruits

0.15B5.0

Sodium N-cyclohexyl sulfamate (cyclamate)

Canned fruits, jams, mixed wine, instant noodle food and condiments

0.65B8.0

Methyl aspartate phenylpropionate (Aspartame)

Dairy products, frozen fruits and vegetables, cereals and starch desserts

0.3B4.0

Sucralose

Prepared dairy products, jams, sufu, coarse cereals products, Baked products, etc.

0.25B5.0

BHA: Butyl hydroxyanisole

Fat, oil and emulsified fat products, coarse grain, instant rice noodles products, etc.

0.2

PG: Propyl gallate

Nuts and canned seeds, gum-based candy, fried noodles, etc.

0.1B0.4

TBHQ: Tert-butyl hydroquinone

Moon cakes, instant rice noodles products, biscuits, baked food fillings

0.2

BHT: Butylated hydroxytoluene

Fried noodles, gum-based candy, air-dried aquatic products, etc.

0.2B0.4

TP: Tea polyphenols

Nuts, grilled meat, salted products, canned aquatic products, vegetable protein drinks, etc.

0.1B0.8

Ascorbic acid

Peeled fresh fruits and vegetables, wheat flour, fruits and vegetables products

0.2B5.0

Mono- and diglycerides of fatty acids

Coffee, vinegar, ice-creams, spreads, breads, cakes, milk and milk beverage, etc.

—

Sodium stearoyl lactylate Sodium phosphates

(BHA), butylated hydroxytoluene (BHT), tert-butyl hydroquinone (TBHQ) and propyl gallate (PG) (Amorati, Foti, & Valgimigli, 2013). These agents are often added to foods to suppress the formation of hydroperoxides. Sweetener, a sugar substitute, is a food additive that provides a sweet taste like that of sugar while containing significantly less food energy, making it a zero-calorie or low-calorie sweetener (Kroger, Meister, & Kava, 2006). Natural sweeteners like erythritol, xylitol, and sorbitol are derived from sugars, which are widely used in dairy products, tea products, alcoholic drinks, seasonings, candy, starch products, processed fruits and vegetables. Artificial sweeteners may be derived through manufacturing of plant extracts or processed by chemical synthesis, such as saccharin sodium, cyclamate, sspartame and sucralose. They are mainly used in fruits, jams, beverages, desserts and dairy products. Examples in stabilizers and emulsifiers include tricalcium phosphate, lecithin, alginic acid and xantham gum, and stearic acid as anticaking agent, emulsifier, bulking and thickening agents and glaze enhancer, respectively (Hasenhuettl and Hartel, 2008). Additives fall into this category have other functions for improving whipping, leavening and color permanence. As for food matrix, whenever two or more ingredients mix together, stabilization becomes an issue. For instance, water and oil can not mix naturally, so when a processed food comes across this problem, an additive can be introduced to encourage cohesive mixing. All the examples show that food additives are intensively used in our daily life and industrial production. They can definitely improve food quality and benefit people’s health in certain ways. However, the vast majority of food safety incidents caused by illegal activities, especially the abuse of food additives and even the illegal use of chemical additives, have caught public attention worldwide. Therefore, the regulation and measurement of food additives are two most effective methods to standardize the market action and enhance the level of food safety.

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7.4

161

Regulation and measurements of food additives

Though people are becoming more dependent on food additives, there has been certain controversy when talking about the risks and benefits of food additives. In people’s perception, natural additives are generally considered safer than synthetic ones. However, natural additives may be similarly harmful in certain individuals. For example, safrole, naturally occurs in sassafras and sweet basil, was firstly used as a food spice to flavor root beer until it was shown to be carcinogenic. Actually, many countries regulate the use of food additives. From the 1870s to the 1920s, boric acid was widely used as a food preservative, but was banned in World War I as it was demonstrated to be toxic in animal and human studies. However, the urgent need for cheap and available food preservatives led to its reuse during World War II, and it was finally banned in the 1950s (Bucci, 1994). Such cases came to the conclusion that only additives with stable safety should be used in foods. Therefore, a series of standards regarding the topic of food additives safety should be covered to introduce new safe additives and bans those with questionable ones. According to laws, regulations and standards, illegal food additives or abuse of food additives are identified in two ways of component identification and content determination. The former aims at finding out whether additives are illegally used and the latter is to know if additives are excessive. From this point of view, how to determine food composition and additive content is an important issue to guide the laws and ensure food safety (Fig. 7.2). Thus, it is critical to establish stable and sensitive analytical methods for the inspection of food additives. At present, traditional analytical methods for the detection of food additives mainly include chromatography such as liquid chromatography, gas chromatography and mass/liquid chromatography. These methods behave advantages of high sensitivity, good stability and reproducibility, but still suffer shortcomings such as professional operations, complex sample pretreatment, relative long test period and high cost. Besides, spectrophotometry like Uv-vis absorbance and fluorescence spectroscopy possess simple operation and good sensitivity, however, the stability is not good and uncertainty sources are complicated (Soova¨li, Ro˜o˜m, Ku¨tt, Kaljurand, & Leito, 2006). High performance capillary electrophoresis exhibits simple operation, high accuracy and sensitivity but requires high quality of electrophoresis paint (Wang, Cao, Fu, Jiang, & Hu, 2019). Recently, ion chromatography has been intensively used in food additives, which FIGURE 7.2 Analytical methods for the detection of all kinds of food additives.

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can determine low level of components even at high substrate concentration, thus simplify or eliminate sample pretreatment process and achieve simultaneous determination of multiple components with different valence states (Kaufmann, Widmer, Maden, Butcher, & Walker, 2018). With the development of nanotechnology, surface enhanced Raman spectroscopy, fluorescence assay, electrochemical detection, enzyme-linked immunosorbent assay, immunochromatography and their related biosensors have been constructed to analyze food additives, which greatly improved the performance of the analytical methods (Wu et al., 2019). These newly developed methods can also realize quantitative and qualitative detection of food additives with simple operations, rapid detection, high sensitivity and good specificity.

7.5

Analysis of food additives

In this chapter, five classes of food additives are introduced, including colorants, preservatives, sweeteners, antioxidants, emulsifiers as well as their application and properties. Then, different analytical methods are carefully discussed to prove the good detection performance for food additive, especially for the additives with certain limit quantity. Amongst, biosensors, a system based on biochemical recognition mechanism that can sensitively converts analyte concentrations into signals, are also introduced in the analysis of food additives.

7.5.1 Analysis of food colorants Food colorants, also known as dyes or pigments that impart color when it is added to food or drink, are usually divided into natural ones and synthetic ones. Compared with natural pigments, synthetic pigments have the advantages of high stability, good color uniformity, low microbial pollution and cost of production. Because of the advantages, synthetic pigments such as amaranth, tartrazine, indigo, sunset yellow are widely applied in food industry. However, continuous intake of such synthetic colorants may cause certain toxicity to human’s body. Experiments demonstrated that a high dosage of the dye such as amaranth might increase the incidence of malignant tumors in rats (Omaye, 2004). The synthetic pigments allowed in some countries include amaranth, tartrazine, indigo carmine and sunset yellow with strictly limited dosage. Fig. 7.3

7.5.1.1 Amaranth Amaranth, a synthetic red azo dye, which usually comes as a trisodium salt in the drinks, candy and pastries. Excessive amaranth can have adverse effects on health, such as high genotoxicity and cytotoxicity. Since 1976, it has been banned in the United States by the Food and Drug Administration as a suspected carcinogen (Color Additives Fact Sheet, U. S., 2001). However, its use is legal in some other countries like United Kingdom and China but the maximum amount is strictly limited due to the potential risks to health. Amaranth is not suitable for fermented food because it is easy to be (1) HO NaOS

SONa

NaOOC

N

N N

N

N

N

Amaranth

Tartrazine

(3) O S O

O

N H

SONa

OH

NaOS

SONa

Na+

FIGURE 7.3 Chemical structures of the representative synthetic food colorants (1: amaranth; 2: tartrazine; 3: indigo carmine; 4: sunset yellow).

(2)

(4) H N

O

Indigo carmine

HO O S O

N

O– Na+

N SONa

NaSO Sunset yellow

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decomposed by bacteria. According to the hygienic standard of food additives by FAO/WHO in 1994, the daily acceptable intake of amaranth is between 0 mg/kg and 0.5 mg/kg. Therefore, it is of great significance to detect the amount of amaranth in food to control food quality and ensure consumer’s health. So far, many techniques like high performance liquid chromatography (HPLC), electrochemical detection and other methods are used for the determination of amaranth in foods. For example, Wang et al. reported an electrochemical sensor for the sensitive and rapid detection of amaranth using multi-walled carbon nanotubes as the sensing film (Wang, Hu, & Cheng, 2010). The application of multiwall carbon nanotubes greatly improved the surface area and accumulation efficiency of electrode interface, which led to a great enhancement effect on the oxidation of amaranth. The detection linear range for amaranth was from 40 nM to 0.8 μM, and the limit of detection (LOD) reached 35 nM. The method was employed to detect amaranth in soft drinks and the results were tested by HPLC. Also, Mei et al. developed an electrochemical method for amaranth analysis based on expanded graphene. Due to the excellent electrochemical properties of graphene, amaranth has been successfully detected in retsina samples with a LOD of 0.005 μM (S/N 5 3) (Wang, Zhang, & Ding, 2013). The electrochemical method provided a simple and easy approach to the sensitive and selective of amaranth in food samples. Moreover, Zhu et al. presented a fluorescence enhancement method for amaranth in the presence of sodium dodecyl benzene sulfonate (SDBS). The method showed a linear range of 1.00 3 1027B1.00 3 1023 M for amaranth with the LOD of 0.327 μM (Zhu, Huang, & Wang, 2014). Compared with other reported analytical methods, this method has a relatively wide linear range and acceptable sensitivity, but it may lack the capacity of resisting disturbance in real sample detection. By introducing carboxy into amaranth to design the immunogen and coating antigen with carrier protein, Zhang et al. prepared a specific monoclonal antibody against amaranth and developed an indirect competitive ELISA for quantitative analysis of amaranth. The method behaved a linear range from 3.0 to 243.0 ng mL21 with the LOD of 3.35 ng mL21 (Zhang, Du, & Meng, 2014). The method was simple, sensitive, specific and accurate, but this method is a little tedious and requires high expertise in the preparation of antibody.

7.5.1.2 Tartrazine Tartrazine, known as lemon yellow, is a kind of water-soluble azo dye and often used in the coloring of food, beverage and so on. Studies have shown that intake of tartarzine can cause a series of biochemical markers changes at both higher doses and low doses, which are significantly harmful to asthma patients and children at higher doses (Amin, Hameid, & Elsttar, 2010). The acceptable daily intake (ADI) for tartrazine is allocated as 7.5 mg/kg /day by JECFA in 1964, but many countries have banned or restricted tartrazine (Walton et al., 1999). Therefore, many methods have been developed to analyze tartrazine in food. For instance, Zhang et al. prepared alumina microfibers and used them to construct electrochemical sensors for the sensitive detection of tartrazine. Owing to the porous structures and large surface area, alumina microfibers exhibited high accumulation efficiency for tartrazine and significantly increased the oxidation signal of tartrazine. The new sensor is used for the detection of tartrazine in different drink samples with a LOD of 2.0 nM (Zhang, Hu, & Liu, 2015). The electrochemical method can be rapid, simple and sensitive in additive analysis. At the same time the oxidation reaction mechanism can be explored using the electrochemical technique. Yang et al. reported a fluorescence method via the fluorescence resonance energy transfer (FRET) between tartrazine and 3-mercapto-1, 2, 4-triazole terminated gold nanoclusters (TRO-AuNCs) (Yang, Na, & Tan, 2016). In the strategy, the fluorescence of TRO-AuNCs can be effectively quenched by tartrazine, and the reduction in the fluorescence intensity of TRO-AuNCs can be calculated to indicate the concentration of tartrazine. The method showed a linear range from 0.08 to 37.5 μM with the LOD of 28 nM was achieved. Finally, the method was verified by juice and honey samples with recoveries at 92.0B105.2%, which suggested its potential application in practical measurement of tartrazine in foodstuff samples. Similarly, Yang et al. described a rapid, sensitive and selective fluorescence method based on the quenching effect of tartrazine towards acriflavine (Yang, Ran, & Yan, 2017). The proposed method manifested satisfied linear relationship and sensitivity to tartrazine with the linear range of 0.056B5 μM and LOD of 0.017 μM (3σ/k). By using a standard addition method, the recoveries from 96.0% to 103.0% can be obtained for tartrazine in real food samples. Thus, in addition to the convenience, the FRET method shows higher sensitivity and stability in the analysis of tartrazine.

7.5.1.3 Indigo carmine As a kind of synthetic colorant, indigo carmine is an organic salt derived from indigo by sulfonation and often used to improve the color of food such as cold drink. Though indigo carmine is a water-soluble non-azo colorant, its water solubility is not that good (25  C, 10 g/L). Recent studies show that indigo carmine might cause irritations vomiting and

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diarrhea to human beings (Lakshmi, Srivastava, Mall, & Lataye, 2009). However, it is difficult to extract it completely from the food samples for analysis, which poses great challenges to the food safety supervision. Therefore, it is critical to develop methods for the effective separation and rapid detection of indigotine. For the separation and purification of indigo carmine, Huddleston et al. discussed the separation and recovery utilizing the aqueous biphasic extraction chromatographic (ABEC) resins (Huddleston, Willauer, Boaz, & Rogers, 1998). Quantitative partition of indigo carmine in ABEC resins has been demonstrated that ABEC can be applied to the analysis, purification, recovery, and recycling of similar small organic molecules. Furthermore, Li et al. reported an electrospinning process for the synthesis of mercapto group functionalized polyvinyl alcohol/SiO2 composite nanofiber membranes as adsorbents (Li, Wang, Wu, Li, & Zhi, 2012). The adsorbents were adopted to adsorb indigo carmine and showed maximum adsorption capacity of 266.77 mg g21 for 500 mg L21 indigo carmine within 20 min, which revealed that the adsorbents have the advantages of high adsorption capacity and short adsorption time. The ideas of the two work can be used for the separation and concentrate indigo carmine for further analysis. Arvand et al. studied the electrochemical behavior of indigo carmine based on N-ethylaniline/carbon paste modified electrode (Arvand, Saberi, Ardaki, & Mohammadi, 2017). The electrochemical signal of indigo carmine was greatly enhanced as N-ethylaniline/carbon paste was introduced. The method showed a linear range from 1 to 100 μM for indigo carmine with the LOD of 0.36 μM. Without complex sample pretreatment, the proposed electrode was successfully applied to the determination of indigo carmine in candy coated chocolate, jelly powder and diazepam tablet samples. By coupling flow injection analysis and boron-doped diamond electrode, Deroco et al. proposed a multiple pulse amperometry for the detection of indigo carmine (Deroco, Medeiros, Rocha-Filho, & Fatibello-Filho, 2018). The results revealed the concentration ranges of 70.0B1000 nM with a LOD of 40.0 nM. Finally, the proposed method was applied in the quantification of indigo carmine in commercial candies at a 95% confidence level. Lately, Manjunatha described a poly(glycine) biosensor for the analysis of indigo carmine, which was prepared via the electropolymerization of glycine on carbon paste electrode (Manjunatha, 2018). The electrochemical biosensor showed the linear ranges of 2 3 1026B1 3 1025 M and 1.5 3 1025B6 3 1025 M with a LOD of 11 3 1028 M, which indicated that the proposed biosensor is attractive and suitable for the analysis of trace indigo carmine. Li et al. developed a resonance Rayleigh scattering (RRS) method for the analysis of indigo carmine using acridine orange (Li et al., 2016). In the presence of indigo carmine, the weak RRS intensity of acridine orange was greatly enhanced. The enhanced RRS intensity by indigo carmine behaved a linear range from 2 to 32 μM, and the LOD is calculated to be 2.4 3 1028 M. Without any significant interference, the RRS method can achieve sensitive and selective determination of trace level of indigo carmine. From the pretreatment of separation and purification to the detection techniques, it can be seen that the high efficiency of pretreatment and good robustness of detection always come first. Fortunately, the above-mentioned detection methods possess the advantages such as simple pretreatment, good purification effect, high sensitivity and reproducibility, showing their potential applications in the analysis of indigo carmine in foodstuffs.

7.5.1.4 Sunset yellow Sunset yellow (orange yellow), a water-soluble synthetic azo dye, is widely used in candy, snacks, sauces, desserts, and preserved fruits. It is often used in combination with amaranth to give a brown colouring in chocolates. Under EU and WHO/FAO guidelines, the daily acceptable intake is 0B4 mg/kg. Studies reported that sunset yellow behaves no carcinogenicity, genotoxicity, or developmental toxicity in the amounts at which it is used (EFSA Panel on Food Additives and Nutrient Sources added to Food ANS, 2014). However, it was banned or restricted as a food additive in countries like Norway, Finland and Sweden due to its lack of evidence for food safety. In recent years, several analytical methods have been reported for the determination of sunset yellow, such as electrochemical sensor, fluorimetric method and surface-enhanced Raman scattering and enzyme-linked immunoassay. Rovina et al. developed an electrochemical sensor for the detection of sunset yellow based on combination of chitosan, calcium oxide nanoparticles and multiwall carbon nanotubes sensing film (Rovina, Siddiquee, & Shaarani, 2016). By using differential pulse voltammetry, sunset yellow can be detected with the linear range of 0.9B10 ppm as well as the LOD of 0.8 ppm. The recoveries of food beverages are 91.8B97.5%, which suggests its accuracy and stability in the analysis of sunset yellow. Moreover, Ghoreishi et al. reported a gold nanoparticles modified carbon paste electrode for the simultaneous determination of sunset yellow and tartrazine (Ghoreishi, Behpour, & Golestaneh, 2012). Under pulse voltammetry, two well-resolved anodic peaks for sunset yellow and tartrazine could be observed, which were used to analyze these two colourants simultaneously. The results showed the LODs of 3.0 3 1028 M and 2.0 3 1029 M for sunset yellow and tartrazine, respectively. The method showed accurate and sensitive determination of the used

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dyes in soft drinks without laborious pretreatment, which make it an effective voltammetric sensor for the simultaneous analysis of sunset yellow and tartrazine in real samples. Besides, a novel surface-enhanced Raman scattering (SERS) substrate composed of SiO2@Au nanoshell was developed by Xie et al., which was then applied to determine sunset yellow and chrysoidine simultaneously (Xie, Chen, & Cheng, 2014). The SERS substrate behaved an excellent SERS enhancement for both sunset yellow and chrysoidine and each molecule can be distinguished by the characteristic peaks in SERS spectrum. The lowest concentration of 1 ppm and 0.5 ppm were measured for sunset yellow and chrysoidine, respectively. Qu et al. reported a SERS method for the analysis of sunset yellow and allura red in beverages based on gold nanorods (Ou et al., 2018). SERS coupled with gold nanorods exhibit high sensitivity towards the two colorants with a limit of quantitation (LOQ) of 0.10 mg/L. With little sample preparation, the method can realize the convenient, cost efficient and quick detection of sunset yellow and allura red. Generally, the SERS method possesses great advantages in terms of simplicity and sensitivity in the analysis of colorants but the background interference is the main restrictions. Fluorescence spectrometry is another powerful tool widely used in the analysis of sunset yellow. For example, by heating N-(2-hydroxyethyl)ethylene diaminetriacetic acid in air, Yuan et al. prepared fluorescent carbon dots whose fluorescent can be selectively quenched by sunset yellow (Yuan et al., 2016). The fluorescent resonance energy transfer between the carbon dots and sunset yellow was exploited to determine sunset yellow ranging from 0.3 to 8.0 μM with a LOD (3σ/k) of 79.6 nM. The fluorimetric method was validated for sunset yellow in soft drinks samples with the recoveries and relative standard deviations of 96.3B101.6% and 1.6B3.8%, revealing its feasibility to analyze the content of sunset yellow in food samples. Enzyme-linked immunosorbent assay (ELISA) technique is mostly used for the detection of various additives in real systems because of their rapidity, mobility, high sensitivity and low detection limit. By using a polyclonal antibody immunized from carboxyl group modified sunset yellow, Xing et al. presented an indirect competitive enzyme-linked immunoassay for the selective determination of sunset yellow (Xing et al., 2012). The method showed the IC50 value of 0.52 ng mL21 and LOD 25 pg mL21 in buffer. Also, the LOD values in real samples of beverage, dried beancurd and braised pork are 0.12, 0.04 and 1.11 ng mL21, which revealed its high sensitivity in sunset yellow detection.

7.5.1.5 Other colorants Other commonly used colorants include ponceau 4 R, allura red and azorubine. They contain azo group as the chromophore in the molecular structure, which can produce reduction peak by cathodic voltammetric determination. Meanwhile, the -OH group on aromatic ring can also make them electrochemically oxidisable by using anodic voltammetry (Lipskikh, Korotkova, Khristunova, Barek, & Kratochvil, 2018). For example, Zhang et al. described an electrochemical sensor based on a multi-walled carbon nanotube (MWNT) sensing film (Zhang, Zhang, & Lu, 2010). In the method, two oxidation peaks could be observed corresponding to Ponceau 4 R and Allura Red (0.56 and 0.68 V). The oxidation signals of the two colorants were remarkably improved by MWNT thin film, yielding the LODs of 15 μg L2 1 and 25 μg L2 1, respectively. Piri et al. constructed a molecularly imprinted electrochemical sensor for the determination of azorubine, which showed good sensitivity and selectivity with a detection linear range of 1B12 mg L2 1 and a LOD of 0.57 mg L2 1 (Piri, Piri, Yaftian, & Zamani, 2018). The molecularly imprinted method can greatly induce the interference from food samples, making the method more accurate and selective to azorubine. Besides, Yang et al. synthesized silver-doped graphite carbon nitride (Ag-g-C3N4) nanoparticles as fluorescence probes to detect curcumin (Yang et al., 2018). The green fluorescence of Ag-g-C3N4 could be quenched by curcumin. The fluorescence intensity decreased with the increase of curcumin concentration, and the LOD was as low as 38 nM. Peksa et al. reported a fast quantitative analytical method for azorubine in food and drinks using surface-enhanced Raman scattering (SERS) (Peksa et al., 2015). The SERS substrate of Au sputtered nanospheres behaved highly sensitive and reproducible for azorubine with the range of 0.5B500 mg L2 1, which can be applied for quantitative analysis of azorubine with minimum effort. The robust SERS substrates can make SERS a perfect method in fast preliminary testing of foods and beverages.

7.5.2 Analysis of preservatives Due to the high nutrient content in foods, microbial, enzyme or chemical changes in the shelf life can easily cause food spoilage (Abdulmumeen et al., 2012). The addition of food preservatives can prevent or delay food spoilage. The food antioxygen permitted in China include benzoic acid and its sodium salt, sorbic acid and its salts, propionic acid and its

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salts, P-hydroxy phenyl acid polymer, nitrite and so on. The work mechanism of these preservatives lies in inhibiting growth of the bacteria or their specific enzymes. The function of antioxidants pulls them into the class of preservatives, but they may not be discussed in this section. We’ll talk about antioxidants like BHA, BHT, TBHQ and PG in a separate section because they have similar chemical structure and are usually used in food together. Studies have found that there is a general adverse effect of benzoate preservatives on the behavior of children, but these studies were not entirely conclusive (Bateman et al., 2004). Whatever, it is essential to develop analytical methods for analyzing preservatives for food safety supervison and risk assessment.

7.5.2.1 Benzoic acid and its sodium salt Benzoic acid and its sodium salt are used as preservatives, which are mainly used in pickled products and beverages. The maximum amount used in foods ranges from 0.2 to 2.0 g/kg. If benzoic acid is excessively added to the food, it will destroy the VB1 in the food and make the calcium insoluble, which can destroy the absorption of calcium by the human body. Furthermore, a long-term intake of benzoic acid will increase the risk of cancer. Therefore, it is necessary to ensure low levels of these preservatives in food to meet regulatory standards. Ding et al. proposed a rapid, accurate and environment-friendly procedure for the HPLC-based determination of benzoic acid salt in soy sauce (Ding, Peng, Ma, & Zhang, 2015). Small amounts of organic solvents were used for liquid liquid extraction and no organic solvents was adopted in the standard solutions, which made it less harmful to environment. Besides, benzoic acid and the internal standard were separated within 8.1 min, followed by the measurements with a relative standard deviation of less than 3% and recoveries of 96.1B104.3%, which demonstrated that the ¨ ztekin has exploited a silica capillary instead of a column to developed method is rapid and accurate. Moreover, O ¨ ztekin, 2018). The detection linear range for achieve rapid sample separation (B3 min) and analysis of benzoic acid (O benzoic acid was from 0.005 to 0.4 mM with a LOD of 0.405 mg L2 1. The capillary electrophoresis method has the advantages of simple procedures, low cost and good reproducibility. Li et al. developed a polydiacetylene (PDA) vesicles-based chromatic biosensor for the detection of sodium benzoate. In the work, benzoic acid analog-modified PDA vesicles were used to construct a competitive colorimetric immunoassay with binding antibodies, then benzoic acid competed with antibodies to produce the color variations that caused by state changes of PDA (Li et al., 2018). Benzoic acid in vinegar and soy sauce diluted with 100 or 200 times could also be detected by this method. The LODs of sodium benzoate were 100 ng mL21 and 0.01 ng mL21 by nakedeye readout and UV vis spectrometry, respectively. The accuracy of this method is comparable with HPLC with the advantages of convenience and small sample consumption, which is suitable for on-site detection. But the preparation and preservation of antibody is a tough work, as well as the stability of the sensor remains to be modified. Microfluidic device is widely used in food analysis, and the paper-based device has the advantages such as lightweight and low in cost. Liu et al. developed an integrated microfluidic platform comprising a microfluidic paper-based analytical device for the detection of benzoic acid (Liu, Wang, & Fu, 2018). Twenty-one commercial food samples were measured to analyze the quantity of benzoic acid, which showed the deviations of concentration measurements were below 6.6%. The proposed integrated microfluidic paper-based chip platform provides a compact and reliable tool for the analysis of benzoic acid. Berger et al. reported a supercritical fluid chromatography (SFC) for the rapid separation and quantification of benzoic acid in beverages, which achieved a wide range of 0.025B5 mg mL21 with correlation coefficients more than 0.999 and RSD less than 1% (Berger and Berger, 2013). The SFC method was found to be up to 7 times faster (B2 min) than HPLC without the use of acetonitrile. The method has the advantages of simplicity and low cost, which is suitable for on-site detection.

7.5.2.2 Parabens Parabens, known as p-hydroxybenzoic acid, are widely used in food, cosmetic and pharmaceutical products, which show no toxicity in subchronic and chronic studies in rodents (Soni, Carabin, & Burdock, 2005). However, some animal studies have reported adverse reproductive effects of parabens. As such, possible detrimental health effects to human could be caused with the use of foods, cosmetics, and pharmaceutical products. To address the concern, many regulatory agencies have established exposure limits for the use of parabens in those products. Thus, it is of high importance to determine parabens in such products to ensure human’s health. Most of the analytical methods for parabens are chromatography technique coupled with certain separation system. For example, Chen et al. reported a vortex-assisted dispersive liquid-liquid microextraction for paraben and then detected paraben using a capillary liquid chromatography-ultraviolet detection system (Chen, Hsu, Lu, Weng, & Feng, 2018). Four parabens in 19 commercial products were successfully determined with good precision (RSD , 5%), short

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extraction time (,6 min) and excellent sample versatility. The proposed method exhibited a detection linear range for parabens from 0.1 to 10 mg L21 with the LOD of 30 μg L21. The technique was very suited to the extraction of parabens from complex food samples. Besides, Villar-Navarro et al. developed an electromembrane extraction (EME) combined with a HPLC procedure for the determination of five parabens in water, which showed detection limits between 0.98 and 1.43 μg L21 (Villar-Navarro, del Carmen Moreno-Carballo, Ferna´ndez-Torres, Callejo´n-Mocho´n, & BelloLo´pez, 2016). EME method behaved high extraction efficiency for parabens without complex instrumentation and with a low-cost procedure, which could be an alternative to conduct pretreatment in food matrices. Moreover, Prapainop et al. described a simple and sensitive fluorometry for total paraben determination using mercaptosuccinic acid capped (MSA) CdTe quantum dots (MSA-CdTe QDs), which could realize the concentration detection by both the visible and fluorescent methods (Prapainop, Mekseriwattana, Siangproh, Chailapakul, & Songsrirote, 2019). Due to the hydrogen bonding formation between carboxylic acid groups on CdTe QDs and the analytes, paraben can selectively quench CdTe QDs fluorescence, thus making it possible for the determination of paraben. Using the method, a wide detection range of 1.0B500.0 mg L21 as well as a LOD of 0.1 mg L21 was achieved for p-hydroxybenzoic acid, hydrolyzed form of parabens. This work demonstrated successful application in determining these preservatives in fish sauce, coconut milk, and beverages, which showed great potential for parabens detection in the complex samples such as cosmetic, household, and food products

7.5.2.3 Nitrite Nitrite, mostly sodium nitrite, is used in the processing of meat products because it can prevent bacterial growth and give the product a desirable red color, such as in corned meat like pork and beef. However, nitrite is adverse to human body, because it can bind with hemoglobin to reduce the oxygen transport capability of blood. Also, nitrite can be readily converted to potent carcinogenic N-nitrosamines by reacting with the amino acids of proteins (Parthasarathy and Bryan, 2012). The lethal level of nitrite is about 22 mg/kg of body weight, thus the maximum allowed nitrite concentration in meat products is 200 ppm. Researchers have paid much attention to the determination of nitrite by using anodic voltammetry because other elements and anions do not interfere with nitrite oxidation peak current. For example, Sudarvizhi et al. developed a chitosan protected tetrasulfonated copper phthalocyanine modified glassy carbon electrode to sensitively detect nitrite in food sample (Sudarvizhi, Pandian, Oluwafemi, & Gopinath, 2018). The abundant adsorbed mediator molecules on the modified electrode greatly enhanced the current of nitrite. The differential pulse voltammetry displayed the peak current against NO22 with a linear range of 0.2B6.3 μM with the LOD of 0.012 μM. Besides, Kung et al. fabricated an amperometric nitrite sensor based on metal organic framework nanmaterials, which showed high electrocatalytic activity for nitrite oxidation (Kung, Chang, & Chou, 2015). The detection linear range and LOD of this work are 20B800 μM and 2.1 μM, respectively. Wu et al. proposed a chemiluminescence method for the detection of nitrite on microfluidic chip (Wu et al., 2016). In the carbon dots-NaNO2-acidified H2O2 system, nitrite can react with H2O2 to form online ONOOH that make carbon dots chemiluminescent. The nitrites in water and beverage samples were successfully analyzed with the LOD of 10 μM. Nam et al. described polyethylene glycol hydrogel superimposed glass fiber membrane strips for nitrite detection based on Griess assay (Nam, Jung, & Kim, 2018). The prepared glass fiber strips played an important part in reducing nonspecific binding and immobilizing color reacted by nitrite. The colorimetric hydrogel sensor showed a detection linear range from 10 μM to 5 mM with a LOD of 10 μM. The coloring method is believed to be a simple, specific and agentsave approach for nitrite ion analysis.

7.5.2.4 Other preservatives Other preservatives like sulfur dioxide and sulfites, sorbic acid and sodium sorbate, propionic acid and propionates are also reported to be detected by various analytical methods. Wang et al. constructed a rapidly responsive and highly selective fluorescent probe for bisulfite (HSO32) detection in sugar samples (Wang et al., 2017). The color of fluorescent probe changed from yellow to colorless with the treatment sulfite, which can be ascribed to the nucleophilic addition to break the π-conjugation. The method exhibited high selectivity towards HSO32 with the detection range of 0B80 μM, as well as a LOD of 0.01 μM, which suggested that the fluorescent probe could be used as an effective tool to determine the bisulfite levels in food samples. Based on capillary electrophoresis with UV spectrophotometric method, de Jesus et al. successfully realized the simultaneous detection of preservatives propionate and sorbate with the separation time less than 5 min (de Jesus et al., 2018). Propionate and sorbate were detected at 235 nm and 250 nm with the LOQs of 4.3 and 1.5 mg kg21, respectively. The proposed method can be a promising tool in the simultaneous determination of preservatives in commercial

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food samples. Guadalupe Coelho et al. carried out the propionate and sorbate analysis by capillary electrophoresis with capacitively coupled contactless conductivity detection (Guadalupe Coelho, Fernanda Souza de Jesus, de Assis Pallos, Alberto Fracassi da Silva, & Pereira de Jesus, 2018). The method showed high separation efficiency of the analytes with 7 min, which yielded LOQs of 0.11 and 0.21 g kg21, as well as the recoveries varied from 88% to 94% and 86% to 102% for propionate and sorbate. The method is extremely appropriate for the analysis of inorganic and organic compounds that lack a strong chromophore group, such as propionate and sorbat.

7.5.3 Analysis of sweeteners Sweeteners is a kind of food additive that can give food sweetness, which can be divided into natural sweeteners (eg. stevia, mogroside, mabinlin, inulin) and artificial sweeteners (eg. saccharin, advantam, aspartame). The effects of those sweeteners on human health include weight gain, metabolic disorder or even the risk of cancer. The good is that it remains inconsistent and inconclusive between the weight gain and sweetener usage. The same conclusion is drawn for the influences of sweeteners on the metabolic disorders and the potential cancer risks in humans (Lohner, Toews, & Meerpohl, 2017). Still, the guidance about daily limit for consuming sweeteners has been provided by FDA (Additional Information about High-Intensity Sweeteners Permitted for Use in Food in the United States, 2018). Therefore, it is necessary to determine sweeteners to ensure the quality and safety control of foods.

7.5.3.1 Steviol glycosides Steviol glycosides is a high-sweet, low-calorie natural sweetener extracted from the leaves and stems of stevia. Stevia can be extracted and purified to obtain the monomer stevia glycoside or the mixture of each monomer. Steviol glycosides is a new natural edulcorant with the advantages of high sweetness, low calorie, resistance high temperature and high stability, which has become an attractive sugar substitute in the food industry. Besides, the use of stevia sweeteners as replacements for sugar might reduce blood glucose and protect the body from diseases such as diabetes and obesity. Thus, to evaluate the structure and capability of Stevia extracts, the analysis of these sweeteners is in great need. The analytical methods for steviol glycosides mainly include chromatography and spectrometry. For example, Lorenzo et al. achieved the purpose of microfiltration and ultrafiltration and quantitative determination of stevia glycosides (Lorenzo, Serrano-Dı´az, Plaza, Quintanilla, & Alonso, 2014). By means of micro- and ultrafiltration, it could obtain an acceptable percentage of the major steviol glycosides. After that, the proposed HPLC achieved good selectivity, sensitivity and accuracy for the quantification analysis of steviol glycosides. The method showed a wide concentration range from 25 to 500 mg L21 for steviol glycosides (stevioside and rebaudioside A) with LODs of 1.07 mg L21. Wald et al. focused on the improvement of high-performance thin-layer chromatography (HPTLC) method and applied it for Stevia analysis (Wald and Morlock, 2017). As a result, linearity was 1B7 μg/zone, LODs were 127 and 387 ng/ zone, LOQs were 393 and 1191 ng/zone for both stevioside and rebaudioside A, respectively. The HPTLC method was shown to effectively support quality control with short analysis time (2.6 min per sample) and low solvent consumption (0.4 mL per analysis). The method was reliable and robust, which was suitable for quantitative detection of steviol glycosides in laboratory and quality control in industry. Bathinapatla et al. constructed an electrochemical biosensor for the electrochemical determination of rebaudioside A in different food samples (Bathinapatla, Kanchi, Singh, Sabela, & Bisetty, 2016). Multiwalled carbon nanotubes/cytochrome c nanocomposites were modified on the electrode surface, showing enzyme-like activity for the reduction of Reb A. The biosensor exhibited showed a linear region of 0.001B0.05 mM and 0.075B1.25 mM for rebaudioside with a LOD of 0.264 μM. Moreover, McCullagh et al. developed an ion mobility mass spectrometry coupled with ultrahigh performance chromatograph (UHPLC-IM-MS) for the analysis of steviol glycosides (McCullagh, Douce, Van Hoeck, & Goscinny, 2018). Based on collision cross sections in nitrogen buffer gas, the specificity could be enhanced in conjunction with retention time and exact mass. Further, the analytical challenge and resolve complexity in food analysis can be understood by this means.

7.5.3.2 Sodium saccharin Saccharin sodium is a commonly used artificial sweetener added in the food such as sauces, soft drinks, candy and other desserts to improve flavor. The ADI of saccharin sodium determined by the JCEFA is 0B5 mg/kg, and excessive consumption can lead to the development of bladder cancer in rats but not clear in humans (Organization and Organization, 2010). Saccharin is allowed in most countries and the label requirement for products containing saccharin is essential. Therefore, it is necessary to analyze the content of saccharin sodium in food.

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Zhao et al. synthesized molecularly imprinted nanoparticles to enrich saccharin sodium with high specificity and efficiency, and saccharin sodium acted as model to evaluate the performance of molecularly imprinted technique (MIP) (Zhao et al., 2015). The linear range for saccharin sodium in samples ranged from 0.5 to 50 μg mL21 with LODs of 0.05 μg g21 for canned peaches, 0.08 μg g21 for sweet sauce and 0.08 μg g21 for grape juice. The results revealed that the surface molecularly imprinted polymers can be used for the selective and sensitive detection of saccharin sodium. Li et al. developed a method for quantitative analysis of saccharin sodium in various food samples using ambient flame ionization coupled with triple quadrupole tandem mass spectrometry (AFI-MS) (Li et al., 2018). AFI-MS possess a high sensitivity for saccharin sodium with the detection range of 4B100 μg mL21 and LODs of 0.12B0.21 μg mL21 in different matrices. This technique could be coupled with mass spectrometers easily, which was appropriate for rapid and high-throughput quantification analysis. Han et al. explored a SERS method with silver nanorod array (SNA) substrates for the rapid and sensitive quantification of saccharin sodium in soft drinks (Han et al., 2017). The SNA substrates were prepared by an oblique angle deposition method with an enhancement factor of B108. The method obtained a linear detection range of 0.5B100 mg L21, as well as a LOD of 0.3 mg L21 for saccharin sodium. This SERS-based detection can be completed within 10 min, which is ideal for the on-site analysis of saccharin sodium. He et al. exploited hollow silver dendritic nanoplates as SERS substrate to improve the SERS activity in analyzing saccharin sodium (He et al., 2019). A linear relationship between SERS intensity and the logarithm of target concentration from 0.487 to 0.037 mM, as well the LOD of 0.012 mM was achieved, indicating the feasibility and sensitivity of quantitative analysis for saccharin sodium. At this point, the nanomaterials based SERS substrates have almost become the cornerstone in the detection.

7.5.3.3 Aspartame Aspartame, an artificial non-saccharide sweetener and one of the most rigorously tested food ingredients, is an ingredient in dairy products, frozen fruits and vegetables, cereals and carbonated soft drinks. The acceptable limit of aspartame for a person is 50 mg/kg of body weight per day, and the allowable content in soft drink is no more than 600 mg L21 (EFSA Panel on Food Additives and Nutrient Sources added to Food ANS, 2013). However, upon ingestion, aspartame breaks down into residual components like phenylalanine and methanol, which may be toxic to humans. Therefore, many methods have been reported for the analysis of aspartame in food, such as SERS, MIT, HPLC and electrochemical sensor. For example, Buyukgoz et al. established a rapid and simple method for the determination of aspartame in soft drinks by SERS (Buyukgoz, Bozkurt, Akgul, Tamer, & Boyaci, 2015). Silver nanoparticles (AgNPs) were synthesized by wet chemical method to enhance the Raman signal of aspartame. A good linear relationship was obtained in soft drinks with the detection range of 0B1.0 mg mL21, as well as the LOD and LOQ of 0.14 mg mL21 and 0.45 mg mL21, respectively. The method behaved a good robustness in aspartame detection with a short analysis time, low sample volume and no pre-treatment. Combining MIT and colloidal sphere lithography, Tiu et al. fabricated a colloidal sphere-patterned polyterthiophene thin film for selective detection of aspartame (Tiu, Pernites, Tiu, & Advincula, 2016). Aspartame was successfully imprinted into the electropolymeric molecular imprinted polymer to produce an artificial recognition, then the quartz crystal microbalance was used to detect the mass variations. The sensor had a good linear response to aspartame in the range of 12.5B200 μM, and the LOD was B31 μM. The artificial nanostructured conducting polymer film could realize a highly sensitive and selective detection of aspartame. Sun et al. developed a HPLC/MS/MS method for the simultaneous detection of aspartame and its four degradation products (Sun, Han, Zhang, & Ding, 2014). Tandem mass spectrometry (MS/MS) owns good sensitivity and stability and HPLC possesses short analysis time and high resolution. The couple of HPLC and MS/MS showed LODs of 0.16 5.8 mg L21 for aspartame and its degradation products with run time of 7 min. The method is rapid, sensitive and specific, which can effectively eliminate matrix interference. Radulescu et al. proposed a bienzyme amperometric biosensor for the rapid and simple detection of aspartame (Radulescu, Bucur, Bucur, & Radu, 2014). Two enzymes of alcohol oxidase and carboxyl esterase with cobalt- phthalocyanine were modified on screen-printed electrodes, which could facilitate the breakdown of aspartame and thus generate detectable characteristic current peak. The method achieved the selective and quantitative detection of aspartame in complex samples with a LOD of 0.2 μM.

7.5.3.4 Other sweeteners Other types of sweeteners like cyclamate, sucralose, and sugar alcohols (sorbitol, xylitol and lactitol) are also commonly used in manufacturing of foods. For example, sodium cyclamate, an artificial sweetener, is 30 times sweeter than sucrose. But it was found to absorb and combine with serum protein and showed the evidence of carcinogenic activity

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(Price et al., 1970). Many countries, such as Canada, the United States and India have banned it as a food additive, and JECFA sets the ADI of sodium cyclamate as 11 mg/kg body weight. Similarly, the safety of sucralose and sugar alcohols to human body is not completely clear. Thus, it is necessary to construct appropriate methods to analyze the content level of these additives to guide the safety and quality control. Yu et al. proposed a method to analyze sodium cyclamate by gas chromatography electron capture detector (GCECD) (Yu, Zhu, Lv, Li, & Huang, 2012). By quantitatively converting cyclamate to N,N-dichlorocyclohexylamine with sodium hyperchlorite, the products were detected through GC-ECD. The LODs and LOQs were 0.005 mg/L and 0.25 mg/kg, 0.2 mg/L and 0.8 mg/kg for yellow rice wine and juice, cake and preserved fruit, respectively. The work presented a simple and sensitive procedure, which was suitable for sodium cyclamate analysis in foods and beverages. Shah et al. developed and verified a rapid and reliable LC-MS/MS method for the determination of sodium cyclamate in various food matrices (Shah, De Jager, & Begley, 2014). The method provided quantitative mass spectral for the analysis of sodium cyclamate with the detection range of 0.010B1.00 μg/mL. The LODs and LOQs were 0.1 and 0.6 ng/mL, 0.3 and 1.6 ng/mL for pomegranate juice and dried fig, respectively. This proposed method can analyze various common adulterated products, including drinks, dried fruits, jam and hard sugar. Wu et al. established an ultrasensitive electrochemical biosensing platform for fructose and xylitol based on the principle of boronic acid-diol recognition (Wu et al., 2017). With the presence of fructose or xylitol, boronic acid groups would hybride with fructose or xylitol, which could hinder redox probe like ferricyanide due to the sterical effect. The peak current change of ferricyanide was related to the concentration of fructose or xylitol. A wide range of 1 3 10212B1 3 1022 M was achieved with LODs of 1 3 10212 M and 6 3 10213 M for fructose and xylitol, respectively. The method is pretty simple and rapid in the analysis of fructose and xylitol. Vistuba et al. reported a subminute method to determine aspartame, cyclamate, acesulfame-K and saccharin in food (Vistuba, Dolzan, Vitali, de Oliveira, & Micke, 2015). Using capillary electrophoresis (CE), all analytes were separated within less than 1 min. The method showed good linearity (r2 . 0.9972), low LODs of 3.3B6.4 mg L21 and recoveries of 91B117% for aspartame, cyclamate, saccharin and acesulfame-K. Besides, Coelho and de Jesus proposed a simple method for analyzing erythritol, maltitol, xylitol and sorbitol using capillary electrophoresis (Coelho and de Jesus, 2016). The calibration curve showed good linearity (r2 . 0.9972), low LOQs of 12.4 μg/g, 15.9 μg/g and 9.0 μg/g for britol, maltol, xylitol and sorbitol, respectively. The simplicity and rapidity make this method an interesting alternative for the analysis of food and pharmaceutical samples.

7.5.4 Analysis of antioxidant Antioxidants are an especially important class of preservatives to assure excellent quality and pleasant appearance, odor, and taste for the consumer. Commonly used food antioxidants include butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), propyl gallate (PG) and tert-butyl hydroquinone (TBHQ), which prevent food from turning rancid and discolored by scavenging free radicals from the peroxidation of oils and fats (Fig. 7.4) (Kahl and Kappus, 1993). However, high doses of these antioxidants may have harmful long-term effects on animals such as cancer O

O

OH

OH

FIGURE 7.4 Chemical structures of the synthetic food antioxidants.

OH

(1) BHA

(2) BHT

OH HO

OH

HO O

HO O (3) PG

(4) TBHQ

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proliferation. Therefore, the analysis of these unavoidable additives in food samples are important. A number of analytical procedures have been developed for the detection of the antioxidants. Herein, three main techniques include elctrochemical methods, and biosensors will be introduced because they are more suitable for on-site analysis.

7.5.4.1 Butylated hydroxyanisole Butylated hydroxyanisole (BHA) is composed of a mixture of two isomeric organic compounds, 2-tert-/3-tert-butyl-4hydroxyanisole. As a food additive, BHA is mainly used for fat, oil and emulsified fat products, miscellaneous grains, instant noodles and so on. Based on evidence of carcinogenicity in experimental animals, BHA is reasonably anticipated to be a human carcinogen. In particular, when administered in high doses as part of their diet, BHA may cause adverse effects in animals like rats (Brewer, 2011). According to the Food and Drug Administration regulations, the levels of BHA are limited to 2B1000 μg g21. So the development of simple and sensitive analytical methods for BHA are essential. For example, Rasheed et al. studied the electrochemical oxidation of BHA by the differential pulse voltammetry technique (Rasheed, Vikraman, Thomas, Jagan, & Kumar, 2015). By using multiwalled carbon nanotube modified platinum as supporting electrode for BHA analysis, the method showed a linear range from 1 3 1026 to 1 3 1027 M with the LOD of 94.9 μM. Moreover, Ng et al. construceted a gold nanoparticle/graphite electrode for the multiple detection of BHA, BHT, and TBHQ in food samples (Ng, Tan, & Khor, 2017). Based on linear sweep voltammetry, the developed method showed a dynamic range extended from 0.6 to 80 μg mL21 for the three compounds, which indicated its desirability in the regulation of food safety control. Bavol et al. reported an amperometric method for the simultaneous of BHA, PG and TBHQ using flow injection analysis (Bavol, Economou, Zima, Barek, & Dejmkova, 2018). The method is precise, fast and behaves sufficiently low LOQs of 0.85, 1.45, and 2.51 μM for BHA, PG and TBHQ, respectively. The above electrochemical methods can be easily applied in the detection of food samples without high demands on instrumentation, which behave great potential in food analysis when applying a simple extraction procedure.

7.5.4.2 Butylated hydroxytoluene Butylated hydroxytoluene (BHT), also known as dibutylhydroxytoluene, is a lipophilic organic compound and primarily used as an antioxidant food additive. BHA can delay the rancidity of food, mainly used in animal and vegetable oils and foods containing animal and vegetable oils and fats. The permissible levels of BHT are limited to 200 μg g21 (Brewer, 2011). Long-term consumption of excessive BHT will cause chronic poisoning, leading to adverse effects such as metabolic disorders. The new detection method for BHT mainly focused on electrochemical method and the coupling technique of chromatography. For example, Emam et al. developed a molecularly imprinted electrochemical gas sensor to analyze BHT in air (Emam et al., 2018). The electrochemical sensor was first fabricated by modifying a thin layer of graphene onto a glassy carbon substrate. Then the deposition of polymer template and target molecule was facilitated by electrochemically initiated polymerization. Subsequent removal of the target molecules produced a conductive thin layer of polypyrrole, which contained molecularly imprinted cavities that were selective for the target molecule. The sensor behaved a very LOD of 0.02 ppb, which can be extended to the analysis of BHT in food samples. Toma´sˇkova´ et al. proposed a new method for the simultaneous detection of BHT and BHA using linear-sweep voltammetry (Toma´sˇkova´ et al., 2014). The measurements were conducted on gold disc electrodes without further modification, and the partial overlaps of both BHT and BHA signals could be separated by mathematical treatment of the corresponding voltammograms. The optimized conditions enabled the quantification of both analytes in the BHA 1 BHT mixture at the low level of Bg kg21. Zhang et al. established a GC MS coupled with derivatization method for the analysis of BHT and transformation products (Zhang, Li, Li, Cui, & Ma, 2018). The method showed LODs and LOQs of 0.02B0.34 μg kg21 and 0.08B1.14 μg kg21 for BHT and its transformation products. At different spiking levels, the recoveries of spiked samples ranged from 71.1% to 118% with relative standard deviations ,10.6%. The method could provide accurate results and applied to the analysis of BHT in food samples as well as the study in their transformation mechanism.

7.5.4.3 Propyl gallate Propyl gallate (PG), or propyl 3,4,5-trihydroxybenzoate, is one of the most widely employed synthetic phenolic antioxidants. As a common oil-soluble antioxidant, PG has been added in fried foods, edible fats and oils. Furthermore, PG strengthens the antioxidative action of BHA so that these two additives can be found together in many foodstuffs. Nevertheless, high doses of PG may cause potential health hazards such as apoptosis and DNA cleavage (Nakagawa,

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Molde´us, & Moore, 1996). The ADI recommended by JECFA is 0B1.4 mg/kg body weight and the maximum level in food is 200 mg kg21. The detection methods for PG mainly include electrochemistry, spectrophotometry and chromatography. Morales et al. reported an amperometric tyrosinase biosensor for the determination of PG in different types of foodstuffs (Morales, Gonza´lez, Reviejo, & Pingarro´n, 2005). By incorporating tyrosinase into electrode with simple physical inclusion, the electrode based on graphite Teflon composite behaves great advantages for the determination of PG. A calibration plot for PG was constructed in the concentration range of 1.0 3 1025B2.0 3 1024 M with LODs of B1026 M in different solutions. Wu et al. proposed an electrochemical biosensor for the simultaneous determination of BHA and PG in foodstuffs (Wu et al., 2016). Based on the modification of spiny Au-Pt nanotubes (SAP NTs) and horseradish peroxidase (HRP) onto electrode, it behaved synergetic catalysis towards BHA and PG. The biosensor showed a wide linear range for BHA and PG with 0.3B50 mg L21 and 0.1B100 mg L21, as well as LODs of 0.046 mg L21 and 0.024 mg L21 (3σ/slope), respectively. The above electrochemical sensors concerning characteristics such as high sensitivity, stability with time, short response time, low cost and good suitability to be used in the analysis of foodstuffs. Moreover, dos Reis Lima et al. designed a photoelectrochemical platform based on indium tin oxide (ITO) electrode for the analysis of PG in edible oil samples (dos Reis Lima et al., 2019). By modifying CdTe quantum dots and poly(dglucosamine) (PDG) on the ITO electrode, higher photocurrent response could be obtained for the detection of PG. The sensor showed a linear range of response and LOD for PG with 0.3B150 μM and 0.13 μM, respectively. Recovery tests in edible oils behaved recovery percentages from 96.2% to 111.3%. Dai et al. developed a “sign-on/off” electrochemical sensor for PG analysis based on poly(thionine) modified molecular imprinted polymer (Dai, Li, Fan, Lu, & Kan, 2016). In principle, PG molecules were oxidized in imprinted cavities and blocked the electron transfer for the redox of PTH, which realized a sign-on from PG current and a sign-off from PTH current. The method could achieve a linear detection range for PG from 5.0 3 1028 M to 1.0 3 1024 M with a LOD of 2.4 3 1028 M. This sensor behaved good selectivity, sensitivity and stability towards PG detection. Yue et al. proposed a new fluorescence method for the detection of PG based on the unique fluorescence quenching property of MoO422 and PG (Yue et al., 2018). In the presence of MoO422 and PG, the fluorescence of g-C3N4 can be quenched nanosheets, which was developed for the quantitative analysis of PG. The method behaved a wide range of 0.5B200 μg mL21 with a LOD of 0.11 μg mL for PG. The method provided a strategy for rapid, specific and sensitive detection of PG, which is helpful for regulating and reducing the risk of overuse of PG in foods.

7.5.4.4 Tert-butyl hydroquinone Tert-butyl hydroquinone (TBHQ) is a synthetic aromatic organic compound with two hydroxyl groups and a tert-butyl group. The structure of TBHQ indicates its stability and strong oxidation resistance. In foods, TBHQ is widely used as an additive in unsaturated vegetable oils and many edible animal fats to clean or quench the free radicals. Many studies have shown that prolonged exposure to very high doses of TBHQ may be carcinogenic, especially for stomach tumors (Gharavi and El-Kadi, 2005). Obviously, excessive use of TBHQ has a negative impact on food quality and public health. According to the FDA regulations in U.S., the limit of TBHQ in fats and oils is 0.02% of fat. The detection methods of TBHQ include traditional methods such as HPLC, GC. Here, new developed sensors with simple operations and short detection time will be discussed. With the development of nanotechnology, researchers have developed various new methods for the rapid detection of antioxidants in foods. For example, Fan et al. developed an electrochemical sensor for selective detection of TBHQ (Fan, Hao, & Kan, 2018). Based on molecular imprinted polymer (MIP) modified exfoliated graphite paper, the sensor exhibited a high sensitivity and selectivity for the analysis of TBHQ. The linear range were 0.08B1.0 μM and 1.0B100 μM with the LOD of 12 nM (S/N 5 3). Besides, Cao et al. constructed a ratiometric electrochemical sensor for the detection of TBHQ in edible oil samples (Cao, Wang, Zhuang, Wang, & Ni, 2019). By introducing MnO2 as the inner reference electrochemical signal, the sensor showed two linear ranges of 1.0B50.0 μM and 100.0B300.0 μM for TBHQ with a LOD of 0.8 μM (S/N 5 3). The sensor can avoid the interference of other antioxidants and achieve rapid and accurate detection of TBHQ in edible oil. Monteiro et al. established a photoelectrochemical sensor for the sensitive detection of TBHQ in vegetable oil (Monteiro, Tanaka, & Damos, 2017). Activated by lithium tetracyanoethylenide, CdSe/ZnS quantum dots behaved higher photocurrent and faster charge transfer towards TBHQ. Owing to the easily transfer of TBHQ electrons to the CdSe/ ZnS valence band, TBHQ showed a higher photocurrent compared to that of other phenolic antioxidants. The linear range of the sensor was 0.6B250 μM, and the LOD was 0.21 μM. Yue et al. proposed a novel fluorescence method for TBHQ analysis in edible oil samples (Yue et al., 2016). Based on the competitive interaction between the photo-induce

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electron transfer effect and the complexation reaction of TBHQ/ Fe(III) ions, the switchable fluorescence of carbon dots can be applied to determinate TBHQ. The sensor showed a wide detection range of 0.5B80 μg mL21 with the LOD of 0.01 μg mL21, as well as the recovery from 94.29% to 105.82%, which demonstrated its benefit in monitoring and assessing the risk of TBHQ in food storage.

7.5.4.5 Other antioxidants Also, other antioxidants like natural (tea polyphenols, α-tocopherol, ascorbic acid) and artificial ones (dodecyl gallate, octyl gallate) are added in food products. Due to safety concerns, people are more favorable to natural antioxidants like ascorbic acid and tea polyphenols. However, researchers have shown increasing attention on the analysis of antioxidants, especially for the synthetic ones. For example, Wang et al. proposed a colorimetric method for the rapid detection of tea polyphenols (TPs) based on protein conjugated gold nanoclusters (AuNCs) (Wang, Liu, Qin, Chen, & Shen, 2016). The AuNCs behaved good peroxidase-like activity towards H2O2, however, TPs could restrain the activity of AuNCs and induce the color variations. The changes in color behaved a linear range of 10 nMB10 μM for TPs with the LOD of 10 nM. The strategy is simple in the analysis of tea polyphenols. He et al. reported a colorimetric sensor for the selective detection of ascorbic acid (He, Wang, Chen, & Liu, 2018). In the strategy, MnO2 nanosheets acted as a nanoenzyme to catalyze 3,3,5,5-tetramethylbenzidine (TMB) to a blue product. In the presence of ascorbic acid, it reduced MnO2 into Mn21 and produced the color variations, thus the changes in color can be judged by naked eyes. The method showed a linear response for AA ranged from 0.25 μM to 30 μM with a LOD of 62.81 nM, which could be applied for fruit and juice samples. Xu et al. developed a dispersive liquid liquid microextraction method for the rapid and simple detection of six synthetic phenolic antioxidants (Xu et al., 2016). Based on HPLC with diode array detection, good detection linearity was found over the range of 0.1B500 mg L21 for all the SPAs with the correlation coefficients above 0.998. The LODs were 10, 15 and 25 μg L21 for PG, TBHQ and BHA, DG (dodecyl gallate) and OG (octyl gallate), and BHT, respectively. The developed method can be a potential candidate for the analysis of synthetic phenolic antioxidant in edible oils.

7.6

Analysis of other food additives

In addition to the food additives discussed above, the additives to be detected by researchers also include emulsifiers or the other ones. Most of the analytical methods are included in chromatography, spectrophotometry, electrophoresis, optical detection, immunoassay, electrochemistry and biosensors. According to the differences in the structure and property of food additives, certain technique or their coupled ones will be utilized to obtain the optimal detection performance. For example, Oellig et al. presented a method for emulsifiers determination like mono- and diacylglycerol, triacylglycerols and free fatty acids by high-performance thin-layer chromatography (Oellig, Bra¨ndle, & Schwack, 2018). Through fluorescence detection, good resolution was obtained in the emulsifier composition down to 25 ng lipid class/ zone, which is equal to 0.1% in the emulsifier for 20 μL application. The method was demonstrated as an efficient tool for a simple lipid class screening in food emulsifiers. Nedeljkovi´c et al. used Raman spectroscopy coupled with partial least squares regression to identify butter adulteration with margarine (Nedeljkovi´c et al., 2016). Judging from the differences between Raman spectra of butter and margarine, butter samples could be directly analysed, which implemented this technique in routine butter quality control purposes. Some of the developed methods achieved good detection performance in selectivity, sensitivity and stability. But they may suffer from the disadvantages such as long detection time, long-term sample preparation, complicated operation and high cost. The case maybe the other way around. Table 7.2 lists the advantages and disadvantages of all kinds of analytical methods for food additives analysis. It seems impossible to develop a technique that with all strong points in one. Therefore, to address the complexity in practical sample analysis, the combination with other techniques is the main developing direction.

7.7

Prospects for the analysis of food additives

Modern analytical techniques are being used more often in food additive analysis with remarkable advancement. However, two main problems are still remained to be solved: one is the complexity of the tested samples and the other one is the limitations of analytical techniques. First, the complexity of tested samples can be divided into different levels: (a) food additives show the complexity with great varieties; (b) food additives are often added in all kinds of

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TABLE 7.2 Comparison of advantages and disadvantages of analytical methods for food additives detection. Methods

Food additives

LOD

Advantage

Disadvantage

Reference

HPLC

preservative

Parabens: 30 μg/L

Good selectivity, high sensitivity, recovery and reproducibility

Expensive equipment, high cost of inspection, long cycle and inability to meet batch inspection

(Chen et al. (2018); Lorenzo et al. (2014); Sun et al. (2014); Xu et al. (2016))

sweeteners

Stevia glycosides: 1.07 mg/L

Rapid, sensitive and accurate in quantification

Cumbersome, expensive, timeconsuming, and need to be completed in lab

(Shah et al. (2014); Zhang et al. (2018))

High sensitivity, wide linear range, fast and convenient, suitable for on-site detection, less sample consumption

Expensive, lack of stability, require sample database.

¨ ztekin (2018); (O Prapainop et al. (2019); Wang et al. (2017); Yang et al. (2017); Yang et al. (2018); ZhYuan et al. (2016); Yue et al. (2016); Yue et al. (2018); u et al. (2014))

Simple, rapid, low cost and less sample consumption

Relative low sensitivity and reproducibility

¨ ztekin (2018); (O Vistuba et al. (2015))

Aspartame: 0.16B5.8 mg/L antioxidant

PG: 10 μg/L TBHQ: 15 μg/L BHA: 25 μg/L

GC-MS or LCMS

Spectrometry

sweeteners

Sodium cyclamate: 0.1 ng/mL

antioxidant

BHT: 0.02B0.034 μg/ kg

colorants

Amaranth: 0.327 μM

Tartrazine: 0.017 μM Sunset yellow: 79.6 nM Curcumin: 38 nM preservative

Benzoic acid: 0.01 ng/mL Parabens: 0.1 mg/L Nitrite: 0.01 μM

antioxidant

PG: 0.11 μg/mL TBHQ: 0.01 μg/ mL

Capillary electrophoresis

preservative

Benzoic acid: 0.405 μg/mL

(Continued )

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TABLE 7.2 (Continued) Methods

Electrochemical method

Food additives

LOD

sweeteners

Aspartame, Cyclamate, Acesulfame-K, Saccharin: 3.3B6.4 mg/L

colorants

Amaranth: 35 nM

Advantage

Disadvantage

Reference

Simple, require low volume samples, high sensitivity, low detection limit, strong anti-interference ability and high recovery rate

Relative low reproducibility and selectivity, short working life, the electrode and electrolyte are difficult to reuse.

(Arvand et al. (2017); Bathinapatla et al. (2016); dos Reis Lima et al. (2019); Fan et al. (2018); Rasheed et al. (2015); Rovina et al. (2016); Sudarvizhi et al. (2018); Villar-Navarro et al. (2016); Wang et al. (2010); Wu et al. (2017); Zhang et al. (2010); Zhang et al. (2015))

Simple operations, low cost, high sensitivity, fast and convenient.

Tedious procedures, relative low reproducibility, require antibody, and easy to be interfered by matrix.

(Xing et al. (2012); Zhang et al. (2014))

Tartrazine: 2.0 nM Indigo carmine: 0.36 μM Sunset yellow: 0.8 ppm Ponceau 4 R: 15 μg/mL preservative

Parabens: 0.98B1.43 μg/L Nitrite: 0.012 μM

sweeteners

Steviol glycosides: 0.264 μM Fructose: 1 3 10212 M Xylitol: 6 3 10213 M

antioxidant

BHA: 94.9 μM PG: 0.13 μM TBHQ: 12 nM

ELISA

colorants

Amaranth: 3.35 ng/mL

(Continued )

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TABLE 7.2 (Continued) Methods

Food additives

LOD

Advantage

Disadvantage

Reference

Good specificity, relative high stability, sensitivity, and recovery, very high flexibility

Require relative complex procedures, harsh conditions, and high price.

(Emam et al. (2018); He et al. (2018); Kung et al. (2015); Morales et al. (2005); Radulescu et al. (2014); Wang et al. (2016))

Rapid, sensitive and simple, rapid response, low volume samples and on-site detection

Lack of stability, require either complex data processing or highquality SERS substrate

(Buyukgoz et al. (2015); Han et al. (2017); Xie et al. (2014))

Sunset yellow: 25 pg/mL Sensors

preservative

Nitrite: 2.1 μM

sweeteners

Aspartame: 0.2 μM

antioxidant

BHT: 0.02 ppb PG: 1026 M TP: 10 nM Ascor acid: 62.81 nM

Surfaceenhanced Raman scattering

colorants

Sunset yellow: 1 ppm

sweeteners

Sodium saccharin: 0.3 mg/L Aspartame: 0.14 mg/mL

foods, which become more complex matrices to be analyzed; (c) the same kind of food additive may present varied states (with monomer or conjugates) in different foods. The limitations of analytical techniques in food additives are dependent on the samples and the detection requirements, which can be summarized as: complicated pretreatment and procedure, low sensitivity, high cost and long time-period of detection. To meet the challenges of complex situation, all kinds of pretreatment techniques include mechanical, microwave, thermal, chemical and biological groups are applied to remove particulate matter and minimize the matrix interferences from food samples. All these techniques aim to prevent food matrices from interfering the identification and quantification of analyte, which can effectively improve the accuracy and stability of detection method. Nevertheless, the careful processing procedures may make the analysis become more laborious and time-consuming, which seems to be contrary to the original intention (convenience and robustness) of analytical methods. From this perspective, ensuring the stable and accurate detection is the most important and very first step to guarantee and regulate the correct use of food additives. Also, great effort has been made to address the limitations of analytical techniques. With different strategies, analytical techniques are being developed or adapted to improve the detection performance such as accuracy, sensitivity, stability, selectivity as well as simplicity. For instance, the sensitivity of analytical methods can be greatly enhanced with the aid of different kinds of functional nanomaterials. By combining mathematical algorithms with Raman spectroscopic technique, it can achieve the multicomponent detection for additives, which make the analysis simpler and more convenient. Similarly, the Raman spectroscopy technique coupling with appropriate chemometrics can realize rapid, noninvasive and cost-effective detection for analytes. Therefore, it is an effective way to improve the analytical techniques for food additive analysis by combining with other strategies. The “ASSURED” criteria of “(i) Affordable, (ii) Sensitive, (iii) Specific, (iv) User-friendly, (v) Rapid and robust, (vi) Equipment-free, and (vii) Deliverable to users who need them” outlined by the World Health Organization gives

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the direction of those analytical techniques (Urdea et al., 2006). Theoretically, microsystems own high efficiency, fast analysis time and low reagent consumption on separation. Thus, miniaturization and integration of already existing techniques is an alternative to the traditional techniques. It is believed that the combination of effective pretreatment techniques with new analytical techniques can develop automatic and intelligent sensing techniques, which predicts a development direction of intelligent sensor to integrate artificial intelligence with sensors. While such ideas promise the application of those techniques in resource-limited field like medical treatment for extreme point of care, these requirements and tactics are very important for the development of analytical platforms in food additives. Only all the relevant techniques work together, can it be possible to develop more efficient methods to analyze food additives.

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Chapter 8

Innovations in analytical methods for food authenticity M. Esteki1, M.J. Cardador2, N. Jurado-Campos2, A. Martı´n-Go´mez2, L. Arce2 and J. Simal-Gandara3 1

Department of Chemistry, University of Zanjan, Zanjan, Iran, 2Analytical Chemistry Department, Faculty of Science, University of Cordoba,

Rabanales Campus, Cordoba, Spain, 3Nutrition and Bromatology Group, Department of Analytical and Food Chemistry, Faculty of Food Science and Technology, University of Vigo, Ourense Campus, Ourense, Spain

8.1

Authentication of food products

The authentication of food products has attracted worldwide attention due to the wide variety of techniques for detection of adulteration, mislabeling, characterization, and misleading origin. A variety of different forms of adulteration including species substitution, manipulation, dilution, mislabeling, and contamination are mostly used to increase financial benefits through extending shelf lives or reducing production costs. Food authenticity is a significant concern for all involved in the food trade including companies, unions, scientific institutions, national and international regulations, food manufacturers, and consumers. There are numerous international and regional legal authorities to ensure that foods are accurately labeled in order to improve public health; moreover, new information is being constantly obtained and analytical instruments are being produced to verify the authenticity of food products (Abbas et al., 2018). According to the scientific reports (Bouzembrak & Marvin, 2016), the most common type of adulteration is replacing food components with less expensive similar ingredients whose identification is not easy for consumers and even by conventional analytical techniques. Food authentication usually involves analyzing samples for specific components to verify label information including geographical origin, storage conditions, and processing methods. Therefore application of reliable and effective analytical methods is required in order to develop new policies, programs, and techniques for verification of food authenticity. Authenticating foodstuffs by granting a Certificate of Specific Character, such as protected designation of origin (PDO) or protected geographical indication (PGI) is particularly important with agri-food products (Abbas et al., 2018; Danezis, Tsagkaris, Brusic, & Georgiou, 2016a). Therefore PDOs and geographical indications facilitate the promotion of agricultural products at international markets, which lead to an increase of profits for producers. New proteomics, genomics, and metabolomic-based methods have recently emerged as complementary approaches to verify the quality of food products and avoiding food fraud (Ortea, O’Connor, & Maquet, 2016). These methods applied modern analytical tools in order to identify all ingredients of foodstuffs through a single experiment. In this chapter, different analytical methods for authentication of food products based on targeted or untargeted approach are reviewed, taking into account the strengths of each technique (Esteki, Regueiro, & Simal-Ga´ndara, 2019) and some aspects such as if: (1) they are more appropriate for quantitative analyses; (2) they should be equipped with powerful software; (3) they should be well suited for food authentication in-house; and (4) they should have the ability to identify significant markers for compositional information.

8.1.1 Traceability for preventing adulteration The ability to link a food product to its processing methods and its ingredients, known as “traceability,” has a significant impact on food quality. It would be very difficult to verify food compositional data, nutrition, and health claims without it. Currently, a wide range of raw materials, food ingredients, and additives are produced by a variety of companies around the world, which are converted into various food products for global marketing. The extensive growth of Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00008-X Copyright © 2021 Elsevier Inc. All rights reserved.

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global food supply and demand in recent years has made traceability a major issue for international, national, and local manufacturers. Moreover, increasing demands from consumers for quality and nutritional value, clear labeling, and safety provide further evidence of the need for robust back-and-forth traceable supply chains (Esteki et al., 2019). Traceability schemes, which are now required by regulation in some regions, mostly lead to improved process control and high-quality manufacturing practices. The systematic implementation of traceability in the food supply chain was noticeably increased in the late 1980s, mostly by the bovine spongiform encephalopathy (BSE) crisis in United Kingdom and later to the debate and active scientific research on genetically modified organisms (GMO). The BSE crisis led to a loss of consumer confidence in food of animal origin and in national food inspection services, which led to an almost complete ban on beef exports from the United Kingdom. Traders tackled this problem by encouraging employing tracking techniques and applying labeling methods that became highly effective in their promotional campaigns. The Meat and Livestock Commission additionally afforded a complete and widespread traceability plan for beef products that created the opportunity for suppliers and traders to authenticate on-pack food composition data and quality claims. Nowadays, the GMO debate has sparked greater interest among consumers in transparency and clear labeling of foodstuffs. In fact, traceability is a crucial issue, since it is important to be sure about the labeling accuracy [e.g., to verify genetically modified “(GM)-free” claims]. The previous issues, along with the increasing globalization of food production, are the main parameters in forming the significant objectives of food tracking schemes, which can be summarized as follows: compliance with national and international regulations such as protocols for ensuring traceability; improving traceability managements for agri-food products; full support for good health and safety claims, evidence-based marketing claims, and precise package marketing claims; ensuring that food labels show all ingredients along with their exact quantities; improving process control; specifying precise and clear process control strategies; improving the quality and stability of the final product; and improving food monitoring systems;

8.1.2 Labeling and compositional regulations Food labeling is a primary marketing tool, as it improves the public’s understanding of the health benefits of following a nutritious diet, which helps them best to differentiate between more or less healthy food and whether these changes in perceived healthiness result in changes of food choices. Labels are intended to provide protection and fair trade for consumers (Turner, 1995). Increasing consumer concerns about food safety have raised public awareness of the undeniable impact of food labeling. However, the need for food labels is more than just a consumer demand. In recent decades, the food industry has undergone significant structural changes in the food production and marketing chain. The need to provide efficient new products considering economic requirements justifies the necessity of organized control at various points through the food supply chain. As a direct response to this need, bar-code-based behavioral programs have been invented to transfer the information needed from food labels (Turner, 1995). Although interest in evaluating the impact of food labeling regulations on consumers’ belief and behavior has grown tremendously (Balasubramanian & Cole, 2002; Cowburn & Stockley, 2005; Levy & Fein, 1998; Ricciuto, Lin, & Tarasuk, 2009; Roe, Levy, Brenda, & Derby, 1999), only limited research has focused on the effect on label information and food manufacturing. Food labeling can be expected to improve the nutritional profile of food products (Lang & Heasman, 2004; Moorman, 1998; Ricciuto et al., 2009). Labeling the characteristics of prepackaged foods as a part of a health promotion operational plan can be a driving force for effective public health strategies to make dietary changes to reduce diet-related diseases (Balasubramanian & Cole, 2002; European Commission, 2006; Ricciuto et al., 2009; Who, 2004). Labels on food packages generally include nutrient information such as fat, saturated fat, trans-fat, cholesterol, fiber, and other nutrients or specific health claims. Food labeling is widely recognized as an effective factor in population diets, because paying attention to food labels increases the demand for healthier and more beneficial foods. Today, this has led to competition in the food industry for nutritional quality (Derby & Levy, 2001; Ricciuto et al., 2009). Food labeling helps consumers make better choices for healthy food based on accurate information. Today, efforts are being made to establish a regulatory framework to create the right business model by emphasizing the importance of food labels for a wide range of ready-made foods. Standard food labels should now contain information on the

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amount of energy, total fat, carbohydrate, protein, sugars, sodium, saturated fat, and other nutritional properties (Ni Mhurchu & Gorton, 2007). Label information should represent the samples available in the market so that consumers have an accurate perception of the products. This has made continuous labeling verification one of the main tasks of regulatory bodies. Food regulatory authorities are constantly updating their systems so that they can be used to identify food components (Premanandh, 2013). Regular monitoring has become inevitable for authorities to prevent future food adulterations. Although labeling policies obviously differ by different manufacturers, it is expected that allergen information will be also included as an essential component in the labels (Premanandh, 2011). However, some labels deliberately exclude essential information about product identity, statement of which frauds should be legally enforced.

8.2

Revision of analytical methods for food authentication

As already mentioned, in the context of food verification, analytical assessments are generally classified into two types: targeted and nontargeted. In the first approach, the analysis is specifically focused on one or more metabolites, whereas in the non-target approach, the main purpose is to distinguish the metabolite patterns with the aim of finding differences and similarities between the samples and establishing reliable models for predicting the specified class of samples. Untargeted approach or food fingerprints may be obtained for food quality, safety, and authenticity purposes. A correct untargeted approach will begin with a well-planned analytical procedure, being the sampling the first step to taking into account. The number of samples examined is the main issue when comparing different studies for validation purposes. Although it may be difficult to determine a sufficient number of samples for analysis, the larger the number, the greater the confidence in the results. Sample preparation, selection of appropriate instrumentation methods, and design of efficient analytical models are also other important parameters to evaluate. A wide range of analytical devices, such as chromatography, mass spectrometry (MS), or spectroscopy, are used to monitor food authenticity. Notice that the combination of orthogonal techniques to analyze the same sample will allow a deeper and comprehensive analysis of the sample of interest. From legal and a business point of view, regulatory authorities are urged to constantly update analytical methods and conditions that allow food verification to be valid, as this can enhance enforcement efforts. Public committees are needed to coordinate with the industry to detect fraud in various food products. The following sections will help to review the current methodologies. It is important to note that it is not possible to give a definitive opinion on the application of the methods. Depending on the examining samples and the purpose of the analysis, each method has its advantages and disadvantages. Therefore the most appropriate method for any particular situation should be selected. This is why the most commonly used methods, along with their strengths and weaknesses, are described in this section, followed by a report on the use of the most recent methods.

8.2.1 Coffee The pleasant taste and flavor of coffee, and its high caffeine concentration make it one of the most popular drinks across the world. The growth in global coffee consumption has been taken into consideration in the past two decades. This increase has mainly been due to the emergence of new products and drink formulations, domestic coffee machines, and the growing number of street coffee shops (Toci, Farah, Pezza, & Pezza, 2016). International Coffee Organization (ICO) is the leading international coffee organization that brings coffee producing and consuming countries together in order to meet the global coffee challenges through international cooperation. Based on ICO definition, green coffee is “all coffee in the naked bean form before roasting,” roasted coffee is “green coffee roasted to any degree and including ground coffee,” and soluble coffee is “the dried water-soluble solids derived from roasted coffee” (Prodolliet & Hischenhuber, 1998). Therefore according to internationally accepted definitions, commercial coffee products should not contain any material other than green coffee beans. However, industrial coffee producers usually use the lowquality beans (geographical origin and defective beans); or roast coffee with lower priced adulterants such as coffee husks, cereals, coffee twigs, corn and barley, coffee and brown sugar, wheat middling, soybean, and rye, which has a direct impact on the quality of the coffee beverage especially on its flavor characteristics (de Moura Ribeiro, Boralle, Redigolo Pezza, Pezza, & Toci, 2017; Nogueira & do Lago, 2009; Pauli et al., 2014). Therefore it is important to provide simple, operational, and reliable methods for detecting and quantifying this type of fraud with the aim of authenticating coffee. The worldwide coffee consumed primarily comes from two varieties: Coffea arabica (arabica coffee) and Coffea canephora (robusta coffee), which make up 75% and 25% of the world market, respectively. Arabica coffee has attracted the attention of consumers because of its high-quality beverages with strong aroma, low bitterness, and good

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flavor, low caffeine content, and lack of off-flavors, such as smoky notes. This makes the price of arabica coffee typically 20% 25% higher than that of robusta. There are several varieties of coffee that can grow throughout the world, such as the Typica and Bourbon varieties (arabica), but some are also special to their producing countries, such as Catuai (Brazil), Novo Mundo, Villa Sarchi (Costa Rica), and Jimma and Harar (Ethiopia). The quality of the raw coffee beans, the amount of defective beans, and the type and quality condition of roasting and grinding process all influence the final product. Moreover, considering the fact that after roasting and grinding, the addition of other materials cannot be visually identified, and investigation of the adulteration of roasted and ground coffee is a highly significant issue. Therefore it is important to distinguish and quantify coffee adulteration (such as the illegal addition of cheaper Robusta coffee) to ensure quality coffee, minimize unfair trade, and increase consumer confidence. Optical and electron microscopy is one of the most highly used conventional methods in laboratories for identification of the adulteration of roasted and ground coffee. Mineral and moisture content, ether-extractable substances, and caffeine analysis are also complementary physicochemical methods for detection of adulteration. These conventional methods require several days to give results and usually have limited intrinsic accuracy and are imprecise in their performance. In this way, other methodologies have been investigated to provide more accurate and precise analyses that would be able to detect various types of possible food fraud. These alternative efficient techniques include infrared (IR) spectroscopy, ultraviolet-visible (UV-Vis) spectroscopy, and chromatographic methods. IR spectroscopy is one of the most powerful techniques normally used for molecular characterization, which is widely used for adulteration detection. Its advantages include data collection speed, a reduced amount of chemical waste, less sample treatment, and ease of maintenance and use than the conventional methods. Different coffee products have complex IR spectra because of severe peak overlapping originated from various chemical components; however, a variety of studies elucidated the capability of this analytical technique for evaluation of multiple adulterants in roasted coffee. Caffeine is the most well-known component of coffee due to its broad application in the food and pharmaceutical industries; therefore it has been the target of several investigations (Do´rea & da Costa, 2005). Near-IR (NIR) spectra were also used to obtain an accurate model to determine the content of caffeine in ground arabica coffee samples made from a wide range of roasted levels. The results demonstrated the possibility of using NIR spectroscopy for at-line applications to predict the amount of caffeine of unknown coffee samples, with a few second analysis time without any destruction of samples (Zhang et al., 2013). Discrimination of the rough green beans into Arabica and robusta has also been carried out followed by the determination of caffeine, theobromine, and theophylline in roasted coffee based on near-IR reflectance spectroscopy (NIRS) spectroscopy (Huck, Maurer, Popp, Basener, & Bonn, 1999). Fourier transform IR (FT-IR) spectroscopy is an alternative fast procedure to determine the caffeine content of roasted coffee with reduced amount of organic solvents (Garrigues, Bouhsain, Garrigues, & De La Guardia, 2000). Among other coffee adulteration types, the most commonly occurring are adulteration via coffee substitution by cheaper products such as malt, cereals, starch, chicory, maltodextrins or glucose, figs and caramel, as well as roasted or even unroasted coffee husks; mixing of expensive coffee beans from one geographical region with cheap beans with another origin; or mixing of two species (addition of cheaper robusta to pure arabica coffee) (Franca, Mendonc¸a, & Oliveira, 2005). In order to identify various coffee blends for authentication issues, NIR spectral data have been successfully used quantification of barley as the adulterant addition in roasted and ground coffee samples (Ebrahimi-Najafabadi et al., 2012). Detection of adulteration of freeze-dried instant coffees has been carried out using diffuse reflectance and attenuated total reflectance FT-IR spectroscopy (Briandet, Kemsley, & Wilson, 1996). Furthermore, added carbohydrates (xylose, glucose, and fructose) have been determined in instant coffee based on FT-IR spectroscopy (Briandet et al., 1996). In another research, detection of adulteration of C. arabica blends with the C. robusta have been done using NIR spectroscopy combined with multivariate data analysis methods (Pizarro, Esteban-Dı´ez, & Gonza´lez-Sa´iz, 2007) Tavares et al. (2012) distinguished 13 coffee blends containing different percentages (0.5% 30%) of coffee husks using mid-IR (MIR) spectroscopy. Adulterated samples were identified by principal component analysis (PCA), and quantitative estimation of adulteration was accomplished by partial least square (PLS) regression. Reis, Franca, and Oliveira (2013b) also developed a promising monitoring method based on MIR spectroscopy for detection of adulteration of ground roasted coffee with roasted corn, spent coffee grounds, roasted barley, and roasted coffee husks. The constructed PLSDA model revealed the superiority of DF over automatic target recognition (ATR) by representing lower misclassification cases. Reis, Franca, and Oliveira (2013a) investigated the feasibility of discrimination among roasted coffee, coffee husks, and roasted maize using diffuse reflectance IR Fourier transform spectroscopy. Generally, the trade values Arabica beans most highly, as they are considered to have a finer flavor than robusta. Therefore identification of mislabeling adulteration is an important issue regarding to consumer protection by the authorities. Discrimination and

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characterization of arabica and robusta coffee blends could be accomplished using NIR reflectance spectroscopy (Downey, Briandet, Wilson, & Kemsley, 1997; Esteban-Dı´ez, Gonza´lez-Sa´iz, Sa´enz-Gonza´lez, & Pizarro, 2007). Characterization of the quality of coffee based on the assessment of bean quality is equivalent to determining the relative number of defective beans among nondefective ones. Therefore it is important to provide a methodology for the rapid assessment of the quality of coffee that would be capable of becoming a powerful analytical tool for regulating the quality coffee. In this regard, an NIR spectroscopy has been proposed for identification of the presence of defective beans in a batch and fast quality assessment of arabica and robusta coffee varieties from different geographical origins (Santos, Sarraguc¸a, Rangel, & Lopes, 2012). Significant studies have been also accomplished to investigate the relationship between the sensory properties of coffee beverages and the chemical composition of the coffee beans. Coffee beverage sensory data and NIR spectral data of arabica roasted coffee samples in combination with chemometrics have been used to predict the flavor, the scores of acidity, cleanliness, bitterness, body, and overall quality of coffee beverage. The analysis of NIR spectral data verified the relationship between the sensory attributes of the beverage and the chemical constituents of the roasted coffee beans. The results demonstrated that caffeine and chlorogenic acids were closely related to bitterness and acidity of coffee, while the flavor, cleanliness, and overall quality were related to the caffeine, polysaccharides, chlorogenic acid, protein, trigonelline, and sucrose present in the roasted coffee beans (Ribeiro, Ferreira, & Salva, 2011). The composition of some carbonyl compounds such as esters, vinyl esters/lactones, acids, aldehydes, and ketones in roasted coffee appears to be correlated with the heating rate and roasting time of the green beans to the first and second cracks (Lyman, Benck, Dell, Merle, & Murray-Wijelath, 2003). The potential of IR reflection spectroscopy with Fourier transform was investigated to distinguish commercial coffee samples with different industrial processes, based on caffeine extraction and degree of roasting Ribeiro, Salva, and Ferreira (2010). Triglycerides (TAGs) are important part of the lipid composition of the coffee oil, representing 75% (in mass basis) of the coffee bean lipids (de Azevedo et al., 2008). Tocopherols, as minor components in coffee beans, are lipophilic constituents present in the coffee oil, which possess antioxidant properties and together with the tocotrienols, collectively known as tocols, all are referred to as vitamin E (Alves, Casal, & Oliveira, 2009). High-performance liquid chromatography (HPLC) is highly considered as a well-suited method for the separation and analysis of triglycerides and tocopherols in oil samples (Andrikopoulos, Brueschweiler, Felber, & Taeschler, 1991). HPLC analysis in both cases could be carried out in a straightforward way by just diluting the oil and injecting it into the instrument (Gonza´lez, Pablos, Martıfin, Leo´n-Camacho, & Valdenebro, 2001). Triglycerides are mostly detected by refractive index detector and tochopherols mainly by UV or fluorescence detectors (Cert, Moreda, & Pe´rez-Camino, 2000). The triglyceride and tocopherol composition of green and roasted coffee beans in coffee samples has been used based RP-HPLC analysis in combination with chemometrics methods with authentication purposes to differentiate coffee varieties (Gonza´lez et al., 2001). In another report, HPLC-UV-Vis in combination with PCA was used to detect adulterations in roasted and ground coffee (Domingues et al., 2014). Tavares et al. (2016) developed an accurate method for detection of coffee adulteration with maize based on tocopherol content. The results showed that using the tocopherol profiles maize adulterant was detectable at levels above 10%. For heavy adulterations, discrimination of husks and cleaned husks was also possible. Schulze, De Beer, De Villiers, Manley, and Joubert (2014) determined the main components in extracts from “unfermented” and fermented Cyclopia maculata using HPLC and chromatographic fingerprinting. Implementation of PCA on unfermented C. maculata extracts fingerprints demonstrated substantial differences between cultivated seedlings and wild-harvested plants. The free and total carbohydrate profiles of coffee products also have been used for characterizing the coffee samples. Free fructose, sucrose, mannitol, free glucose, total xylose, and total glucose could be used as carbohydrates markers (Prodolliet and Hischenhuber, 1998). Domingues et al. (2014) evaluated the quality of coffee based on carbohydrate composition as the chemical biomarkers. High-performance anion-exchange chromatography was used to characterize the pure roasted coffee bean and the adulteration carbohydrates profiles. Soybeans and wheat were used as adulterants. The constructed PCA and LDA models were sensitive to distinguish genuine coffee and to quantify the adulterants. Fructose and glucose were identified as the main chemical markers for soybean and wheat adulteration, respectively, while high levels of galactose and mannose were identified in pure roasted coffee. Recently, UV-Vis spectroscopy has been applied to the detection and quantification of coffee adulteration. Dankowska, Domagała, and Kowalewski (2017) investigated the fluorescence and UV-Vis spectroscopies for quantifying roasted C. arabica and C. canephora var. robusta in coffee blends. LDA as used for classification of two types of coffee and their mixtures using fluorescence and UV spectral data. The best PCA-LDA performance was achieved for combination of UV and fluorescence data. This means that fluorescence and UV-Visible spectroscopy have a complementary effect on the quantification of roasted C. arabica and C. canephora var. robusta in blends.

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Cha´vez, Ascheri, Carvalho, Godoy, and Pacheco (2017) used UV-Vis spectroscopy and the successive projections algorithm for variable selection in combination with LDA in order to identify the adulteration in the form of husk and sticks in ground roasted coffee samples. In this way, they analyzed ground roasted coffee extracts in hot water (the endproducts). High classification sensitivity demonstrated that the developed method provides a simple and fast technique for detecting coffee adulteration. Suhandy and Yulia (2017) classified coffee samples as either pure pea-berry or pure normal coffee using UV-Vis spectroscopy. The spectral data for pea-berry and normal coffee were analyzed using PLSDA with 100% classification sensitivity for both the calibration set and the test set. The loadings of the spectral data showed that the wavelengths 230, 250, 270, 310, and 350 nm are the main effective wavelength for classification and identification of coffee type. In addition, absorbances at these wavelengths were highly correlated with those for caffeine, caffeic acid, and chlorogenic acid, which are some of the major chemical components of roasted coffee. Del Fiore et al. (2010) discriminated arabica and robusta coffee beans using hyperspectral image (wavelength region of 400 1000 nm) analysis based on changes in relative reflectance in coffee beans roasted for a variable time. Classification of 96% of the samples in the calibration set and 94% of those in the test set was obtained using LDA model. The overall results elucidated that hyperspectral imaging is a powerful technique for detection of coffee blend adulteration. With the availability of a variety of high-performance analytical tools, the metabolomics method should be more efficient by choosing the appropriate analytical platform. Due to the excellent sensitivity of gas chromatography mass spectroscopy (GC-MS) and the availability of various efficient ionization methods, this technique has been used widely in metabolomics studies. The ability to identify the metabolic peaks is a significant advantage for GC-MS-based metabolomics approach, especially in nontargeted monitoring methods (Chang & Ho, 2014). Huang et al. (2007) used multivariate data analysis methods for analysis of GC-MS chromatographic fingerprints in order to assess the quality of tobacco flavors. Fifty-two compounds in coffee flavor sample were identified using spectral correlative chromatography. Similarity analysis, sample stability, and the relative retention times and the relative peak areas were used for evaluation of the method. The proposed method successfully differentiated coffee flavor from coco flavor using PCA. Arana, Medina, Esseiva, Pazos, and Wist (2016) differentiated coffees from Colombia versus nearby countries including Brazil and Peru, in order to support the recent PGI of Colombian coffee. They developed a robust proficient system using GC-C-isotope ratio mass spectrometry (IRMS) and GC-MS combined with multivariate data analysis methods. PLS-DA and PCA results showed that the number of variables is highly effective on the performance of the proposed models. Other activities on authentication of coffee have been reported in Table 8.1.

8.2.2 Honey Honey is a natural and highly complex mixture produced by honeybees (Apis mellifera L.) under relatively uncontrolled conditions from various secretions of plants. The botanical origin of honeys is often categorized into just two classes, on the basis of secretions of plants used for their synthesis: (1) blossom or floral honey is produced by honeybees from the nectar of blossoms (the nectar of flowers); and (2) honeydew honey made from secretions of living parts of plants or excretions of plant-sucking insects on plants (Pita-Calvo & Va´zquez, 2017). Honey is not only consumed for its taste and nutritional value, but also for its medicinal properties and health benefits. Several studies have been shown the hypoglycemic, antibacterial, antihypertensive, hepatoprotective, antiinflammatory, gastroprotective, antifungal, and antioxidant effects of honey (Estevinho, Pereira, Moreira, Dias, & Pereira, 2008; Mandal & Mandal, 2011). The chemical composition and properties of honey are highly depending on the botanical origin of the source of nectar secretions and pollens (Kaˇskonien˙e & Venskutonis, 2010). Reducing sugars, mainly fructose and glucose, and water represent the largest portion of honey composition; however, other valuable nutritional compounds, such as vitamins, flavors, minerals, enzymes, free amino acids, and numerous volatile organic substances, are present as minor components (da Silva, Gauche, Gonzaga, Costa, & Fett, 2016). The chemical composition of honey is highly depending on several factors such as the botanical origin, season, geographic area, mode of storage and the extraction technology. Therefore it is reasonable to expect that differences among honeys from different geographical regions occurred from the different compositions of pollen or nectar, which exert a significant influence on the chemical composition of honey (CastroVa´zquez, Dı´az-Maroto, de Torres, & Pe´rez-Coello, 2010). The price of natural honey has been raised much more than other sweeteners, such as beet sugar or refined cane sugar, due to its high cost of production, nutritional value and unique honey flavor. This has made honey a potential target for fraud in various forms. Changing the composition of honey by addition of other sweeteners, such as glucose syrup, high fructose corn syrup, or saccharose syrups in any part of the production can be an attractive way to achieve financial profits. Adulteration of honey by substituting with cheap sweet ingredients is a critical issue in the honey

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TABLE 8.1 Summary of analytical purpose and method reviewed along the chapter for food authentication. Food

Purpose of analysis

Analytical method

Ref

Milk

Characterization and authentication

GC-FID

Molkentin (2007)

Cow milk

Characterization and authentication

HRGC-FID

Cossignani et al. (2011)

Milk

Study of modifications of milk constituents during processing

HS-SPME-GC-MS MS-based electronic nose UV spectrophotometry HPLC-MS/MS MALDI-TOF MS

Fenaille et al. (2006)

Milk

Shelf life prediction of processed milk

SPME-MS

Marsili (2000)

Milk

Authentication

GC-FID

Rodrı´guez-Bermu´dez et al. (2018)

Milk and butter

Characterization and authentication

GC-FID

Gutie´rrez et al. (2009)

Raw goat milk cheese

Analysis of the formation of the aroma during maturation

SPME-GC-MS

Delgado et al. (2011)

Cheese

Discrimination of commercial cheeses from fatty acid profiles and phytosterol contents

GC-FID GC-MS

Kim et al. (2014)

Natural cheese

Modeling of the category using sensory prediction

GC-FID

Ochi, Bamba, Naito, Iwatsuki, and Fukusaki (2012)

Dairy product

Determination of their shelf life

GC-MS SPME-GC-MS

Verzera et al. (2008)

Milk

Influence of heat treatment on the volatile compounds of milk

GC-MS TCT-GC-FID

Contarini et al. (1997)

Milk

Fingerprint study of short-chain fatty acids in human milk, infant formula, pure milk and fermented milk

GC-MS

Jiang et al. (2016)

Camel milk

Identification of volatile components in Chinese Sinkiang fermented camel milk

SAFE SDE HS-SPME-GC-MS GC-O

Li et al. (2012)

Milk

Keeping-quality assessment of pasteurized milk

GC-FID GC-MS

Vallejo-Co´rdoba and Nakai (1994)

Honey

Geographical origin authentication

GC-TOF MS

Stanimirova et al. (2010)

Honey

Differentiation of Manuka honey from Manuka honey and from Jelly Bush honey

HS-SPME-GC-MS UPLC-PDA-MS/MS

Beitlich, KoellingSpeer, Oelschlaegel, and Speer (2014)

Honey

Botanical discrimination and classification of honey samples using fingerprinting volatile compounds

HS- SPME-GC-MS

Aliferis, Tarantilis, Harizanis, and Alissandrakis (2010)

Honey

Application of carbohydrate analysis to verify honey authenticity

GC-FID HPAE-PAD

Cotte, Casabianca, Chardon, Lheritier, and Grenier-Loustalot (2003)

Honey

Analysis of sugars applied to the characterization of monofloral honey

HPAEC-PAD GC-FID

Cotte, Casabianca, Chardon, Lheritier, and Grenier-Loustalot (2004b)

Milk and dairy products

Honey

(Continued )

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Innovative Food Analysis

TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Honey

Floral classification of Yucatan Peninsula honeys using volatile compounds information

HS-SPME-GC-MS

Cuevas-Glory, Ortiz, Pino, and Sauri-Duch (2012)

Honey

Classification of honey based on rapid aroma profiling

zNose

Lammertyn, Veraverbeke, and Irudayaraj (2004)

Honey

Differentiation between nectar honey and honeydew honey

GC-FID GC-MS

Sanz, Gonzalez, De Lorenzo, Sanz, and Martı´nez-Castro (2005)

Honey

Estimation of the honeydew ratio in honey samples from their physicochemical and volatile characterization

HS-SPME-GC-MS

Soria, Gonza´lez, De Lorenzo, Martı´nezCastro, and Sanz (2005)

Honey

Study of volatile compounds in honey

TCT-GC-MS

Soria, Martı´nez-Castro, and Sanz, (2009)

Honey

Identification of monofloral honeys

HPLC-ECD

Zhao et al. (2016a)

Honey

Floral classification of honey

HPLC-DAD-MS/MS

Zhou et al. (2014)

Honey

Authentication from HPLC amino acid profiles

HPLC-FLD

Cotte et al. (2004a)

Honey

Identifying plant phenolic metabolites and floral origin of rosemary honey

HPLC-DAD

Gil et al. (1995)

Honey

Characterizing the botanical origin of honey

HPLC-PAD

Nozal et al. (2005)

Honey

Differentiation between wild harvested and cultivated seedling plants

HPLC-DAD LD-MS

Schulze et al. (2014)

Honey

Discrimination of polish unifloral honeys

PTR-MS HPLC-DAD

Ku´s and van Ruth (2015)

Honey

Rapid determination of invert cane sugar adulteration in honey

FT-IR spectroscopy

Irudayaraj, Xu, and Tewari (2003)

Honey

Detection of adulterants such as sweeteners in honey

NIR spectroscopy

Zhu et al. (2010)

Honey

Discrimination of Corsican honey

FT Raman spectroscopy

Pierna, Abbas, Dardenne, and Baeten (2011)

Honey

Detection of honey adulteration with high fructose corn syrup and maltose syrup

Raman spectroscopy

Li et al. (2012)

Honey

Authenticity of botanical origin of honey

H-NMR

Gerhardt et al. (2016)

Honey

Detection of honey adulteration by high fructose corn syrup and maltose syrup using Raman spectroscopy

Raman spectroscopy

Li et al. (2012)

Coffee beans

Authentication of Asian palm civet coffee

GC-MS

Jumhawan, Putri, Yusianto, Bamba, and Fukusaki (2016)

Green tea

Prediction of product quality

GC-MS

Pongsuwan, Bamba, Yonetani, Kobayashi, and Fukusaki (2008)

Green tea

Classification of Japanese green tea by qualities

GC-TOF MS

Pongsuwan et al. (2007)

Coffee

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Green tea

Classification of Japanese green tea by qualities

GC-MS

Jumtee, Komura, Bamba, and Fukusaki (2011)

Green tea

Prediction of product quality

GC-FID

Jumtee, Bamba, and Fukusaki (2009)

Tea

Geographical origin of tea

GC-MS

Ye, Zhang, and Gu, (2012)

Coffee

Authentication

HPLC-RID HPLC-FLD

Gonza´lez et al. (2001)

Coffee

Establishment of the effects of roasting conditions on coffee of different geographical origin

HPLC-DAD NIR spectroscopy

De Luca et al. (2016)

Coffee

Raising coffee beverage quality by using selected yeast strains during dry fermentation

PCR-DGGE HPLC-UV HS-SPME-GC-FID Sensory analysis

Evangelista et al. (2014)

Coffee

Establishment of the coffee origin

HPLC-MS Bio-Rad DC protein assay UV spectrophotometry GC-FID HPLC-UV

Choi, Choi, Park, Lim, and Kwon (2010)

Roasted coffee

Characterization of roasted coffee

HPLC-UV HS-GC-MS HS-GC-FID

Bicchi, Binello, Legovich, Pellegrino, and Vanni (1993)

Roasted coffee

Characterization of green and roasted coffees from their chlorogenic acid fraction

HPLC-UV

Bicchi, Binello, Pellegrino, and Vanni (1995)

Sorghum and roasted coffee

Identification of bioactive compounds and assessment of antioxidant capacity in sorghum-roasted coffee blends

HPLC-DAD UV spectrophotometry

Cha´vez et al. (2017)

Coffee beans

Differentiation of the variety and processing conditions of green coffee beans

GC-TOF MS HPLC-DAD HPLC-MS

Kuhnert et al. (2011)

Espresso coffee

Characterization and classification of the espresso coffee from different botanical varieties and roast type

HPLC-DAD Sensory analysis

Maeztu et al. (2001)

Green coffee

Assessment of the ripeness in Catuai and Tipica green coffee

HPLC-MS HPLC-UV HPSEC-UV HPLC-RID HS-SPME-GC-MS

Smrke, Kroslakova, Gloess, and Yeretzian (2015)

Green and white tea

Discrimination of tea categories

UPLC-DAD-MS

Zhao et al. (2011)

Green tea

Evaluation of (dis)similarity

HPLC-UV

Alaerts et al. (2012)

Prediction of the total antioxidant capacity

HPLC-DAD UV spectrophotometry

Van Nederkassel, Daszykowski, Massart, and Vander Heyden (2005)

Prediction of the total antioxidant capacity

HPLC-DAD UV spectrophotometry

Daszykowski, Vander Heyden, and Walczak (2007)

Green tea

Green tea

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Green tea

Prediction of the total antioxidant capacity

HPLC-DAD UV spectrophotometry

Dumarey, van Nederkassel, Deconinck, and Vander Heyden (2008)

Green tea

Identification of Chinese Ziyang green tea from different plantations

HPLC-UV

He et al. (2015)

Green tea

Prediction of the total antioxidant capacity

HPLC-DAD UV spectrophotometry

Dumarey, Smets, and Vander Heyden (2010)

Tea

Identification of the geographical origin

UPLC-MS

Fraser et al. (2013)

Tea

Identification of the processing method

HPLC-UV HPLC-MS

Zheng et al. (2009)

Roasted and ground coffee

Detection of many adulterants in roasted and ground coffee

FT-IR spectroscopy

Reis et al. (2013b)

Roasted coffee

Discrimination between defective and nondefective roasted coffees

FT-IR spectroscopy

Craig, Franca, and Oliveira (2012)

Roasted coffee

Simultaneous detection of multiple adulterants in ground roasted coffee

ATR-FT-IR spectroscopy

Reis, Botelho, Franca, and Oliveira (2017)

Green coffee

Classification based on metabolomics

C-NMR

Wei et al. (2012)

Instant coffee

Quality control and authenticity of instant coffee

H-NMR

Charlton, Farrington, and Brereton (2002)

Wine

Differentiation of wines according to cultivar

SBSE-TD-GC-MS

Tredoux et al. (2008)

Wine

Differentiation of wines according to cultivar

GC-FID FTMIR spectroscopy

Louw et al. (2009)

White wine

Differentiation of wines according to cultivar

GC-TOF MS H-NMR

Skogerson et al. (2009)

Slovak white wines

Classification of white varietal wines

GC-MS

Kruzlicova et al. (2009)

Italian wine

Differentiation of wines using their brand through the volatile fingerprinting

HS-SPME-GC-MS

Dall’Asta, Cirlini, Morini, and Galaverna (2011)

Wine

Authentication of geographical origin

GC-MS

Salvatore, Bevilacqua, Bro, Marini, and Cocchi (2013)

White wine

White wine varietal authentication

HS-SPME-GC-MS

Springer et al. (2014)

Cherry wine

Discrimination of cherry wines

HS-SPME-GC-MS

Xiao et al. (2014)

Red wine

Discrimination and classification of red wines according to their variety

HPLC-TOF MS

Vaclavik, Lacina, Hajslova, and Zweigenbaum (2011)

Trappist beer

Classification of different brands of beer

UPLC-TOF MS

Mattarucchi et al. (2010)

Red wine

Discrimination of Spanish wines according with their origin

HPLC-DAD HPLC-FLD MS

Serrano-Lourido, Saurina, Herna´ndezCassou, and Checa (2012)

Red wine

Classification of Chilean wine varieties

HPLC-DAD

Beltra´n et al. (2006)

Alcoholic beverages

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Red wine

Geographic classification of Spanish and Australian tempranillo red wines

Visible spectroscopy NIR spectroscopy

Liu, Cozzolino, Cynkar, Gishen, and Colby (2006)

Chinese wine

Classification of Chinese wine varieties

H-NMR

Fan et al. (2018)

Wine

Classification based on grape variety, year of vintage, and geographical origin

H-NMR

Monakhova, Godelmann, Kuballa, Mushtakova, and Rutledge (2015)

Wine

Classification based on grape variety, the geographical origin, and the year of vintage of wine

H-NMR SNIF-NMR, O-NMR C-NMR

Monakhova et al. (2014)

Red wines

Classification of red wines in terms of variety and vintage

H-NMR C-NMR Isotopic analysis

Geana et al. (2016a)

Wine

Classification based on grape variety, geographical origin, and year of vintage

H-NMR

Godelmann et al. (2013)

Wine

Profiling of wine blends

H-NMR

Imparato, Di Paolo, Braca, and Lamanna (2011)

Slovenian wine

Determination of authenticity, regional origin, and vintage of Slovenian wines

IRMS SNIF-NMR

Ogrinc, Koˇsir, Kocjanˇciˇc, and Kidriˇc, (2001)

Walnut

Study of fatty acid profile of walnuts and discrimination patterns based on irradiation dose and packaging

GC-FID NMR

Sinanoglou et al. (2015)

Nuts

Monitoring the lipid oxidation in nuts

HS-SPME-GC-FID HS-SPME-GC-MS Electronic nose

Pastorelli et al. (2007)

Walnuts

Comparison of ex situ volatile emissions from intact and mechanically damaged walnuts

HS-SPME-GC-MS

San Roma´n, Bartolome´, Gee, Alonso, and Beck (2015)

Pistachio nuts

Investigation of volatile compounds and characterization of flavor profile

HS-SPME-GC-MS Sensory analysis

Kendirci and Onoˇgur (2011)

Fermented soybean paste

Metabolite profiling during fermentation

GC-MS GC-FID

Park et al. (2010)

Walnuts

Free and esterified sterols in walnuts and hazelnuts in three stages during kernel development

GC-MS

Taneva et al. (2013)

Pistachio nuts

Quantification of mixtures of aflatoxins

HPLC-DAD

Vosough and Salemi (2011)

Peanuts

Identification of the interrelationships among tocopherols in commercial runner market type peanuts grown in the United States

HPLC-FLD

Shin et al. (2010)

Almonds

Establishment of the fatty acid and phenolic profiles of almonds grown in Serbia

UHPLC-DAD MS/MS

ˇ c et al. (2017) Coli´

Jatropha curcas L. seeds and nuts

Investigation of the allergenicity in a new 2S albumin from J. curcas L. seeds

HPLC-UV SDS-PAGE

Maciel et al. (2009)

Nuts

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Tiger nut milk

Exploration of the quality and nutritional value of tiger nut milk using the untargeted metabolomics

UHPLC-HRMS

Rubert et al. (2017)

Almonds

Nutritional and chemical characterization of almonds

HPLC-ELSD HPLC-DAD GC-FID

Barreira et al. (2012)

Nuts

Examination of the urinary changes in individuals with metabolic syndrome following nut consumption for 12 weeks using metabolomics

HPLC-QTOF MS HPLC- LTQ Orbitrap

Tulipani et al. (2011)

Nuts

Evaluation of the allergenicity and tolerance to proteins in Brazil nuts

HPLC-UV SDS-PAGE

Maciel Melo, XavierFilho, Silva Lima, and Prouvost-Danon, (1994)

Walnuts

Examination of the changes in phenol content in walnuts

HPLC-UV

Cosmulescu and Trandafir (2011)

Pistachios

Authenticity control of pistachios

H-NMR C-NMR

Zur, Heier, Blaas, and Fauhl-Hassek (2008)

Date palm fruits

Study of metabolomics fingerprints of 21 date palm fruit varieties

GC-MS UPLC-QTOF MS

Farag et al. (2014)

Melon

Spatial metabolite analysis in melon

GC-TOF MS H-NMR

Biais et al. (2009)

Tomato fruit

Exploration of the influence of chilling and protective heat-shock treatments on metabolite content

GC-MS

Luengwilai et al. (2012)

Melon fruit

Discriminating the climacteric behavior based on aroma volatiles

HS-SPME-GC-MS INDEX-MS-Electronic-nose

Chaparro-Torres et al. (2016)

Mango fruits

Detection and discrimination of two fungal diseases of mango (cv. Keitt) fruits based on volatile metabolite profiles

GC-MS

Moalemiyan et al. (2007)

Strawberry

Detection of the adulteration of soft fruit purees

HS-SPME-GC-FID

Reid et al. (2004)

Pineapple

Study of changes in the volatile fraction of pineapple due to ripening

HS-SPME-GC 3 GC-qMS

Steingass et al. (2015)

Snake fruit

Maturity discrimination of snake fruit based on volatiles

GC MS GC-O Electronic nose

Supriyadi et al. (2004)

Korla pear

Quality control of Korla pear based on flavor fingerprinting

HS-SPME-GC-MS GC-O

Tian et al. (2014)

Sweet potatoes

Discrimination of pure, powdered, purple sweet potatoes, and their samples adulterated with the white sweet potato flour

NIR spectroscopy

Ding, Ni, and Kokot (2015)

Grape

Study of grape variety, geographical origin, and year of vintage

H-NMR

Godelmann et al. (2013)

Metabolic fingerprinting investigation

GC-MS

Xue et al. (2012)

Fruits

Herb and spices Tussilago farfara

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Schizone petatenuifolia Briq

Quality control

GC-MS HS-SPME-GC-MS

Chun et al. (2010)

Curcuma longa L

Quality assessment

GC-MS

Li et al. (2009)

Glycyrrhiza glabra

Comparative metabolite profiling and fingerprinting of medicinal liquorice roots

GC-MS

Farag et al. (2012)

Notoptery giumincisum Ting

Study of traditional Chinese medicine volatile oils from different geographical origins

GC 3 GC-TOF MS GC 3 GC-FID

Qiu et al. (2007)

Matricaria recutita L.

Classification of different chemotypes of chamomile flower-heads (M. recutita L.)

HS-SPME-F-GC-FID HS-SPME-GC-FID HS-SPME-GC-MS

Rubiolo et al. (2006)

Dried roots of Angelica acutiloba (Yamato-toki in Japanese)

Metabolic profiling of A. acutiloba roots for quality assessment based on cultivation area and cultivar multivariate pattern recognition

GC-TOF MS

Tianniam et al. (2008)

Medicinal herbs

Determination of the geographical origin of medicinal herbs

ICP-AES ICP-MS H-NMR

Kwon, Bong, Lee, and Hwang (2014)

Apple juice

Discrimination of juices by apple varieties and geographical origin

HS-SPME-GC-MS

Guo, Yue, and Yuan (2012)

Apple juice

Discrimination between apple varieties

H-NMR

Belton et al. (1998)

Tomatoes

Approach to differentiate conventionally and organically grown tomatoes

H-NMR

Hohmann, Christoph, Wachter, and Holzgrabe (2014)

Orange juice and pulp

Discrimination between orange juice and pulp: identification of marker compounds

H-NMR

Le Gall, Puaud, and Colquhoun (2001)

Mushrooms

Study of volatile biomarkers for wild mushrooms species discrimination

HS-SPME-GC-MS

Malheiro, Guedes de Pinho, Soares, Ce´sar da Silva Ferreira, and Baptista (2013)

Tubers

Exploration of the effect of agricultural production systems on the potato metabolome

GC-MS LC-MS

Shepherd et al. (2014)

Tomato and concentrate paste

Study of the geographical origin of tomatoes

HS-SPME-GC-MS

Feudo et al. (2011)

Peach juice

Characterization of peach juices obtained from different cultivars grown in Italy

HPLC-UV-RID UV spectrophotometry

Versari, Castellari, Parpinello, Riponi, and Galassi (2002)

Apricot juice

Characterization of Italian commercial apricot

HPLC-RID HPLC-DAD

Versari et al. (2008)

Orange juice

Detection of adulteration in freshly squeezed orange juice

Electronic nose IR spectroscopy GC-MS

Shen et al. (2016)

Beef

Hormone characterization in meat

GC-MS

Hartwig et al. (1997)

Beef

Hormone quantization in serum and urine

HPLC-MS/MS

Draisci et al. (2000)

Beef

Quality control of hormone in plasma determination method

Immunoassay

Simontacchi et al. (1999)

Fruit juice

Meat

(Continued )

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Innovative Food Analysis

TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Pork

Sex determination employing blood, meat, and hair

PCR

Fontanesi et al. (2008)

Pork

Breed authentication employing blood

PCR CE

Zhao et al. (2018a)

Pork

Authentication of meat

PCR-RFLP

Fontanesi et al. (2016)

Pork

Breed authentication employing blood

AFLP CE

Alves et al. (2002)

Chicken

Genotype characterization

qRT-PCR

Du et al. (2017)

Beef

Genotype characterization employing blood

Microarray

Campos et al. (2017)

Beef

Breed authentication employing animal tissue

PCR CE

Zhao et al. (2017)

Beef

Breed authentication employing blood

PCR

Dalvit et al. (2008)

Beef

Breed authentication employing blood

PCR

Rogberg-Mun˜oz et al. (2014)

Beef

Authentication of meat grades

NIR spectroscopy

Alomar et al. (2003)

Dry-cured Iberian ham

Authentication of breed and feeding regime employing fat

GC-IMS

Martı´n-Go´mez et al. (2019)

Pork

Sulphonamides determination in meat

PLE CE-MS/MS-QIT

Font et al. (2007)

Beef

Sulphonamides andtrimethoprim determination in meat

CE-MS CE-MS/MS

Soto-Chinchilla et al. (2007)

Beef

Antibiotics determination in meat

CE-MS

Juan-Garcı´a et al. (2007)

Chicken

Quinolones determination in meat

PLE SFE-CE-MS/MS

Lara et al. (2008)

Chicken

Quinolones determination in meat

SFE-CE-MS

Juan-Garcı´a et al. (2006)

Chicken and pork

Tetracyclines determination in organic meat

Fluorescence microscopy

Kelly et al. (2006)

Dry-cured Iberian ham

Authentication of feeding regime employing slices

GC-IMS

Arroyo-Manzanares et al. (2018)

Lamb

Authentication of feeding regime employing adipose tissue

DH-GC-MS

Sivadier et al. (2008)

Iberian pig

Authentication of feeding regime employing adipose tissue

NIR spectroscopy MIR spectroscopy

Arce et al. (2009)

Pork

Authentication of organic feeding regime employing mineral content of meat

ICP-MS

Zhao et al. (2016b)

South African lamb

Authentication of meat origin through the feeding regime

IRMS

Erasmus et al. (2016)

Beef

Authentication of feeding regime using muscle, hair, and urine

SIRA NMR

Monahan et al. (2012)

Rabbit

Authentication of meat from wild animals employing rare earth elements

ICP-MS

Danezis et al. (2017)

Chicken

Detection of irradiation in skin and chicken products through determination of hydrocarbons and fatty acids

GC-MS

Maija et al. (1992)

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Chicken

Detection of irradiation in meat through determination of o-tyrosine

HPLC-LASER

Miyahara et al. (2012)

Chicken

Detection of irradiation in meat through determination of hydrocarbons and 2alkylcyclobutanones

SFE GC-FID GC-MS

Horvatovich et al. (2000)

Chicken

Detection of irradiation in bone fraction

ESR spectroscopy

Marchioni et al. (2005)

Beef

Detection of thawed meat through determination of HADH activity

UV spectrophotometry

Chen et al. (1988)

Chicken

Detection of thawed meat through determination of HADH activity

UV spectrophotometry

Billington et al. (1992)

Pork

Detection of thawed meat through enzyme profile

APIZYM system

Toldra´ et al. (1991)

Pork and beef

Detection of thawed meat through enzyme profile

APIZYM system

Ellerbroek et al. (1995)

Beef

Detection of thawed and irradiated meat through determination of DNA damage

Comet assay

Park et al. (2000)

Chicken

Determination of DNA damage in chilled meat

Comet assay-DEFT

Cerda and Koppen (1998)

Pork

Determination of the effects of freezing, thawing, and cooking in meat of different qualities

NMR

Mortensen et al. (2006)

Pork

Detection of frozen and thawed meat

NMR

Guiheneuf et al. (1997)

Beef, lamb, and pork

Determination of freezing and thawing effects on meat

MRI

Evans et al. (1998)

Chicken

Detection of frozen meat

2-D NIR spectroscopy

Liu et al. (2004)

Beef

Detection of thawed meat using its drip juice

NIR spectroscopy

Downey and Beaucheˆne (1997a)

Beef

Detection of thawed meat

NIR spectroscopy

Downey and Beaucheˆne (1997b)

Chicken, pork, and turkey mixes

Detection of thawed meat and meat from different species

MIR spectroscopy

Al-Jowder et al. (1997)

Beef

Detection of frozen meat

NIR spectroscopy DESIR spectroscopy

Thyholt and Isaksson (1997)

Beef, sheep, chicken, goat, and buffalo mixes

Determination of meat and meat products origin for detection of adulteration

Raman spectroscopy

Boyaci et al. (2014)

Beef

Detection of adulteration with horse meat

Raman spectroscopy

Boyaci et al. (2014)

Beef

Detection of adulteration with horse meat

FT-Raman spectroscopy IR spectroscopy

Zaja˛c et al. (2014)

Meat mixes of different species

Detection of adulteration with chicken meat

OFFGEL electrophoresis SDS-PAGE HPLC-MS/MS MALDI-TOF MS AQUA analysis

Sentandreu et al. (2010)

Beef, buffalo, and sheep mixes

Determination of meat and meat products origin for detection of adulteration

OFFGEL electrophoresis, SDS-PAGE MALDI-TOF MS UPLC-QTOF MS

Naveena et al. (2017)

(Continued )

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Innovative Food Analysis

TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Pork sausage

Detection of adulteration with soy protein

ELISA SDS-PAGE

Gonza´lez-Co´rdova et al. (1998)

Meat mixes of chicken, pork, beef, and turkey

Detection of adulteration with soy protein

RP-HPLC ELISA

Castro et al. (2007)

Minced pork, beef, chicken, and lamb

Detection of binding agents (fibrinopeptides)

LC-MS/MS

Grundy et al. (2008)

Minced pork, beef, chicken, and lamb

Detection of bovine binding agents (fibrinopeptides)

LC-MS/MS HPCL-MALDI-TOF MS

Grundy et al. (2007)

Chicken feed

Detection of melamine adulteration

SERS HPLC

Lin et al. (2008)

Mechanically separated meat

Detection of bone and cartilage particles

Staining and microscopy

Branscheid et al. (2009)

Sheep

Determination of collagen in meat through the quantification of hydroxyproline

LC-MRM-MS

Colgrave et al. (2008)

Minced beef

Detection of adulteration of meat with offal

REIMS MS/MS LC-Q-TOF MS

Black et al. (2019)

Minced beef

Detection of adulteration of meat with offal

FT-IR spectroscopy

Hu et al. (2017)

Italian “Valle d’Aosta” PDO lard

Authentication and differentiation from other non-PDO lards

FT-NIR spectroscopy GC-FID HS-SPME-GC-MS

Chiesa et al. (2016)

Shrimp

Identification of shrimp species through the determination of sarcoplasmic proteins

Isoelectric focusing MS/MS

Ortea et al. (2010)

Fish

Fish species identification employing muscle parvalbumins

Isoelectric focusing

Rehbein et al. (2000)

Fish

Fish species authentication employing parvalbumins

MALDI-TOF MS

Mazzeo et al. (2008)

Shrimp

Shrimp species identification

MALDI-TOF MS

Salla and Murray (2013)

Fish

Genetic authentication of Thunnus species and Katsuwonus pelamis in food products

PCR ELISA

Santaclara et al. (2015)

Surimi

Traceability and species identification in surimi-based products

Next generation sequencing

Giusti et al. (2017)

Caviar

Identification of pure Sturgeon Caviar and hybrids

Single nucleotide polymorphisms

Boscari et al. (2014)

Surimi

Traceability and authentication of surimi species authentication

PCR-DNA barcoding

Galal-Khallaf et al. (2016)

Sea products

Fish

Species identification in fish fillet products

PCR-DNA barcoding

Di Pinto et al. (2015)

Fish (hake and plaice species)

Species identification in packaged frozen fishery products

PCR-DNA barcoding

Di Pinto et al. (2016)

Cod, pollock, and saithe

Differentiation of Gadidae fish species using COI and cytb barcode regions

DNA barcoding combined with high resolution melting analysis

Fernandes et al. (2017)

Shrimp, clam, sea cucumber, and fish

Differentiation of wild and farmed fish by stable isotope analysis

ICP-MS

Li et al. (2016b) (Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Salmon

Differentiation of wild and farmed Salmon employing compositional and isotope analysis

GC Isotope ratio MS High-resolution 2H sitespecific natural isotope Fractionation/nuclear magnetic resonance spectroscopy

Aursand et al. (2000)

Salmon

Differentiation of wild and farmed Salmon

H-NMR

Masoum et al. (2007)

Salmon

Wild Salmon authenticity

H-NMR

Capuano et al. (2012)

Eel

Differentiation of wild and farmed Eel employing fatty acid profile and carbon and nitrogen isotopic analyses

IRMS

Vasconi et al. (2019)

Salmon and trout

Traceability and authentication of the production origin of Salmonids employing stable isotope, fatty acid, and carotenoid analyses

Isoelectric focusing PCR-DNA barcoding HPLC Isotope-ratio-MS

Molkentin et al. (2015)

Sea cucumber

Differentiation of wild and cultured sea cucumber using amino acids carbon stable isotope fingerprinting

GC-combustion-IRMS

Zhao et al. (2018b)

Red mullet, cod, and samlet

Differentiation of high-quality from lower quality fish species

NIR spectroscopy

O’Brien et al. (2013)

Red mullet and plaice

Differentiation of valuable fish species from the cheaper ones in fish fillet

FT-NIR spectroscopy FT-IR spectroscopy

Alamprese and Casiraghi (2015)

Mullet, cod, and trout

Differentiation of high-quality from lower quality fish species in fillets and patties

NIR spectroscopy

Grassi et al., 2018

Virgin olive oils

Virgin olive oils characterization by free fatty acids evaluation as quality parameter

NIR spectroscopy

Marquez et al. (2005)

Virgin olive oils

Prediction of free acidity content in virgin olive oil as quality parameter

NIR spectroscopy

Cayuela et al. (2009)

Olive oil

Determination of the olive oil stability by using free fatty acids content, peroxide value, oxidative index, and conjugated dienes

Visible spectroscopy NIR spectroscopy

Cayuela Sa´nchez et al. (2013)

Olive oil

Determination of free fatty acids content in olive oil as quality parameter

Visible spectroscopy NIR spectroscopy

Garcı´a Martı´n (2015)

Virgin olive oils

Control of geographic origin and quality (free fatty acid content and peroxide value) of virgin olive oils

IR spectroscopy

Bendini et al. (2007)

Oils

Classification of oils in different categories by using the free fatty acid content

Raman spectroscopy

Muik et al. (2003)

Extra virgin olive oils

Determination of peroxide value as a quality parameter

MIR spectroscopy

Pizarro et al. (2013)

Virgin olive oils

Evaluation of peroxide values as quality parameter, minor components, and sensory characteristics

NIR spectroscopy

Inarejos-Garcı´a et al. (2013)

Extra virgin olive oils

Detection of low-quality extra virgin olive oils by fatty acid methyl esters or fatty acid ethyl esters

IR spectroscopy

Valli et al. (2013)

Edible vegetable oils

(Continued )

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Innovative Food Analysis

TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Virgin olive oils

Quantification of triacylglycerols and fatty acids as purity indicators and geographic origins classification

NIR spectroscopy

Galtier et al. (2007)

Extra virgin olive oil

Determination of adulterant by using information of triacylglycerols and fatty acids as purity indicators

NIR spectroscopy

Azizian et al. (2015)

Extra virgin olive oils

Determination of triacylglycerols and fatty acids for identifying the registered designation of origin

NIR spectroscopy MIR spectroscopy

Dupuy et al. (2010)

Virgin olive oils

Control of the origin and authentication of virgin olive oils with triacylglycerols and fatty acids information

Raman spectroscopy

Korifi et al. (2011)

Extra virgin olive oils

Differentiation according to the origin by evaluating the metabolomic fingerprinting

FT-IR spectroscopy

Tapp et al. (2003)

Extra virgin olive oil

Determination of extra virgin olive oil adulteration with lower priced vegetable oils using the metabolomic fingerprinting

FT-IR spectroscopy

Vlachos et al. (2006)

Extra virgin olive oil

Determination of extra virgin olive oil adulteration with lower priced vegetable oils using the metabolomic fingerprinting

Visible spectroscopy NIR spectroscopy

Downey et al. (2002)

Extra virgin olive oil

Confirmation of geographical origin using the metabolomic fingerprinting

NIR spectroscopy

Woodcock et al. (2008)

Virgin olive oils

Assessment of the adulteration with lower priced vegetable oils using the metabolomic fingerprinting

Raman spectroscopy

Lo´pez-Dı´ez et al. (2003)

Extra virgin olive oil

Determination of phenolic compounds

NMR

Christophoridou and Dais (2009)

Extra virgin olive oil

Determination of sterols

NMR

Hatzakis et al. (2010)

Extra virgin olive oil

Classification according to the geographical origin using the metabolomic profile

NMR

Mannina et al. (2010)

Refined oil

Evaluation of adulteration with lower priced vegetable oils using the metabolomic profile

NMR

Agiomyrgianaki et al. (2010)

Virgin olive oils

Determination of geographical origin using the metabolomic fingerprinting

NMR

Alonso-Salces et al. (2010)

Olive oils

Prediction of the geographical origin using the metabolomic fingerprinting

NMR

Longobardi et al. (2012)

Virgin olive oils

Classification according to the quality using volatile indicators

IMS

Garrido-Delgado et al. (2015)

Virgin olive oils

Classification according to the quality using volatile indicators

IMS

Contreras et al. (2019)

Olive oils

Evaluation of adulteration with lower priced vegetable oils using volatile indicators

MS

Goodacre et al. (2002)

Olive oils

Evaluation of adulteration with lower priced vegetable oils using triacylglycerols

MS

Go´mez-Ariza et al. (2006)

Olive oils

Evaluation of adulteration with lower priced vegetable oils using volatile indicators

MS

Alves et al. (2013)

Virgin olive oils

Classification according to the quality using volatile indicators

Quartz crystal microbalance sensor

Escuderos et al. (2011) (Continued )

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199

TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Virgin olive oils

Classification according to the quality and monitoring of rancidity using volatile indicators

Electronic nose

Savarese et al. (2013)

Olive oils

Classification according to sensory intensity perception

Electronic tongue

Veloso et al. (2016)

Coconut oil

Assessment of the adulteration with corn, sunflower, olive, or palm oil using spectral fingerprint

IR spectroscopy

Rohman and Man (2009)

Palm oil

Evaluation of the adulteration with recycled cooking oil using the spectral fingerprint

IR spectroscopy

Lim et al. (2018)

Peanut oil

Evaluation of the adulteration with palm oil using the spectral fingerprint

IR spectroscopy

Ren et al. (2014)

Coconut, soybean, canola, safflower, olive, and corn oils

Discrimination among different edible oils using the spectral fingerprint

MIR spectroscopy NIR spectroscopy Raman spectroscopy

Yang et al. (2005)

Sesame oil

Verification of sesame oil using spectral data

NMR

Kim et al. (2015)

Camellia oil

Verification of camellia oil using intensity of 15 NMR signals

NMR

Shi et al. (2018)

Peanut oil

Verification of peanut oil using single component relaxation time and peak area proportion

NMR

Zhu et al. (2017)

Sunflower oil

Verification of sunflower oil using phospholipid, fatty acid, and saccharide data

NMR

Monakhova and Diehl (2016)

Rapeseed, sunflower, peanut, and soy oil

Authentication using peroxide, anisidine values, and total tocopherols data

Potentiometric electronic tongue

Semenov et al. (2019)

Sesame oil

Authentication using the fingerprints

IMS

Zhang et al. (2016)

Coconut, soybean, canola, safflower, olive, and corn oils

Discrimination among different edible oils using the spectral fingerprint

MIR spectroscopy NIR spectroscopy Raman spectroscopy

Yang et al. (2005)

Common wheat (Triticum aestivum)

Characterization of gliadins employing protein extract

2-DE MS/MS

Mamone et al. (2005)

Wheat

Differentiation of common (T. aestivum) and durum (Triticum durum) and detection of adulterations

UHPLC-Q-TOF MS

Righetti et al. (2018)

Wheat

Differentiation of common, hulled (Triticum spelta), and durum wheat

DART-HRMS

Miano et al. (2018)

Rice

Differentiation of common and wild varieties employing steryl ferulates

UPLC-HR-Q-TOF MS

Zhu and Nystro¨m (2015)

Wheat

Differentiation of common and durum kernels

NIR-HIS NIR spectroscopy

Vermeulen et al. (2018)

Wheat

Differentiation of varieties employing flour and kernels

NIR spectroscopy

Ziegler et al. (2016)

Wheat

Differentiation of varieties

NIR spectroscopy

Miralbe´s (2008)

Wheat, taro, and sago flours

Differentiation and detection of mixtures

NIR spectroscopy

Rachmawati et al. (2017)

Oats

Detection of oat grains mixed with other cereals

NIR-HIS

Erkinbaev et al. (2017)

Cereal

(Continued )

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Innovative Food Analysis

TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Triticum species

Authentication employing powdered mixtures

CE-TBP

Silletti et al. (2019)

Wheat

Differentiation of common and durum cultivars in pasta

PCR electrophoresis

Bryan et al. (1998)

Wheat

Detection of adulteration of common wheat with durum

PCR

Sonnante et al. (2009)

Wheat flour

Detection of durum flour for pasta production adulterated with common wheat

PCR

Carloni et al. (2017)

Wheat

Differentiation of durum and soft common wheat kernels and pasta for adulteration detection

PCR

Casazza et al. (2012)

Italian “Altamura” PDO bread

Characterization of volatile compounds, color, and texture

DHS-GC-MS

Bianchi et al. (2008)

Wheat

Characterization of hulled varieties employing phenolic compounds, glycerophospholipids, and glycerolipids

LC-QTOF MS

Righetti et al. (2016)

Thai rough rice

Differentiation of varieties according to their physicochemical properties

Wet-based analyses of crude protein, apparent amylose content, and alkali spreading value

Attaviroj and Noomhorm (2014)

Triticum species, rye, oat, maize, millet, sorghum, rice, buckwheat, and quinoa flours

Characterization of betaine profile

HPLC -MS/MS

Servillo et al. (2018)

Wheat, spelt, and rye bread

Detection of varieties addition through peptide profiling

LC-MS/MS

Bo¨nick et al. (2017)

Italian PDO/PGI breads

Characterization of flour, dough, and final product

1

Brescia et al. (2007)

Bakery products

Detection of adulteration of the fat source (margarine/butter) employing lipid fraction

Raman spectroscopy NIR spectroscopy

¨ c¸u¨ncu¨oˇglu et al. U (2013)

Wheat gluten and wheat bran

Detection of melamine

SERS

Mecker et al. (2012)

Maize gluten

Detection of adulterants

Microscopy Electronic nose HPLC GC-MS/MS

Frick et al.(2009)

Common soft wheat (T. aestivum)

Characterization of cultivars employing their lignan profile

CE-MS

Dinelli et al. (2007)

Rice

Authentication of organic rice employing trace elements

q-ICP-MS

Barbosa et al. (2016)

Maize

Differentiation of transgenic cultivars employing zein proteins

CE-MS

Erny et al. (2007)

Maize

Differentiation of organic cultivars employing metabolomic profile

GC-MS

Ro¨hlig and Engel (2010)

Maize

Differentiation of transgenic cultivars employing metabolomic profile

CE-UV CE-TOF-MS

Levandi et al. (2008)

Paddy rice

Detection of pesticide tricyclazole

HPLC-UV SERS

Tang et al. (2012)

H HR-MAS NMR

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Durum wheat

Detection of fungicide epoxiconazole

NIR spectroscopy

Soto-Ca´mara et al. (2012)

Wheat and rice

Authentication of origin

EA-SIRMS

Wu et al. (2015)

Wheat

Authentication of origin

EA-SIRMS

Luo et al. (2015)

Wheat, barley, rye, triticale, oat, and maize

Authentication of origin

IRMS ICP-MS

Goitom Asfaha et al. (2011)

Rice (Oryza sativa)

Authentication of origin

IRMS ICP-MS

Chung et al. (2018)

Rice (Thai jasmine, KDM 105)

Authentication of origin

EA-IRMS Instrumental neutron activation analysis

Kukusamude and Kongsri (2018)

Rice (O. sativa)

Authentication of origin according to grain quality

IRMS

Chen et al. (2016)

Rice (O. sativa)

Authentication of organic and pesticide-free cultivars

IRMS GC-MS/MS LC-MS/MS

Chung et al. (2017)

Wheat, barley, faba bean, and potato

Authentication of organic cultivars and their origin

ICP-OES ICP-MS

Laursen et al. (2011)

Sorghum starch

Characterization of cultivar origin according to granule resistance and water solubility

Wet-based analyses potentiometry 2B chromatography

Craig and Stark, (1984)

Rice (Thai jasmine)

Authentication of origin

HR-ICP-MS

Cheajesadagul et al. (2013)

Rice (O. sativa)

Authentication of origin

ICP-AES

Chung et al. (2015)

Wheat kernels and flour

Authentication of origin

NIR spectroscopy

Zhao et al. (2013)

Seeds and seed flours

Characterization of a technique for detection and quantification of GMOs

Nucleic acid sequencebased amplification implemented microarray analysis

Dobnik et al. (2010)

Soybean

Characterization of the technique and quantification of GMOs

Droplet digital PCR assays

Koˇsir et al. (2017)

Rice (O. sativa)

Detection and identification of GMOs through pCAMBIA vector determination

PCR SYBR

Fraiture et al. (2014)

Rice and maize

Detection of GMOs

DNA walking

Fraiture et al. (2015a)

Rice (O. sativa)

Validation of a method for GMOs detection and identification through pCAMBIA vectors determination

DNA walking

Fraiture et al. (2015b)

Maize

Detection of GMOs

Use of commercial kits, qPCR, and basic local alignment search tool

Liang et al. (2014)

Rice (O. sativa) and rice noodles

Development of a statistical framework meant for characterization of GMOs

Next generation sequencing

Willems et al. (2016)

GMO maize NK603

Characterization and molecular profiling

UPLC-MS/MS GC-MS/MS

Mesnage et al. (2016)

Determination of major phenolic compounds

HPLC-UV

Brown et al. (2011)

Food supplements Echinacea raw materials

(Continued )

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TABLE 8.1 (Continued) Food

Purpose of analysis

Analytical method

Ref

Cranberry fruit products

Determination of anthocyanins

HPLC-UV

Brown and Shipley (2011)

Ginkgo biloba supplements

Detection of adulterants such us rutin, quercetin, and an unknown flavonol glycoside using spectral fingerprint

HPLC-UV

Harnly et al. (2012)

Notoginseng root extrac

Authentication of notoginseng root using the content of 12 saponins

HPLC-UV

Wang et al. (2009)

Dietary supplements for weight loss

Detection of adulteration by quantification of sibutramine, phenolphthalein, and synephrine

NMR

Vaysse et al. (2010)

Panax ginseng, Panax quinquefolius, and Panax notoginseng extracts

Differentiation of three ginseng species

MS

Chen et al. (2011)

Bearberry, Arctostaphylos uva-ursi (L.), and pungen

Differentiation of Bearberry, A. uva-ursi (L.) from pungen using fingerprint identification

HPTLC NMR HPLC-DAD

Gallo et al. (2013)

2-D NIR, 2-D near-infrared; CE, capillary electrophoresis; CE-MS, capillary electrophoresis-mass spectroscopy; CE-TBP, capillary electrophoresis-tubulinbased polymorphism profiling; CE-TOF-MS, capillary electrophoresis-time of flight-mass spectroscopy; CE-UV, capillary electrophoresis-ultraviolet; DART-HRMS, direct analysis real time high-resolution mass spectroscopy; ELISA, enzyme-linked immunosorbent assay; ESR, electronic spin resonance; FT, Fourier transform; FT-IR, Fourier transform infrared; FT-NIR, Fourier transform near infrared; GC, gas chromatography; GC-FID, gas chromatography with a flame-ionization detector; IRMS, isotope ratio mass spectrometry; GC-IMS, gas chromatography-ion mobility spectrometry; GC-MS/MS, gas chromatography-mass spectrometry/mass spectrometry; GC-O, gas chromatography-olfactometry; GC-TOF MS, gas chromatography-time of flight-mass spectroscopy; H-NMR, proton nuclear magnetic resonance; HPAE-PAD, anion exchange chromatography with pulse amperometric detection; HPLC, highperformance liquid chromatography; HPLC-ECD, high-performance liquid chromatography-electrochemical detector; HPLC-LASER, high-performance liquid chromatography-light amplification by stimulated emission of radiation; HPLC-MS, high-performance liquid chromatography-mass spectroscopy; HPLCPAD, high-performance liquid chromatography-pulse amperometric detection; HPLC-QTOF MS, high-performance liquid chromatography-quadrupole timeof-flight mass spectroscopy; HPLC-TOF MS, high-performance liquid chromatography-time-of-flight mass spectroscopy; HPLC-UV, high-performance liquid chromatography-ultraviolet; HPTLC, high-performance thin-layer chromatography; HRGC-FID, high-resolution gas chromatography flame ionization detector; ICP-AES, inductively coupled plasma atomic emission spectrometry; ICP-MS, inductively coupled plasma mass spectrometry; ICP-OES, inductively coupled plasma optical emission spectrometry; IMS, ion mobility spectrometry; IR, infrared; IRMS, isotope ratio mass spectrometry; LC-MS, liquid chromatography-mass spectrometry; LC-QTOF MS, liquid chromatography-quadrupole time-of-flight mass spectroscopy; MIR, mid-IR; NIR, near infrared; NMR, nuclear magnetic resonance; PCR, polymerase chain reaction; SAFE, solvent-assisted flavor evaporation; SBSE, stir bar sorptive extraction; SERS, surface-enhanced Raman spectroscopy; SDE, simultaneous distillation extraction; SFE, supercritical fluid extraction; SIRA, stable isotope ratio analyses; TCT-GC-FID, thermal desorption and cold trap gas chromatography flame ionization detector; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis; TCT-GC-MS, thermal desorption and cold trap gas chromatography mass spectrometry; TD, thermal desorption; UHPLC-HRMS, ultra-high performance liquid chromatography-high-resolution mass spectroscopy; UPLC-MS, ultra-high performance liquid chromatography-mass spectroscopy; UV, ultraviolet.

industry (Downey, Fouratier, & Kelly, 2003; Kelly, Downey, & Fouratier, 2004; Li, Shan, Zhu, Zhang, & Ling, 2012). Detection of this kind of adulteration is crucial, because the sugar composition of these cheap syrups is sometimes close to honey. Adulteration of honey by feeding bees with artificial sources such as sugar or syrup (Kast & Roetschi, 2017) and incorrect information about the honey’s geographic and botanical origin is another significant issue, which can cause serious problems for honey producers and consumers (Bougrini et al., 2016). Therefore confirm the quality and authenticity of honey is a significant issue for commercial and health reasons. Authentication of honey generally deals with quality control, determination of physicochemical parameters, detection of adulteration or residues, and determination of botanical and geographical origin (Siddiqui, Musharraf, Choudhary, & Rahman, 2017). Traditional honey authentication, which have been routinely performed in commercial honey trading, mostly involved tedious and time-consuming analytical procedures requiring sample preparation and analytical skills for completion. Nowadays, various advanced analytical methods such as chromatographic and spectroscopic methods are becoming the most attractive and commonly used methods for authentication of honey in order to monitor and evaluate its composition (Soares, Amaral, Oliveira, & Mafra, 2017). Honey has high monosaccharide content leading to immediate energy production by the body. It consists primarily of fructose (about 38.5%), glucose (about 31%), and water (about 20%). Glucose and fructose are still by far the major sugars in honey that are simple sugars, but 22 others have been found, including maltose (about 7.1%), sucrose (about 1.3%), maltulose, turanose, isomaltose, laminaribiose, nigerose, kojibiose, gentiobiose, and trehalose as disaccharides. Maltotriose, erlose, melezitose, 1-kestose, isopanose, isomaltotriose, panose, and theanderose have been found as the trisaccharides in

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honey (Doner, 1977). Honey adulterated by sugar cannot be easily detected by direct analysis of sugars, because its components are the main components of honey. However, with this adulteration, some chemical and biochemical parameters of honey, such as electrical conductivity, enzymatic activity, and contents of specific compounds such as Hydroxymethyl furfuraldehyde (HMF), glucose, fructose, sucrose, maltose, isomaltose, proline, and ash would be changed (Soares et al., 2017). Therefore normal variation of these parameters should be considered for evaluation of unknown samples. It should be noted that some chemical characteristics, such as HMF content and enzymatic activity, vary in different honeys and could also change in honeys when exposed to heat or stored in a warm environment (Tosi, Ciappini, Re´, & Lucero, 2002). The amounts of amino acids and proteins of honey are relatively low; however, the amino acid profile of honey could be characteristic of its quality and also its botanical origin (Hermosıfin, Chico´n, & Dolores Cabezudo, 2003). Honey contains almost all physiologically important amino acids, among which proline is the dominant free amino acid in all types of honey. Proline content of honey constantly decreases during storage; therefore the amount of proline in honey has been proposed as an indicator of honey ripeness (Bogdanov et al., 1999). The amount of proline in honey is related to the degree of nectar processing by the bees. It could be used as an indicator of honey adulteration. The amount of proline in regular honeys should be more than 200 mg kg21, and values below 180 mg kg21 indicate honey fraud (Bogdanov et al., 1999). The content of HMF, which is a decomposition product of fructose, is widely recognized parameter in evaluating freshness and overheating of honey (Aparna & Rajalakshmi, 1999). Trace amounts is present in fresh honey; however, its concentration increases with increasing storage time and prolonged heating. The process of conversion of fructose to HMF depends on pH, and in blossom honey, this conversion occurs faster than in honeydew because of the higher pH (Bogdanov et al., 1999). Using short-term heating, even at higher temperatures, the HMF content is minimum. The research results demonstrated that NIR and MIR spectroscopy is a valuable, rapid, and nondestructive tool for the quantitative analysis of some of the chemical parameters in honey (Escuredo, Carmen Seijo, Salvador, & Inmaculada Gonza´lez-Martı´n, 2013; Garcı´a-Alvarez, Huidobro, Hermida, & Rodrı´guez-Otero, 2000; Ruoff et al., 2007). These two methods have been used to predict the content of HMF, proline, and sugars such as maltose, turanose, nigerose, erlose, trehalose, isomaltose, kojibiose, melezitose, raffinose, gentiobiose, melibiose, maltotriose, as well as pH value, electrical conductivity, and free acidity (Garcı´a-Alvarez et al., 2000; Pataca, Neto, Marcucci, & Poppi, 2007). Guler, Bakan, Nisbet, and Yavuz (2007) determined the biochemical properties, such as proline content and electrical conductivity beside sugar (sucrose, fructose, and glucose) content, in order to discriminate pure blossom honey from adulterated samples obtained by overfeeding the bees with sucrose syrup. The results demonstrated that proline content and electrical conductivity could be successfully used for detection of this adulteration; however, sugar content could not be used for this purpose since more than 95% of the sucrose consumed through feeding of the bees is converted into fructose and glucose (Guler et al., 2007). Recently, anion exchange chromatography with pulse amperometric detection (HPAEC-PAD), liquid chromatography (LC) with a refractometer detector, gas chromatography with a flame-ionization detector (GC-FID), and GC-MS have been used as alternative chromatographic methods for sugar analysis in honey. Some researchers have showed the presence of disaccharides and a small number of trisaccharides and tetrasaccharides in honey, and the absence of oligosaccharides with a high degree of polymerization. Considering that several sugar syrups are obtained by the enzymatic hydrolysis of starch, they can contain a large amount of oligosaccharides with a high degree of polymerization (Megherbi, Herbreteau, Faure, & Salvador, 2009). Therefore although major sugars are not suitable as honey authentication markers, oligosaccharides with a high degree of polymerization could be proposed as suitable indicators for detection of honey adulteration with starch syrups (Pita-Calvo, Guerra-Rodrı´guez, & Va´zquez, 2017). The amount of acid in honey is relatively low (they only amount to less than 0.5%) but has a key role in the taste of honey. Gluconic acid is the most significant acid; however, other organic acids such as lactic, formic, butyric, tartaric, pyruvic, acetic, citric, oxalic, succinic, malic, maleic, α-ketoglutaric, glucose-6-phosphate, pyroglutamic, and glycolic acid are also present in minor amounts in honey (Mato, Huidobro, Simal-Lozano, & Sancho, 2003). The investigation of these components as potential candidates is attractive, because they are involved organoleptic properties, such as color and taste, and its physical and chemical properties, such as acidity and electrical conductivity (Mato, Huidobro, Simal-Lozano, & Sancho, 2006a). HPLC, GC, and capillary zone electrophoresis are the most widely used analytical methods for determination of organic acids in honey (Cherchi, Spanedda, Tuberoso, & Cabras, 1994; Mato, Huidobro, Simal-Lozano, & Sancho, 2006b; Sua´rez-Luque, Mato, Huidobro, Simal-Lozano, & Sancho, 2002). The presence of enzymes makes honey different from other sweetening agents. These enzymes are added by honey bees during the process of natural honey ripening (Aurongzeb & Azim, 2011). Some of the most important honey enzymes are invertase, diastase (amylase), glucose oxidase, catalase, and acid phosphatase (Solayman et al., 2016; Weirich, Collins, & Williams, 2002). Heating of honey, which is the only practical method to prevent or delay crystallization, can destabilize or destroy these enzymes (Subramanian, Umesh Hebbar, & Rastogi, 2007). These enzymes play an important role in defining the physical and biological properties of various honeys.

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The mineral content or ash in floral honey varies between 0.04% and 0.20%, and it contributes to the color of the honey, which may vary from light to dark (Gonza´lez-Miret, Terrab, Hernanz, Ferna´ndez-Recamales, & Heredia, 2005). Potassium, sodium, zinc, iron, calcium, and copper are the major mineral present in honey (Ajibola, Chamunorwa, & Erlwanger, 2012). Inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) are two main analytical methods for determination of minerals in honey (Chudzinska & Baralkiewicz, 2010; Terrab, Hernanz, & Heredia, 2004). Food processors, retailers, food service companies, and consumers are actually eager to know about the origin and quality of honey. PDOs are also beneficial to ensuring honey authenticity. The geographical origin of honey affects its physiological properties, composition, and its nutritional value. Therefore specifying the origin of honey can lead to a better understanding of consumers about its quality. The botanical origin of honey is another important parameter in determining its quality; therefore it has a significant impact on its price. Traditionally, the botanical origin of honey has been determined solely through information on its sensory, flavor, pollen, and physicochemical properties (Rodopoulou et al., 2018). However, the results of analysis using these methods are usually represented with some uncertainty due to the various number of factors involved. Therefore it would be more appropriate to identify honey for the botanic source with respect to chemical components such as organic acids and sugars, proteins, metals, volatile organic compounds, flavonoids, and other phenolic compounds (da Silva et al., 2016). These would be based mostly on new powerful analytical techniques for combination with multivariate data analysis. Ku´s and van Ruth (2015) assessed the feasibility of discrimination of honey from six different floral varieties (cornflower, buckwheat, black locust, rapeseed, lime, and heather) based on application of PCA and k-nearest neighbors algorithm (kNN) on HPLC-diode array detector (DAD) fingerprints. They discriminatined floral origins successfully, using HPLC fingerprints obtained at 210 nm as the most efficient wavelength for discrimination based on botanical origin. Cavazza, Corradini, Musci, and Salvadeo (2013) differentiated monofloral honey by various botanical origins based on phenolic fingerprints obtained by HPLC-UV after solid phase extraction. They found that the relative amount of phenolic compounds has a special potential in discriminating honey samples with different botanical origins. PCA was used for the exploratory analysis and then LDA was applied as a supervised pattern recognition method. The resulting classification models discriminated honey samples accurately, according to their botanical origins. Zhao et al. (2016a) examined phenolic acids in jujube, chaste, and longan honey samples. They used PCA and LDA as unsupervised and supervised pattern recognition methods on HPLC-electrochemical detector fingerprints. Seventy-seven samples were accurately discriminated based on their floral origin using phenolic acid fingerprints and LDA method. Zhou et al. (2014) investigated on the traceability of rape and chaste honey samples using HPLC-DAD-MS/MS according to the floral origin; 360 and 270 nm were identified as the appropriate wavelengths for differentiation of chaste and rape honey using chromatographic fingerprints. The authors used PCA, PLS, and PLS-DA in order to classify samples by their floral origin. The results also showed that ferulic acid, morin, and kaempferol are significant markers in this differentiation. Cotte et al. (2004a) examined amino acids for discrimination of honey samples according to botanical origin. HPLC was used to obtain fingerprints and PCA was applied to fingerprints of pure honey samples. Lavender honey was accurately distinguished but other six varieties were discriminated moderately. Gil, Ferreres, Ortiz, Subra, and Toma´s-Barbera´n (1995) determined honey floral origin based on biochemical markers including phenolic metabolites in rosemary honey and floral nectar using HPLC-UV. All samples have a common flavonoid profile consisting of 15 compounds. Flavonoid profiles of the honey samples were also similar to propolis, indicating that this plant is the major source of the flavonoids they contained. Since kaempferol can be achieved from various flower nectars, the presence of this flavonol in rosemary honey could not be known as verification for this floral origin. However, the presence or absence of trace amounts of kaempferol (,0.3 μg/g) may afford additional evidence of a different floral origin. Natural honey is susceptible to adulteration, because its production is a laborious process, which is time-consuming and involves a lot of costs. Honey authenticity has become a serious public issue that needs to be addressed effectively in order to protect consumers from the consequences of unscrupulous food production. Honey adulteration methods include feeding bees with pure sugar cane or sugar syrup during honey production, dilution with various sugar syrups, and inconsistent and ¨ zdemir, 2018; Guler et al., 2014; inaccurate labeling of honey about its geographical origin or botanical variety (Ba¸sar & O Utzeri et al., 2018). Other activities on authentication of honey have been reported in Table 8.1.

8.2.3 Milk and dairy products Milk and dairy products with highly nutritive value make a major contribution to a well-balanced diet for different consumer groups such as children and pregnant women (Jenkins & McGuire, 2006). Milk has a high nutritional value because of its containing carbohydrates, proteins, minerals, vitamins, enzymes, and fats (Zamberlin, Antunac,

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Havranek, & Samarˇzija, 2012). Milk protein is an important factor for the production of other dairy products as it has a significant impact on the physical properties, flavor, and other attributes of resulting fermented products. Therefore it could be stressed that protein content of the milk has directly affected the quality of industrial milk (Bhat & Bhat, 2011). Moreover, milk proteins may provide peptides with demonstrated antioxidant activity and essential amino acids, which is necessitate for human growth (Chakrabarti, Jahandideh, & Wu, 2014; Chatterjee, Sarkar, & Boland, 2014). Milk is a relatively common target for profit-making fraud because of its high cost of production and its unique characteristics (Afzal, Mahmood, Hussain, & Akhtar, 2011). Usually, milk is adulterated by mixing lower valued milk with higher valued milk, increasing its volume, masking inferior quality, or replacing authentic components (Nascimento, Santos, Pereira-Filho, & Rocha, 2017). Milk adulteration is a deliberate strategy intended to reduce total production costs by mixing different types of milk, or by adding water and foreign oils or fats (Kumar, Lal, Seth, & Sharma, 2010). Milk manipulations include adding sugar, salt, or high-molecular-weight substances such as whey or starch to mask added water (Santos, Pereira-Filho, & Rodrı´guez-Saona, 2013). Commercial ultrahigh temperature (UHT) milks may be mixed by adulterants such as chlorine, hydrogen peroxide, formalin, starch, etc. (Souza et al., 2011). Occasionally, nitrogen-rich materials are also used to mimic proteins in milk and dairy products, because standard analytical methods cannot distinguish nitrogen from protein and nonprotein sources (De Lourdes Mendes Finete, Gouveˆa, De Carvalho Marques, & Netto, 2013; Draher, Pound, & Reddy, 2014). Therefore the need to develop effective techniques for detecting different types of fraud in milk is essential for the benefit of consumers. Tracing manufactured dairy products is able to confirm the processing conditions and their geographical origin (Neˇcemer, Potoˇcnik, & Ogrinc, 2016) and, moreover, can be of help to prevent the labeling of ordinary milk as an organic farming product (Chung, Park, Yoon, Yang, & Kim, 2014). The geographical origin and the processing technology of dairy products could also be attributed to their authentication (Osorio, Koidis, & Papademas, 2015; Schmidt & Mayer, 2018). Labeling of conventional milk as a product of organic farming is another aspect of authenticity of milk and dairy products, which should be considered (Erich et al., 2015). Therefore dairy product authentication is one of the primary concerns for the monitoring (Sardina, Tortorici, Mastrangelo, Di Gerlando, & Tolone, 2015) to ensure that dairy products are properly labeled (Di Domenico, Di Giuseppe, Wicochea Rodrı´guez, & Camma`, 2017). Traceability of dairy products is also an important issue indicating the presence of undesirable compounds such as antimicrobials, mycotoxins, organochlorine pesticide, antibiotic residues, and heavy metals in order to protect consumers from harmful contamination (Motarjemi, Moy, Jooste, & Anelich, 2014). Because safety in dairy products has become a growing public health issue, all participants in the supply chain should be encouraged to create a robust traceability system to be implemented within the supply chain in order to assure quality and safety in different milk products. Tay, Fang, Chia, and Li (2013) used LC-quadrupole ion trap and LCquadrupole time-of-flight-MS in order to detect analyzed added detergent powder in infant milk formula. They identified dodecylbenzene sulfonate as the main marker of detergent powder and successfully determined this adulterant in milk powder formula using PLS regression. Jablonski, Moore, and Harnly (2014) authenticated milk powder by analyzing skim milk powder (SMP) and mixtures of SMP with soy (SPI), hydrolyzed wheat protein (HWPI), pea (PPI), and brown rice (BRP) as adulterants using HPLC chromatographic fingerprints. Soft independent modeling of class analogy (SIMCA) model allowed detection of adulteration of SMP with low levels (1% and 3%) of SPI, and with higher levels of HWPI, PPI, and BRP. Lu, Lv, Gao, Shi, and Yu (2015) distinguished milk and nonmilk proteins using amino acid fingerprinting combined with chemometrics. Microwave-assisted hydrolysis and ultraperformance liquid chromatography (UPLC) were used to obtain amino acid fingerprints. PCA and PLS-DA discriminated milk protein from nonmilk protein from peanut, whey, soy, fish, corn, egg yolk, beef extract, cattle bone, and collagen. The proposed method was accurately used to detect and quantify adulteration of protein in milk. Maximum shelf life of fermented milk products and fresh cheeses depending on manufacturing method and packaging technologies is 4 5 weeks during refrigerated storage. Verzera, Romeo, Ziino, and Conte (2008) investigated the shelf life of fresh cheeses and fermented milk based on monitoring of volatile compounds using solid phase microextraction (SPME)-GC-MS. PCA was used to explore the main differences in the composition of volatile organic compounds (VOCs) during storage. Subsequently, the possible maximum critical days of shelf life and the cheese ‘‘freshness’’ were determined. Contarini, Povolo, Leardi, and Toppino (1997) differentiated the milks with special heat treatments including “in-bottle” sterilization, direct UHT, and pasteurization method using dynamic headspace capillary GC coupled with multivariate data analysis methods. The results elucidated that the volatile compounds, provided significant information about the temperature and the time of storage.

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Pure milk, fermented milk, infant formula, and human milk mostly used as feed sources or supplementary foods contain a wide range of nutrients necessitate for both children and adults. Meanwhile, there is increasing evidence that short-chain fatty acids (SCFAs) have attracted much attention recently because of a beneficial role in human health according to their positive physiological effects. Jiang et al. (2016) discriminated fermented milk, infant formula, pure milk, and human milk, based on their SCFAs composition. GC-MS fingerprinting method coupled to PCA was used in this purpose to study SCFAs in dairy products and human milk. Differentiation was mainly based on the differences in concentration levels of formic acid, acetic acid, propionic acid, and hexanoic acid. Li et al. (2012) investigated in the volatile composition of fermented camel milk (from Sinkiang, China) in six different conditions using GC/MS. PCA was applied to compare the pretreatment methods. The results elucidated that different pretreatment methods may lead to different volatile component profiles. Kim et al. (2014) examined the feasibility of differentiation of natural mozzarella cheese (NMC) from cheese substitutes based on fatty acid profiles and phytosterol contents. NMCs, imitation mozzarella cheeses (IMCs), and mixed cheeses (MCs) composed of NMCs and IMCs and processed cheeses were used as studied samples. The results of PCA demonstrated that the NMC samples were differentiated from the IMCs based on phytosterol content, and the NMC samples were completely distinguished from the IMC and MC clusters based on their fatty acid compositions. Vallejo-Co´rdoba and Nakai (1994) predicted the shelf-life period of milk using an efficient analytical system. VOCs (determined by dynamic headspace GC), sensory properties, and psychotropic bacterial counts were examined during storage of pasteurized milk. A principal component regression model was used to estimate the shelf life of milk samples based on their VOC profiles. The satisfactory results of prediction less than 2 days was achieved. Delgado, Gonza´lezCrespo, Cava, and Ramı´rez (2011) investigated on VOCs obtained from SPME-GC-MS in order to examine the goat milk cheese of the ‘‘Queso Ibores’’ from Spain. Alcohols, esters, acids, and ketones were detected through four steps of maturation including days 1, 30, 60, and 90, for studied milk samples. Octanoic, hexanoic, and butanoic acids were considered as the most discriminative aroma compounds of Ibores cheese, and carboxylic acids were the main volatiles in the headspace of Ibores cheese. The results also showed that the total amounts of VOCs increased through the first 60 days of maturation. Marsili (2000) determined the shelf life of pasteurized milk, whole-fat chocolate milk, sampled and homogenized reduced-fat milk over a 7-month period. SPME and GC-MS were applied to extract and detection of VOCs. PLS regression was applied on mass fragmentation profiles with the satisfactory ability to predict the shelf life of milk samples. Other activities on authentication of milk and dairy products have been reported in Table 8.1.

8.2.4 Alcoholic beverages Wine authentication is the monitoring and recognition process by which the quality of wine is verified as in compliance with its label description and, moreover, with national and international regulations. Authentication of wine basically involves obtaining appropriate effective parameters to identify the processing methods, the origin, and grape variety. The relatively high price and widespread production of wine have made it an attractive target for adulteration. Wine adulteration includes implementing nonappropriate techniques in the production process, using different grape varieties, application of dried or poor-quality grapes, and fraudulent or inaccurate labeling (Stanziani, 2009). Wine can also be adulterated through addition of colorants, sugar, water, alcohol, or other substances (Holmberg, 2010). Cane sugar or beet can be added before or during fermentation in order to increase the ethanol content of wine and raise its price (Arrizon & Gschaedler, 2002). This method is only permitted in some regions where exposure to sunlight is inadequate during the growing season, leading to reduced sugar contents in grapes. Moreover, their use increases the stability of wine against changes of temperature and oxidation during aging and commercialization (Ferreira, Du Toit, & Du Toit, 2006). However, adding sugar to wine in order to raise its ethanol content is not permitted in some wine-producing areas (France and Italy, for instance; Christoph, Rossmann, & Voerkelius, 2003). Accurate labeling of the wine can allow consumers to select their proper choices based on the variety of grape, the geographical origin, and the quality and the aroma of the wine (Springer et al., 2014). The purity of the botanical origin is of particular importance in some wines, so accurate labeling on the variety of grape can be very helpful. Since consumers often place great trust in labels, abuse by manufacturers in the form of inaccurate descriptions on labels is another form of fraud. Consumers nowadays are particularly sensitive to grape varieties, because they know that the sensory properties of ´ lvarez-Casas, Pa´jaro, Lores, & Garcı´a-Jares, 2016). In fact, the diswine are strongly dependent on the grape variety (A tinction between the different grape varieties used in wine making has become an important issue and has led to more winemakers being able to identify correctly grape varieties on their labels. Grape varietal adulteration involves

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admixing must achieved from grape varieties other than that represented on the label to a certain extent greater than the threshold imposed by regulatory authorities (Pennington, Ni, Mabud, & Dugar, 2006). Traditional methods for authentication of wine involve sensorial panels, determination of the total soluble solids with hydrometry and/or refractrometry, determination of alcohol using different techniques such as ebullimetric analysis or GC, or determination of the various phenolic compounds using different operating systems (Cubero-Leo´n, Pen˜alver, & Maquet, 2014). Nowadays, the wine industry needs efficient analytical tools for rapid, accurate, and inexpensive analysis to verify the authenticity of high value products. Detection of adulteration in wine due to its complex matrix is not possible only through the determination of finite chemical parameters, so advanced analytical methods with acceptable speed and accuracy must be used to confirm its identity and detect fraud. Recently, fingerprinting techniques coupled to chemometrics have been applied as a new generation method for wine authentication purposes. Nuclear magnetic resonance (NMR) spectrometry, GC, HPLC, and MS spectrometry have been widely used for this purpose (Table 8.1). For example, application of chemical fingerprints based on isotopic, organic, and inorganic patterns for varietal and geographical discrimination of three Argentinean red wines have been reported successfully (Di PaolaNaranjo et al., 2011). Their phenolic patterns was determined by HPLC-MS/MS, multielemental composition by ICPMS, and δ13C and 87Sr/86Sr by IRMS and thermal ionization mass spectrometry, respectively. Geana et al. (2016b) detected adulteration of sweet red table wines from chemical parameters. They successfully identified addition of water, sugar, synthetic sweeteners, or synthetic red dyes by determining alcoholic strength, stable isotopes (δ13C and δ18O), and 5-(hydroxymethyl)-2-furaldehyde in the wines. Fernandes et al. (2015) verified botanical origin of German white wine based on volatiles fingerprinting. A total of 198 wine samples were analyzed applying HS-SPME-GC-MS. The results of PLS-DA model showed that white wine varietal classifications were accurately accomplished with reasonable sensitivity and specificity. Xiao et al. (2014) used headspace (HS)-SPME-GC-MS and chemometrics in order to characterize the sensory properties of three classes of the wines using volatile organic patterns of cherry wines. Esters, alcohols, acids, aldehydes, and ketones containing 35 volatiles were characterized in studied samples. The results showed that the combination of HS-SPME-GC-MS with multivariate data analysis methods provide a proper way for characterization and classification of cherry wines. Laghi, Versari, Marcolini, and Parpinello (2014) discriminated red wines obtained from organic and biodynamic management by means of proton nuclear magnetic resonance (1H-NMR) and metabonomic investigation. The authenticity, the grape variety, the geographical origin, and the year of vintage of wines produced in Germany were also investigated by 1H-NMR spectroscopy in combination with multivariate data analysis methods (Godelmann et al., 2013). Another alcoholic complex beverage is beer. It is made mainly using malt, yeast, hops, and water. Beers can be produced in different countries and belong to different types (ale, lager, etc.), which have several variations within their specific process, such as the type of yeast used, the production temperature, and the fermentation time (Cubero-Leo´n et al., 2014). The traceability, authentication, and the quality control of beer is a major international concern because of the impact of any change in the production process; it is always exposed to various levels of distortions and adulterations. The high cost of production and trading of beer makes it susceptible to fraud in order to reduce these costs and meet demands for higher profits. At the present time, beer manipulation has become more sophisticated by taking different forms, for example, mixing high-quality beer with low-quality one to achieve more profit. Therefore in order to control the quality and authenticity of the products, complete control over the beer production process using appropriate analytical methods is necessary (Esslinger, Riedl, & Fauhl-Hassek, 2014). Few studies have been conducted so far to verify the authenticity of beer products. UV-Vis spectroscopy, GC-FID, HPLC, and FLD are commonly used methods to study amino acid content, HPLC-UV-Vis to study phenols, and, most recently, LC-MS, GC-MS, and direct analysis real time (DART)-time of flight (TOF)-MS and NMR for obtaining metabolite profiles (Table 8.1). For instance, an NMR and chemometric analytical approach has been successfully used to classify beers according to their brand identity within the European TRACE (Tracing Food Commodities in Europe) project (Mannina et al., 2016). LC-MS was also used to authenticate a selected Trappist beer, and it was clearly distinguished from other types of beers using multivariate data analysis methods (Mattarucchi et al., 2010). IR spectroscopy has also been proved successful for the authenticity of beers with minimal sample preparation steps (Engel, Blanchet, Buydens, & Downey, 2012). Other activities on authentication of alcoholic beverages have been reported in Table 8.1.

8.2.5 Nuts Nuts exhibit a broad range of functional properties due to their vitamins, minerals, unsaturated fatty acids, fiber, and phytochemicals such as polyphenols, tocopherols, squalene, and phytosterols that could have potential positive effect on human health and nutrition. More than 75% of the total fatty acids of nuts are unsaturated. β-sitosterol is dominant

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sterol in most of the nuts and α-tocopherol is the main tocopherol isomer in nuts. Nuts are rich in compounds with antioxidant properties and their consumption prevents incidence of many diseases such as cardiovascular diseases and cancers. Walnuts, Brazil nut, cashew nut, peanut, pecan, and pistachio nuts are rich in γ-tocopherol, while pistachio and pine nut have the highest total phytosterol. Walnuts also contain large amount of phenolic compounds compared with other nuts (Dehghan, Mohammadi, Mohammadzadeh-Aghdash, & Ezzati Nazhad Dolatabadi, 2018). Nuts should be considered as one of the most susceptible food products to fraudulent mechanisms, since they can be replaced with old or expired samples or poor-quality samples with cheaper prices. This may lead to cause serious problems for consumers, especially those who suffer from allergies and intolerance. Adulteration of nuts and seeds contributed 4% of the 177 adulteration cases reported in the European Union in 2016 (Campmajo´ et al., 2019). Nowadays, the exact physical, morphological, and chemical characteristics of kernels are significant issues in marketing, so that certain varieties with particular characteristics could be more appropriate for specific marketing management, industrial processing, and ultimately consumers. Therefore the need to monitor the authenticity of nuts through quality control processes is absolutely clear. Nut powder is generally used in a variety of processed foods, particularly in bakery and confectionery products. Most of the nut powders, due to their high prices, are target of illegal practices such as mixing with cheaper nuts. Today, there is an increasing need for efficient and inexpensive quality control systems for the identification of foreign food materials in the powdered food processing lines. This is especially important for detecting foods that have allergic effects, such as peanuts. The manufacturing industries that deal with the processing of powdered food products have a very high potential for adulteration by substitution of them with other cheap or poor-quality nuts, which may lead to unintentional ingestion by the sensitized population. Therefore the necessity for inline systems for identifying different types of seeds and nuts traces at the early stages of food production is completely clear. One another important issue in authentication of nuts is the determination of their cultivars and their geographical origin, because these parameters have a significant impact on the quality of nuts. Klockmann, Reiner, Bachmann, Hackl, and Fischer (2016) investigated the ability to discriminate hazelnut varieties based on geographical origin using UPLC/QToF-MS. Hazelnuts from Italy, Germany, France, Turkey, and Georgia were used for this purpose. They used four different LC/MS methods and 20 key metabolites. SIMCA, PCA-LDA, and support vector machines (SVM) were used for model building. The results demonstrated that a combination of SIMCA and SVM provided the best results. The predictive ability of the constructed models was assessed by using authentic and nonauthentic hazelnut samples. In recent years, fatty acid profiling has become a promising approach to distinguish high fat containing foods. Regarding the chemical composition of nuts, fatty acid fingerprinting could be applied as a means of investigation of adulteration in nuts powder samples. For instance, Esteki et al. (2017a) examined the feasibility to discriminate Iranian walnuts according to their geographical origins by using combination of GC fatty acid fingerprints and chemometrics methods. The data set is achieved from walnuts of six regions in Iran. The results of PCA-LDA as a supervised pattern recognition model revealed that discrimination was feasible using their corresponding fatty acid fingerprints. A high percentage of accuracy in classification of the training set indicated the significant relationship between the fatty acid fingerprints and the geographical origin of walnuts, while the high sensitivity of the test set demonstrated the capability of the model to determine the geographical origin of the unknown samples. Zunin, Leardi, and Boggia (2009) examined the quantification of the amounts of pecorino and pine nuts in a typical Italian basil-based pasta sauce named as pesto genovese using headspace sorptive extraction and GC-MS. To construct and evaluate of the models, two sample groups were prepared with ingredients from different batches. The PLS regression on training and the prediction set revealed the capability and robustness of the proposed method. The most effective variables were also specified using PCA. Sinanoglou et al. (2015) examined the effects of different packaging conditions and different doses of γ-irradiation by monitoring proximate composition and fatty acid profile of walnuts (Juglans regia L.). The amount of moisture, ash, protein, fatty acid profile, and fat content of walnuts were immediately determined after irradiation. The GC-FID chromatograms demonstrated that by increasing irradiation dose, monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) decreased while saturated fatty acid (SFA) increased. Moreover, MUFA/SFA and PUFA/SFA ratios decreased in comparison with nonirradiated samples. The overall results revealed that fatty acid nutritional values would be retained by applying irradiation doses lower than 5 kGy, and MAP packaging may lead to greater stability of walnuts. Spectroscopic methods that are used for authentication of nuts are mostly based on NIR, MIR, FT-IR, Raman or NMR spectroscopy, or hyperspectral imaging. For instance, Eksi-Kocak, Mentes-Yilmaz, and Boyaci (2016) used a rapid and nondestructive method for determination of green pea adulteration in pistachio nut granules using Raman hyperspectral imaging combined with chemometrics.

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Ghosh et al. (2016) investigated the potential of NIRS (896 1686 nm) and chemometrics to classify 30 different cereals and 19 different nuts based on their spectral signatures. They proposed NIR spectroscopy is a robust method for specificity analysis of peanuts from different cereals and nuts. The high fat contents of dry foods may be subject to oxidation of lipids, which may be deteriorated through the appearance of poor taste. In this context, hexanal is a common product that is considered as an appropriate marker for evaluating the shelf life of such high fat products. Hexanal can be determined using headspace analytical methods due to its high volatility. Pastorelli et al. (2007) investigated the capability of electronic nose in comparison with SPMEGC-FID for examining the development of hexanal in hazelnuts during storage in different environmental conditions. The overall results indicated the capability of SPME-GC-FID as an efficient monitoring tool for estimating the shelf life of nuts. Kendirci and Onoˇgur (2011) used SPME-GC-MS, VOCs, and sensory flavor fingerprints in order to characterize five varieties of pistachio nuts cultivated in Turkey. The main VOCs of the fresh pistachio nuts were identified as α-pinene (15.53 48.57%), limonene (3.15 30.04%), α-terpinolene (1.66 23.06%), and β-myrcene (3.50 8.95%), using multivariate data analysis methods. Park et al. (2010) evaluated the alteration of predefined metabolites during the fermentation of Cheonggukjang using metabolite profiling data set obtained by GC-MS. A total of 20 amino acids, 9 fatty acids, and 12 organic acids were recognized as targeted metabolites in Cheonggukjang. It was shown that the amounts of most amino acids decreased through the early stage of fermentation; however, by proceeding fermentation process, they would be increased. Moreover, the amount of most organic acids decreased, while fatty acid contents increased during the fermentation process. Discrimination of Cheonggukjang samples according to fermentation process was carried out successfully using PCA analysis method. Itaconic acid, malic acid, 2-hydroxyglutaric acid, citric acid, c-aminobutyric acid, tartaric acid, tryptophan, leucine and b-alanine are identified as the main compounds for differentiation of the samples. Taneva, Momchilov, Marekov, Blagoeva, and Nikolova (2013) examined fatty acids, esters, and free sterols fractions in Bulgarian hazelnuts and walnuts. Nuts were collected in three periods of time including filling and enlargement, immediately after harvest and after postharvest drying and processing. GC-MS was used to determine sterols content, including campesterol cholesterol, sitosterol, stigmasterol, and 5-avenasterol. Differences between sterol fractions of walnuts and hazelnuts were investigated using PCA. The stage of kernel development was found to have no statistically significant effect on the composition of sterols in both sterol fractions. Another significant mechanism used in the field of food product verification is to examine the metal content of various samples. Determining the composition of minerals is an important approach that demonstrates nutritional value and its relation to food quality, because the presence of some of these elements is essential for the human body (Cu, Zn, Fe, Co, and Se) and the presence of others (As, Cd, and Pb) will cause health problems. Therefore since the elemental profile of many food products, such as nuts, would be changed by adulteration., elemental profiling can be used to verify many foods (Coetzee, Van Jaarsveld, & Vanhaecke, 2014; Danezis et al., 2016a). Esteki, Vander Heyden, Farajmand, and Kolahderazi (2017b) detected the adulteration of almond powder samples with peanut using multielemental fingerprinting based on ICP-OES measurements combined with chemometric methods. Chen et al. (2014) examined the potential of mineral elements and chemometric methods as a tool to classify Chinese honeys according to their botanical origin was examined. Fingerprinting based on chemical composition including tocopherols, fatty acids, and triacylglycerols (TAGs) of nuts and multivariate data analysis has become one of the most powerful systematic approaches to determine authenticity (Amaral, Casal, Alves, Seabra, & Oliveira, 2006; Bacchetta et al., 2013). Shin, Pegg, Phillips, and Eitenmiller (2010) investigated on commercially grown U.S. Runner peanuts of 10 different cultivars. They examined the discrimination in tocopherol amounts obtained by HPLC with fluorescence detector. Loading plots revealed that PC1 encompassed α-, β-, and δ-T, whereas PC2 is comprised of γ-T mainly. Rubert et al. (2017) examined the effect of UHT treatments on the shelf life of tiger nut milk by investigating the resulting nutrient profiles. They used cold solvent extraction followed by ultra-high performance liquid chromatography (UHPLC)/high-resolution MS (HRMS) and chemometrics in this research. Treated and fresh tiger nut milk were compared in terms of phosphatidic acid, monoacylglycerol, mono-diglycerides, esters, L-arginine, and biotin MS-based metabolomic fingerprinting combined with chemometrics recognized nutrient losses caused by the UHT treatment and discrimination between tiger nut milk products. Barreira et al. (2012) detected differences among commercial non-PDO cultivars and with the PDO Ameˆndoa Douro by characterizing almonds harvested over a period of 3 years in Tra´s-os-Montes (Portugal). They used HPLC with evaporative light-scattering detector in order to examine fatty acids, fiber, tocopherols, and TAGs (TAGs). 1,2-dioleoyl-3linoleoylglycerol and 1,2,3- trioleoylglycerol were identified as the major TAG and α-Tocopherol was found to be the

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main tocopherol. PCA and LDA were used to investigate the differences between PDO and non-PDO cultivars. The overall results showed that TAG analysis coupled with LDA provided the best model for distinguishing almond cultivars. Other activities on authentication of nuts have been reported in Table 8.1.

8.2.6 Fruit and vegetables Fruit and vegetables are a rich source of health-promoting components such as antioxidants (vitamin C, betacarotene, and flavonoids), essential vitamins, minerals, and fiber. A significant portion of vitamins and minerals in the diet comes from consuming fruits and vegetables. Increased fruit and vegetables intake appears to reduce the risk of many degenerative diseases including cardiovascular disease, cancer, and premature ageing (van’t Veer, Jansen, Klerk, & Kok, 2000). Consumers are now paying particular attention to the quality of food products, and therefore they are highly interested in the nutritional value, good taste, and flavor of the fruit and vegetables they consume. According to consumers, the term “quality” implies the degree of excellence of the fruit with a perfect shape, size, colour, aroma, and an absence of defects such as cuts, bruises, or decay (Sivakumar & Bautista-Ban˜os, 2014). Various chemical components of fruits such as polyphenols and phenolic acids (Pardo-Mates et al., 2017), aliphatic and aromatic amino acids, polyols, fatty acids, organic acids, sugars, phenolic acids, sterols, tocopherols, and minerals could be used to authenticate different fruits (Crozier et al., 2007; Dembitsky et al., 2011). Authentication of vegetables and fruits may include verification of their quality based on their chemical composition, the verification of the organic agriculture, geographical origin, and cultivar, which all are the important factors in determining the market value of fruits and vegetables. Withania somnifera is one of the most valuable medicinal plants in India with special pharmaceutical utilizations. Bhatia, Bharti, Tewari, Sidhu, and Roy (2013) analyzed NMR and GC-MS metabolic fingerprinting of four varieties of W. somnifera. GC-MS combined with 1H NMR spectroscopy identified different metabolites including aromatic and aliphatic amino acids, sugars, fatty acids, polyols, organic acids, sterols, tocopherols, and phenolic acids in W. somnifera. DOXP, shikimic acid, MVA, and phenyl propanoid biosynthetic metabolic pathways were identified as the primary and secondary metabolites in this research. PCA applied on fingerprints demonstrated clear differentiation between the primary and secondary metabolites of different varieties of W. Somnifera. Significant differences also were found between the concentration of metabolites in different varieties. Farag, Mohsen, Heinke, and Wessjohann (2014) investigated the primary and secondary metabolites of different date varieties from Egypt using GC-MS. Forty-nine metabolites were extracted from the fruit skin based on metabolomics results. The main hydroxycinnamic acid conjugate was caffeoyl shikimic acid and the major identified flavonols were apigenin and glycosides of luteolin and quercetin conjugates. Cluster analysis and PCA results indicated that flavonols and sugars were the most effective parameters in varieties differentiation. Biais et al. (2009) employed metabolomics profiling obtained by 1H NMR and GC-EI-TOFMS in order to characterize melon fruit. PCA analysis of quantitative 1H NMR of polar extracts revealed that the main metabolites in fruit flesh were including organic acids, sugars, and amino acids. Multiblock hierarchical PCA was used for comparing of GC-EITOFMS and 1H NMR results. Luengwilai, Saltveit, and Beckles (2012) characterized metabolites associated with chilling tolerance produced after employingthe thermal shock on tomato fruit pericarp. Decreasing of chilling injury symptoms was used to choose the optimum condition of thermal-shock treatment. Sixty-five metabolites were identified in fruit pericarp based on GC-MS metabolite profiling. Samples with different heat treatments were discriminated using PCA. The results indicated that heat treatment can prevent chilling of the samples by altering the levels of fruit metabolites including fructose-6-phosphate, valine, arabinose, and shikemic acid. Chaparro-Torres, Bueso, and Ferna´ndez-Trujillo (2016) employed SPME by GC-MS and MS-based electronic nose to characterize melon volatile compounds at harvest time. These VOCs mostly included alcohols, esters, and aldehydes. The results of PLS-DA showed that GC-MS had a more appropriate differentiation performance than E-nose. Moalemiyan, Vikram, and Kushalappa (2007) investigated the feasibility of automatic diagnosis of mango diseases based on analysis of headspace of mango cv. Keitt inoculated with Lasiodiplodia theobromae, Colletotrichum gloeosporioides, and mock and nonwounded-noninoculated mango using GC-MS. A total of 37 metabolites were identified using mass spectral profiles. Cross-validation method was employed in order to verify the accuracy of the classification model. Reid, O’Donnell, and Downey (2004) investigated the feasibility of differentiating pure strawberry samples and adulterated strawberry samples with apple puree using SPME followed by GC in combination with multivariate data analysis methods. Partial least squares regression (PLSR) elucidated a clear differentiation of adulterated samples. Rfarnesene, hexanoic acid, and 2-hexenal were identified as the most effective compounds within sample differentiation.

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Steingass, Carle, and Schmarr (2015) used HS-SPME followed by comprehensive 2-D gas chromatography and MS in order to examine the ripening-dependent changes of pineapple. The obtained profile patterns represented as the contour plots were subjected into image processing and chemometrics methods. PLS-DA and PLSR were used to differentiate different ripening stages, while PCA and HCA were applied to classify the samples. The constructed model was able to perform a rapid discrimination between postharvest-ripened sea-freighted pineapples and premature green-ripe fruits. Volatile fingerprints of the fully ripe air-freighted pineapples were found similar to postharvest-ripened sea freighted pineapples based on PCA results. Supriyadi et al. (2004) identified the specific aroma compounds of snake fruit (Salacca edulis Reinw cv. Pondoh) using GC-olfactometry (GC-O). Six methyl esters, carboxylic acids, a furaneol, and an alcohol were identified as as the most characteristic odorants. Methyl esters exhibited fruity and sweet door in snake fruit, while carboxylic acids particularly exhibited the sweaty door characteristics. The headspace of snake fruit at different steps of maturation was analyzed using an electronic nose containing 18 sensors and fingerprint MS. Differentiation of maturation levels was carried out based on three sensors and five esters selected as fingerprint parameters. Both methods were able to discriminate the fruits at different maturation stages. Tian, Zhan, Deng, Yan, and Zhu (2014) developed flavor fingerprint using GC-O and SPME-GC-MS in order to describe a quality control method for Korla pear. Twenty-three batches of Korla pear were collected from different regions in Xinjiang, China. In order to obtain flavor fingerprints including common door-active compounds, the results of the GC-MS assessment of the samples were combined with GC-O analysis. Development of a flavour fingerprint following by the discrimination of Korla pears was successfully carried out using PCA method. Fraige, Pereira-Filho, and Carrilho (2014) identified the anthocyanin profile from their HPLC/MS fragmentation patterns and absorbance spectra of different grape varieties and also different geographical origin. Wine grape varieties were successfully discriminated by PCA. Vinifera grapes were also discriminated from a hybrid grape mainly due to the anthocyanin diglucosides. Saˆrbu et al. (2012) assessed the discrimination of fruit species and subspecies for fruits including kiwi and pomelo based on fingerprints obtained by HPLC with mass spectrometer detector using PCA-LDA. Li, Meng, and Li (2016a) examined anthocyanins in different varieties of blueberries from various regions in China. Absorbance spectra and HPLC-MS fragmentation patterns identified a total of 13 anthocyanins. The major components included delphinidin, petunidin, and malvidin. The results elucidated that anthocyanin proportions were cultivar-dependent; however, all blueberry cultivars exhibited a similar anthocyanin fingerprint. Each species was successfully discriminated based on its anthocyanin contents using PCA analysis method. Other activities on authentication of fruit and vegetables have been reported in Table 8.1.

8.2.7 Fruit juices There is an increased concern in the fruit juice industry; because it has become one of the largest agro-food businesses worldwide. Concern over availability of high-quality juice obtained from different fruit varieties with high nutritional composition has increased in parallel. From the economic point of view, product quality is a key component of profitability, competitivity, and effectiveness. Consumers pay particular attention to the quality of the fruit juice, and the producers are looking for a superior product to meet their expectations. Regulatory bodies also seek to verify the degree of conformity between the actual composition of the fruit juice and the label used on the product to ensure the quality and authenticity of the product. The industrial and economic significance of fruit juice and puree has made them potential targets for deliberate fraud. Therefore it is very important to characterize fruit juices in order to accurately determine their chemical constituents and detect their fraud. Fruit juices can be adulterated through addition of water, sugar, or pulp wash or by mixing with other cheaper juices (Achilli, Zhang, & Acworth, 2013; Navarro-Pascual-Ahuir, Lerma-Garcı´a, Simo-Alfonso, & Herrero-Martı´nez, 2015). Juices can also be adulterated by adding special additives such as flavorings and colorants (e.g., citric or tartaric acid; Jandri´c et al., 2014; Weikle, 2012). Various strategies have been developed for authentication and identification of counterfeits in fruit-based products. Due to the complexity and variety of fruit products and their production processes, an efficient analytical description can only be performed by identifying certain components of the fruit or fruit juice. Cuevas, Pereira-Caro, Moreno-Rojas, Mun˜oz-Redondo, and Ruiz-Moreno (2017) developed a robust method for authenticating European premium organic orange juices. They carried out the classification of commercial orange juices based on metabolomic fingerprinting of VOCs obtained from HPLC-high resolution/MS. PCA and HCA were used for exploratory data analysis and PLS-DA model was employed to classify orange juices. Flavonoids, fatty acids, aldehydes, and esters were identified as significant markers for authentication of organic juices. Martı´n et al. (2013)

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examined the physicochemical parameters including, water solubility, organic carbon partition coefficient, vapour pressure, density, and octanol/water partition coefficient in order to investigate the correlation between pesticide concentrations in apple juice and pesticide levels applied to the raw fruits. A mixture of pesticides was applied to an apple tree, and LC/MS MS and GC/MS MS were used to determine the residual pesticide levels in apple samples collected 10 days later. PCA revealed correlation of the pesticide processing factors with octanol/water partition coefficients, organic carbon, and the water solubility. Versari, Parpinello, Mattioli, and Galassi (2008) investigated carbohydrates, amino acids, organic acids, and furanic and phenolic compounds obtained by HPLC in order to analyze Italian apricot juices obtained by conventional, integrated, and organic farming. Furanic compounds were identified as the most effective quality markers during processing and storage of the juice samples. PCA was able to discriminate organically produced juice from the other two types of juice using chemical compositional data. Calani et al. (2013) utilized the phenolic profile alongside genetic, agronomical, and morphological variables in order to perform a qualitative characterization of pomegranate juices. More than 65 punicalagins, flavonoids, anthocyanins, phenylpropanoids, and ellagic acid derivatives were identified in four centuries old Punica granatum L. ecotypes from northern Italy. They also used UHPLC/MS for comparison with P. granatum cv. Dente di Cavallo. Despite the limited geographical distribution of the pomegranate varieties, they succeeded in the phytochemical fingerprinting discrimination of the samples with chemometric methods. Vaclavik, Schreiber, Lacina, Cajka, and Hajslova (2012) used LC/MS-MS for the comprehensive fingerprinting of various fruit juices. They successfully classified them and detected adulteration from metabolomic data. In fact, the ensuing model allowed detection of adulteration of orange juice with grapefruit or apple juice to be detected. Achilli et al. (2013) examined metabolomic fingerprints of herbs or fruit juices by using multivariate data analysis methods. Gradient HPLC with electrochemical array detection was also used in order to identify potential adulterants in oregano herb and orange juice. The developed method was able to detect adulteration of oregano containing as little as 5% of deliberately blended thyme and marjoram and of 5% of orange juice blended with another juice or mixed with orange peel. Cuevas et al. (2017) examined European organic orange juices based on the volatile profile and the metabolomics fingerprinting obtained by HS-SPME-GC-MS. PCA, HCA, and PLS methods were applied on the obtained fingerprints in order to characterize orange juices. Some flavonoids, esters, fatty acids, and aldehydes were identified as significant markers in discriminating organic juices. Appropriate accuracy was achieved for classification of organic orange juices. Matsushita, Zhao, Igura, and Shimoda (2018) investigated the authentication of commercial spices based on a simple and solvent-free method. SPME and then GC-MS were used to obtain the VOCs of some spices such as white pepper, dill, coriander, allspice, caraway, fennel, black pepper, star anise, cumin, and clove anise were extracted. The GC fingerprints were analyzed using multivariate data analysis methods such as PCA. Cardeal, de Souza, Silva, and da; Marriott (2008) used HS-SPME followed by GC 3 GC/TOFMS in order to study the aroma compounds of Cachaca samples during the fermentation process. Determination of turning points of the process, the comparison of coracao (core), cabeca (head), and cauda (tail) fractions, and quality control of the process of ageing were accomplished based on fingerprint monitoring. For this purpose, unique compounds were characterized in different steps of the distillation process. Seventy compounds were visually distinguished during the distillation process progression, which their retention indices showed good agreement with other literature data. Other activities on authentication of fruit juices have been reported in Table 8.1.

8.2.8 Herb and spices Spices and herbs are common food adjuncts, which mostly used as flavoring, seasoning, or coloring agents (Rubio´, Motilva, & Romero, 2013). The flavoring components consist of such compounds as alcohols, aldehydes, esters, terpenes, phenols, organic acids, and others, some of which have not yet been fully identified. While it brings the color and taste of the food, some spices have long been considered to have medicinal value and are effectively used in the indigenous medical systems (Lai & Roy, 2004). The spices fenugreek, garlic, ginger, onion, red pepper, and turmeric are effective as antispasmodic, antithrombotic, hypocholesterolaemic, antiallergic, and antiserotonergic (Panpatil, Tattari, Kota, Nimgulkar, & Polasa, 2013). Furthermore, fenugreek has been found to be effective in human diabetic subjects, whereas garlic and onion may attenuate postprandial lipemia. Capsaicin and curcumin, the active principles of red pepper and turmeric, respectively, are also known for their hypolipidemic property. Capsaicin, curcumin, fenugreek, ginger, and onion may also enhance biliary secretion of bile acids, which play a major role in fat digestion and absorption (Srinivasan, Sambaiah, & Chandrasekhara, 2004).

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Adulteration of spices, herbs, and their high value products is a matter of primary concern due to globalization in trade. Adulteration of spices may be caused either by default or by design with closely related species to achieve economic gain, leading to quality deterioration and erosion of the biological property. Besides many economic circumstances such as commercial gain, there are serious consequences of possible health hazards of spices, herbs, and their products, as many of the contaminants can cause acute and chronic health disorders in humans. Among the widespread adulteration practices, artificial organic colorants such as azo dyes are more important, since they are mostly known to first degrade into genotoxic products (Brown & De Vito, 1993). The application of artificial colorants is an old method, motivated by the special attention of consumers to the visual appearance of these products, despite the potential risks of artificial additives. Nowadays, various modern analytical methods have been used for detection of different types of adulteration in this field. Di Anibal, Odena, Ruisa´nchez, and Callao (2009) proposed a very simple and fast method for detecting Sudan dyes (I, II, III, and IV) in commercial spices, based on characterizing samples through their UV visible spectra and using multivariate classification techniques. Di Anibal, Ruisa´nchez, and Callao (2011) proposed a combination of high-resolution 1H NMR and chemometric treatment for detection of adulteration in spices with Sudan dyes. The variables were reduced and selected based on the difference between the NMR spectra from the noncontaminated commercial spices and the spices spiked with one of the four Sudan dyes. Curry, turmeric, and mild and hot paprika, distributed in five classes, noncontaminated spices and spices spiked independently with one Sudan dye, were investigated as the commercial spices. PLS-discriminant analysis (PLS-DA) was applied to the most important NMR variables selected. The prediction probabilities provided by PLSDA were satisfactory for all the classes. Black, Haughey, Chevallier, Galvin-King, and Elliott (2016) developed an FT-IR screening method coupled to data analysis using chemometrics and a second method using LC-HRMS, with the latter detecting commonly used adulterants by biomarker identification in order to detect the fraudulent adulteration of herbs. Donno et al. (2016) examined the chemical composition of Rubus idaeus herbal preparations using HPLC with diode array detection. They used PCA and clustering analysis for identification of the main phytochemicals in raspberry bud extracts from different cultivars. Organic acids, vitamins, and catechins were found as the most effective variables for discriminating cultivars. Teo, Tan, Yong, Hew, and Ong (2009) utilized microwave-assisted extraction (MAE) and pressurized hot water extraction for evaluation of the quality of medicinal plants. For this purpose, chromatographic fingerprinting obtained from HPLC equipped with photodiode array detection was also used. The extraction performance of rebaudioside A and stevioside from Stevia rebaudiana Bertoni grown under different conditions was compared. The results showed that MAE provided distinct chemical fingerprints containing greater numbers of extracted chemical components. Combination of MAE and chromatographic fingerprinting provided a sensitive method for discriminating medicinal plants grown under different conditions. Fingerprinting analysis of herbal medicines with complex chromatographic profiles can be used as an efficient approach for their characterization. Application of chemometric methods is more reliable than visual similarity assessment mainly for fine differences between the fingerprints. Liquorice (Glycyrrhiza glabra) is a very popular medicinal plant with antiinflammatory properties and anticancer and antiviral activity. This medicinal root is reach in phytochemical constituents including saponins, polysaccharides, terpenoids, flavonoids, isoflavonoid, and polyamines. The complete bioactive constituents of this plant have not yet been fully identified, a step that is needed for determination of medicinal effects. There are over 30 species in the Glycyrrhiza genus, most including phytochemical or pharmacological properties. Farag, Porzel, and Wessjohann (2012) investigated on chemical composition of Glycyrrhiza species using metabolic fingerprinting techniques. NMR, MS, and UV spectra of extracted components were connected with NMR and MS data from G. glabra, Glycyrrhiza uralensis, Glycyrrhiza echinata, and Glycyrrhiza inflat using multivariate data analysis methods. Glycosidic conjugates of liquiritigenin/isoliquiritigenin, 4-hydroxyphenyl acetic acid and glycyrrhizin were specified as major peaks in MS and 1H NMR spectra leading to discriminating species. PCA analysis showed that both NMR and GC-MS were found to be useful methods for discrimination based on geographical origin or genetic of the samples. The chemical composition of Schizonepeta tenuifolia Briq. consists of various VOCs, which are attributed to multiple pharmacological effects. Chun, Kim, Lee, Jung, and Hong (2010) assessed the profiles of VOCs of Sch.t. Briq using HS-SPME-GC-MS technique. The GC-MS identified 21 chemical compounds including major components such as 2hydroxy-2-isopropenyl-5- methylcyclohexane, (1)-menthone, (2) pulegone, cis-pulegone oxide, and schizonal. Sch. t. Briq. samples were purchased from markets in China and Korea. PCA was performed on volatile organic fingerprints to discriminate the samples based on their geographical origin. PCA was also used for identification of the major volatiles attributed to the classification of different categories.

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Li, Zhou, Zhao, Wang, and Hu (2009) examined the quality of Curcuma longa L. (C. longa) using GC-MS fingerprint analysis combined with PCA and HCA. The GC-MS fingerprint of 33 batches of samples of C. longa was obtain from different geographic origins. PC1 differentiated longa collected from Guizhou and Fujian, containing 71.83% of data variance, and PC2 with 11.13% of data variance lead to improve separation. Qiu et al. (2007) investigate the chemical constitutes of volatile oil in the rhizomes and radixes of Notopterygium incisum Ting ex H.T. chang from different regions, using GC 3 GC TOFMS and GC 3 GC FID PCA analysis revealed that the abundances of oxygenated sesquiterpenes and monoterpenes were mainly attributed in sample discrimination. Analysis of fingerprints also revealed that Sichuan and Qianghuo provinces have significant difference in chemical composition in comparison with the herbs of other geographical origin. Rubiolo et al. (2006) differentiated successfully flower-heads of different chemotypes of chamomile using HS-SPME-GC combined with PCA. Tianniam, Tarachiwin, Bamba, Kobayashi, and Fukusaki (2008) examined the potential of primary metabolites for discrimination of Angelica acutiloba (or Yamato-toki) roots with different quality. Sugars, amino acids, and organic acids were identified as the main metabolites based on GC-TOF-MS data. PCA results showed that Yamato-toki roots samples could be differentiated based on their cultivation areas. Malic acid, phosphoric acid, proline, and citric acid were identified as the most accumulated in high and medium quality, while fructose and glucose were found mostly in poor-quality toki roots. The PCA results demonstrated the moderate-quality samples were mostly influenced by malic acid. Other activities on authentication of herb and spices have been reported in Table 8.1.

8.2.9 Meat One of the main interests of meat authentication is the economic benefit it offers to local producers, providing a way to differentiate meats of diverse commercial qualities in a vast market. Simultaneously, authentication prevents abuses of fraudulent brand names on the market, promotes competition among producers, and provides transparent information to consumers (van Caenegem & Cleary, 2017). The presence of processed ingredients and nondeclared additives in the final product is a crucial point in authentication of meat, because they increase the risk of food intolerances. For all these reasons, the serious consequences derived from illegal practices during production make authentication of meat a mandatory task. Meat recognized within geographical indications and traditional specialties in the European Union can be subdivided in fresh meat and offal or in processed meat, which can be salted, smoked, cooked, or dry-cured. These products are susceptible to fraud in the market due to its unique characteristics that make them particularly attractive to the consumer. In Europe, meat and derivatives obtained from specific animal species and whose entire manufacture takes place in a particular location following strict regulations are described as PDO products (Regulation EU No 1151/2012, 2012). Current European legislation (Council Regulation EC No 510/2006, 2006) gathers regulations and specific guidelines for each individual PDO meat product on EU database of agricultural products and foods (Council Regulation EC No 834/2007, 2007). Organic product rules are also compiled in official European regulation (Commission Regulation EC No 889/2008, 2008). All meats and meat products must follow strict rules in terms of antibiotics presence (Council Directive 96/23/EC, 1996). In order to be considered as an authentic fresh meat or meat product, the result of chemical analyses has to fulfill all the conditions gathered in its corresponding regulation. The authenticity indicators mentioned in official regulations can be subdivided as related to animal of origin, processing, or final product.

8.2.9.1 Indicators related with the animal of origin Breed and sex can be determined directly or, to a lesser extent, indirectly determining chemical markers. The most common methods for direct sex determination are gene-based. This can be achieved by analysis of sexual hormones (Draisci, Palleschi, Ferretti, Lucentini, & Cammarata, 2000; Hartwig, Hartmann, & Steinhart, 1997; Simontacchi, Marinelli, Gabai, Bono, & Angeletti, 1999) or gene amplification by polymerase chain reaction (PCR)-based procedures, for example, in pigs (Fontanesi, Scotti, & Russo, 2008). On the other hand, breed has been habitually assessed by genetic analyses such as genotyping, amplified fragment length polymorphism, single nucleotide polymorphism, and random amplification of polymorphic DNA, which have been employed in swine (Alves, Castellanos, Ovilo, Silio´, & Rodrıfiguez, 2002; Fontanesi, Scotti, Gallo, Nanni Costa, & Dall’Olio, 2016; Zhao et al., 2018a), chicken (Du, Ding, Li, & Fang, 2017), and beef (Campos et al., 2017; Dalvit et al., 2008; Rogberg-Mun˜oz et al., 2014; Zhao et al., 2017). Other outspread techniques for breed determination purposes are spectroscopy-based, such as NIRS, which has been successfully used to discriminate beef meat from different breeds (Alomar, Gallo, Castan˜eda, & Fuchslocher, 2003).

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Furthermore, gas chromatography-ion mobility spectrometry (GC-IMS) has increased its relevance in food research and was successfully employed to discriminate Iberian pig’s breed while sampling the finished product (dry-cured Iberian ham) (Martı´n-Go´mez, Arroyo-Manzanares, Rodrı´guez-Este´vez, & Arce, 2019). Moreover, feeding regime of the animal of origin and feed composition also serve as major authenticity indicators. Meat from animals grown in free-range on organic regimes is valued more highly than meat from conventional or confined animals fed with concentrate feed. In addition, animals that are used to obtain organic meat cannot receive any hormonal treatments or antibiotics and cannot be fed with products or subproducts of animal origin (e.g., fish or blood flour) or transgenics. Besides, pesticides cannot be employed on the pasture where these animals are fed. On the other hand, conventional production does not have these requirements. Regarding organic meat, most of the indicators found are related to antibiotics. Their use and that of other forbidden substances in the animal of origin can be detected in the final meat product. For this purpose, metabolites or active principles are determined with instrumental techniques. Of all of them, the most widely employed is capillary electrophoresis (CE) combined with mass spectrometry detection (CE-MS). With this technique, several sulfonamides have been determined in pork (Font, Juan-Garcı´a, & Pico´, 2007) and in beef along with trimethoprim (Soto-Chinchilla, Garcı´a-Campan˜a, & Ga´miz-Gracia, 2007), sulfonamides, β-lactams, and quinolones (Juan-Garcı´a, Font, & Pico´, 2007). The presence of quinolones in chicken meat was also assessed with this technique (Juan-Garcı´a, Font, & Pico´, 2006; Lara, Garcı´a-Campan˜a, Ale´s-Barrero, & Bosque-Sendra, 2008). Additionally, through the examination under UV illumination, tetracyclines (specifically, oxytetracycline, and chlortetracycline) were determined in pork and chicken meat (Kelly, Tarbin, Ashwin, & Sharman, 2006). Nevertheless, CE-MS methods are currently the most relevant in this field. Regarding feeding regime as an indicator, it can be determined with data extracted from the piece of meat or from the animal prior to its sacrifice (e.g., hair, urine, etc.). Nevertheless, the most common methods for its determination in meat include a previous chromatographic separation, or they are spectroscopy-based. For the first case, GC-IMS was included in a method, which was able to authenticate an extensive feeding regime of Iberian pig employing nontargeted analysis of VOCs (Arroyo-Manzanares et al., 2018). Likewise, dynamic headspace-gas chromatography-mass spectrometry (DH-GC-MS) has been used to determine VOCs related to animal diet in adipose tissue (Sivadier, Ratel, Bouvier, & Engel, 2008). Concerning spectroscopic techniques, IR spectroscopy was also described as a useful tool for feeding regime authentication employing pigs fat (Arce et al., 2009). Besides that, the mineral content in meat was determined with individual wet-based analyses and correlated with strictly organic diets or diets that included feed with added minerals (Zhao, Wang, & Yang, 2016b). Yet, recent studies on this field have achieved a direct authentication of animal feed with hydrogen, oxygen, carbon, nitrogen, and sulfur isotopes employing isotope-ratio mass spectrometry (IRMS) (Zhao et al., 2014) or its variations. Specifically, an extensive grazing diet for beef and lambs was verified with this technique when the animal was sacrificed (Erasmus, Muller, van der Rijst, & Hoffman, 2016). For the same purpose, the tail hair of the live animal was analyzed employing stable isotope ratio analysis and the results were correlated with a free-range diet (Monahan et al., 2012). Similarly, game meat can also be distinguished from common meat. For this purpose, ICP-MS has served to identify and quantify rare-earth elements and correlate them with wild animal products (Danezis et al., 2017). Moreover, a recent review comprehensively described PCR-based methods for the genetic authentication of game meat (Fajardo, Gonza´lez, Rojas, Garcı´a, & Martı´n, 2010). Ultimately, meat geographical origin and its climate and terrain composition (e.g. fertilizers, saltiness, organic matter, humidity, etc.) can also be determined taking into account other indirect indicators previously described, such as the feeding regime of the animal (eg. autochthonous herbaceous plants, protein crops, dried fruits, pasture, etc.) and the subsequent meat composition. Yet, methods based on stable isotopes are able to directly authenticate the meat origin in beef, chicken, and swine (Vinci, Preti, Tieri, & Vieri, 2013).

8.2.9.2 Indicators related with meat processing Irradiation is a process that aims to improve the safety and shelf life of foods and that must be mentioned in the label. The byproducts of this process can be used as indicators of mislabeled products. The most frequently described methods to determine radiation byproducts include a chromatographic separation prior to the detection, for example, GC-MS with detection of hydrocarbons (Maija, Sjo¨berg, Tuominen, Kiutamo, & Luukkonen, 1992) for chicken meat, HPLC determination of o-tyrosine with light amplification by stimulated emission of radiation (LASER) fluorometric detection (Miyahara et al., 2012), or supercritical fluid extraction combined with GC coupled to MS or FID for detection of 2alkylcyclobutanones and volatile hydrocarbons in chicken (which are a subproduct of irradiation; Delince´e, 2002; Horvatovich, Miesch, Hasselmann, & Marchioni, 2000). Similarly, the official analysis procedure for the determination of 2-alkylcyclobutanones to detect chicken and pork meat irradiation is described in European standard EN-1785

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(Foodstuffs, 2003a). In addition, European standard EN 1784 (Foodstuffs, 2003b; GC analysis of hydrocarbons) method can be applied for chicken, pork, and beef irradiation detection. Furthermore, spectroscopic methods have been successfully applied for this purpose, such as electronic spin resonance (ESR) spectroscopy (Andersen & Skibsted, 2018; Marchioni, Horvatovich, Charon, & Kuntz, 2005) and the method included in regulation EN 1786 (Foodstuffs, 1996), which is meant for the detection of irradiation in chicken bones using ESR spectroscopy. Moreover, the method described in the regulation prEN 13784 (based on DNA comet assay screening) also aims to detect chicken and pork meat irradiation. Freezing is also employed to lengthen the shelf life of meat, but at the same time, it influences its quality substantially. Consequently, most of the analytical methods described in bibliography are aimed to detect this process through the assessment of genetic damage or changes in enzymatic activity in cells. Additionally, spectroscopic methods have an important role in this topic. Firstly, enzymes such as β-hydroxyacyl-CoA-dehydrogenase (HADH) can be determined with UV spectrophotometry in order to detect disruptions of mitochondrial activity, which takes place when freezing or grinding meat pieces. However, this methodology only is able to differentiate meat that has been frozen under 212oC % and it cannot be applied for ground meat analysis. HADH activity has been employed to differentiate fresh and frozen beef (Chen, Yang, Der, & Guo, 1988) following the methods developed by Gottesmann and Hamm (1983), and Billington, Bowie, Scotter, Walker, and Wood (1992). The activity of esterase-lipase, β-glucuronidase, α-glucosidase (Toldra´, Torrero, & Flores, 1991), and β-galactosidase (Ellerbroek, Lichtenberg, & Weise, 1995) has also proved to be influenced by thawing in pork meat, as determined by the API ZYM system. Secondly, single cell electrophoresis (comet assay) with fluorescence microscopy detection has been successfully employed to detect thawing damage in cells. The determination mechanism is based on the head/tail intensity proportion of DNA “comets” extracted from lysed cells, which is correlated with DNA damage in frozen beef (Park et al., 2000) and chicken (Cerda & Koppen, 1998). Likewise, freezing can also be detected by additional vibrational spectroscopy techniques, such as NMR as this process decreases the transverse (T2) (Mortensen, Andersen, Engelsen, & Bertram, 2006) and longitudinal (T1) relaxation time in pork (Evans, Nott, Kshirsagar, & Hall, 1998; Guiheneuf, Parker, Tessier, & Hall, 1997), beef, and lamb (Evans et al., 1998), while magnetic transfer rates increase. Similarly, deoxy-, met-, and oxymyoglobin levels are influenced by thawing of chicken as determined by NIR (Evans et al., 1998). Lastly, IR spectroscopy and subtypes have also been used to discriminate thawed beef (Downey & Beaucheˆne, 1997a, 1997b; Liu, Barton, Lyon, Windham, & Lyon, 2004), frozen grounded chicken (Al-Jowder, Kemsley, & Wilson, 1997), and frozen/refrozen beef while employing dry extracts (Thyholt & Isaksson, 1997). Most of these methods were described by Ballin and Lametsch (2008).

8.2.9.3 Indicators related to the final product The final product also has specific indicators that suggest if the meat has been altered in any way before consumer acquisition. One of the most important authenticity indicators is the animal species from which the meat has been obtained. Undeclared meat substitution and mixes of meat from different species in processed products could represent a serious threat to public health. While most of the analytical techniques meant to detect this sort of adulteration are gene-based, Raman methods have a relevant role and have been used to predict animal species in meat through the fat characteristics (Boyaci et al., 2014) and fresh meat properties (Zaja˛c, Hanuza, & Dymi´nska, 2014). On the other hand, proteomic approaches based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) have been also employed to detect chicken in meat mixes (Sentandreu, Fraser, Halket, Patel, & Bramley, 2010). Moreover, a previous electrophoresis step was included in a similar method in order to detect meat mixes from different animal species (Naveena et al., 2017). Similarly, the presence of additives must be controlled, since some PDO meat products do not admit any of them or only admit slight quantities of specific ones. Nonetheless, the incidence of undeclared substances in meat is a still serious problem, for instance, the addition of vegetal protein to meat, which is a common practice. Some relevant examples of this practice are the fraudulent addition of soy protein, which has been detected in pork sausages with enzyme-linked immunosorbent assay (ELISA) (Gonza´lez-Co´rdova, de la Barca, Cota, Vallejo-Co´rdoba, & Immunochemical, 1998) and in meat mixtures of different species with HPLC (Castro, Garcı´a, Rodrı´guez, Rodrı´guez, & Marina, 2007). These and other methods for detection of vegetal protein in meat have been thoroughly described in reviews (Belloque, Garcı´a, Torre, & Marina, 2002; Koppelman, Lakemond, Vlooswijk, & Hefle, 2004). Likewise, the presence of undeclared colorants, aromas, preservatives, and other nonmeat additives (e.g., water) is also undesirable. In particular, phosphates are usually added to meat in order to increase water binding and simulate natural water/protein ratios when water was artificially added. These additives that influence the amount of water in meat can be detected studying the water/protein proportion alongside total protein value. An official ISO wet-based analysis method for water amount (ISO 1442:1997,

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1997) already exists, and, if combined with the official ISO protein amount analysis (ISO 937:1978, 1978), it can provide the real water value and allow fraud detection. Conversely, blood-clotting enzymes (e.g., thrombin) that are added to meat in order to simplify the obtention of accurate meat portions can be detected by means of related fibrinopeptides. The determination of fibrinopeptides A and B through LC-MS/MS is employed to detect the addition of these enzymes in pork (Grundy et al., 2008) and beef (Grundy et al., 2007). Likewise, the presence of melamine and other dangerous substances added to meat in order to obtain more profit can also be detected with surface-enhanced Raman spectroscopy (SERS) and HPLC techniques, which have been used in combination with the purpose of detecting melamine in chicken meat (Lin et al., 2008). Furthermore, the mix of higher quality meats with low-value parts is another widespread problem. As well, the presence of undeclared content of bone (e.g., cartilage) in processed meats contribute to this form of fraud. Meat mixes with bone, which is usually a discarded part in the meat industry, has been correlated with a higher calcium content when determined through staining (Branscheid, Judas, & Ho¨reth, 2009). What is more, mixes of meat cuts of diverse value can be detected if the present amount of collagen is indirectly determined through 4-hydroxiproline employing LC-MS/MS (Colgrave, Allingham, & Jones, 2008). For this purpose, it already exists an official reference method (ISO 3496:1978, 1978), which, on the contrary, is spectroscopy-based. Not to mention another important problem, which is the offal addition to meat. In this case, the instrumental techniques used are mostly based on MS detection. For instance, a rapid evaporative ionization MS method was able to detect brain, heart, kidney, large intestine, and liver tissues mixed with beef meat determining lipid biomarkers, which were later identified with MS/MS (Black et al., 2019). Nonetheless, another study was able to detect offal in beef meat employing FT-IR (Hu, Zou, Huang, & Lu, 2017). Besides, there are studies focused on methods to determine the composition of specific PDO meats. These methods usually combine analytical techniques. For instance, Italian lard was authenticated combining NIR and GC-FID for fatty acids determination and GC-MS for its VOCs profiling (Chiesa et al., 2016).

8.2.9.4 Current trends in meat authentication Scientific articles from the last 10 years, focused on meat authentication and gathered in this section, are arranged over a limited number of relevant indicators. These are breed (mainly determined by genetic methods, followed by Raman), presence of antibiotics (mainly determined by CE-MS), feed (mainly determined by chromatographic techniques combined with diverse detectors), and geographical origin (mainly determined by techniques based on stable isotopes, such as IRMS). Nevertheless, as Danezis, Tsagkaris, Camin, Brusic, and Georgiou (2016b) already pointed out, MS still is a technology widely used for food authentication due to its high sensitivity, selectivity, throughput, and multianalyte capability. Despite the significant advances in the meat authentication field, it currently suffers from a lack of advances in data treatment. Therefore the most relevant research trends include chemometrics and data-mining for the development of novel prediction models. Additionally, current research is focused on techniques not based in traditional wet-based methods. Furthermore, future meat analyses should emphasize rapid and easy sample preparation methods and be focused on the creation of biomarker databases for authentication purposes, and techniques that allow higher sample amounts in order to describe new variability sources and the development of robust methods that can be employed in different laboratories with common instruments and simple protocols.

8.2.10 Sea products The substitution of valuable species with cheaper ones is the most frequent economic fraud in sea products (Siddiqi & Nollet, 2018). However, authentication comprises many other topics, such as geographical origin, processing conditions (fresh, frozen fish, etc.), production method (wild or farmed), or the inclusion of seafood as an ingredient not mentioned in the label, which can cause serious health effects in the case of allergenic substances (Fiorino et al., 2018). Identification of a fish species has been traditionally made by using morphological characters (e.g., skin, eyes, body shape and size, shape, and number of fins among others). However, the morphological characters are modified when fish is processed (filleted, smoked, or canned) making identification very difficult. Even when all morphological characters are intact, identification of some very closely related fish species is rather difficult (Civera, 2003). Because of these difficulties, there is a need for analytical methods for species identification. Currently, proteins are widely used as markers for fresh, refrigerated, and frozen fish and seafood. However, this strategy is usually limited in processed products subjected to heat treatments due to the protein thermolability (Sotelo & Pe´rez-Martı´n, 2006). The classical proteomic strategy integrated 2-D electrophoresis and MS (Ortea, Can˜as, Calo-Mata,

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Barros-Vela´zquez, & Gallardo, 2010; Rehbein, Ku¨ndiger, Pineiro, & Perez-Martin, 2000). In fact, analysis of the sarcoplasmic proteins is considered the validated method for species identification by the Association of Official Analytical Chemistry (Association of Official Analytical Chemists & Helrich, 1990) and isoelectric focusing profiles of sarcoplasmic proteins from different fish species have been collected by U.S. Food and Drug Administration in the Internet library Regulatory Fish Encyclopedia (Regulatory Fish Encyclopedia RFE, 2011). Nevertheless, nowadays, MS is the preferred method for the characterization, sequencing, and identification of peptides and proteins that are specific to a species (Ortea et al., 2012). Moreover, these targeted MS approaches make feasible the rapid detection of parvalbumins, which besides being indicators of the authenticity, are also the major fish allergen in any sea products (Mazzeo & Siciliano, 2016). More recently, molecular profiling strategies have emerged as a powerful tool for fish authenticity (Mazzeo et al., 2008). In this sense, methods based on matrix-assisted LASER desorption/ionization with time-of-flight MS allows to obtain specific fingerprints of the fish composition and obtaining protein or peptide profiles (Mazzeo et al., 2008; Salla & Murray, 2013). The mass spectra obtained shows characteristic signal patterns unique for the species under analysis (Mazzeo et al., 2008). This methodology has several advantages with respect target methods, because it leads to fish identification within minutes and do not demand preliminary information on the sample (origin and putative species) or the identity of proteins generating the biomarker pattern. Moreover, the robustness of the method was demonstrated by the authentication of fish samples subjected to harsh heat treatment and processed sea products such as sole fillets and fish sticks. DNA-based techniques have emerged in the last decades as an alternative solution to the limitations of the use of proteins as markers. The DNA-based methods present several advantages, such as high sensitivity, high specificity, and large-scale throughput. Although DNA is also degraded during heat treatments, it is possible to obtain small fragments with sufficient differences in sequence to differentiate even between closely related species (Rasmussen & Morrissey, 2009; Santaclara et al., 2015). The classical methods mainly used for sea products authentication comprise PCR-based methodologies, including DNA sequencing, restriction fragment length polymorphism, and reverse transcription PCR. However, recently, novel genomics-based techniques have emerged trying to improve the performance of classical DNA-based techniques in terms of specificity, sensitivity, multiplexing, and sample throughput. These strategies include next-generation sequencing (NGS), droplet digital PCR, high-resolution melting, and DNA-barcoding. For example, NGS allowed the identification of all present species in commercial surimi samples (Giusti, Armani, & Sotelo, 2017), important for allergic consumers because of the potential presence of no declared mollusks or shellfish species. On the other hand, DNA barcoding has been especially focused on the differentiation of species from sea products, such as caviar, surimi, fillets, and packaged frozen products (Boscari et al., 2014; Di Pinto et al., 2015; Di Pinto et al., 2016; Galal-Khallaf, Ardura, Borrell, & Garcia-Vazquez, 2016). Moreover, these methods allow most commercially available species to be identified, including fresh, frozen, or processed sea products. Mitochondrial DNA should be the choice for fish products subjected to heat treatments because of its higher number of copies per gram of tissue and the possible higher tolerance to heat compared with nuclear DNA (Borgo, Souty-Grosset, Bouchon, & Gomot, 1996). Currently, mitochondrial loci genes are the preferred DNA barcodes for the discrimination of fish species (Fernandes, Costa, Oliveira, & Mafra, 2017). Stable isotope analysis thorough techniques such as IRMS represents an innovative method to identify geographical origin and to distinguish the wild from the farmed species (Li, Boyd, & Sun, 2016b). This methodology is based on the fact that the stable isotope ratios, mainly 15N/14N and 13C/12C, in different tissues or lipid fraction are significantly influenced by geographical origin (e.g., fresh water versus marine ecosystems), soil geology, and diet composition (Aursand, Mabon, & Martin, 2000; Capuano et al., 2012; Masoum et al., 2007). However, the stable isotope analysis presents limitations to determine the geographical origin of juvenile organisms whose stable isotopic values in white muscle resulted less variable (Vasconi et al., 2019). To solve this problem, the C and N isotope ratio analysis is sometimes combined with the fatty acid profile to differentiate between wild or farmed sea fish, and freshwater fish species, since fatty acid composition is also affected by the geographical origin and feeding (Molkentin, Lehmann, Ostermeyer, & Rehbein, 2015; Vasconi et al., 2019; Zhao, Liu, Li, Zhang, & Qi, 2018b). To accelerate the process of fish authentication, fingerprinting methods based on spectroscopic or chromatographic techniques are emerging as powerful and effective nontargeted strategies (Esteki et al., 2018). A fingerprint produces a characteristic profile containing information of all the complex chemical composition of the analyzed sample in a nonselective way. Chemometrics, as a multivariate data analysis tool, is often coupled to fingerprints to interpret the large amount of information obtained in a single analysis. In this sense, some studies have demonstrated the potential of NIR spectroscopy for fish authentication (Alamprese & Casiraghi, 2015; Grassi, Casiraghi, & Alamprese, 2018; O’Brien, Hulse, Pfeifer, & Siesler, 2013). However, despite the literature grown in NIR spectroscopy applied to food frauds and authentication purposes, this is still a field to explore in the authentication of fish (Grassi et al., 2018; Qu et al., 2015).

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8.2.11 Edible vegetable oils Edible vegetable oils could be splitted into two groups (Spain: Royal Decree 308/1983, 1983): olive oils and oilseed oils. For the former group, there are different subtypes of olive oils such as virgin olive oil (VOO), refined olive oil, and pomace olive oil. Virgin olive oils are also subdivided into three subcategories of decreasing quality named extra virgin olive oil (EVOO), VOO, and lampante olive oil (LOO). The latter is not suitable for direct human consumption. Oilseed oils involve a wide variety of oils obtained from authorized oilseeds and subjected to complete refining such as soybean oil, peanut oil, sunflower oil, and cotton oil. The authentication of edible vegetable oils has become an important issue because of their influence on our diet, since they are used extensively to cook in salads or as an ingredient in food products due to their quality, potential health, and nutritional benefits. These properties increase their price and make them a preferred target for fraudsters. Specifically, the case of olive oils should be highlighted, because these are one of the most expensive and appreciated products and, therefore, more susceptible to fraud, since they provide greater profits. There are two main adulteration types related to edible vegetable oils (Azadmard-Damirchi and Torbati, 2015): first, the mix of cold press oil with refined oil, since the former oils have not been in contact with any solvents and they are able to retain minor compounds which positively influences not only their quality but also their price and second, the complete substitution of a more expensive oil by a cheaper one, such as the replacement of EVOO with VOO. Additionally, there are different subtypes of replacement frauds such as addition, dilution, false declaration of varieties or species, botanical or geographic origin, and production process, among others (Moore, Spink, & Lipp, 2012). The fraudulent practices described above entail an economic deceit to consumers and could even suppose a health risk for them due to allergies or intolerances. The main authenticity indicators of the currently sold vegetable oils are the proportion of fatty acids and their characteristics and major and minor components. Moreover, since each edible vegetable oil may have some special components at a known level that serve as descriptors for each type of oil, the determination of their presence and amount should be considered as a detection tool for authentication. European Commission Regulation has clearly defined the quality and purity indicators in order to characterize olive oils and olive pomace oils (Commission Regulation EEC No 2568/91, 1991). The official analytical methods to control these indicators are, in general, time-consuming and require the employment of solvents with the subsequent generation of waste. For instance, volumetric analysis or chromatographic systems with conventional detectors (FID) and electron capture dissociation, among others have been established by regulatory bodies and associations to characterize olive oils and olive pomace oils (Tena, Wang, Aparicio-Ruiz, Garcı´a-Gonza´lez, & Aparicio, 2015). Now, the trend is toward finding alternative analytical methods and techniques, which are environment-friendly, fast, and cheap without losing veracity. Analytical techniques such as UV-Vis spectroscopy, fluorescence, vibrational spectroscopies (NIR, MIR, and Raman) (Hu, He, Zhang, Yang, & Liu, 2019; Rohman, 2017), NMR spectroscopy, IMS, MS, and others are considered as promising tools to assess the quality of olive oils. Different approaches have been applied to perform this evaluation such as target analysis of only one indicator or that of a family of metabolites, metabolic profiling of a number of metabolites belonging to different classes without separation procedures, or metabolomic fingerprinting, without identification of individual metabolites. First, UV-Vis and vibrational techniques (Wang, Sun, Zhang, & Liu, 2016) have been employed to evaluate target quality indicators such as free fatty acids by using NIR (Cayuela, Garcı´a, Garcı´a, Caliani, & Caliani, 2009; Marquez, Dı´az, & Reguera, 2005), Vis/NIR (Cayuela Sa´nchez, Moreda, & Garcı´a, 2013; Garcı´a Martı´n, 2015), IR (Bendini et al., 2007) or Raman (Muik, Lendl, Molina-Dı´az, & Ayora-Can˜ada, 2003); peroxide values by employing MIR spectra (Pizarro, Esteban-Dı´ez, Rodrı´guez-Tecedor, & Gonza´lez-Sa´iz, 2013), and NIR (Inarejos-Garcı´a, Go´mez-Alonso, Fregapane, & Salvador, 2013); and fatty acid methyl esters or fatty acid ethyl esters employing IR (Valli et al., 2013). Moreover, purity indicators of authenticity such as TAGs and fatty acids have been assessed by using NIR (Azizian et al., 2015; Galtier et al., 2007), MIR (Dupuy, Galtier, Ollivier, Vanloot, & Artaud, 2010), and Raman scattering (Korifi, Le Dre´au, Molinet, Artaud, & Dupuy, 2011). Metabolomic fingerprinting approaches have been also carried out with these techniques, such as FT-IR (Tapp, Defernez, & Kemsley, 2003; Vlachos et al., 2006), NIR (Downey, McIntyre, & Davies, 2002; Downey, Kelly, & Petisco-Rodrı´guez, 2006; Woodcock, Downey, & O’Donnell, 2008), or Raman (Lo´pez-Dı´ez, Bianchi, & Goodacre, 2003). The application of NMR spectroscopy has also been demonstrated to be useful to assess the quality, authenticity, and geographical origin of olive oil. On one hand, phenolic compounds (Christophoridou & Dais, 2009) or sterols (Hatzakis, Dagounakis, Agiomyrgianaki, & Dais, 2010) have been evaluated as target indicators. Moreover, metabolic profiles have been studied with NMR, for instance, the simultaneous evaluation of the content in terpenes, aldehydes,

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sn-1,3 diacylglycerol, sn-1,2 diacylglycerol, squalene, unsaturated fatty chains, saturated fatty chains, wax, and bsitosterol (Mannina, Marini, Gobbino, Sobolev, & Capitani, 2010); or fatty acids, iodine number, phenolic compounds, diacylglycerols, sterols, and free fatty acids (Agiomyrgianaki, Petrakis, & Dais, 2010), among others. Finally, a metabolomic fingerprinting approach has also been explored by using this family of techniques (Alonso-Salces et al., 2010; Longobardi et al., 2012). Furthermore, the VOCs fraction is another important authenticity indicator, although its assessment is difficult, since most of the times the aroma is not just due to one chemical compound. Currently, the official sensory profile evaluation of EVOO, VOO, and LOO (Commission Regulation EEC No 2568/91, 1991) categories is performed by regulated expert panel tests (Spain: Royal Decree 227/2008, 2008), which consist of assessing the smell and taste of VOOs. However, this procedure is costly and slow and there are few olive oil tasters at an international level, hence the urgency of obtaining results as soon as possible by bottlers. This fact has given rise to the development of analytical and chemometric methodologies to support the activity of panel tests in order to avoid the mislabeling of VOOs (Contreras, del, Jurado-Campos, Arce, & Arroyo-Manzanares, 2019; Garrido-Delgado, Dobao-Prieto, Arce, & Valca´rcel, 2015). In this sense, analytical techniques such as MS (Alves, Botelho, Sena, & Augusti, 2013; Go´mezAriza, Arias-Borrego, Garcı´a-Barrera, & Beltran, 2006; Goodacre, Vaidyanathan, Bianchi, & Kell, 2002), IMS (Garrido-Delgado et al., 2015), or additional chemical sensor technologies (Escuderos, Sa´nchez, & Jime´nez, 2011; Savarese, Caporaso, & Parisini, 2013; Veloso, Dias, Rodrigues, Pereira, & Peres, 2016) are some examples of alternatives proposed to evaluate authenticity through volatile indicators. While some authors have tried to use only some identified volatile metabolites or group of known metabolites such as TAGs (Go´mez-Ariza et al., 2006) to address the authentication of olive oils, the most common approach is to use untargeted methods to draw conclusions. It is important to note that the combination of these alternative analytical techniques with multivariate statistical techniques is crucial to obtain conclusions about the authenticity of the oils analyzed (Avramidou, Doulis, & Petrakis, 2018; Go´mez-Caravaca, Maggio, & Cerretani, 2016; Messai, Farman, Sarraj-Laabidi, Hammami-Semmar, & Semmar, 2016). In fact, so far, the data treatment process is the limiting factor for transferring these authentication methods to the industry. In the case of oilseed oils, the Codex Alimentarius Commission defines the fatty acid composition as an essential factor of quality of this group of oils (Codex Alimentarius, 1999), and it is usually the selected indicator for authentication. The trending techniques employed to assess fatty acid content are similar as for olive oils. Some current examples are presented here: vibrational techniques as, for example, IR has been used to detect coconut oil adulteration with corn, sunflower, olive, or palm oil (Rohman & Man, 2009), for palm oil authentication (Lim, Abdul Mutalib, Khaza’ai, & Chang, 2018), or peanut oil authentication (Ren, Lin, Shi, Shen, & Wang, 2014). Moreover, vibrational techniques has been used to carry out untargeted analysis for authentication of some oilseed oils such as corn, peanut, canola, coconut, and others (Yang, Irudayaraj, & Paradkar, 2005). On the other hand, methods based on NMR have been published for instance for the verification of sesame oil (Kim et al., 2015), camellia oil (Shi et al., 2018), or peanut oil (Zhu, Wang, & Chen, 2017). These methods were based on information related to fatty acids as indicators of authenticity. Other indicators such as phospholipids and saccharide profiles have been used for sunflower oil authentication by using NMR (Monakhova & Diehl, 2016). Sensory evaluation has not been applied extensively to the authentication of oilseed oils. Therefore research on this field is supposed to be an opportunity to develop new methods based on volatile phase information in the future. Some examples are the application of a sensor based on a potentiometric e-tongue to evaluate three parameters; peroxide, anisidine, and total tocopherols values for authentication of different oilseed oils (rapeseed, sunflower, peanut, and soy; Semenov et al., 2019). IMS has been also used to identify adulterations of sesame oil based on its fingerprint (Zhang et al., 2016). Efforts currently tend to focus on improvement of statistical procedures to avoid a casual relationship between results obtained with alternative analytical methods and the authentic category of edible vegetable oils (Aparicio, Conte, & Fiebig, 2013). Furthermore, on occasion, the required validation tests are not carried out. Therefore increasing the number of samples should be also considered to improve this aspect and obtain rational conclusions (Bajoub, Bendini, Ferna´ndez-Gutie´rrez, & Carrasco-Pancorbo, 2018). Consequently, there is a growing need for the development of fast and simple methods for authentication of oilseed oils in analytical laboratories.

8.2.12 Cereals Examples of widespread cereals are common wheat (Triticum aestivum), barley (Hordeum vulgare), rye (Secale cereale), oats (Avena sativa), millet (with many varieties), rapeseed (Brassica napus), spelt (Triticum spelta), sorghum

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(Sorghum bicolor), maize (Zea mays), and rice varieties (wild, glutinous, basmati, and jasmine, among others). Apart from the raw cereal, fraud can be detected in its processed products, for example, grits, starch, and flour. Examples of fraud in these are adulteration through admixing or undeclared compositions (e.g., gluten-free products that contain gluten). Among others, from the current legislation (Regulation EU No 1151/2012, 2012), cereal variety, chemical composition, and origin can be addressed as the most relevant indicators of authenticity and evaluation for cereal and cereal products. First, cereals have many varieties, which belong to different species. The importance of variety discrimination is that the PDO products (e.g., “Pane di Altamura”) are manufactured with high-quality flours obtained from specific varieties. In order to differentiate them, apart from the widespread genetic analysis, the most described methods are mainly based on HRMS or NIR. Therefore MS has been employed combined with electrophoresis to analyze endosperm storage proteins, albumins, and gliadins with the aim of authenticate wheat varieties (Mamone et al., 2005). Likewise, common wheat (T. aestivum) and durum wheat (Triticum durum) have been differentiated with untargeted lipidomics employing ultrahigh performance liquid chromatography-quadrupole time-of-flight (UHPLC-QTOF) mass spectrometry (Righetti et al., 2018) and DART-HRMS (Miano et al., 2018). Additionally, rice varieties can also be differentiated with UHPLC-QTOF through the determination of their amount of steryl ferulates (e.g., g-oryzanol), which is correlated with rice variety (Zhu and Nystro¨m, 2015). In this work, subsequent classification was achieved with the development of orthogonal partial least squares discriminant analysis chemometric models. As can be observed, all the methods mentioned above had cutting-edge MS detection in common. Second, NIR has been also widely used to authenticate and differentiate cereal varieties. Among all cereals, usually wheat is the main focus (Miralbe´s, 2008; Vermeulen, Suman, Ferna´ndez Pierna, & Baeten, 2018; Ziegler et al., 2016). Its flour has also been discriminated from others types with NIR (Rachmawati, Rohaeti, & Rafi, 2017). Likewise, gluten-free oat was equally distinguished from other cereals with this technique (Erkinbaev, Henderson, & Paliwal, 2017). Discrimination between cereal varieties is particularly important for bread and pasta labeled with PDO (e.g., the “Petit e´peutre de Provence” from France or the Italian “Farro della Garfagnana”), which have an economic importance in their area of production. High-quality bread and pasta are made from ancient wheat varieties known as farro (emmer, einkorn, and spelt), which are sometimes replaced by cheaper ones (Miano et al., 2018; Silletti et al., 2019). The issue of farro varieties authentication has been fairly investigated and the methods proposed are mainly based on DNA techniques such as end-point PCR (Bryan, Dixon, Gale, & Wiseman, 1998), quantitative polymerase chain reaction (qPCR) (Carloni et al., 2017; Sonnante et al., 2009), and tubulin-based polymorphism profiling (TBP) (Casazza et al., 2012). GC-MS analyses have also been carried out for characterization of PDO breads through using VOCs profile (Bianchi, Careri, Chiavaro, Musci, & Vittadini, 2008). More recently, a DNA fingerprinting approach based on TBP, exploiting interspecific polymorphisms of β-tubulin introns, has successfully addressed the issue of multiple wheat variety authentication in raw materials as well as in processed food, providing a complete qualitative analysis (Silletti et al., 2019). This DNA fingerprinting analysis has important advantages with respect to the other strategies of authentication, since it is almost immediate and does not require any additional statistical elaboration. TBP-based fingerprinting analyses can, therefore, provide a first screening step that could possibly be followed by target quantitative analysis, performed by qPCR or other methods, to discriminate between accidental contaminants, mislabeling, or adulteration. Nowadays, advanced analytical tools in metabolomics have permitted the simultaneous analysis of hundreds of metabolites. In this sense, a nontargeted metabolomic method has been developed for the characterization of ancient wheats (Righetti et al., 2016). This method is based on solid liquid extraction followed by a reversed phase-LC-QTOF and multivariate data analysis. Discriminant metabolites including alkylresorcinols, glycerophospholipids, and glycerolipids were identified allowing a metabolic characterization of ancient wheat grains. More recently, the same research group has also developed a new unconventional nontargeted method to discriminate wheat varieties based on DART-HRMS, exploiting an orbitrap mass analyzer and multivariate data analysis (Miano et al., 2018).

8.2.12.1 Chemical composition Oil content, proteins, water content and absorption capacity, gluten index, wax in starch, salt content, and pH are the most relevant parameters of cereal and cereal products composition. Most of the developed methods described in the literature use GC-MS. Moreover, HPLC or CE has been used jointly with polyacrylamide gel electrophoresis to determine cereal kernel proteins. Subsequently, their profile can be employed to differentiate rice varieties. In this case, protein profile, kernel size, amylose, and moisture content analyses were combined with a following DA chemometric model (Attaviroj & Noomhorm, 2014). In addition, flour composition has been examined in terms of N-methylpipecolic acid and homostachydrine using liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS)

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in order to detect rye in blends, as these compounds have only been determined in rye (Servillo et al., 2018). Furthermore, LC-ESI-MS/MS has been used to determine peptide biomarkers and correlate them with wheat, rye, and spelt in breads (Bo¨nick, Huschek, & Rawel, 2017). As can be seen, MS detection was shared in all the procedures described above. NMR combined with IRMS (Brescia et al., 2007) was also successfully employed for these purposes. On the other hand, the presence of nondeclared additives or substitution of materials in raw cereal or its products is another common type of fraud. In these cases, NIR or Raman methods are the most utilized. For instance, the use of ¨ c¸u¨ncu¨oˇglu, margarine instead of butter in bakery products was detected using both Raman and NIR spectroscopies (U ¨ Ilaslan, Boyaci, & Ozay, 2013). Margarine is cheaper than real butter, but highly hydrogenated and of considerably lower quality. Moreover, the presence of nondeclared toxic melamine in cereal has been detected with diverse techniques, such as SERS in wheat gluten and bran (Mecker et al., 2012) or SERS combined with HPLC-MS in wheat gluten (Lin et al., 2008). This compound, along with added urea and cyanuric acid, was also determined in fraudulent maize gluten employing a stereomicroscope combined with electronic nose (Frick, Dubois, Chaubert, & Ampuero, 2009). Likewise, modern and old wheat cultivars differ in their lignan composition. While traditional variety seeds contain more arctigenin, hinokinin, and syringaresinol, modern ones contain more secoisolariciresinol and pinoresinol, as determined by CE-MS analysis (Dinelli et al., 2007).

8.2.12.2 Cereal origin Organic cereal cultivars require a control of forbidden substances, soil fertility indexes and nutrient composition, and the use of specific seeds, among other conditions. Thus the differentiation of organic and conventional cereals is essential. Currently, MS detection is the leading technique for this purpose, besides electrophoresis (which can be used as a previous separation step) and stable isotopes techniques (e.g., IRMS). For instance, ICP-MS has been used to determine the quantities of 20 elements, and subsequently, these were employed to classify rice as organic or conventional (Barbosa et al., 2016). Thus this methodology was able to detect the use of forbidden fertilizers in order to grow organic crops faster. Furthermore, transgenic or GM organisms are restricted in organic cultivars and their discernment is a must. For instance, CE separation combined with ESI-MS has been employed to differentiate GM from nontransgenic maize cultivars. Using CE-ESI-MS, the fingerprint of zein proteins (prolamins) was studied and found out to be different depending on conventional or GM product (Erny, Marina, & Cifuentes, 2007). A similar study was carried out with GC-MS, where metabolites from GM and conventional corn kernels were compared (Ro¨hlig & Engel, 2010). CE separation has also been used in combination with TOF-MS to discriminate GM maize using metabolites as biomarkers (Levandi, Leon, Kaljurand, Garcia-Can˜as, & Cifuentes, 2008). In this case, Cry1Ab gene was added in order to provide protection against worms. As can be seen, CE and MS have been useful in this field. On the other hand, not every GM cereal analysis was targeted. For instance, atomic spectroscopy has been also used for multielement and stable-isotope determination with subsequent multivariate chemometric models (Laursen, Schjoerring, Kelly, & Husted, 2014). Moreover, organic cultivars forbid the use of pesticides. Several methods have been developed to detect them in cereals. For example, SERS was employed to determine tricyclazole, which is a fungicide that has been successfully detected in paddy rice (Tang et al., 2012). This compound has also been detected in durum wheat using NIR (Soto-Ca´mara, Gaita´nJurado, & Domı´nguez, 2012). Furthermore, cereal origin determination serves also as a potential tool for authentication, especially useful for imported commercial products. In this field, there is a great variety of techniques employed. IRMS is one of the most widely employed techniques in order to verify cereal or flours origin, such as wheat (Goitom Asfaha et al., 2011; Luo et al., 2015; Wu et al., 2015), rice (Black et al., 2016; Di Anibal et al., 2011; Donno et al., 2016; Teo et al., 2009), corn, barley, rye, oat, and triticale (which is a hybrid of maize with rye). ICP coupled with optical emission spectrometry and MS (Laursen et al., 2011) have been used to determine barley, sorghum starch (Craig & Stark, 1984), and rice origin (Cheajesadagul, Arnaudguilhem, Shiowatana, Siripinyanond, & Szpunar, 2013). ICP combined with atomic emission spectroscopy (Chung, Kim, Lee, & Kim, 2015) also served as a technique for determination of rice origin and varieties, based on multielement fingerprinting. NIR is also able to determine barley origin (Zhao, Guo, Wei, & Zhang, 2013).

8.2.12.3 Future trends in cereal authentication In summary, nowadays, the most relevant analytical methods used for cereal authentication include MS detection and multielement determination. However, it is necessary to develop novel nondestructive methods based on spectroscopic techniques (e.g., NIR or Raman; Danezis et al., 2016b) for varietal differentiation, as it has already happened in the

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case of meat. These techniques provide faster results than chromatography and cheaper than genetic analysis, and their simplicity allows their use in routine without requiring sample pretreatment. Their combination with chemometric models and imaging techniques will make it possible to fingerprint large amounts of product soon. In this way, additional studies in this sense are crucial, as cereal grains usually are traded in enormous batches of homogenized product or transformed in flours. Therefore the detection of fraud is a complex process that requires the development of exhaustive sampling methodologies.

8.2.12.4 Genetically modified organisms Genetically modified organisms (GMOs), also called transgenics, are defined as organisms in which the genetic information has been altered. While their presence in food is regulated and the label needs to highlight specifically that the product contains GMOs (Directive 2001/18/EC, 2010; Regulation EC No 1830/2003, 2003), and the detection of GMOs still represents a challenge. At present, the strategies followed to detect GMOs in foods are mainly based on proteomics, genomics, and metabolomics (Bo¨hme, Calo-Mata, Barros-Vela´zquez, & Ortea, 2019). DNA-based methods are the most used to identify GMOs in foods. Several genes for different events can be successfully used as indicators of the presence of GMOs in food products (Dobnik, Morisset, & Gruden, 2010). Real-time PCR is the only valid method that is applied for the maintenance of GMO regulation in the EU. Nevertheless, this approach presents certain limitations. On the one hand, the laboratory work could become quite laborious and complex, since usually only one method per reaction is used. Moreover, PCR strategy is based on knowledge of the targeted sequences, which limits the analysis when the sample presents unknown and nonauthorized GMOs (Fraiture et al., 2016). The development of novel DNA-based technologies has opened huge possibilities for the simultaneous detection ˇ & of different GMOs in a single analysis and the decrease of limits of detection (Koˇsir, Spilsberg, Holst-Jensen, Zel, Dobnik, 2017). For example, multiplex PCR assays have been described to detect various target GM events simultaneously (Georgiou, 2017). Moreover, the presence of unknown/unauthorized GM events can be determined by the DNA walking technique (Fraiture et al., 2014; Fraiture et al., 2015a; Fraiture et al. 2015b). However, the DNA walking methods cannot be easily implemented in routine analysis due to their experimental complexity as well as insufficient specificity, sensitivity, or yield (Fraiture et al., 2016). In order to detect unknown GMOs in foods, more recently, NGS technology has been investigated, which offers a high throughput (Fraiture et al., 2016). Among all NGS strategies, two main approaches are distinguished, namely, targeted-sequencing strategy, in which sequences of interest are first selected to be then sequenced, thus requiring some prior knowledge; and whole-genome sequencing, where a DNA library contains the entire genome that is fully sequenced. The generated reads can then be treated with bioinformatics (Liang et al., 2014; Willems et al., 2016). Proteins are also suitable indicators to identify the presence or absence of specific genotypes, since many proteins are related to posttranscriptional modification (Martins-Lopes, Gomes, Pereira, & Guedes-Pinto, 2013). Proteomics can be used for detecting differential expression of proteins between GMO and non-GMO organisms via immunoassays, such as ELISA technologies involving fluorescent dyes, which is the classic assay reported by many researchers. More recent methodologies use techniques such as LC-MS/MS to obtain the protein profiling. For instance, a proteomics approach allowed the detection of changes in expression of 117 proteins in maize as a result of the insertion of a protein conferring resistance to glyphosate (NK603 maize; Mesnage et al., 2016). However, due to their lesser stability, protein-based methods are usually considered unsuitable for the detection of a wide range of GMOs, especially for processed foods (Lian & Zeng, 2017; Qian, Wang, Wu, Ping, & Wu, 2018). Nowadays, the strategies are aimed to develop new and more informative analytical methodologies based on advanced massive analysis of molecular data. Metabolites such as alkaloids, steroids, or phenolic and terpenic compounds can be suitable markers of genetic diversity both qualitatively and quantitatively (Montet & Ray, 2017). Each genotype has its own profile within the secondary metabolites profile (metabolic fingerprints) that can be obtained by HPLC and GC coupled or not with MS (Montet & Ray, 2017).

8.2.13 Food supplements The consumption of food supplements has grown in recent years as a way to complement the diet due to the fast-paced lifestyle of consumers (Greger, 2001). The European directive 2002/46/EC (European Union, 2002) affirms that a wide range of ingredient and nutrients may be part of food supplements including, but not limited to, vitamins, minerals, amino acids, essential fatty acids, fiber, and various plants and herbal extracts. Therefore all these substances could be indicators of authenticity of food supplement products.

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The great concern with the issue of the authenticity of food supplements is around those obtained from botanical or herbal preparations. The ideal situation would be to dispose of standard reference materials to characterize raw materials and reduce the risk of misidentification or the use of inappropriate amounts of nutrients (Sander, Sharpless, & Wise, 2006). Unfortunately, it is not always possible due to the wide variety of plants that could potentially be employed to produce food supplements. Because of this, some botanical extracts are not well characterized before entering the market, causing an accidental adulteration of food supplements. Intentional adulterations can occur when a botanical product is in short supply and other botanical materials are substituted for it (Rader, Delmonte, & Trucksess, 2007). However, the greatest intentional adulterations are usually economically motivated, for example, by substitution of some plant species by botanically similar and cheaper ones (Mudge, Betz, & Brown, 2016). A clear example is the adulteration of bilberry extract (Vaccinium myrtilus) used for the manufacturing of food supplements, by addition of amaranth dye to increase the apparent content of anthocyanin (detected by measuring spectrophotometric absorbance at 528 nm), which is forbidden due to the possibility of it being carcinogenic (Penman et al., 2006). A high-performance thin-layer chromatography (HPTLC) method was proposed to confirm the identity of extracts in parallel to avoid this adulteration (Gardana, Ciappellano, Marinoni, Fachechi, & Simonetti, 2014). There are several considerations to ensure the authenticity of botanical food supplements such as the identification of the starting material, the knowledge of the production process, and the standardization and nature of the final product (Schilter et al., 2003). However, the bottleneck of the authentication of these products is the correct identification of the starting material, which is based on the identification of the taxonomic and geographic origin (Schilter et al., 2003) or morphological and microscopic examinations of the plants. However, rigorous botanical identification of raw materials, based largely on morphological characteristics, is often difficult or impossible, which creates an opportunity to adulterate the content of food supplements (Applequist & Miller, 2013). Since these methodologies are not sufficient to characterize a botanical preparation, analytical techniques such as HPLC (Brown and Shipley, 2011; Brown, Chan, Paley, & Betz, 2011; Harnly, Luthria, & Chen, 2012; Mudge et al., 2016; Wang et al., 2009), NMR (Vaysse et al., 2010), MS (Chen, Harnly, & De Harrington, 2011), HPTLC (Sherma and Rabel, 2018), and other separations such as GC or CE (Khan and Smillie, 2012) are also employed to authenticate the composition of botanical food supplements. For instance, rutin, quercetin, and an unknown flavonol glycoside are indicators of adulteration for Gingko biloba supplements and they were identified successfully using HPLC-UV (Harnly et al., 2012). Quantification of sibutramine, phenolphthalein, and synephrine is possible as adulterants of dietary supplements marketed as natural slimming products by using NMR (Vaysse et al., 2010). In addition, the differentiation of three ginseng species was achieved by direct injection in an MS without a lengthy chromatographic separation (Chen et al., 2011). The simultaneous use of some of these techniques could also be used to address adulteration issues, as was the case of HPTLC, NMR, and HPLC-DAD, which were employed together to distinguish bearberry; Arctostaphylos uva-ursi from Arctostaphylos pungens. The former is a medicinal plant that has influence on urinary disorders, while the latter is similar morphologically, but its market value is lower (Gallo et al., 2013). With regard to specific adulterants of food supplements from an official point of view, the most frequently found by the European Union rapid alert system for food and feed was sildenafil and its analogues, sibutramine and its derivates, 1,3-dimethylamylamin, yohimbine, and tadalfil. HPLC-MS and high field NMR were the most used and recommended techniques for their identification (Walker, Naughton, Deshmukh, & Burns, 2016). The top analysis to authenticate food supplements are based on molecular technologies for species identification. In fact, molecular authentication is found to be accurate, sensitive, and reliable. Currently, it is the dominant trend in food supplement authentication. In recent years, there have been improvements in these DNA-based techniques and new alternatives have appeared, such as isothermal amplification for onsite diagnosis, NGS for high-throughput species identification, high-resolution melting analysis for quick species differentiation, or DNA array techniques for rapid detection and quantitative determination (Lo & Shaw, 2018). In addition, the developing of analytical instrument platforms to simultaneously verify different indicators or fingerprints of food supplements could be an appropriate strategy to confront their authentication. However, above all, the improving of the efficiency of DNA-based techniques to develop faster on-site methodologies should be the tendency.

8.3

Conclusions

In recent years, the consumers have demonstrated a strong desire for safe and healthy food products. Today, due to the lifestyle of most people in modern societies, the need for processed foods has increased dramatically. This issue can alone emphasize the importance of quality control of food products, because employing of compounds that may threaten the human health, such as some preservatives for instance, will bring irreparable costs to human life on a global

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scale. Considering serious reports on specific diseases can be an alarm in order to pay more attention to this remarkable issue. Fortunately, strict oversight of food production processes in advanced societies has been implemented to an acceptable extent, but unfortunately, this is still too far from ideal situations in many underdeveloped or even developing countries. The discovery and estimation of food chemical contaminants, in order to ensure food safety and consumer satisfaction, require the development of effective analytical methods to confirm the composition, quality, and authentication of different food products. Modern analytical methods provide opportunity to collect large amounts of data for various samples in order to represent valuable structural information to assess adulteration and authenticity. This book chapter attempts to systematically report, compare, and evaluate the current food authentication research activities for a variety of food products. This book chapter shows the ability of UV-Vis and fluorescence spectroscopies, vibrational spectroscopy, hyperspectral imaging, NMR spectroscopy, and chromatographic methods mostly combined with MS to achieve chemical and structural information for characterization and classification of food products and also detection and quantification of food contaminants. These analytical techniques are widely used as a broad range rapid, highthroughput, sensitive, and accurate analysis for a wide range of samples. In fact, the authors are trying to narrow the gap between research and practice by representing the content of this book chapter.

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Chapter 9

Food traceability Burcu Guldiken1, Simge Karliga2, Esra Capanoglu2, Perihan Yolci-Omeroglu3 and Senem Kamiloglu4 1

Department of Food and Bioproduct Sciences, University of Saskatchewan, Saskatoon, SK, Canada, 2Department of Food Engineering, Faculty of

Chemical and Metallurgical Engineering, Istanbul Technical University, Istanbul, Turkey, 3Department of Food Engineering, Faculty of Agriculture, Bursa Uludag University, Bursa, Turkey, 4Science and Technology Application and Research Center (BITUAM), Bursa Uludag University, Gorukle, Bursa, Turkey

9.1

Introduction

Today, food products are supplied from the producers or farmers to the regions, various locations, or countries at different distances. Therefore a food’s journey from a producer to a costumer or a retailer takes a lot of time. Transparency in the food supply chain ensures food safety and quality by providing the information on the origin and the transport chains to the final end user including consumers, food industries, and retailers. For instance, temperature is the major parameter that affects safety and quality of easily decomposable foods; therefore it is necessary to monitor and control temperature during the supply chain. The food industry should establish quality assurance systems based on the consumer expectations and legal requirements. While assuring the food safety and quality, these systems improve food product integrity and transparency throughout the whole food supply chain. Moreover, these systems, generally named as traceability systems, include proactive monitoring systems throughout the chain to provide effective food recalling whenever required (Alfian et al., 2017; Kadir, Shamsuddin, Supriyanto, Sutopo, & Rosa, 2015). The major aims in a traceable system are to (1) solve food safety problems; (2) to have full recalling opportunities; and (3) eliminate unconsumed food as soon as possible during the recalling process. Olsen and Borit (2013) defined traceability as “the ability to access any or all information relating to that which is under consideration, throughout its entire life cycle, by means of recorded identifications” (Badia-Melis, Mishra, & Ruiz-Garcı´a, 2015). A more official definition is provided in European Food Law as (EC 178/2002) as “‘traceability’ means the ability to trace and follow a food, feed, foodproducing animal, or substance intended to be, or expected to be, incorporated into a food or feed, through all stages of production, processing, and distribution” (Regulation E. No 178/2002, 2002). The EU Food Law (EC 178/2002) makes an obligation to have adequate level of identification or labeling for food or feed on the market to enable traceability. Moreover, it is the major responsibility of food and feed business operators to manage food safety by establishing proper quality assurance systems, which facilitate traceability. In case of emergency, they shall immediately withdraw the products from the market and, if necessary, recall them from the customer (Regulation E. No 178/2002, 2002). Member State Authorities shall trace the risk backward and forward along with the food chain and notify Rapid Alert System for Food and Feed. European Commission requests information from operators to enable traceability and coordinates action by national authorities. From past to present, consumers have become more conscious on what they consume on their diet and started to demand safe and healthy foods and to avoid contaminated food. Dioxin crisis in the Netherlands in 2004 was the first important food safety crisis occurred in European Union and led to an awareness for the consideration of selecting proper tools for traceability (https://ec.europa.eu/food/sites/food/files/safety/docs/gfl_req_factsheet_traceability_2007_en.pdf). Based on a consumer survey conducted in four EU member countries, traceability was perceived as an important tool in verifying products and stated as the primary need for fresh and perishable products such as meat, dairy products, fruits and vegetables, fish, and eggs (Song, Liu, Wang, & Nanseki, 2008; Van Rijswijk & Frewer, 2012). Traceability systems are considered as the main tool for eliminating the concerns of consumers, manufacturers, and legal authorities on food safety (Olsen & Borit, 2018; Verbeke, Frewer, Scholderer, & De Brabander, 2007). In this concept, various techniques have been developed to assure the traceability of different food products. For instance, radioInnovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00009-1 Copyright © 2021 Elsevier Inc. All rights reserved.

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frequency identification (RFID) is used for seafoods in the United States, while the internet-based “Critter Trace System” is used by British government. Similarly, China uses traceability system for agricultural products, and since 2001, Japan has traceability system for beef production (Qiao, Wei, & Yang, 2013). Traceability methods include document-based systems, information and communication technologies, alphanumerical codes, barcodes, holograms, RFID, nuclear techniques, and nanotechnology (Dandage, Badia-Melis, & Ruiz-Garcı´a, 2017), which are discussed in detail below. In addition, analytical methods that are used in food traceability analysis are highlighted.

9.2

Traceability systems

9.2.1 Document-based systems The contemporary food supply chain should provide information that consumers and other relevant organizations need to know, such as issues related to food characteristics, country of origin, animal welfare, and genetic engineering. Documented data are needed to prevent foodborne diseases and recall products when necessary. In order to minimize damage, certification should be made at every stage of traceability (Bosona & Gebresenbet, 2013). This makes it easier to identify affected products, the kind of event occurred and the time and location of occurance in the supply chain, and the responsibles. Traceability systems ensure the accessibility of integrated data throughout production, storage, distribution, quality control, and sales processes, and therefore data and information are crucial for managing food crises (Aung & Chang, 2014; Bosona & Gebresenbet, 2013). Only one-way traceability does not allow traceability of food circulation throughout the supply chain (Bosona & Gebresenbet, 2013). Traceability provides both backward and forward tracking of products and information on product’ history indicating the movement of the product in the supply chain. However, this circulation also needs to be documented. The data obtained should be documented, and an integrated database should be used for performance evaluation (Bosona & Gebresenbet, 2013; Opara, 2003). Today, mandatory food traceability laws are implemented in some countries, and the EU has done so by the General Food Law (EU 178/2002) as explained above. The obligatory data for this law require specific information such as lot number, product ID, product description, supplier ID, quantity, unit of measure, buyer ID, optional data, supplier name, contact information, entry date, country of origin, package date, commercial unit, vehicle ID, logistics service provider ID, recipient’s name, and date of shipment (Bosona & Gebresenbet, 2013; Regulation E. No 178/2002, 2002). Some countries such as China and Japan target specific data collection and use standards, which assure food safety, food sustainability, and customer information (Zhang & Bhatt, 2014). Even though electronic data evaluation and automatic data capture levels are high in food industries in developed countries, standardization is still insufficient (Bosona & Gebresenbet, 2013; Zhang & Bhatt, 2014). In addition, Hazard Analysis and Critical Control Points (HACCP) is the international reference system for food safety management, and based on seven key principles, which is widely used worldwide. HACCP is a structural approach that involves the careful recording of all details and actions to document that a safety management system is operating and that all hazards in food processing are under full control. Obviously, information systems can simplify the use of such a structured approach, and so many HACCP software have been developed (McMeekin et al., 2006). There are two different traceability data flow. The first model (one step up-one step down flow model) provides the required information such as the origin and quality of the products and filters the unnecessary information, which provides a convenient way for food traceability; however, in the second model (aggregated information flow model), all the collected information follows the product up to end stage in the supply chain (Bosona & Gebresenbet, 2013; Zhang & Bhatt, 2014). Most companies, especially small and medium enterprises, use paper-based registration systems, although an electronic-based traceability system has performance advantages over a paper-based system (Chryssochoidis, Karagiannaki, Pramatari, & Kehagia, 2009). It generally accommodates paper-based or electronic document-based record-keeping systems in most food supply chains. Even in fully automated systems, paper-based recording systems can coexist (Nishantha, Wanniarachchige, & Jehan, 2010). Information flow affects the quality of the traceability system. The quality of clear information is also important. For example, in the food industry where raw materials from different suppliers are used, the batch mixing stage is important depending on the batch size of the food recall and the batch-specific net information recorded during the batch mixing stage. Or, in the fresh product supply chain, the level of supply chain in which the product is packaged and labeled, such as packhouses, determines the availability and quality of information required for the traceability

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system. In the meat industry, it is also important to capture and store information after cutting process. Traceability reference number such as a beef label, batch number (to be assigned at slaughter and deboning level), the slaughterhouse, and slaughter or deboning facility license number can be kept as mandatory information (Bosona & Gebresenbet, 2013; Van der Vorst, 2006). In order to perform the traceability system better, the documented data must have certain features. The features are: Escribing the amount of data recorded is “Breadth,” indicating up and down traces in the chain is “Depth,” describing the degree of security of data is “Precision,” and indicating the access to data and the transmission rate of the data is “Access” (Bosona & Gebresenbet, 2013; McEntire et al., 2010). When the storage of data is complete, great effort and time are required to verify, normalize, correct, and merge extracted traceability data. More specifically, at this stage, traceability data are converted to specific operating and accounting rules, functional and decision-making requirements, quality regulations, and upcoming transactions. Various technologies can be used to encode the required traceability information, set standards, and ensure that the various documents prepared are valid. The type of data format is very important because errors are usually not detected until something goes wrong, which often leads to poor information quality. In addition, the interaction between the internet and the database world is a necessary mechanism, and with the electronic spread of traceability information, traceability users can benefit from intelligent software tool technology to directly monitor and evaluate supply chain performance in terms of quality (Folinas, Manikas, & Manos, 2006). Documentation of traceability will be carried out by recording the answers of “who, what, when, where, why, and how” questions, as regulators around the world are trying to develop new requirements for food traceability (Zhang & Bhatt, 2014). Moreover, traceability and its documentation will become more and more critical in the near future and will lead the industry to make an investment to develop documentation technology (Bosona & Gebresenbet, 2013).

9.2.2 Information and communication technology Digitalization is one of the most essential transformation steps in universal agri-food chains. For sustainable food production information chain and communication technologies (ICT) is critical. ICT is composed of the latest technologies such as radios, TVs, telephones, computers, internet technologies, and databases. Thus the basic functions of ICT can be defined as electronically capturing, transmitting, and displaying data and information. A variety of technologies can be used under ICT, such as mobile/cloud computing, the Internet of Things (IoT), location-based monitoring (remote sensing (RS), geographic information, drones, etc.), social media, and big data (data network, open connection; El Bilali & Allahyari, 2018). It is well known that, to export a product to developed countries, the traceability of the product is an obligation. With existing paper-based traceability systems, food chain records cannot be interconnected effectively and cannot ensure accuracy and sharing of data on time. Therefore monitoring and monitoring system should be connected to information systems (Singh, Luthra, Mangla, & Uniyal, 2019). In order to export to the EU or trade within EU, The European Commission demands health certificates or trade documents for the consignments of animals, food, feed, and plants. Therefore Trade Export and Control System (TRACES) is developed by EU as a multilingual online management tool to control and plan routes quickly and efficiently. National qualified authorities submit related documents online through TRACES; therefore both the EU border control authorities and the control authorities at the destination can inquiry the consignments and other related certificates that provide allowance to their products to enter and travel through the EU. In the case of the transport of animals, additional checks and controls on animal well-being can be carried out. In addition, TRACES allows traceability for the customer (https://ec.europa.eu/food/animals/traces/how-doestraces-work_en). Therefore, it can be stated that effective implementation of ICTs will make a significant contribution to improve traceability across supply chains (Singh et al., 2019). The use of ICTs to promote traceability of food products and to ensure food safety is being implemented in the United States in addition to EU Member Countries (Van der Vorst, 2006). Since agriculture is the first step of food production, application of ICT is very important to manage traceability at this step of the chain. The Action Plan 2017 of G20 Agriculture Ministers included a section dedicated to ICT in agriculture for a commitment to improve the efficiency and sustainability of the agricultural sector. Modern precision farming, mainly based on satellite navigation and positioning technology, new sensor technologies, and IoT, applies sensors, geographic information systems (GIS), global positioning systems (GPS), and advance software. Advances in spatial science and technology, such as GIS, RS, and GPS, provide important opportunities for traceability, which is important not only for food safety but also for ethics (Coff, Barling, Korthals, & Nielsen, 2008; El Bilali & Allahyari, 2018; Opara, 2003).

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IoT aims to connect physical objects to the internet using a low-power network connection embedded with sensors. IoT technology enables data transmission using a variety of wireless standards such as Bluetooth (Smart), Wi-Fi, ZigBee, Near Field Communication (Nukala et al., 2016). In food production, IoT technologies alter traditional supply chains into smart chains, and throughout the food supply chain, these technologies provide food safety and quality (Chen, 2018; Nukala et al., 2016). Transport containers are very important to maintain the quality of perishable products, and for this reason, several research studies have developed IoT-based smart containers for tracking food during transportation chain. The tremendous data from the sensors of the objects can be used in decision-making and the creation of an information network for efficient management of food across the supply chain. Such a network can be obtained by integrating cloud computing with IoT devices (Nukala et al., 2016). ICTs positively affect the organization, integration, and coordination of food chains at the local, regional, and global levels, reducing process costs in the food chain and ensuring their successful coordination. Food and Agriculture Organization’s Virtual Extension, Research and Communications Network was developed based on ICT to facilitate networking, knowledge sharing, and interaction among agricultural institutions, producer organization, and other actors of the agricultural innovation system at global level. ICT applications are important not only for traceability but also for preventing waste in the food chain. Thus ICT applications help organizations develop an effective information sharing system to ensure better use of resources and environmental protection (El Bilali & Allahyari, 2018; Singh et al., 2019). It also provides new relationships based on greater equality and transparency between producers and consumers, and ICTs such as mobile phones shorten the distance between food chain actors. Ownership of data is an important issue for the use of digital technologies both on the supply (producers) and on the demand (consumers) side of the food chain (El Bilali & Allahyari, 2018). Challenges such as cultural differences, difficult, or impossible interoperability between various systems in the supply chain or farm level, and the integration of complex and large amounts of data can affect the use of ICTs in the food system. It is an important advantage to adapt mobile technologies from ICTs to individual contexts and to ensure that they are adequate, relevant, and accessible. To maximize the benefits of ICTs in food chains and in developing countries, user-friendly, relevant, local, and affordable practices and services are necessary to be developed (El Bilali & Allahyari, 2018). As a preventive quality and safety management tool, ICT is one of the technological innovations that can be applied in the traceability system. Managers need to be aware of future developments in this area to implement appropriate traceability systems (Opara, 2003). In the near future, food traceability techniques such as bio tracking, nano sensor, GPS and GIS are expected to be used more widely (Dandage et al., 2017).

9.2.3 Alphanumerical codes Alphanumerical codes are a combination of alphabetic and numeric characters of different sizes, usually found on the product label (Dandage et al., 2017). Alphanumerical codes allow customers to learn more about the products they purchase through a website (Aung & Chang, 2014). In each EU Member States, it is mandatory for the competent state authority to adopt and enact laws relating to the identification and registration of animals. All animals are provided with health certificates prior to transport between Member States and inspected at the point of entry (Regulation E. No 1760/2000, 2000). In order to facilitate inspections and perform them quickly, alphanumerical codes appear as an alternative to the ear tags in the traceability of animals such as cattle. In such an event, identification code defined for ear tags in the regulation remains constant (McGrann & Wiseman, 2001; Regulation E. No 1224/2009, 2009). Food composition databases have become increasingly important in the field of international health, and alphanumerical codes are required to systematically compile these data. In a study performed in Thailand, 1740 food-related alphanumerical codes were developed and end-user data files were created (Puwastien, 2002). In addition, in India, a company began to use a system of alphanumerical codes printed on reusable containers for easy identification and provision of fast service to customers (Dandage et al., 2017). For these alphanumerical codes, which are used in many applications, standardization and labeling costs are two main problems (Aung & Chang, 2014). Public health, the environment, consumer interests, genetic modification, technological advances, and the emergence of new diseases require fast and accurate information. In addition, the increase and continuation of international trade and technological advances in transportation speed increase the risk of diseases occurring worldwide. However, alphanumerical code method, which is easy to read, durable, easy to use, and comfortable technology, does not allow tampering from outside and increases human health and welfare (Barcos, 2001). Accordingly, in the near future, this food traceability technique, which is a harmless process throughout the entire food chain, will be used in many countries.

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9.2.4 Barcodes Barcodes, first patented in Philadelphia in 1952, have become a key technology in food traceability in recent years (Qiao et al., 2013; Tzoulis & Andreopoulou, 2013). They may be in the form of a machine-readable code in numerical form or may consist of a pattern of variable-width parallel lines that can be printed on a product. In particular, Quick response (QR) barcodes are used as an effective way to securely share information (Rewatkar & Raut, 2014; Tzoulis & Andreopoulou, 2013). The QR code is a two-dimensional (2D) matrix barcode that can save both more than 1,800 text information and information such as website URL, calendar event, contact information, geographic location, and are printable at high quality and readable quickly (Rewatkar & Raut, 2014). Developing economy and enhancements in people’s living standards have brought consumers’ attention to safety and high-quality food products. For the use of barcodes, consumers first install 2D barcode reading software on the smart terminals and then scan the 2D barcode on the product packaging. The reading software sends a request to the web service interface provided by the traceability system and returns the traceability information of the product to the terminal after program separation. Thus the consumer can learn about the product (Qiao et al., 2013). Most common traceability systems include RFID electronic labels or one-dimensional (1D) barcodes. For low-cost food products such as vegetables, operating costs increase with RFID due to the requirement of special reader for the RFID tag. Recognition software is necessary to read for both 1D and 2D barcodes; however, 1D barcode does not provide high accuracy of traceability information (Qiao et al., 2013). On the other hand, QR codes and DNA barcodes are more reliable and provide high level of information with high accuracy. As a result of recent advances in genomic science, a number of tools have been introduced that can assist in traceability, thereby improving the DNA barcode (Migone & Howlett, 2012). The DNA barcode is a molecular-based system that allows scientists to identify specific species by comparing short genetic markers in sample DNA with reference sequences. There are some applications of DNA barcodes for the identification and traceability of mammals such as cattle, pigs, lambs, deer, horses, and bird meats such as chicken and turkey (Galimberti et al., 2013). In addition, molecular markers, which can be used to trace the origin of many imported products, such as fish, meat, coffee, palm oil, organic foods, and genetically modified organisms (GMO) foods, prevent illegal production and also can be used to trace an animal from farm to warehouse (Migone & Howlett, 2012). In China, a Wheat Flour Milling Traceability System including 2D barcode and RFID technology was developed (Qian et al., 2012). Barcode systems are modeled not only for raw products but also for processed products such as wine (Bezerra, Pandorfi, Gama, De Carvalho, & Guiselini, 2017; Gogliano Sobrinho, Cugnasca, Fialho, & Guerra, 2010). In 2013, the cattle/beef traceability system model was proposed and evaluated in the supply chain by applying this model (Feng, Fu, Wang, Xu, & Zhang, 2013). At the same time, a traceability system model was developed in the vegetable supply chain based on Unified Modeling Language (Tzoulis & Andreopoulou, 2013). In the studies conducted at ZooPlantLab in Italy, DNA barcodes were used for the characterization and traceability of small crops. It has been also reported that the DNA barcode may be effective in identifying plant species that cause intoxication or poisoning in consumers (Galimberti et al., 2014). In a study, it was determined that DNA barcoding methods are very important and have a high potential for developing an effective seafood traceability framework, sustainable fisheries management, and monitoring potential substitution fraud throughout the food chain. The importance of traceability of seafood is set out in the current EU legal framework (Paracchini et al., 2017; Regulation E. No 1224/2009, 2009). In Brazil, using the principles of the DNA barcode, it was discovered that fish were mislabeled in Japanese restaurants and fish markets, and labeling fraud was prevented (Staffen et al., 2017). In addition, a molecular research revealed that Palombo, a type of fish, is frequently replaced by less valuable shark species. In the test, it was determined that about 80% of the sieved fish products did not correspond to related DNA barcodes (Galimberti et al., 2014). It is stated that the use of molecular tools to characterize and trace is not only for meat products but also dairy products are widely accepted (Galimberti et al., 2013). The use of DNA barcode targeting the mitochondrial regions of DNA for the identification of species is well defined and documented, and the lengths of sequenced fragments required should generally be greater than 600 bp. Genome sequences can be obtained from the NCBI FTP page (Paracchini et al., 2017). The DNA barcode does not require prior genome information for the species being investigated, and analytical procedures can be easily performed by any molecular biology laboratory (Galimberti et al., 2014). The success of the DNA barcode depends on the molecular variability between species and the presence of reference sequences, high-quality DNA sequences of known species (Galimberti et al., 2013). In addition, when full barcodes are very expensive to obtain and identification comparisons are within a limited taxonomic group, 100 bp short sequences, such as short barcodes, have been confirmed to be effective in identifying samples (Hajibabaei et al., 2006).

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The barcode traceability system, which has become a common part of today’s life, is simple and cost-effective (Tzoulis & Andreopoulou, 2013). As a result of several applications, researchers concluded that DNA barcode can be used as a universal tool for food traceability. However, further studies are needed to obtain high-quality reference sequence pools (Galimberti et al., 2013).

9.2.5 Holograms The hologram, a physical structure that distributes an image to light, is a developing tool in the field of intelligent packaging, providing process improvement (Dandage et al., 2017; Demartini et al., 2018; Sohail, Sun, & Zhu, 2018). It is made from holographic foil, an optically variable device and usually a polyester film base. The perception of holographic image from the human eye is ideal for brand promotion and security. When the packages are bent against the light source, the holographic image is revealed. The hologram manufacturer can increase the complexity of the hologram, making it difficult for counterfeiters to reproduce products (Pareek & Khunteta, 2014). The hologram can be used with virtual reality and augmented reality. Augmented reality-based systems can support a variety of services such as instant instruction to mobile devices, while virtual reality applications can be used for virtual prototyping, web-based virtual processing, assembly, diagnostics and learning, and various production processes (Demartini et al., 2018). The industry has used a range of security techniques and fonts, such as microtext, blistering, customized varnishes, holograms made with holographic materials, against counterfeit production, and packaging, which have high annual losses. The hologram is a big and important part of the security label and market and is an ideal choice for product authentication. Hologram offers brand authentication as well as protective features against external interference. If the hologram is attempted to be removed, the top polyester layer will be lifted while the hologram layer remains on the package (Pareek & Khunteta, 2014). So, these labels can be used to provide tamper protection, product reliability, detection, theft prevention, counterfeiting protection, and product traceability (Mlalila, Kadam, Swai, & Hilonga, 2016). Holographic images and logos can keep the originality of the product (Choi, Rogers, & Jones, 2015; Otles & Yalcin, 2008). So, it is an effective solution that empowers consumers, brand owners, and government officials to easily identify original products against counterfeit products (Dandage et al., 2017). There are numerous challenges for small-scale manufacturers in terms of traceability. Difficulties such as low awareness, steep learning curves, and the lack of a rigid regulatory framework for the local market limit their ability to proactively follow the traceability process and thus reduce their participation. Due to the high cost of certification and traceability in the initial stages, not only small producers but also some large corporations or companies do not want to participate in the traceability system. However, supply chain traceability becomes more useful by converting the data obtained by different labeling techniques such as hologram into a cloud-based standardized data that are shared online in different components (Singh, Karthik, Nar, & Piplani, 2017). Thanks to the hologram, one of the effective monitoring technologies, plant personnel can be guided by applying a simple sequence of operations, reducing human errors. Consistent reporting can be achieved at all levels, ensuring safety throughout the entire process and monitoring employee welfare (Dandage et al., 2017; Demartini et al., 2018). In addition, the hologram label allows the tracking of products and tracking of wholesaler activities (Singh et al., 2017). Holograms are often used in the pharmaceutical industry for highly sophisticated pharmaceutical products, but their applications in the food packaging sector are very limited (Sohail et al., 2018). Recently, some researchers and practitioners have suggested that the food industry can also use these technology-enabled features, so teams can work globally, new product ideas become easier, product portfolio expands, packaging and recipe features can be simplified, and production planning and supply chain knowledge can be managed more conveniently (Pinna, Taisch, & Terzi, 2016). Such solutions enable food companies to accelerate innovation, increase profits from product promotions, reduce risks, and ultimately increase competitive advantage (Demartini et al., 2018). Although RFID tags commonly used in the food industry are difficult to emulate, they are not as difficult to imitate as barcodes. Because of their structure, they are easy to copy. Therefore different techniques have been developed to combine hologram labels with barcodes, thereby greatly improving counterfeiting performance without loss of barcode information. For example, companies such as DuPont in Albania have implemented 3D security holographic technology (Vukatana, Sevrani, & Hoxha, 2016). Furthermore, as reported by Agrawal Arun, the secretary of the Authentication Solution Providers Association, a safety hologram should be used in daily household foods such as tea, salt, legumes, spices, and flour to focus on food safety (Dandage et al., 2017). New and efficient traceability systems, such as hologram, can create greater awareness of food quality standards and result in savings at one level of the supply chain, as well as controlling human error (Dandage et al., 2017). With the

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advancement of technology, holograms for food applications are expected to be available in a wider range in the near future (Sohail et al., 2018).

9.2.6 Radio-frequency identification RFID technology is a general term for technologies that use radio-frequency waves to identify an object and is also a flow control technology that provides traceability of products using several simple and inexpensive components in all stages of the production chain in the food industry (Costa et al., 2013; Kumar, Reinitz, Simunovic, Sandeep, & Franzon, 2009). Low frequency (9 135 kHz), high frequency (13.553 15.567 MHz), amateur radio band (430 440 MHz), ultrahigh frequency (860 930 MHz), and microwave frequency (2.45 5.8 GHz) are commonly used by RFID systems. RFID systems have been started to be used for the food industry in recent years in addition to their applications for the payment of tolls, inventory tracking, securing car keys, and labeling products enabling fast payment (Costa et al., 2013). Until recently, many logistics companies have used traditional paper labels for traceability and a tape chart recorder placed in two or three marked boxes per shipment for temperature monitoring. However, the main disadvantage of this system is that the box must be opened for manual reading, and the cost is high (Abad et al., 2009). On the other hand, RFID tags are very small (reading distance of several millimeters), can contain additional data such as product and manufacturer details, and can convey measured data of various environmental factors including temperature and relative humidity (Regattieri, Gamberi, & Manzini, 2007). The tag created from wireless microchips is an isolated system, its materials are aseptic and food compatible, and the connection between the tag and the products can be realized very quickly. In liquids, the tag is usually attached to the storage or packaging system, while the bonding system is highly effective, provided that it is a neutral adhesive, especially for robust products. RFID tags with different forms, such as flexible or rigid, provide a suitable data transfer rate. With the use of these labeling systems, manual scanning and control operations are no longer necessary, thus facilitating handling and storage, enabling effective control of the supply chain. Besides, all operations will be simple and fast, so profit loss is reduced (Abad et al., 2009; Regattieri et al., 2007). Furthermore, RFID readers can separate many different tags in the same area without any human assistance (Van Rijswijk & Frewer, 2012; Verbeke et al., 2007), which makes them very important for monitoring food quality in the supply chain. In this way, RFID enables an increase in information transfer rate and saving production and distribution costs despite of their higher cost (h 0.5 20). All these properties make them more advantageous compared to popular barcodes (Aung & Chang, 2014; Kumar et al., 2009; Pigini & Conti, 2017). An RFID tag, also known as a transponder, consists of a microchip, an antenna, and an encapsulation material and is a small device that can be connected to an object for identification and monitoring of the object. These components have different functionality, that is, the microchip stores data, the antenna transmits and receives data. The microchip and antenna attached to the so-called inner layer are covered with a protective material such as paper, plastic, or film. The size of a tag is usually determined by the size of the antenna because the microchip is usually tiny in size. The tags are housed in a protective housing of many different shapes and sizes. Tags are available on the market that are 0.4 3 0.4 mm2 thinner than a sheet of paper. The size of the stored data can be varied according to the tag type, from a few dozen bits to 32 kb (Costa et al., 2013; Kumar et al., 2009). Various researches have shown that RFID has been successfully applied to real-time monitoring and decision support systems for various products and perishable products (Stankovski et al., 2012; Voulodimos, Patrikakis, Sideridis, Ntafis, & Xylouri, 2010; Wang, Wang, & Yang, 2010). Decathlon, one of the world’s leading sporting goods and equipment retailers, started to use RFID tagging in all distribution centers and stores in 2010 at every stage of the supply chain. This allows Decathlon to monitor products across the entire supply chain, from factories to distribution centers and stores. Heartlands Hospital in the United Kingdom has also established an RFID monitoring system to identify patients with standard RFID tags. With this system, the doctor can automatically view the patient information (Chen, Chen, Yeh, Chen, & Kuo, 2008). Moreover, a wide list of application examples of RFID tags has been reported in the literature in the horticulture, meat, dairy, fishing, bakery, and beverage sectors (Pigini & Conti, 2017). For instance, in Italy, RFID has been used for traceability of pork. Moreover, RFID tags obtained by adding a chemical sensor to the flexible label have been used throughout a fresh fish logistics chain from South Africa to Europe to estimate the freshness or life of the fish and to provide healthy food to the consumer (Abad et al., 2009; Fenu & Garau, 2009). In addition, wafer packaging factories in Taiwan use RFID technology to monitor wafer production. Also, in Japan, the government is working on an “e-Japan” plan and plans to make this food traceability system popular and internationally standardized (Chen et al., 2008).

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The main problems with RFID technology are the high cost of the tags and the lack of standardization, but with the new technologies, prices are constantly falling, and various studies are carried out to determine the working standards (Abad et al., 2009). In addition, some sensor data may be lost or corrupted for many reasons, such as hardware or network problems; however, data mining techniques can be used to prevent these data losses (Alfian et al., 2017). Moreover, an increase was observed in RFID cost from $ 1 billion to $ 4 billion between 2003 and 2008, and this increase is predicted to construct a strong evidence for the possible increase in forthcoming years (Costa et al., 2013). The advantages of RFID technology, its popularity, versatility, and increasing the flow of information to businesses will bring revolutionizing changes in retail and logistics to the sector, and eventually, the Universal Product Code identity that is always known as “barcode” will replace QR code technologies (Costa et al., 2013; Michael & McCathie, 2005).

9.2.7 Nanotechnology Nanotechnology allows us to work at atomic, molecular, and macromolecular levels (on a scale of 1 100 nm) and understand, create, and use new properties of material structures, devices, and systems (Roco, 2003). Nanoparticle-based smart inks, nano-sized barcodes, and reactive nano sheets, which are products of nanotechnology, can be used for different purposes such as traceability, verification, and prevention of adulteration in different sectors, especially in food (Neethirajan & Jayas, 2011). Nanotechnology and the application of nanoparticles in food processing are rapidly emerging. According to reports, five of the world’s top 10 food and beverage companies are currently investing in nanotechnology research and development (Farhang, 2009). The food and beverage companies that are interested in nanotechnology include companies such as Altria, Nestle, Kraft, Heinz, and Unilever. The nano food industry is currently located particularly in the United States, Australia, New Zealand, South Korea, Taiwan, China, and Israel. In addition, all food and food packaging applications are currently managed by the United States, Japan, and China (Chaudhry et al., 2008). Nanotechnology can provide innovative solutions to the latest food safety issues. This new technology is used in many new applications in food processing and food safety. For instance, nanosensors are used to detect pathogens and contaminants to provide food safety. In addition, nanomaterials are developed with superior thermal and mechanical properties for food packaging to improve the protection of foods against external mechanical, thermal, chemical, or microbiological factors (Farhang, 2009). Recent developments in the market include a new generation of packaging materials based on nanomaterials with RFID. These displays are smart labels to help quickly and accurately distribute a wide range of products with limited shelf life. RFID is also available, including polymeric transistors using nanoscale organic thin-film technology. This technology automatically provides information from the product and provides data flow to the relevant system (Chaudhry et al., 2008). Nanopackaging materials integrated with communication devices including RFID, barcode labels, and nanosensors are expected to create a new era in food safety and quality. It is also envisaged that the integration of nano-based communication devices such as RFID tags and barcodes with wireless sensors in packaging materials can be the transforming power of packaging technology and food supply chain (Mlalila et al., 2016). In 2015, South Korean researchers developed a nano-DNA barcode system with products adhering to specific nanoparticles. This new system, which has been developed against the traditional black and white barcode, can contain more information. This nanotechnology-based system can effectively prevent counterfeiting of newly developed nanoparticles with invisible barcodes thanks to nanotechnology, while at the same time providing real source information with food traceability system (Bai & Liu, 2015). Nano-sized barcodes can be used to identify the product source, various important dates, HACCP data, and related files. Nanotechnology can help food industries prove their identity in authentication and monitor the properties of targeted products to prevent counterfeiting or contamination. To assist in tracking and monitoring, nanotechnology can produce complex and invisible nanobarcodes containing aggregated information that can be directly encoded into food products and packaging. This nanobarcode technology also ensures food safety by allowing brand owners to monitor their supply chains without having to share their supply chains with distributors and wholesalers (Ayala-Zavala, Gonza´lez-Aguilar, Ansorena, Alvarez-Pa´rrilla, & de la Rosa, 2014). Looking at the industry, NanoInk, a US-based company, has developed a molding technique called Pen Dip Pen Nanolithography to encrypt information directly into food products and packaging. Addison, also of American origin, has developed and marketed nanoscale markers that can be included in the product packaging. In addition, gold and nickel nanodiscs were made not only in the food industry but also in applications such as pharmaceuticals and DNA detection, as well as biological labels to encrypt information to be used as labels for tracking food products (AyalaZavala et al., 2014).

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Nanotechnology-based monitoring devices can be integrated into multiple functional devices that provide other important information such as sensors for the detection of pathogens, spoilage microorganisms, allergens, chemicals, and other contaminants in foods, as well as the presence and level of nutrients (Chen & Yada, 2011; Neethirajan & Jayas, 2011). In addition, nanoscale labeling devices can be used to record and retrieve information about product history. Such practices will help manufacturers, retailers, and consumers be involved in decision-making process on food safety, food quality, nutritional values, and others (Chen & Yada, 2011). Concerns arise from the lack of knowledge with regard to the interactions of nano-sized materials at the molecular or physiological levels and their potential effects and impacts on consumer’s health and the environment. It should be well known that uncertainty about the potential impacts of new technologies and lack of information or lack of clear communication of risks and benefits may raise public concern (Chaudhry et al., 2008). An important advantage of the manufacturing of nanotechnological products is the minimal use of materials that can reduce production costs (Chen & Yada, 2011). Thus a viewer is developed which can provide molecular traceability of any food quickly and with the naked eye and can use costly reagents. Furthermore, not only traceability but also the accuracy of information related to foodstuffs can be defined with this technology (Valentini et al., 2017). Thus consumers will be provided with real-time information about the quality and safety status of the products, as well as quick recalls when quality and safety standards are followed. Furthermore, traceability systems based on providing detailed documentation about a product in the future may expand to create an increasing demand in the agricultural and other life sciences and food industries. At this point, nanotechnology-based products have significant potential in increasing the speed and sensitivity of traceability in knowledge-based agriculture (Opara, 2003). However, it should be also noted that public trust and acceptance are the key factors that determine the success or failure of nanotechnological applications for the food industry (Chaudhry et al., 2008).

9.2.8 Nuclear techniques Standardization of restrictive government marketing, insecure policies, and unbalanced actions for food security, underdeveloped and unorganized infrastructure, and supply chains in the marketplace have made it necessary to develop an effective food traceability system, especially in less developed countries (Badia-Melis et al., 2015). Existing food labeling systems cannot always guarantee that the food is original, good quality, and safe and may become particularly vulnerable in the event of unconscious or deliberate misuse. Monitoring can only be effective if implemented by a sector that adopts and covers this approach. To ensure efficient product tracking, industries have always needed an easy-toimplement and cost-effective traceability technique (Chen et al., 2017). Nuclear technique is one of the systems that can be used to monitor the production process and the final product to reduce food safety and fraud situations (BadiaMelis et al., 2015). The main features of nuclear technique are the determination of food source and monitoring of food by both genomic and isotopic techniques (Badia-Melis et al., 2015). The term stable isotope is often used when referring to the nuclei of a given element and refers to isotopes of the same element (Chen et al., 2017). Isotopes are atoms of the same element with different masses. Different isotopes of the same element have an equal number of electrons and protons but a different number of neutrons, causing different masses. Stable isotopes are divided into two groups by atomic mass, light (bioelements) and heavy isotopes (Danezis, Tsagkaris, Camin, Brusic, & Georgiou, 2016). In the light isotope group, the most studied ratios are 2H/1H, 13C/12C, 15N/14N, and 18O/16O, while 34S/32S is used less (Chen et al., 2017). In the heavy isotopes group, the most commonly used rate for food authentication is 87Sr/86Sr and more rarely 206 Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, and 143Nd/144Nd (Danezis et al., 2016). Isotopic ratio has emerged as a powerful tool for monitoring the geographical origin of agri-food products in particular. Different degrees of success have been achieved in defining and differentiating agri-food products such as meat, milk, cereal products, wine, and fat (Chen et al., 2017). The analysis of stable isotopes of bioelements has been recognized since the 1990s as formal methods for detecting the adulteration of wine, honey, juice, and maple syrup with cheaper components such as water or sugar syrup. Other examples of isotopic ratio applications include distinguishing natural and synthetic vanillin (Danezis et al., 2016). In another research, isotopic composition was used to determine the geographical origins of wheat samples obtained from different growing regions (Rashmi, Shree, & Singh, 2017). In addition, these new techniques have been used in fruits and vegetables, wheat flour, and can be used not only in agricultural product, but also in different groups of products such as beef and lamb, milk, wine, olive oil, and seafood (BadiaMelis et al., 2015; Chen et al., 2017). In food authentication, isotopic ratios have a significant role due to the effect of climatic conditions, geographic origin, soil properties, etc., on the stable isotope ratios. For instance, isotopic data of H and O, N and C, and S in food

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connected to the geographic origin, climate and agricultural applications, geological properties, volcanism, and some anthropogenic effects (Danezis et al., 2016). Recently, stable isotopic fingerprinting techniques have attracted attention due to their independent nature, which cannot be affected from human factor. Therefore it has become a gradually reliable and useful tool for food traceability, particularly in China (Chen et al., 2017). Food genomics provide many tools for traceability and identification of various products (Chen et al., 2017). DNA can be extracted from different tissues such as blood, muscle, and liver, or highly processed food products can be traced by DNA-based methods due to the high stability of DNA. DNA-based methods include specific amplification of DNA fragments by polymerase chain reaction (PCR; Danezis et al., 2016). Furthermore, nuclear methods are essential for the development of a useful seafood traceability framework and for the protection of fish biodiversity (Paracchini et al., 2017). As it is known, there is a need for traceability systems that provide many different information such as the origin, processing, retail sale, and final destination of foodstuffs. Such systems will increase the confidence of consumers and enable regulatory authorities to identify and withdraw foods that may be harmful to health and cannot be consumed (Schwa¨gele, 2005). In this respect, nuclear technology is one of the most promising systems in food control, which is gaining more and more importance (Chen et al., 2017).

9.3

Traceability analysis

9.3.1 Immunoassays Immunochemical methods provide visual or instrumental measurement of the interaction of antibodies with protein molecules (Miraglia et al., 2004). Immunoassays can be considered as sensitive, dynamic, specific, and low-cost analytical methods for protein detection in complex structures (Brett, Chambers, Huang, & Morgan, 1999). In addition, immunoassays can be used for traceability of allergens, hormones, toxins, and GMO in foods (Stave, 1999). Enzyme-linked immunosorbent assay (ELISA) technique can be highly specialized and precise at low concentrations, and use of kits provide fast and high throughput analysis (Kerbach et al., 2009). Determination of genetically modified soybean in dried soybean powder was investigated by Lipp, Anklam, and Stave (2000) using ELISA. One of the drawbacks of immunoassays for the detection of GMOs is the matrix effect, which alter the process of protein isolation such as homogenization, grinding, or boiling in different solvents, detergents, etc. (Stave, 1999). Different commercial ELISA kits are available for different aims. Lateral flow devices or dipsticks can be applied to get rapid results and ELISA permit semi-quantitative analyses (Chassaigne, Nørgaard, & van Hengel, 2007). In addition, the efficiency of ELISA kits for the detection of allergens may not be identical. Six commercial peanut ELISA kits were evaluated for their performance to recover peanut from standard reference material and their detection ability on four major peanut allergens (Ara h 1, Ara h 2, Ara h 3, and Ara h 6) by Jayasena et al. (2015). This study revealed that sensitivity of kits may be different and proper kit selection may enhance the efficiency of the kits. For instance, sensitivity of five of the kits was found to be the highest to Ara h 3 followed by Ara h 1. However, the other kit showed great sensitivity to Ara h 2 and Ara h 6, which are thermal stable allergens (Jayasena et al., 2015). Furthermore, Gao et al. (2016) developed a sensitive sandwich ELISA method which can be used to detect masked allergens in processed foods. In addition, sandwich ELISA is also used to quantify Gly m 4 which is a key soybean allergen with high variability in varieties grown in different locations (Geng et al., 2015). On the other hand, current developments in ELISA method allow rapid, sensitive, on-site analysis of food allergens. Weng, Gaur, and Neethirajan (2016) developed a microfluidic ELISA platform combined with a custom-designed optical sensor, which decreased the total analysis time up to 15 20 min and sample size to 5 10 μL to detect wheat gluten and Ara h 1. Consequently, developments in immunoassays increase their usage and validity for GMO and allergen analyses.

9.3.2 DNA-polymerase chain reaction methods The weakness of protein-based analysis on the determination of authenticity on heavily processed foods can overcome via DNA-based methods, which can be applied to various food matrices (Galimberti et al., 2013). In addition, analytical methods can be affected from environmental conditions, climate changes, and production techniques; however, DNAbased methods give more accurate results depending on the stability of DNA at different environmental and processing conditions (Madesis, Ganopoulos, Sakaridis, Argiriou, & Tsaftaris, 2014). Methods using particular DNA sequences as

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markers can be split into two groups as (1) hybridization-based markers in which species specific DNA profiles identified and compared with the DNA fragments of a known target; and (2) PCR-based markers (Galimberti et al., 2013). The PCR analysis is basically composed of denaturation of the double stranded DNA at 94 C 96 C for 30 60 s, annealing of the primers to single-stranded template DNA at 45 C 65 C for 30 60 s, and elongation of DNA at 72 C with the Taq DNA polymerase enzyme (Holzhauser, Stephan, & Vieths, 2006). PCR-based methods include the amplification of target loci and separation of fragments via electrophoresis to identify the banding patterns (Galimberti et al., 2013). Restriction Fragment Length Polymorphism, Amplified Fragment Length Polymorphism, and Random Amplified Polymorphic DNA, DNA fingerprinting techniques, are commonly used due to their low cost and easy application compared to other techniques (Simple Sequence Repeat (SSR), Single Nucleotide Polymorphisms, Inter-SSR, Sequence Tagged Sites; Zhang, Zhang, Dediu, & Victor, 2011). The stability of the DNA is the starting point of the DNA-based methods. During the production and storage of the food product, the unity of DNA must be maintained to enable the determination of specific base pair (bp) sequences, which are distinctive for a species (Meyer & Candrian, 1996). Since 1990, PCR has been performed to determine the trace levels of allergenic compounds in foods (Holzhauser et al., 2006). PCR (mostly real time) is an alternative method for the determination of allergic food components below a concentration of 10 mg/kg if there is no option of applying ELISA or if required to analyze multiple allergenic foods (Kerbach et al., 2009). It is easy to determine the allergens in foods ignoring the process effect by PCR methods; however, calibration curves of reference materials are required for quantification based on copy number (Walker, Burns, Elliott, Gowland, & Mills, 2016). Furthermore, traces of soybean in meat products ( . 9.8 pg of soybean DNA) can be determined using matrix normalized real-time PCR approach (Costa, Amaral, Grazina, Oliveira, & Mafra, 2017). The detection of allergens in processed foods is essential. Even proteins are lost, short DNA sequences can be determined in highly processed food products (Kerbach et al., 2009). GMO is defined by European Commission as an organism except human beings in which the genetic material has been changed by unnatural way rather than mating and/or natural recombination (Commission of the European C. Directive 2001/18/EC, 2001). Minimum labeling threshold value can be determined as 0.9% of the GM ingredient for all processed food products (Commission of the European C. Regulation EC No 1830/2003, 2003). Up to now, EU register of GM food and feed includes authorized GMOs for cotton (13), maize (30), oilseed rape (5), soybean (20), and sugar beet (1), which had to be traced through the food and feed supply chain (Commission E., 2019). The minimum threshold limit is only considered after the positive results of the first screening (Anklam, Gadani, Heinze, Pijnenburg, & Van Den Eede, 2002). Modern technologies focus on either the transgenetic DNA inserted or the novel proteins expressed in GMOs (Miraglia et al., 2004). All possible authorized and authorization pending events should be determined in the screening step, which includes assays targeting unique sequences generated during the insertion of the exogenous DNA in the plant genome (Rosa et al., 2016). Traceability of GMO can be achieved via protein-based or DNA-based analytical methods. The difference between protein-based and DNA-based analyses is the ability to detect a genetic modification by DNA-based analysis even if the modified gene is inactive in the cells (Miraglia et al., 2004). In general, there are two approaches as quantitative and threshold to detect GMO in foods (Stave, 1999). Quantitative approach gives percentage of GMOs in the samples; however, threshold approach gives semi-quantitative results to determine the level of GMO above or below a given threshold (Lipp et al., 2000). For instance, a DNA-based screening method on the determination of two genetic elements, the sequence of the 35S promoter is received from the cauliflower mosaic virus, and the nopaline synthase (NOS) terminator from Agrobacterium tumefaciens was published by Pietsch, Waiblinger, Brodmann, and Wurz (1997). Since these 35S promoter and NOS terminator are present in various GMOs, this method cannot identify the specific GMOs in the samples (Lipp, Brodmann, Pietsch, Pauwels, & Anklam, 1999). Gel electrophoresis (GE) technique, commonly used for the detection of amplified or hybridized DNA or RNA, can be used to estimate the quantity and size of the DNA (Miraglia et al., 2004). PCR-Denaturing Gradient Gel Electrophoresis (DGGE) is a useful method due to its capability to provide microbial fingerprints to differentiate samples. PCR-DGGE allows to connect yeast communities present on the fruit and geographical origin of fruits and other harvests using 26S rDNA fingerprinting (El Sheikha et al., 2009). Thus geographical traceability of fruits is achievable. In addition, PCR-DDGE can be used as a global rapid analytical traceability method for fish and fish products as a means of stable biological markers from the specific locations for different seasons (Le Nguyen, Ngoc, Dijoux, Loiseau, & Montet, 2008). This approach is also suitable to determine the origin of cheese and nuts, using the link between their fungal or bacterial communities and their origin (Brereton, 2013). Therefore, fungal or bacterial commodities from different parts of a product, affected from harvesting conditions, production process, and

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storage conditions after harvesting, can be evaluated separately and can be correlated with the environment (Le Nguyen et al., 2008).

9.3.3 Omics Traditional methods are still valid for food traceability; however, new approaches such as genomics, proteomics, and metabolomics are helping to complete the existing methodologies (Ortea, O’Connor, & Maquet, 2016). Use of chromatographic and spectroscopic methods for food traceability together with other approaches is becoming more popular. The definition of proteomics is the large-scale analysis of proteins generally by biochemical methods (Pandey & Mann, 2000). In addition, proteome, the total complement of proteins of a particular biological system at a certain time, can be altered by different stimuli (Ortea et al., 2016). Proteomic analysis can be divided in three different analytical approaches: (1) separation of proteins by two dimensional gel electrophoresis (2D-GE) and mass spectrometric (MS) identification; (2) separation of proteins by liquid chromatography (LC) or capillary electrophoresis and MS identification; and (3) protein microarray technique (Vaidyanathan & Goodacre, 2003). Separation of proteins from food sources may be challenging due to the presence of external proteins. For instance, there are some drawbacks of wine quality control by protein analysis since isolation of proteins from wine samples include proteins from grapes, yeast, bacteria, and fungi that can be traced back to the winemaker or the vineyard and may indicate natural infections or unsuitable handling (Nunes-Miranda, Igrejas, Araujo, Reboiro-Jato, & Capelo, 2013). Zolla, Rinalducci, Antonioli, and Righetti (2008) investigated the proteomic profiles of a transgenic maize variety (event MON 810) in two subsequent generations (T05 and T06) with their respective isogenic controls (WT05 and WT06). The authors analyzed the controls in order to specify the environmental effects on maize. Furthermore, comparing the WT06 and T06 provided information about the effects of DNA manipulation. This study showed the different responses of transgenic and its isogenic controls to the same environment. Thus this study reveals that proteomics can be used to differentiate the traditional and genetically modified products (Zolla et al., 2008). Determination of GMOs in highly processed foods can be impossible in some cases and analysis methods are required to determine GMOs in such highly processed or complex matrices. Determination of GMOs in vegetable oils with changed fatty acid profiles was performed using instrumental analysis such as chromatography and near-infrared spectroscopy (NIR; Anklam et al., 2002). Determination of allergens should be highly specific and selective to detect target proteins at 1 10 mg allergen per kg food (Pedreschi, Nørgaard, & Maquet, 2012). Although determination of allergens in food products can be performed with ELISA and PCR methods in food industries and official food control labs, it is not that easy to determine the allergens in food matrix and processed foods with these methods as they may cause false positives or negatives (Kerbach et al., 2009; Pedreschi et al., 2012). Besides, there are tremendous advantages of MS analysis of proteins in food products: (1) simultaneous determination of various biologically associated molecules; (2) availability of processing stable peptide markers; (3) potential quantification using markers with standard addition or isotopically labeled peptide standards; (4) reproducible and robust analysis; and (5) use of naturally occurring reference materials comprising of different types of food matrix provides highly validated methods (Johnson et al., 2011). The most known approaches for MS-based protein identification are called as bottom-up and top-down (Cunsolo, Muccilli, Saletti, & Foti, 2014). Allergens Ara h 1, Ara h 2, and Ara h 3 constitute more than 30% of the total protein of peanuts (Chassaigne et al., 2007). Determination of allergens in baked goods is difficult due to the matrix and processing effects. Pedreschi et al. (2012) used both targeted (Shotgun proteomics) and nontargeted proteomics to present the difficulties of determining peanut allergens (Ara h 1, Ara h 2, and Ara h 3) in baked cookies. The authors detected peanut allergens at low levels ($10 μg peanut/g matrix) in a processed sample. Ice cream spiked with peanut protein was analyzed using LC/tandem mass spectroscopy (LC-MS/MS) to determine the peanut allergen within a model matrix (Shefcheck & Musser, 2004). This study specified the unique peptide biomarker (Ara h 1) for peanut allergen. Moreover, LC-MS/MS can detect peanut allergens at low concentrations to 24 ppm (mg peanut/kg) within complex components such as phenolics in spices (Vandekerckhove et al., 2017). Chassaigne et al. (2007) investigated the effect of roasting on the detection of allergens via MS analysis. The authors determined specific peptides and peptide modifications using highly sensitive and accurate quadrupole time-of-flight mass spectroscopy (QTOF-MS/MS) that allows detection of food allergens even in processed foods by means of properly selected peptides. This showed the importance of selection of markers in allergen detection. Customers may face with multiple allergens in the final food product due to the contamination during processing and that arises from raw materials. Heick, Fischer, Kerbach, Tamm, and Popping (2011) performed LC-MS/MS method to detect multiallergens (milk, egg, soy, peanut, hazelnut, walnut, and almond) in flour and bread to evaluate the effect

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of baking process. The authors compared ELISA and LC-MS/MS analyses and discovered that both methods are successful to detect peanut, hazelnut, walnut, and almond in processed and unprocessed products. However, LC-MS/MS provided higher sensitivity to milk allergens in comparison to ELISA in processed foods. In another study conducted by Planque et al. (2016), ultrahigh performance LC coupled to tandem mass spectrometry (UHPLC-MS/MS) was used to determine trace amounts of milk (casein, whey protein), egg (yolk, white), soybean, and peanut allergens in different complex and/or heat-processed food products (chocolate, ice cream, tomato sauce, and processed cookies). The detection limits of UHPLC-MS/MS were reported for casein, whey, peanut, soy, egg white, egg yolk as 0.5, 5, 2.5, 5, 3.4, and 30.8 mg/kg, respectively. This study revealed that multiallergen determination can be performed by UHPLC-MS/MS in highly processed foods and complex matrices. Agronomical practices may affect the metabolites of crops (Gallo et al., 2014; Picone et al., 2016). Nuclear magnetic resonance (NMR)-based foodomics presents detailed information on food products from field to fork. Among other methods, NMR is a precise and sensitive technique providing huge information about metabolic profiles of food products with minimum or without sample preparation (Corsaro et al., 2016). The effects of agronomical practices and postharvest conditions on metabolites of grapes can be determined by NMR spectroscopy (Gallo et al., 2014; Picone et al., 2016). Furthermore, NMR-based foodomics can be used to authenticate the grape varieties (Fotakis & Zervou, 2016). Moreover, metabolites detected by High Resolution Magic Angle Spinning NMR Spectroscopy were used for the identification of the mozzarella quality and freshness as well as its origin (Mazzei & Piccolo, 2012). Adulterations of herbs can cause serious problems (Zhao & Li, 2007); hence, differentiation of herbs which have different medicinal effects although they are from closely related species is vital as studied by Gao et al. (2012). In this study, classification of seven Lonicera species flower buds was performed with metabolic profiling by rapid resolution LC combined with QTOF-MS. Trace elements contain data about soil type and environmental growing conditions for agricultural products (Gonzalvez, Armenta, & De La Guardia, 2009). Besides, mineral composition of plants is useful for the determination of geographical traceability of the product. Inductively coupled plasma-mass spectrometry (ICP-MS) analyses of 12 different elements (Li, B, Na, Ga, Rb, Sr, Zr, Nb, Cs, Ba, Sm, and Hf) in 27 different Italian saffron spices from three Italian regions provided information for the geographical traceability of saffron (D’Archivio, Giannitto, Incani, & Nisi, 2014). Besides, spectroscopic fingerprint measurements were performed to classify extra virgin olive oils using UV-Vis spectroscopy by calculation of extinction coefficients (Pizarro, Rodrı´guez-Tecedor, Pe´rez-del-Notario, Esteban-Dı´ez, & Gonza´lez-Sa´iz, 2013).

9.3.4 Isotope ratio analysis Since World War II, isotope ratios, the ratio of atoms of the identical element, which has different number of neutrons, provide information for archeology, geochemistry, medical studies, and food traceability (Felton, 2004). Quite a while, stable isotope analysis has been used particularly for age determination in geographical sciences; however, stable isotope analysis mostly isotope ratio mass spectrometry (IRMS) has been widely used for the traceability of foods (Li, Dong, Luo, Xian, & Fu, 2016). The stable isotopes do not decay over time as radioactive isotopes and protect their abundance over time. In addition, isotopic composition of a natural material hides a “natural fingerprint,” which occurs due to a change in isotopic composition by incomplete turnover processes (Boner & Fo¨rstel, 2004). In several studies, stable isotopic ratio analysis together with other chemical-based methods such as IRMS, ICP-MS, chromatography, and NIR are used for the determination of agricultural products and food authenticity and traceability (Badia-Melis et al., 2015; Kamiloglu, 2019). In stable isotope analysis, mostly hydrogen, carbon, nitrogen, sulfur, and strontium are used to identify the geographical origin of foods (Baffi & Trincherini, 2016). The prominent benefit of using Sr isotopes among other elements is to remain unaltered after food processing (Tommasini et al., 2018). Below, some examples for the use of isotope ratio analysis in food traceability are provided for different food products.

9.3.4.1 Meat Traceability of meat cannot be performed only with the registration system of animals after the slaughtering of the animal and slicing the meat into pieces; therefore natural isotopic composition of a meat can be used to trace the origin (Boner & Fo¨rstel, 2004). Stable isotope analysis to provide information about geographical traceability was first applied in Europe with single element analysis and enhanced with multiple element analysis (Zhao et al., 2014). For instance, geographical origin of a meat can be determined with the tissue water. Stable isotope ratio analysis with hydrogen and oxygen was used to determine the geographical origin of the beef (Schmidt et al., 2005). This idea is based on the cycle

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of the evaporation of meteoric water which was transferred via feed and drinking water to the animal and provides the required information about geographical isotope variation (Zhao et al., 2014). It is reported that tracing back of meat samples to their region was accomplished by analyzing 2H/1H and 18O/16O ratios (Boner & Fo¨rstel, 2004). Similarly, 18 O/16O ratio was used to differentiate the beef samples of cattles from Germany, United Kingdom, and Argentina based on the fact that local ground water traces kept in the tissue water with feeding (Hegerding, Seidler, Danneel, Gessler, & Nowak, 2002). However, this isotope ratio was not able to provide meaningful results for the discrimination of the samples from different regions of Germany. In another study, additional isotope ratios 15N/14N and 34S/32S were also analyzed in order to obtain the information about the soils according to geological composition, atmospheric sulfur deposition, and cultivation, which provides differentiation of local geographical information (Boner & Fo¨rstel, 2004).

9.3.4.2 Cereals Photosynthetic pathways (difference between C3 and C4 plants), age, maturation level of a plant, as well as environmental factors including relative humidity, water stress, precipitation level, and temperature influence δ13C values of a plant (Asfaha et al., 2011; Farquhar, Ehleringer, & Hubick, 1989; O’Leary, 1995; Smith & Epstein, 1971). On the other hand, the crops cultivated in organic farming or conventional farming can be differentiated according to their δ15N values due to the fact that synthetic nitrogen fertilizers derive nitrogen from atmospheric nitrogen (δ15Natm 5 0%), which lowers their δ15N values (Bateman, Kelly, & Jickells, 2005). In 2005, European Commission funded the FP6 project TRACE to develop methods to enhance the use of geochemical markers for the geographic traceability of different cereal samples (wheat [Durum wheat, winter wheat, Emmer wheat, Epuautre, and spring wheat], barley, rye, triticale, oat, and corn) from 10 different sampling sites in Europe between summer 2005 and summer 2007 which provided wide range of information about the suitability of isotope ratio analysis and discrimination of cereals (Asfaha et al., 2011). Determination of geographical origin of durum wheat semolina samples (Canada, Australia, Turkey, North Italy, Central Italy, South Italy) was studied using the natural abundance of isotopic ratio of carbon, oxygen, and nitrogen that indicating the correlation between these isotopic ratios and the geographical origin (Brescia et al., 2002). Another study conducted by Chen et al. (2016) presented results about the differentiation of rice cultivation areas by the isotopic-based traceability method with stable isotopes (δ13C, δ15N, δD, and δ18O). This study revealed that δ13C and δ15N were not able to provide useful information to discriminate the cultivation areas.

9.3.4.3 Olive oil Differentiation of olive oils from Nıˆmes PDO (Protected Designation of Origin) and Morocco was achieved with the analysis of 87SR/86SR ratios of the samples (Medini, Janin, Verdoux, & Techer, 2015). Besides, squalene can be produced from both olive oil and deep-sea sharks. The source of squalene was investigated with the isotope ratio analysis of 13C/12C and 2H/1H by Camin, Bontempo, Ziller, Piangiolino, and Morchio (2010). This study showed the possibility of the differentiation of the origin of squalene by δ13C analysis using IRMS for samples with high purity ( . 80%) and IRMS interfaced to GC/combustion system for samples with lower purity.

9.3.4.4 Dairy products Behkami, Zain, Gholami, and Bakirdere (2017) created a database for cow milk geographical traceability by using isotopic ratio analysis of cattle hair and milk samples. Authors have reported high correlation between δ15N and δ13C in milk and animal tissue (hair), and they were able to separate northern and southern samples. In another study, milk samples from seven regions in Australia and New Zealand were analyzed for the ratios of 13C/12C, 15N/14N, 18O/16O, 34 32 S/ S, and 87Sr/86Sr to determine the applicability of multielement isotope analysis on cow’s milk (Crittenden et al., 2007). The authors have indicated that skim milk and casein fractions of skim milk have different 13C, 15N, and 34S concentrations; however, 87Sr/86Sr ratios remained unchanged in both skim milk and casein as expected. Besides, δ13C values were correlated with the latitude.

9.3.4.5 Wine Discrimination of regions in wine samples was achieved with 87Sr/86Sr ratios (Di Paola-Naranjo et al., 2011). In addition, the isotope analysis δ18O of wine water was reported as an efficient marker for samples from different regions (Dutra et al., 2011). The 2H and 13C fingerprints of ethanol may also provide information to determine the origin of red wines (Gime´nez-Miralles, Salazar, & Solana, 1999). Moreover, Lambrusco wines from different vintages were

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differentiated with the ratio of (Durante et al., 2015).

9.4

87

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Sr/86Sr, and this isotopic ratio was reported to have high correlation with soil samples

Conclusions

Several food traceability techniques including document-based systems, information and communication technologies, alphanumerical codes, barcodes, holograms, RFID, nuclear techniques, and nanotechnology have been developed over the past years. Most of these techniques provide control in situ and during transfer; however, they are not capable of fulfilling all the requirements of today’s food supply chains. Nevertheless, some of these technologies are under testing stage and require great resources, which is not affordable for all the units of the food chain. Therefore in the short term, it is assumed that a continuous traceability during the supply chain is difficult to be achieved. However, in the future, a centralized traceability information system across the supply chain should be established in order to provide accessibility at any time when acting is necessary.

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Chapter 10

Targeted and untargeted analytical techniques coupled with chemometric tools for the evaluation of the quality and authenticity of food products Romdhane Karoui Universite´ d’Artois, UMR BIOECOAGRO 1158, Institut Re´gional en Agroalimentaire et Biotechnologie Charles Viollette, Faculte´ des Sciences JeanPerrin, Lens, France

10.1

Introduction

Undesirable modifications occurring in food products during the technological process and/or storage could be divided into deterioration and spoilage. The deterioration phenomenon refers to changes in quality induced by physicochemical and/or biochemical reactions taking place with or without the intervention of extrinsic factors such as CO2, O2, light, temperature, and so on. The spoilage involves changes in quality due to action bacteria, mold, and yeast known as biological agents (Karoui, 2018). Nowadays, the production of food products has faced many challenges due to changes in consumer behavior and eating habits (Guillemin et al., 2016). Indeed, the consumer is looking for safe products not only with fresh-like and pleasant taste but also with health benefits (limited concentration of pesticide, low percentage of allergies, etc.). Thus food quality and authenticity have become increasingly of great importance for consumers, governments, and the food industry due to the increase in food fraud (Christensen, Nørgaard, Bro, & Engelsen 2006). The term “quality” comprises several properties and characteristics such as physical, compositional, and microbial characteristics that are influenced by numerous intrinsic and extrinsic factors, as among them, there are the season, nutritional status, and so on (Hassoun & Karoui, 2015). Therefore the determination of the quality of food products during processing and storage is a critical attempt due to the presence and interaction between different components (e.g., proteins, lipids, water and carbohydrates). Quality of foods could be determined by different analytical methods including physical (sizes, weight, density, etc.), physicochemical (pH, vitamin content, fat content, etc.), thermophysical properties (enthalpy, etc.), microbiological, rheology (firmness, elasticity, fragility, etc.), sensory (appearance, taste, flavor, texture, etc.), etc. Other techniques are more and more applied in the recent years for the evaluation of structure at the molecular scale including front-face fluorescence spectroscopy (FFFS), ultraviolet-visible (UV-VIS) spectroscopy, nearinfrared (NIR) spectroscopy, mid-infrared (MIR) spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, etc. and microscopic level (X-ray tomography, etc.) since it is well known that structure affected the quality of food products. In addition, these spectroscopic techniques are fast, of relatively low cost, and environmentally friendly, often require little or no sample preparation and are relatively easy to operate and provide a great deal of information with only one test, making them suitable for online and/or atline process control. This chapter provides a comprehensive overview of the application of different analytical techniques coupled with multivariate statistical methods for the evaluation of the quality and authenticity of different food products (Fig. 10.1). An actual example illustrating the utilization of these techniques is also discussed.

Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00010-8 Copyright © 2021 Elsevier Inc. All rights reserved.

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10.2

Rheological methods

The texture is an important parameter for the acceptability of food and food products by the consumer. The rheology refers to the properties and characteristics of solid (hardness, firmness, etc.) and fluid (viscosity, etc.) foods (Table 10.1).

Gas chromatography Sensory analysis Fluorescence spectroscopy

Principal component analysis Factorial discriminant analysis

Validation of the methods

Liquid Chromatography Rheology

Spectroscopic techniques

Food products Reference techniques

Analytical methods

Correlation between 2 data sets Partial least squares regression

FIGURE 10.1 Different analytical techniques coupled with chemometric tools used for the evaluation of food quality and authenticity.

Partial least squares regression

Infrared NIR

MIR

TABLE 10.1 A summary overview of reference methods used for the determination of the quality of food products. Analytical techniques

Objectives

Main results

References

Texture profile analysis

Monitoring the freshness state of whiting fillets kept in different conditions

The hardness, gumminess, and chewiness decreased throughout storage which the authors attributed to the autolytic enzymes that hydrolyze muscle proteins and breakdown the connective tissue, making the muscle softer. No significant difference (P . 0.5) was noted regarding the springiness and cohesiveness parameters

Hassoun and Karoui (2015)

Rheology

Studying the viscoelastic properties of goat and cow skim milks and infant formula gels induced by acidification using Glucono-δ-Lactone

Cow infant formula gels exhibited a significant higher elasticity and breaking stress than goat infant formula gels

Wang et al. (2019)

High-performance liquid chromatography coupled with mass spectrometry

Monitoring duck, goose, and chicken in processed meat products

High ability of the technique to authenticate goose and chicken meat, simultaneously with beef and pork, in the presence of turkey meat

Fornal and Montowska (2019)

Headspace gas chromatography coupled to ion mobility spectrometry

Detection of the adulteration of pure honey (n 5 56) from those adulterated with sugar cane (n 5 71) or corn syrups (n 5 71).

By utilizing orthogonal partial leastsquares-discriminant analysis, models were constructed using 80% of the samples, and the remaining 20% were used for validation. A percentage of correct classification of pure honey from the adulterated ones was of 97.4%

ArroyoManzanares et al. (2019)

Differential scanning calorimetry

Authentication of meat samples coming from free-ranged and caged rabbits

The deconvolution of differential scanning calorimetry signals in the range 30 C 90 C revealed that free-range rearing lead to an increase of myosin amount in meat, with a peak percentage of 8.5 6 1.9 compared to 4.3 6 2.2 in caged rabbit meat

Secci et al. (2019)

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The texture of fish muscle plays a huge role in the quality attributes that depend on several parameters such as hardness, cohesiveness, springiness, chewiness, resilience, and adhesiveness, as well as the internal crosslinking of connective tissue (Cheng & Sun, 2014). Hassoun and Karoui (2016) evaluated the freshness level of whiting fillets kept in different storage conditions. It was found that the hardness, gumminess, and chewiness decreased throughout storage due to the autolytic enzymes, which hydrolyze muscle proteins and breakdown the connective tissue, making the muscle softer. The obtained results were confirmed, later, by Rodrigues et al. (2017), who pointed out an increase of springiness and a decrease of firmness, hardness, and chewiness parameters during the storage of five framed Brazilian freshwater fish species named Pseudoplatystoma corruscans (Pintado), Leporinus freiderici (Piau), Cichla ocellaris (Tucunare´), Prochilodus lineatus (Curimbata´), and Brycon cephalus (Matrinxa˜). These parameters have, also, been used for the determination of chicken breast meat during fermentation and ripening process by using TA-XT2i texture analyzer. The authors observed a gradual decrease in hardness during the ripening process due to the proteolysis of meat proteins, resulting in the generation of a large number of water-soluble substances and the damage of the gel network of proteins. In addition, after ripening, the hardness of chicken breast meat fermented with Penicillium roqueforti declined by 42.7% compared with the control indicating that ripening accelerated the proteolysis of chicken proteins. Recently, Guo et al. (2019) used TA-XT2i texture analyzer to determine the impact of P. roqueforti on texture, microstructure, protein structure, water mobility, and volatile flavor compounds of chicken breast meat during ripening. One of the main conclusions of this study was that the relationship between the structure determined at the molecular level and texture performed at the macroscopic level. Indeed, the reduction in α-helix and the increase in β-sheet structure level induced a decrease in hardness and springiness and an increase in gumminess. The difference in milk composition from various species results in different physicochemical properties of milk products. For example, yogurt made with goat milk is characteristic by a more porous microstructure and softer gel than yogurt produced with cow milk. In addition, variation in structural properties of dairy products made with different protein sources presents a huge consequence on the gastric stage of digestion. In this context, Wang, Shan, Han, Zhao, and Zhang (2019) have studied the mechanical properties of goat and cow skim milk and infant formula gels induced by acidification using Glucono-δ-Lactone. It was found that the complex modulus G* (1 Hz) was greater for skim cow milk and the infant formulas made from cow milk compared to the goat milk. On the other hand, Mohsin et al. (2019) incorporated, for the first time, at the lab-scale synthesized xanthan in camel milk yogurt. It was found that the addition of biosynthesized xanthan affected the most important quality parameters like rheology, viscosity, texture, syneresis, and sensory parameters. The use of biosynthesized xanthan at a level of 0.75% resulted in the best on overall parameters. The reduction in NaCl content in food products has recently received more and more attention from consumers due to its health effects. However, NaCl plays an important role in food quality (sensory, functional properties, etc.), and therefore the diminution of its level in food products is considered a challenge. In this context, Loudiyi et al. (2018b) conducted a research study on 20 Cantal-type cheeses produced with different salt levels: 0.5% NaCl, 1% NaCl, 2% NaCl, 1.5% NaCl/0.5% KCl, and 1% NaCl/1% KCl. One of the main conclusions stated by the authors was that the use of NaCl at a level of 1% or less changed drastically the viscoelastic properties of Cantal cheeses. The physicochemical, rheological, and sensory properties of cheese impact the texture and consumer acceptance of the product. During cheese manufacturing, the glycolysis, lipolysis, and proteolysis, along with other changes, induced some physicochemical modifications. In this context, Ramı´rez-Lo´pez and Ve´lez-Ruiz (2018) studied the effect of the incorporation of goat milk at 10, 20, 30, and 40% in cow milk to produce Panela cheeses. Significant differences in textural and rheological parameters were detected as a function of the milk ratios and the storage time. Based on these results, a partial substitution of cow milk with goat milk in a proportion greater than 30% resulted in cheese with textural, and sensory characteristics and provide a healthy alternative to consumers desiring fresh goat cheese increasing opportunities for local market demands that contribute to the economic sustainability of rural areas.

10.3

Chromatographic techniques

10.3.1 High-performance liquid chromatography The color of food is one of the main criteria that makes it appealing for people to purchase. That is why, in several cases, synthetic colorants were added to the food products and/or edible packaging to improve its appearance. However, exposure to synthetic dye additives in food is dangerous and must be controlled and regulated (Liu et al., 2019). In this context, Liu et al. (2019) developed and validated a technique based on an ultrahigh liquid performance chromatography system—equipped with tandem quadrupole mass spectrometry to determine 20 allergenic disperse dyes in

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foodstuffs and interesting results were obtained. Under optimized conditions, validation results showed excellent linearity (5 1000 μg L21, R2 $ 0.997), limits of detection (LODs, 1.1-10.8 μg/kg), recoveries (60.2-110.3%) and precision (RSDs # 12.6%) for the 20 disperse dyes under investigation. The developed method was successfully applied to analyze 20 disperse dyes in real foodstuffs demonstrating the validity and applicability of the method. Esteki, Shahsavari, and Simal-Gandara (2019) reviewed the use of high-performance liquid chromatography to authenticate different food products. Indeed, the detection of adulteration and mislabeling of food products are a challenge that needs urgent solutions to protect consumers’ rights. For instance, Fornal and Montowska (2019) studied the feasibility use of species-specific peptide-based liquid chromatography-mass spectrometry methods for monitoring duck, goose, and chicken in processed meat products (Table 10.1). The authors pointed out that this technique could be used for monitoring duck, goose, and chicken meat (10 specific peptides), simultaneously with beef and pork (seven peptides), in the presence of turkey meat. Ivanova, Merkuleva, Andreev, and Sakharov (2019) explored the use of highperformance liquid chromatography to detect the presence of hydrogen peroxide, a component widely used for disinfection purposes by food industry enterprises, in milk for the extension of its shelf life. Indirect quantitative evaluation of hydrogen peroxide was carried out according to the amount of triphenyl oxide produced after the reaction between hydrogen peroxide with triphenylphosphine. It has been found that the detection limit of high-performance liquid chromatography is 0.28 mg L21 and the recovery of hydrogen peroxide varied between 97.8% and 103.8%. In the same context, Finete, Gouveˆa, Marques, and Pereira Netto (2015) pointed out the usefulness of the high-performance liquid chromatography for the detection of melamine, used as an adulterant, in bovine ultra-high temperature whole milk. Extracts of milk samples fortified with melamine at three concentration levels, two of which corresponded to the levels established by World Health Organization for melamine in foods, led to an overall mean recovery of 95.4 6 1.2%. The authors considered that this recovery value satisfies the performance criteria established by the Codex Alimentarius for analytical methods, and consequently, the technique could be used for the determination of food residues, demonstrating the usefulness and effectiveness of the proposed method. The separation and quantification of different bovine caseins named α, β, and κ by high-performance liquid chromatography with ultraviolet detection set at 280 nm were optimized and validated in a study conducted by Veloso, Teixeira, and Ferreira (2002). The detection limits were of 0.006, 0.019, and 0.015 mg mL21 for κ-casein, α-casein, and β-casein, respectively, in line with previous findings of Czerwenka, Muller, and Lindner (2010) who used liquid chromatography-mass spectrometry method for detecting a fraudulent addition of cow’s milk to water buffalo milk and mozzarella by using β-lactoglobulin as a marker. Out of 18 commercial buffalo mozzarella samples, 3 samples were found to be adulterated with high levels of cow’s milk. The technique was also used to determine aflatoxins, citreoviridin, deoxynivalenol, fumonisins ochratoxin A, zearalenone, and some metabolites/derivatives in rice, maize-based products, and wheat-based products (Andrade, Dantas, Moura-Alves, & Caldas, 2017).

10.3.2 Gas chromatography Pesticide residues in fruits and vegetables are one of the highest concerns of consumers who need food safety. In this context, Hadian, Eslamizad, and Yazdanpanah (2019) used gas chromatography coupled with mass spectrometry to study 48 pesticide residues from different chemical structures. The pesticide present in 85 fruits and vegetables was extracted with ethyl-acetate, and the obtained extract was cleaned by using high-performance gel permeation column chromatography and solid phase column. The mean recoveries of the pesticides varied in the 81% 136% range, while the detection and quantification limits of pesticide residues ranged from 0.003 to 0.06 μg g21 and from 0.01 to 0.1 μg g21 for the vegetables and fruits, respectively. It was found that pesticide residues were more frequently found in vegetables (65.5%) than in fruits (26.7%). Food products are a very easy target for adulteration practices. In this context, Arroyo-Manzanares et al. (2019) applied headspace gas chromatography coupled with ion mobility spectrometry to detect pure honey (n 5 56) from those adulterated with sugar cane (n 5 71) and corn syrups (n 5 71) (Table 10.1). The authors divided the spectral collection in calibration (80%) and validation (20%). By utilizing orthogonal partial least-squares-discriminant analysis, correct classification amounting to 97.4% of pure honey from the adulterated ones was achieved. Peng et al. (2015) succeeded by using gas chromatography to detect and quantify sesame oil with other vegetable oil of low quality; Indeed, by using the support vector machine algorithm, the prediction results showed that the detection limit for the authentication is as low as 5% in mixing ratio and the root-mean-square errors for prediction varied between 1.19% and 4.29%. In a similar approach, Bratu et al. (2012) assessed the potential use of gas chromatography to authenticate three types of Telemea cheese (cow, goat, and sheep). After the application of fat and performing gas chromatography measurements, the authors applied principal component analysis to the data tables and observed some discrimination

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between samples as a function of ruminant species. Later, Kim, Park, Lee, and Kim (2016) confirmed the obtained results following the use of three markers named fatty acids, triacylglycerols, and cholesterol to detect milk fat adulteration in the 10% 90% range. The fatty acid, triacylglycerol, and cholesterol profiles of the mixtures of milk and nonmilk fat analyzed by gas chromatography indicated that concentrations of the fatty acids with oleic acid (C18:1 n9c) and linoleic acid (C18:2 n6c), triglycerides with C52 and C54, and cholesterol were proportional to the adulteration ratios. Indeed, it was found that pure milk has a higher cholesterol concentration than the adulterated ones. In contrast, oleic acid (C18:1 n9c), linoleic acid (C18:2 n6c), and C52 and C54 contents were found to be lower in pure milk fat than in adulterated mixtures suggesting the use of these parameters as rapid biomarkers for the detection of milk fat adulteration.

10.4

Thermal analysis methods

Thermophysical properties of foods and beverages are very important properties to estimate the processing time for refrigerating, freezing, heating, or drying foods and beverages (Parniakov et al., 2016). Dynamic scanning calorimetry and thermogravimetry are useful tools to determine the enthalpy of phase transition. The change in a food’s enthalpy is utilized to determine the energy that must be added or removed to observe a state change. It is well known that above the freezing point, enthalpy consists of sensible energy, while below the freezing point, enthalpy consists of both sensible and latent energy. Matencio et al. (2019) have used different analytical techniques, namely, dynamic scanning calorimetry and thermogravimetry to determine the impact of the fortification of juice and milk with oxyresveratrol and natural cyclodextrins (α, β, and γ) on their quality. The samples were kept up to 1 month in darkness. It was found that each cyclodextrin presented a similar profile, decreasing up to 100oC due to sample dehydration and degradation B320oC. % proteins, minerals, etc. and has a high digestibility % and nutritional Goat milk is an exquisite source of fatty acids, value, as well as therapeutic and dietary properties. The low percentage of allergies makes goat milk a very promising ˙ and valuable food ingredient, in different food products. In this context, Dolatowska-Zebrowska, Ostrowska-Lige˛za, Wirkowska-Wojdyła, Bry´s, and Go´rska (2019) explored the use of thermal properties including differential scanning calorimetry, pressure differential scanning calorimetry, and thermogravimetry to determine polymorphic forms of goat’s milk fat, oxidative stability, and the percentage composition of goat’s milk chocolate. The use of thermogravimetry and derivative thermogravimetry curves allowed differentiating between goat milk fat and fat extracted from goat’s milk chocolate. Indeed, the authors indicated that both the thermogravimetry and derivative thermogravimetry curves for goat milk fat in oxygen indicated the existence of high-melting triacylglycerols. One of the main conclusions of their study was that chocolate made with goat milk presented hypoallergenic properties, good taste, and slightly better digestibility in comparison with typical milk chocolate. The composition of muscle in protein, fatty acid profiles, as well as color and tenderness plays an important key in the final quality of meat. The quality of meat depends on numerous factors including stocking density, floor type, and animal diet. In this context, Secci, Ferraro, Fratini, Bovera, and Parisi (2019) assessed the potential use of differential scanning calorimetry to discriminate between meat originating from free-ranged and caged rabbits (Table 10.1). A total of 36 rabbits were divided randomly into two groups: (1) housed in open-air cages; and (2) ground free-range. After 62 days of farming, 12 rabbits per group were slaughtered, and the longissimus thoracis and lumborum muscle were analyzed by differential scanning calorimetry. The deconvolution of differential scanning calorimetry signals in the range 30 C 90 C revealed that free-range rearing leads to an increase of myosin amount in meat, being its peak percentage equal to 8.5 6 1.9 against 4.3 6 2.2 in meat from caged rabbits. On the other hand, the differential scanning calorimetry was utilized with success for detecting refined hazelnut oil and high oleic sunflower oil in extra virgin olive oil (Chiavaro et al., 2008; 2009). The incorporation of these oil in the 20% 40% range in extra virgin olive oil showed different profiles confirming previous findings of Wetten, Herwaarden, Splinter, Boerrigter-Eenling, and Ruth (2015) who succeeded to detect sunflower oil in extra virgin olive oil since pure extra virgin olive oil exhibited a cold endothermic peak that was absent in soybean oil. The authors concluded that mixing soybean oil with extra virgin olive oil, in the 2% 10% range, could be detected with success.

10.5

Fluorescence spectroscopy

Fluorescence spectroscopy is a sensitive nondestructive analytical technique allowing to determine the molecular environment of food products. Fluorescence spectra could be used in two modes named classical right-angle fluorescence spectroscopy and FFFS. By using right angle fluorescence spectroscopy, the absorbance of the sample should be less

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than 0.1. At a higher absorbance, a decrease in fluorescence intensity and a distortion of emission spectra are observed. To overcome such problems, FFFS can be used, where: (i) only the surface of the material is illuminated and examined; and (ii) the emitted photons are collected at an angle comprised between 30 and 60 to the surface of the sample (Table 10.2).

10.5.1 Milk and milk products It is well known that intrinsic fluorophores, including the aromatic amino acids and nucleic acids, vitamins A and B2, nicotinamide adenine dinucleotide, and numerous other unknown compounds that are found at a low concentration are present in milk and milk products (Blecker, Jiwan, & Karoui, 2012). Kamal and Karoui (2017) applied FFFS to characterize changes in camel milk following thermal treatments in the 55 75 range from 0.5 to 30 min (Table 10.2). Spectra of nicotinamide adenine dinucleotide, fluorescent Maillard reaction products, and vitamin A were acquired. By applying the principal component analysis, separately, to each data table, only the excitation spectra of vitamin A allowed some differentiation between milk samples according to heat treatment intensity and holding time. Then, the authors applied common components and specific weights analysis to all the fluorescence spectra and a clear differentiation between camel milk samples preheated at 70 C and 75 C from the others was observed confirming previous findings of: (1) Boubellouta and Dufour (2008) who succeeded to discriminate cow milk samples according to heating in the 4 C 50 C range and acidification (pH ranging from 6.8 to 5.1); and (2) Blecker et al. (2012) who observed a clear differentiation between raw and heated milk (60 C and 80 C during 20 min). From the obtained results, the authors concluded that FFFS could be used as a nondestructive tool for differentiating between raw and pasteurized milk confirming previous findings of Ntakatsane, Yang, Lin, Liu, and Zhou (2011) who succeeded to discriminate between milk samples according to heat treatment. In addition, the authors pointed out a high link (R2 5 0.84) between the level of glycation of α-lactalbumin determined by chromatography-mass spectrometry and the emission spectra acquired after excitation set at 360 nm suggesting a high relationship between fluorescence spectra and glycation level. Later, Mungkarndee, Techakriengkrai, Tumcharern, and Sukwattanasinitt (2016) confirmed the obtained results since by using the excitation wavelength set at 375 nm, a clear separation of milk samples according to their heat treatment (pasteurized, sterilized, ultra-high temperature, and recombined milk) and to their type (fermented, soy, and corn milk) was observed. Boubellouta, Galtier, and Dufour (2009) studied the impact of the incorporation of calcium, phosphate, and citrate at different levels on the molecular structure of skimmed milk. It was found that the addition of calcium-induced changes at the molecular level was different from that of phosphate. Wang and Zhang (2011) pointed out the ability of FFFS to differentiate between reconstituted skim milk and fresh milk since an increase in the furosine content was observed with the increase of reconstituted skim milk amount. One of the main conclusions of this study was that FFFS could be used as a nondestructive analytical technique to authenticate milk confirming previous investigations of Mignani et al. (2008) who succeeded to detect the presence of M1 aflatoxin in milk. Indeed, following the scanning of emission spectra after excitation set at 365 nm, the authors observed an increase in the maximum fluorescence intensity located B525 nm with the increase in the level of M1 aflatoxin added to the milk (50, 100, and 250 ppm). Rouissi, Dridi, Kammoun, De Baerdemaeker, and Karoui (2008) utilized FFFS to authenticate milk samples, collected from ewes fed ad libitum with two iso-energetic diets (20% barley, 3% vitamin and mineral premix, and 77% soybean meal or scotch bean) as a function of their lactation period and diet composition (Table 10.2). By applying principal components analysis to the fluorescence spectra, some differentiation between milk samples according to the lactation period and diet composition was noted. Later, the same research group succeeded to differentiate between ewes milk according to their genotype: Sicilo-Sarde and Comisana (Hammami et al., 2010; Zaı¨di et al., 2008) (Table 10.2). Recently, FFFS has been used as a tool for the quality assurance process analytical technology of infant milk formula (Henihan, O’Donnell, Esquerre, Murphy, & O’Callaghan, 2018). Milk powders were produced with ratios of protein, fat, and lactose fixed at 1, 3, and 3, respectively. The milk samples were submitted to different heat treatments (72 C, 95 C, and 115 C) for 15 s. The three batches were kept in 15 6 2 C and 37 6 2 C and spectra were acquired on month 0, 3, 6, and 12. By applying partial least-square regression, successful results were obtained for the prediction of predrying heat treatment and storage time, since root mean square error of cross-validation values of 1.5 months and 6.7 C was observed, respectively. Surface free fats were predicted with a root mean square error of cross-validation range of 0.12% 0.20% (w/w of powder) in rehydrated infant milk formulas. The obtained results confirmed those of Liu et al. (2013) who succeeded to: (1) predict danofloxacin and flumequine levels in ultra-high temperature and pasteurized milk from 2-D fluorescence data and partial least-squares-discriminant analysis regression; and (2) discriminate

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TABLE 10.2 A summary overview of molecular techniques used for the determination of the quality of food products. Analytical techniques

Objectives

Main results

References

Front face fluorescence spectroscopy

Investigation of the impact of heating and holding time (55 75 range from 0.5 min up to 30 min) on the quality of camel milk

- By applying a series of chemometric tools, some differentiations between milk samples were observed with vitamin A spectra

Kamal and Karoui (2017)

- By applying common components and specific weight analysis to all the spectra, a clear differentiation between camel milk samples preheated at 70 C and 75 C from the others was observed Authentication of ewe’s milk according to the genotype, and feeding system

By applying, principal components analysis to the fluorescence spectra allowed the differentiation between milk according to the genotype (Sicilo-Sarde and Comisana) and the feeding system

Hammami et al. (2010); Rouissi et al. (2008); Zaı¨di et al. (2008)

Impact of the substitution of NaCl by KCl on the quality of Cantal-type cheeses

-The level and the nature of salt induced changes at the molecular level of Cantal cheeses

Loudiyi et al. (2017, 2018a)

-During heating (20 C 60 C) and cooling (60 C 20 C) of Cantal-type cheeses with different salts (NaCl and KCl), three independent components were ascribed to coenzyme/Maillard reaction products, tryptophan, and vitamin A, following the use of by applying independent components analysis

Mid infrared spectroscopy

Determine the freshness state of frozen intact horse mackerel (Trachurusjaponicus).

By applying a series of chemometric tools, the authors pointed out that fluorescence spectroscopy allowed reasonable prediction of freshness index since an R2 of 0.89 was obtained and 87.5% of correct discrimination of fish samples was observed

Elmasry et al. (2015)

Authentication of honey

By applying a series of chemometric tools such as the partial least-square regression and support vector machines algorithms, a good classification of honey samples was obtained by the partial least-square-regression model. Total accuracy for calibration and prediction sets was all above 96%

Gan et al. (2015)

Differentiation between Italian extra virgin olive oil collected from different regions (Lombardy, Tuscany, and Calabria)

Correct classification rates of 86 and 96% were observed for monovarietal and mixture of extra virgin olive oil

Sinelli et al. (2008)

Prediction of carbohydrates, ash, and extractives contents of straw

Based on R2, the models for total glycans, glucan, and extractives showed good and excellent prediction

Tamaki and Mazza (2011)

Authenticate meat and meat products: detection of the presence of pork meat in beef meat in the 5% 90% range

By applying the partial least-square kernel with MIR, the ratios of absorbance at 1654 cm21/1745 cm21, 1540 cm21/1745 cm21, and (1395 1 1450 cm21)/1175 cm21 were found to be correlated with pork amount present in beef meat

Abu-Ghoush et al. (2017)

(Continued )

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TABLE 10.2 (Continued) Analytical techniques

Objectives

Main results

References

Visible and near infrared spectroscopy

Detection of the adulteration of milk with water

The established calibration model realized on a set of samples with different adulteration was tested on a validation data set and R2 of 0.96 and root mean square of prediction of 0.01 were obtained

Da Silva Dias et al. (2018)

The authors suggested the use of the prototype to predict the concentration of water in milk samples Prediction of fat in pork meat

The best equations obtained for intact loin in both modes of analysis (full and optimal spectral range) displayed standard error of cross-validation of 1.06% and 1.09% and determination coefficient of cross-validation of 0.69 and 0.77 for fat

Ca´ceres-Nevado et al. (2019)

Monitoring hake (Merluccius merluccius) mince with different thermal histories (fresh, frozen, and cooked)

The modifications of T2 signals were found in terms of changes in relaxation times and relative abundance of the relaxation components

Duflot et al. (2019)

Nuclear magnetic resonance

The relaxation rate of the major component (1/T21) increased significantly upon frozen storage or pH increase, whereas water or NaCl addition had an opposite effect A linear relationship was found for pH and protein concentration with 1/T21

milk samples heated at 70 C, 80 C, and 90 C for different holding times: 0, 5, 10, 15, and 30 min. Different models were established and the best one was observed with an R2 of 0.87 by using tryptophan, Maillard compound, and riboflavin fluorescence spectra. The obtained results were confirmed recently by Panikuttira, Payne, O’Shea, Tobin, and O’Donnell (2019) who succeeded to predict the holding times needed for acidified skim milk coagulum. A strong correlation was observed between the predicted times developed using time parameters extracted from the prototype sensor profiles and the measured storage modulus times extracted from the rheometer (R2 5 0.97, standard error of prediction 5 2.8 min). The FFFS has also been used for studying the quality of milk products such as cheese and butter. Indeed, understanding the structure of cheese, particularly protein and fat structures, the interactions of cheese components during ripening could provide useful information in determining what constituents play a key role in the final quality of the dairy products. In this context, Loudiyi et al. (2017) applied different analytical techniques including synchronous fluorescence spectroscopy to determine the impact of the substitution of NaCl by KCl on the quality of Cantal-type cheese produced at the pilot scale (Table 10.2). One of the main conclusions depicted by the authors was that the substitution of NaCl by KCl modified the molecular structure (intensity, width, and/or position of the fluorescence bands) of cheese samples. The melting temperatures of the model cheese fat from both spectral and rheology data showed similar values indicating a high relationship between the structure of the cheese at the molecular level and the texture. The obtained results were, later, confirmed by the same research group (Loudiyi et al., 2018a) during heating (20 C 60 C) and cooling (60 C 20 C) of Cantal-type cheeses with different salts (NaCl and KCl) underlying the potential of fluorescence spectroscopy in combination with chemometrics, as a fast, nondestructive innovative method that can be applied to monitoring thermal and melting behavior of cheeses (Table 10.2). In another study conducted by Botosoa and Karoui (2013), differentiation between 20 French Emmental kinds of cheese belonging to different brand products was reported following the use of fluorescence spectroscopy.

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Karoui, De Baerdemaeker, and Dufour (2008) investigated the ability of FFFS to predict some physicochemical parameters (pH, fat, dry matter, fat in dry matter, total nitrogen, and water-soluble nitrogen) and melting point of semihard (n 5 3) and hard (n 5 6) cheeses. By applying partial least-squares regression with the leave-one-out cross-validation to the fluorescence spectra, excellent predictions were obtained from the tryptophan and vitamin A for fat (R2 5 0.99 and 0.97, respectively), dry matter (R2 5 0.94 and 0.96, respectively), fat in dry matter (R2 5 0.92 and 0.99, respectively), and total nitrogen (R2 5 0.91 and 0.91, respectively). The vitamin A spectra allowed to predict with the excellent manner the water-soluble nitrogen (R2 5 0.96) and melting point (R2 5 0.97), while only good predictions for these two parameters (R2 5 0.90 and R2 5 0.87, respectively) were obtained from tryptophan spectra. The results for pH were good (R2 5 0.82) and approximate (R2 5 0.76) with tryptophan and vitamin A, respectively. The obtained results were, later, confirmed by Ozbekova and Kulmyrzaev (2017) who succeeded to predict different physicochemical and rheology parameters of semihard cheeses from tryptophan residues and vitamin A spectra. One of the main conclusions of their study was that vitamin A spectra could be used as an intrinsic probe for the prediction of the melting temperatures, moisture, protein, and fat contents and yield stress and flow stress, since R2 values of more than 0.9 were obtained. Cheese could be adulterated with fat originating from the plant. In this context, Dankowska, Małecka, and Kowalewski (2015) assessed the potential of FFFS to detect the presence of plant oils in 21 polish kinds of cheese and 5 cheese-like products. The authors have applied multiple linear regression models to determine the level of adulteration, and the lowest root means square error of prediction (1.5%) and root mean square error of cross-validation (1.8%) were obtained with offset Δλ 5 60. A similar approach was conducted by Ntakatsane, Liu, and Zhou (2013) who adulterated milk fat with vegetable oil in the 0% 40% range using fluorescence spectroscopy and gas chromatography. Following the use of principal component analysis, the authors depicted that pure milk fat and the adulterated ones could be differentiated according to saturated fatty acid profile, tryptophan, tocopherol, and riboflavin. By applying partial least-squares regression to the fluorescence spectra, the lowest detection limit was 5%. The saturated fatty acid gave better prediction performance (R2 5 0.73 0.92) than the unsaturated fatty acid (R2 5 0.20 0.65). Recently, Kokawa et al. (2015) used FFFS to predict the ripening stage, proteolysis index, and free amino acids of 16 cheese samples presenting a ripening time of 7 349 days. By applying partial least-squares regression, the coefficient of determination for cross-validation was 0.93, 0.79, and 0.90 for the maturation time, proteolysis index, and free amino acids, respectively.

10.5.2 Meat and meat products Research on the application of FFFS to determine the quality of meat and meat products has mostly been focused on collagen, adipose tissues, and protein. Wulf, Schneider, Surowsky, Hengl, and Schlu¨ter (2008) assessed the potential use of FFFS to monitor the quality of porcine musculus longissimus dorsi during storage at two temperatures: 5 C and 12 C. Different emission spectra were acquired after excitation sets at 280, 340, and 420 nm. The spectra acquired on meat samples stored at 5 C in the dark exhibited different fluorescence signals 10 days after slaughtering in the 550 750 nm that have been attributed to porphyrin component. Increasing the storage temperature up to 12 C resulted in an increase in the meat fluorescence intensity after 6 days postmortem. Sahar, Boubellouta, Portanguen, Kondjoyan, and Dufour (2009) have explored the potentiality of FFFS to analyze the quality of cooked longissimus dorsi muscle at 237 C for different times (i.e., 0, 1, 2, 5, 7, and 10 min). Synchronous fluorescence spectra were recorded in the 250 550 spectral ranges at different offsets varying from 20 to 160 nm. By applying parallel factors, the authors depicted two components that were attributed to tryptophan and fluorescent Maillard reaction products. The same research group have later succeeded to determine heterocyclic amino acid contents in cooked meat samples (Sahar, Portanguen, Kondjoyan, & Dufour, 2010) and to differentiate samples as a function of cooking temperature and time: 66 C, 90 C, and 237 C for 0, 1, 2, 5, 7, and 10 min (Sahar, Rahman, Kondjoyan, Portanguen, & Dufour, 2016) confirming the previous findings of Gatellier, Sante´Lhoutellier, Portanguen, and Kondjoyan (2009) who pointed out that fluorescence emission spectra acquired after excitation set at 360 nm could be used as a marker of meat oxidation promoted by cooking. In order to determine the best cooking conditions for reducing as much as possible the loss of nutriments, Yamaguchi, Nomi, Homma, Kasai, and Otsuka (2012) studied the relationship between Maillard reaction products and cooking conditions. Three cooking systems in the 0 30 min range were used: pan-broiled meat (200 C), baked meat using a gas oven (170 C), and fried meat. One of the main conclusions of this study was that the excitation/emission wavelengths set at 333/425 nm gave fluorescence intensities 10 times higher than that fixed at 350/450 nm. The authors ascribed this phenomenon to the fluorescent Maillard reaction products that have been suggested to be used as an efficient probe for determining heat treatment applied to meat.

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Durek et al. (2016) monitored the quality of lamb and pork samples during storage at 5 C up to 20 days postmortem. The authors scanned spectra of nicotinamide adenine dinucleotide, protoporphyrin IX, and zinc protoporphyrin. It was found that the fluorescence intensities of nicotinamide adenine dinucleotide are related to microbial contamination on meat surface, while those of protoporphyrin IX and zinc protoporphyrin would be better suitable for a contamination monitoring during meat production. Similarly, Shirai, Oshita, and Makino (2016) used FFFS in the excitation-emission mode for the evaluation of the cleanliness level in meat processing plants. To achieve their goal, the authors have acquired spectra of adenosine triphosphate after excitation set at 286 nm. By applying the second derivative to the spectra, the fluorescence wavelengths related to adenosine triphosphate decreased with the adenosine triphosphate content. In a second step, the authors assessed to predict adenosine triphosphate level and plate count by applying partial leastsquares regression. Good results were obtained since coefficient correlations of 0.87 and 0.89 were observed for the prediction of adenosine triphosphate content and plate count, respectively. The obtained results were confirmed recently by Wu, Dahlberg, Gao, Smith, and Bailin (2018) who succeeded to detect meat spoilage following the monitoring changes occurring in nicotinamide adenine dinucleotide and flavin adenine dinucleotide spectra that are associated with microbial metabolism, the most important process of the bacteria in food spoilage. Aı¨t-Kaddour, Loudiyi, Ferlay, and Gruffat (2018) evaluated the performance FFFS to authenticate three beef muscles. By applying a series of chemometric tools (partial least-squares-discriminant analysis, support vector machine associated with partial least squares, and support vector machine associated with principal component analysis), the authors noted five excitation wavelengths set at 290 (tryptophan), 322 and 335 (vitamin A), 350 (vitamin E), and 382 nm (connective tissue). The best results were obtained after excitation set at 382 nm. The same research group (Aı¨t-Kaddour et al., 2016) assessed the potential use of FFFS to predict total fat and fatty acid composition of beef longissimus thoracis muscles originating from 36 animals of three breeds. By applying partial least-squares regression to the fluorescence spectra and gas-liquid chromatography, the FFFS failed to predict with high accuracy saturated fatty acid, monounsaturated fatty acid, and monounsaturated fatty acid since R2 $ 0.66, R2 $ 0.48, and R2 # 0.48 were observed, respectively. The obtained results were in line with those of Andersen, Wold, Gjerlaug-Enger, and VeisethKent (2018) who depicted the limited ability of FFFS and NIR compared with the Raman spectroscopy to determine the quality of porcine longissimus lumborum. A novel fluorescence strategy for adenosine triphosphate detection based on S1 nuclease, FAM-labeled ssDNA and graphene oxide was used to evaluate the freshness of meat samples (Liu et al. 2019). A linear correlation between the fluorescence spectra and the adenosine triphosphate levels ranging in 20 3500 μM was obtained with a detection limit of 3.2 μM.

10.5.3 Fish and fish products After the death of fish, a series of complicated chemical, biochemical, and microbial processes occur, resulting in a loss of fish quality. In addition, due to its high level of lipid, particularly in polyunsaturated fatty acid, lipid oxidation occurs and generates primary and secondary oxidation products decreasing the quality of fish and fish products. Research studies regarding the application of FFFS as a method for determining the freshness level of fish and fish products are limited. Airado-Rodrı´guez, Skaret, and Wold (2010) monitored the quality of cod caviar paste kept up to 21 days under different conditions (light with 100% N2, dark with 21% O2, and 100% N2). For the fluorescence spectra scanned after excitation set at 382 nm, the authors depicted an intense peak in the 410 500 nm region, especially for samples exposed to light and another one B470 nm; the former peak was attributed to the reaction of unsaturated aldehydes with proteins, while the latter corresponded to the products formed during oxidation. A high correlation was found between fluorescence spectra and sensory and thiobarbituric acid reactive substances values since an R of 0.96 and 0.89 was observed for rancid flavor and thiobarbituric acid reactive substances, respectively. Later, Hassoun and Karoui (2015) confirmed these results during the monitoring of the freshness state of whiting (Merlangius merlangus) fillets stored under different refrigerated conditions (presence/absence of light and partial/total vacuum). By applying principal component analysis to the fluorescence spectra, a clear differentiation between samples as a function of both storage time and condition was observed. Later, the same research group (Hassoun and Karoui, 2016) monitored fillet whiting kept in normal air (control group) and modified atmosphere packaging (50% N2/50% CO2 and 80% N2/20% CO2) up to 15 days at 4 C. The results indicated that modified atmosphere packaging treatment, particularly, 50% N2/ 50% CO2 prolonged the shelf life of fillets by reducing pH values and thiobarbituric acid reactive substances content and retarded the softening of fish texture compared to control group. On the other hand, Eaton, Alcivar-Warren, and Kenny (2012) succeeded to classify two species of shrimp collected from four different countries (Ecuador, Philippines, Thailand, and the United States) following scanning emission

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spectra in the 240 600 nm and excitation in the 230 600 nm. By applying parallel factors and soft independent modeling of class analogy approaches, the authors succeeded to correctly identify the country of origin for 95% of the samples. Elmasry et al. (2015) utilized FFFS to determine the freshness level of frozen intact horse mackerel (Trachurusjaponicus) (Table 10.2). Excitation-emission spectra were recorded on samples at 230 C, and then, vacuum samples were kept in a chamber at a temperature varying between 2 C and 4 C for up to 12 days. The fluorescence spectra were acquired after 1, 4, 7, and 12 days. By applying a series of chemometric tools, the authors noted that FFFS allowed reasonable prediction of freshness index, since an R2 of 0.89 was obtained and 87.5% of correct discrimination of fish samples was observed. The obtained results were confirmed, later, by Karoui, Hassoun, and Ethuin (2017) who succeeded to discriminate between fresh and frozen-thawed fish having different freshness level since the authors found: (1) a high correlation (R . 0.8 for canonical variates 1 and 2) between traditional measurements based on color, textural, and chemical parameters and fluorescence data sets; and (2) out 78 spectra, 72 spectra were well attributed to their groups following the use of factorial discriminant analysis. Recently, Boughattas, Lefur, and Karoui (2019) succeeded to differentiate among skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (Thunnus obesus) canned tunas made with the sunflower oil medium. By applying factorial discriminant analysis to the concatenated spectra data sets, correct classification amounting to 74.6% was observed on the calibration data sets. The ability of FFFS to predict some parameters was also investigated. In this context, Liao, Suzuki, Xu, Kuramoto, and Kondo (2016) succeeded to predict K values by using a fluorescence excitation-emission matrix spectroscopy of 52 Japanese Dace. Indeed, by applying partial least-squares regression, coefficient of calibration, prediction, root mean square error of cross-validation and prediction of 0.93%, 0.92%, 8.68%, and 6.63% were obtained, respectively demonstrating the accuracy of the technique to evaluate the freshness state of fish. The obtained results were in agreement with the findings of Liu et al. (2012) who pointed out the ability of FFFS in synchronous mode to predict pyrene concentrations in the gills of carp fish, since an R of 0.99 was observed between pyrene concentrations (1 1000 mg L21) in n-hexane solution and fluorescence spectra.

10.5.4 Edible oils The quality of olive oil ranges from the high-quality extra virgin olive oil to the low-quality olive-pomace oil. In this context, Sikorska et al. (2008) explored the potential of FFFS to determine the quality of extra virgin olive oil collected from the Coratina cultivar and stored in two conditions: with the presence of light and in dark at two temperatures fixed at 15 C and 25 C. The fluorescence spectra were acquired on months 0, 1, 2, 4, 6, 8, 10, and 12. The authors observed the presence of two maxima (excitation: 290 nm and emission: 320 nm) that have been attributed to tocopherol and a second band with excitation and emission located at B405 and 670 nm that were ascribed to the pigments of the chlorophyll group. In a similar approach, Herna´ndez-Sa´nchez, Lleo´, Ammari, Cuadrado, and Roger (2017) monitored the evolution of commercial extra virgin olive oil, belonging to different cultivars (Arbequina, Hojiblanca, blend, etc.) and kept in dark or transparent bottles by using a prototype. One of the main conclusions of this study was that the emission of chlorophyll could be used to determine the level of oxidation of olive oil. On the other hand, synchronous fluorescence spectroscopy was used to determine the quality of frying oil (Tan et al., 2017). By applying partial least-squares regression with leave-one-out cross-validation, a high correlation was obtained (R2 . 0.96). Poulli, Mousdis, and Georgiou (2009) determined the impact of heat treatment at 100 C, 150 C, and 190 C for 30 min, 2 h, and 8 h on the quality of edible oils: extra virgin olive oil, pomace, sesame, corn, sunflower, and soybean oils and a commercial blend of oils. In spite of the relatively low duration of heat treatment of edible oil (8 h), the authors succeeded to differentiate samples according to the oil type. In a similar approach, Tena, GarcıaGonzalez, and Aparicio (2009) monitored the quality of virgin olive oil during 94 h of heating and observed a decrease in the bands located between 630 and 750 nm, associated with chlorophylls observed during heating time, in agreement with the findings of Mishra, Lleo´, Cuadrado, Ruiz-Altisent, and Herna´ndez-Sa´nchez (2018) who, by monitoring the level of oxidation of three cultivars (Arbequina, Picual, and Cornicabra) during storage, observed a decrease in the fluorescence intensity at 671 nm due to the degradation of chlorophyll and an increase in the fluorescence intensities at 420, 440, 464, and 515 nm due to the formation of oxidation products. Argan olive oil is rich in vitamin E and essential fatty acids and used for its both nutritive and cosmetic properties. Addou, Fethi, Chikri, and Rrhioua (2016) used FFFS to differentiate between pure and adulterated argan oil (up to 27% of adulterated argan oil). By using an excitation wavelength set at 532 nm, the sensitivity of the FFFS to detect the adulteration of argan oil was 0.43%. The obtained results confirmed previous findings of Dankowska and Małecka (2009) who succeeded to detect adulteration of extra virgin olive oil by four olive oils, where adulteration range was

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fixed in the 3% 25%. Recently, Uncu and Ozen (2019) succeeded, by using different analytical methods, namely fluorescence, infrared, and VIS, to both detect and quantify adulteration of fresh olive oils adulterated with old olive oils from the previous harvest in the 10% 50% range (v/v). Indeed, over 90% of correct classification rate was obtained.

10.5.5 Cereals and cereal products Botosoa, Che´ne`, & Karoui (2013a) monitored lipid oxidation of sponge cakes produced at the pilot scale during storage up to 20 days (i.e., 1, 3, 6, 9, 16, and 20 days). The authors observed some modifications in the tryptophan spectra located at 382, 435, and 467 nm. The former band corresponds to the maximum emission of tryptophan, while the two latter were ascribed to the fluorescent Maillard reaction products. The authors observed a high correlation between tryptophan spectra and texture determined by texture profile analysis (R2 5 0.99), suggesting that the modifications occurring at the molecular level induced changes at the macroscopic level. Later, the same research group investigated other fluorescent probes (Botosoa et al., 2013b). By applying the principal component analysis to the fluorescence spectra, a clear differentiation between cake samples according to their age was observed. In addition, a strong correlation (R2 5 0.73) was obtained between the content of anisidine determined by physicochemical measurements and the fluorescence intensity at 521 nm. Lenhardt et al. (2017) analyzed 88 flour samples belonging to 34 wheat, 18 corn, 5 rye, 9 rice, 4 oats, 5 spelt, 4 barley, and 9 buckwheat by using FFFS. The spectra were scanned after excitation set at 255 420 nm and emission in the 300 600 nm. The samples exhibited fluorescence band in the: (1) 300 410 nm emission range for 255 305 nm excitation; and (2) 380 490 nm emission range for 320 400 nm excitation. By applying parallel factors, four components were observed: the first component presented excitation and emission maxima set at 270 and 350 nm, respectively, while the third component had excitation maximum set at 290 nm and emission maximum at 371 nm. These bands were ascribed to the aromatic amino acids and nucleic acids. The second and the fourth components with excitation and emission maxima set at 340 and 370 nm and 426 and 458 nm, respectively; the authors ascribed these bands to tocopherol, pyridoxine, and 4-aminobenzoic acid, respectively. On the other hand, Wold, Airado-Rodrı´guez, Holtekjølen, Holopainen-Mantila, and Sahlstrøm (2017) used different excitation wavelength characteristic of pericarp (excitation: 470 nm; emission: 490 650 nm), aleurone (excitation: 350 nm; emission: 400 600 nm), and endosperm (excitation: 280 nm; emission: 300 540 nm), for determining the nutritional quality of barley. By applying partial least-squares regression to the fluorescence spectra and phytic acid determined by the reference method, the authors stated a high correlation with R2 of 0.96 and prediction errors of 6 0.16 2 0.18/100 g, suggesting the ability of the method to predict phytic acid. In another approach, Ahmad, Nache, Waffenschmidt, and Hitzmann (2016a) succeeded by using excitation and emission spectra in the 270 550 nm and 310 590 nm, respectively, to predict the middle curve of farinogram with R2 of 0.75. The obtained results were in line with the findings of the same research group who succeeded to: (1) predict rheological and baking parameters of 12 wheat flours (Ahmad, Nache, Waffenschmidt, & Hitzmann, 2016b); and (2) detect low-levels of gluten in cereal products, since high correlation coefficient (R 5 0.9) was obtained (Ahmad, Nache, & Hitzmann, 2017).

10.6

Mid-infrared spectroscopy

The MIR region corresponds to the 4000 400 cm21. Absorptions in the fingerprint region are mainly caused by bending and skeletal vibrations, which are particularly sensitive to large wavenumber shifts, thereby mitigating against unambiguous identification of specific functional groups. The application of MIR, in combination with chemometric tools, to determine the quality of food and food products has increased in the last decade. The development of Fourier transform (FT) MIR affords the possibility to obtain unique information about protein, fat, carbohydrate levels, etc. in different food products.

10.6.1 Dairy products Nowadays, there is a need for the dairy processing industry to have rapid tools for real-time control of production lines to check whether in-process material, during a given processing step, meets the necessary compositional or functional specifications to reach a predetermined quality standard in the final product. In this context, the MIR spectroscopy is considered as fast, is of relatively low cost, and provides a great deal of information with only one test. Karoui, Hammami, Rouissi, and Blecker (2011) assessed the potential of MIR to differentiate between ewe’s milk samples according to their feeding systems. Some differences were observed in 3500 2800, 1700 1500, and

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1500 900 cm21. To extract information from the data tables, the authors applied factorial discriminant analysis and 71.7% of the milk samples were correctly classified according to the feeding system. The obtained results confirmed the previous findings of Maaˆmouri et al. (2008) who succeeded by combining the 3000 2800 cm21 and 1500 900 cm21 to differentiate between milk samples of ewes fed soybean meal from those fed scotch bean meal. The obtained results were confirmed, later, by: (1) Valenti et al. (2013), who pointed out the capability of MIR to distinguish milk from hay- and pasture-based systems and those from maize silage- and pasture-based systems; however, the same research study stated that MIR failed to discriminate milk samples collected from the upland and lowland regions; and (2) Capuano, Elgersma, Tres, and van Ruth (2014), who succeeded by using FT-MIR coupled with partial leastsquares-discriminant analysis to discriminate between milk collected from cows fed fresh and aged grass and organic milk from conventional milk. The adulteration of milk becomes more and more sophisticated, since different components could be added. In this context, Cassoli, Sartori, Zampar, and Machado (2011) studied some adulterated raw milk with three different compounds: sodium bicarbonate, sodium citrate, and nonacid cheese whey by MIR in the 1000 5000 cm21 spectral region. It was found that the MIR was able to detect milk adulteration with 0.05% and 0.075% of sodium bicarbonate and citrate sodium, respectively. However, the low sensitivity of MIR was observed when milk samples were adulterated with nonacid whey, even when added at high concentrations (20%). By using the same approach, Santos, Pereira-Filho, and Rodriguez-Saona (2013) succeeded, by applying soft independent modeling of class analogy, to detect and quantify adulterated milk with whey, urea, hydrogen peroxide, synthetic urine, and synthetic milk in the 1700 1500 cm21. The obtained results were in agreement with those of: (1) Jawaid, Talpur, Sherazi, Nizamani, and Khaskheli (2013), who accomplished by using FT-MIR to detect melamine at 2.5 ppm in liquid and powder milk samples; (2) Nicolaou, Xu, and Goodacre (2010), who achieved to detect the binary and tertiary milk mixtures composed of sheep’s, goat’s, and cow’s milk; (3) Mauer, Chernyshova, Hiatt, Deering, and Davis (2009), who succeeded to detect the melamine at 1 ppm in infant formula powder; and (4) Aparecida de Carvalho et al. (2015), who detected the presence of cheese whey in milk powder. Recently, Liu, Ren, Liu, and Guo (2015) achieved, by using a new comprehensive index, called Q, to differentiate between artificial adulterated milk from the nonadulterated ones. One of the main conclusions of this study was that index Q could accurately detect milk adulteration with maltodextrin and water (as low as 1.0% of adulteration proportions) and with other nine kinds of synthetic adulterants (as low as 0.5% of adulteration proportions). The obtained results confirmed the findings of Jaiswal et al. (2015), who succeeded to detect the presence of soymilk in milk at a level of 2% and to detect the presence of margarine adulteration in butter (Koca, Kocaoglu-Vurma, Harper, & Rodriguez-Saona, 2010). The authors used soft independent modeling of class analogy in the 2800 3040 and 900 1800 cm21, and a distinctive band located at B1741 cm21 allowed to differentiate between butter and margarine. The obtained results were confirmed later by: (1) Gori, Cevoli, Fabbri, Caboni, and Losi (2012), who pointed out the ability of MIR to discriminate between butter samples according to their production seasons; and (2) Bassbasi, De Luca, Ioele, Oussama, and Ragno (2014), who succeeded to authenticate Moroccan butter collected from different regions. Boubellouta, Karoui, Lebecque, and Dufour (2010) studied by different analytical techniques Saint-Nectaire PDO and Savaron cheese made by different manufacturing and ripened in different conditions. The authors achieved to discriminate between the cheese samples in accordance with the findings of Botosoa and Karoui (2013) who succeeded to differentiate between French Emmental cheese of different brands. A similar approach sampling was used by LermaGarcıa, Gori, Cerretani, Simo-Alfonso, and Caboni (2010), who applied MIR to classify Italian Pecorino cheeses according to their ripening stage and manufacturing process (Fossa and nonfossa cheeses). By considering three categories of cheeses (hard nonfossa, hard fossa, and semihard nonfossa), an excellent resolution was achieved according to both ripening time and manufacturing process. A model based on the use of linear discriminant analysis was established and good results were obtained suggesting the use of MIR as a rapid tool to authenticate cheeses. The infrared spectroscopy has been used to predict fat content by varying the level of fatty acid chain length and unsaturation (Kaylegian et al., 2009). The obtained results were confirmed, recently, by Margolies and Barbano (2018), who predicted some physicochemical parameters including fat, protein, moisture, and salt contents of Cheddar blended with a dissolving solution containing pentasodium triphosphate and disodium metasilicate to achieve a uniform, particle-free dispersion of cheese. The authors applied partial least-squares regression to the MIR spectra, and standard error of prediction values of 0.28, 0.23, 0.036, and 0.19 was obtained for respective moisture, fat, salt, and protein confirming previous findings of Ferrand-Calmels et al. (2014), who succeeded to predict fatty acid composition of 349 cow, 200 ewe, and 332 goat milk samples. The authors concluded that the fatty acid composition of milk could be determined by using MIR, a particular fatty acid present in medium and high concentrations, since the coefficient of determination higher than 0.9 was noted following external validation.

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10.6.2 Meat and meat products Meat and meat product is a highly perishable product and poses health threats when its quality and safety are not determined. In this context, MIR has been utilized with success to determine the poultry meat spoilage (Ur Rahman, Sahar, Pasha, ur Rahman, & Ishaq, 2018). On the other hand, Abu-Ghoush et al. (2017) used MIR in the 400 4000 cm21 to detect the presence of pork meat in beef meat in the 5% 90% range (Table 10.2). By applying the partial leastsquares-Kernel, the ratios of absorbance at 1654/1745, 1540/1745, and (1395 1 1450)/1175 cm21 were found to be correlated with pork amount present in beef meat confirming previous investigations of: (1) Zhao, Downey, and O’Donnell (2014), who succeeded to differentiate between fresh and frozen beef offal adulterants containing heart, liver, kidney, and lung, since 100% of correct classification was obtained; and (2) Alamprese, Casale, Sinelli, Lanteri, and Casiraghi (2013), who compared different analytical techniques for their capability to detect minced beef adulteration with turkey meat. Following the application of a series of descriptive and predictive chemometric tools, the MIR and NIR techniques were found to give the best results confirming previous investigations of Meza-Marquez, Gallardo-Velazquez, and Osorio-Revilla (2010), who by using MIR succeeded to detect and quantify the adulteration of mincemeat with horse meat, fat beef trimmings, and textured soy protein. On the other hand, the MIR has been used to predict some physicochemical parameters of meat and meat products. Lozano et al. (2017) succeeded to predict fat (R2 5 0.92) and protein (R2 5 0.75) levels. In addition, bands B2925, 2854, and 1746 cm21 were found to be in connection to fat, whereas those B3288, 1657, and 1542 cm21 are associated with proteins.

10.6.3 Cereals and cereal products Starch could be submitted to different transformation during both heating and cooling. In this context, Dewaest et al. (2016) have employed MIR to native potato, maize, waxy maize, wheat and pregelatinized (potato and maize) starches that cover a wide range of amylose/amylopectin ratios and crystallinity levels. Each suspension was heated from 30 C to 110 C at 4 C min21 and spectra were collected in the 4000 600 cm21. By applying independent component analysis to MIR spectra, five bands located B993, 1003, 1014, 1024, and 1047 cm21 were observed. One of the main conclusions of this study was that four peaks were found to be linked to the expected evolution of the amorphous (993 and 1003 cm21) and ordered (1014 and 1024 cm21) starch molecules indicating that MIR has the capability to monitor changes during heating and cooling of starch. Recently, different barley varieties collected from different localities and harvested in 2009 (n 5 18), 2012 (n 5 18), 2014 (n 5 63), and 2015 (n 5 63) growing seasons were studied by Porker, Zerner, and Cozzolino (2017) in the 4000 375 cm21. By applying a series of chemometric tools named soft independent modeling of class analogy, linear discriminant analysis and partial least-squares-discriminant analysis, acceptable to excellent differentiation between barley varieties, originated from a specific location was observed. Later, Kuhnen, Ogliari, Dias, and Fernando (2010) confirmed these findings, since the authors accomplished to differentiate between 26 maize landraces originating from southern Brazil by considering two spectral regions: 1650 1500 and 3000 600 cm21. On the other hand, the MIR was used to predict acid detergent fiber, neutral detergent fiber, and total nitrogen in triticale, peas, and mixtures of triticale/pea (Calderon, Vigil, Reeves, & Poss, 2009). The MIR succeeded to predict total nitrogen (R2 5 0.94 and 0.88 for calibration and validation, respectively) and acid detergent fiber (R2 of calibration and validation was of 0.87 and 0.82). However, the less successful result was obtained for neutral detergent fiber, since R2 values of 0.80 and 0.65 were observed for the calibration and validation, respectively. Tamaki and Mazza (2011) explored the use of MIR to predict carbohydrates, ash, and extractives contents in straw samples. Based on R2, the models for total glycan, glucan, and extractive showed good to excellent prediction, while the xylan model indicated good and acceptable predictive performance (Table 10.2). However, the MIR failed to predict galactan, arabinan, and mannan components and predict ash with an approximate level.

10.6.4 Edible oils The ability of MIR to discriminate olive oil according to their quality was investigated by different researchers. Indeed, Galtier et al. (2008) have studied the profile of virgin olive oil according to their composition and geographic origin (“Aix-en-Provence,” “Haute-Provence,” “Valle´e des Baux de Provence,” “Nice,” “Nıˆmes,” and “Nyons”). By applying a series of chemometric tools, the clear discrimination of samples was observed confirming the results obtained with gas chromatography and high-performance liquid chromatography.

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Sinelli, Casiraghi, Tura, and Downey (2008) explored a series of analytical techniques to differentiate between Italian extra virgin olive oil originating from different regions (Table 10.2). The MIR showed its ability to discriminate between monovarietal and mixture of extra virgin olive oil, since overall correct classification rates of 86% and 96% were observed. However, the authors stated that the NIR showed high potentiality to differentiate extra virgin olive oil than MIR, which is in agreement with the findings of Bevilacqua, Bucci, Magrı, Magrı‘, and Marini (2012) who found better results with NIR than that with MIR. On the other hand, Borra`s et al. (2016) assessed the potential use of MIR for determining some positive attributes (fruitiness, bitterness, pungency, grassy green, astringent, sweet, and apple) and defective attributes (musty, winey-vinegary, fusty, rancid, and metallic). In their study, 343 olive oil samples collected during four consecutive crop years from 2010 to 2014 were analyzed by sensory and MIR spectra. The authors applied partial least-squares regression to MIR and mass spectrometry, and the best results were obtained for two positive attributes, namely, fruity and bitter and two defective descriptors (fusty and musty). The obtained results could be considered as promising, since the legal categorization of virgin olive oil only requires the determination of fruity and defective descriptors. Pizarro, Esteban-Dıez, Rodrıguez-Tecedor, and Gonzalez-Saiz (2013) examined the use of different spectral regions (3737.8 3316.1, 2745.3 2501.4, 1422.8 1293.7, and 1044.2 547.9 cm21) to predict the oxidation level of extra virgin olive oil and interesting results were obtained. Machado et al. (2015) continued this work by assessing the potential of MIR to predict phenol content and antioxidant activity in olive fruits and oils from “Cobranc¸osa” cultivar. By using partial least-squares regression, the authors established regression models based on the use of two spectral regions 3050 2750 and 1800 790 cm21.

10.6.5 Sugar and honey FT-MIR has proved to be a promising screening method to authenticate honey samples according to their geographical and/or botanical origin. Etzold and Lichtenberg-Kraag (2008) collected 1075 honey samples collected between 1999 and 2005 and originating from the beekeepers of the eastern part of Germany. The authors explored: (1) monoforal honey from rape (Brassica spp.), lime tree (Tilia spp.), false acacia (Robinia pseudoacacia), heather (Calluna vulgaris), cornflower (Centaurea cyanus), clover (Trifolium spp.), sunflower (Helianthus annuus), and honeydew honey; and (2) multifloral honey and blends of the monofloral. By applying principal component analysis to the spectral collection, differentiation between most of the honey samples from rape, false acacia, heather, and honeydew in consideration of the physical chemical and sensorial properties was observed. The obtained results were confirmed by Gan et al. (2016) who employed electronic nose, electronic tongue, NIR, and MIR techniques to authenticate 259 honey samples (105 and 154 adulterated ones) (Table 10.2). By applying a series of chemometric tools, a clear classification of honey samples was obtained by electronic nose and electronic tongue analysis by support vector machine model and NIR and MIR analysis by partial least-squares regression model. Total accuracy for calibration and prediction sets was all above 96% in NIR, MIR, and electronic tongue by partial least-squares-discriminant analysis model, in agreement with the findings of Sultanbawa et al. (2015), who by using MIR succeeded to predict methylglyoxal content (R2 5 0.75) of honey supporting the investigation of Herna´ndez, Vela´zquez, Revilla, Abarca, and Martınez (2015). Indeed, the latters succeeded to detect and quantify oxytetracycline and sulfathiazole contamination in honey samples that was in line with the investigationsof Das et al. (2017), who used two analytical techniques named MIR and electrical impedance spectroscopies to detect the presence of sucrose syrup, varying in the 10% 70% level, in honey samples. One of the main conclusions of this study was that the band at 1056 cm21 was present in pure and adulterated honey samples and proposed this band for the detection and quantification of adulterated honey with syrup, since the intensity of this band increased with the increase in syrup content in pure honey.

10.7

Visible and near-infrared

10.7.1 Milk and dairy products Milk can be adulterated by the addition of water, which is one of the oldest and most obvious ways. In this context, Da Silva Dias, Da Silva, De Souza Maudeira Felicio, and De Franca (2018) detected with success the presence of water in milk by using a prototype in diffuse reflectance mode since R2 of 0.96 and root mean square of prediction of 0.01 were obtained, suggesting the use of the MIR prototype to detect the concentration of water in milk samples (Table 10.2). In a similar approach, Karunathilaka, Yakes, He, Chung, and Mossoba (2018) falsified milk powder with different adulterants that are classified into four categories: (1) low molecular weight and nitrogen-rich compounds; (2) plant proteins; (3) inorganic salts; and (4) nonfat solids. A series of 0.0% 2.0% (w/w) gravimetric blends of milk powder and the low molecular weight, nitrogen-rich compounds, or inorganic salts were prepared, and NIR spectra were scanned by using

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two benchtops FT-NIR spectrometers and a handheld NIR device. Classification models yielded 100% sensitivities for the benchtop spectrometers. Better specificity was obtained for the benchtop FT-NIR instruments than for the handheld NIR device, which suffered from lower spectral resolution and a narrower spectral range. One of the main conclusions of this study was that FT-NIR spectroscopy coupled with soft independent modeling of class analogy classification models could be considered as a promising tool for the rapid screening of commercial milk powder for the detection of potential adulteration. These results confirmed previous findings of Mabood et al. (2017) who succeeded to detect the presence of cow milk in camel milk by using FT-NIR combined with multivariate methods. The partial least-squaresdiscriminant analysis model was built to check the discrimination between the pure and adulterated camel milk samples and interesting results were obtained since R2 of 0.97 was obtained. Regarding the results obtained with partial leastsquares regression, a root means square error of cross-validation of 1.76% was obtained indicating the ability of NIR as a rapid method to detect the presence of cow milk in camel milk. This result confirms the previous investigations of Chen, Tan, Lin, and Wu (2017), who pointed out the ability of NIR combined with chemometric tools to detect the presence of melamine in milk. The comprehension of the structure of cheese and the interaction between the different components during the technological process and ripening provide useful information on determining what defines a product with good quality and make it possible to predict quality attributes before entering the market. In this context, Sustova, Mlcek, Luzova, and Kuchtik (2019) monitored the acidity of different cheese varieties and successful results were obtained. Indeed, for the prediction of pH values determined by NIR, the correlation coefficient of 0.97, 0.98, and 0.98 was observed for blue cheese, Olomouc curd read smear cheese, and fresh goat cheese, respectively. A study conducted by Woodcock, Fagan, O’Donnell, and Downey (2008) reviewed the ability of NIR and MIR to determine the quality and authenticity of cheese. The authors pointed out the usefulness of NIR coupled with chemometric techniques, for monitoring coagulation, syneresis, and ripening as well as the determination of authenticity, composition, sensory, and rheological parameters, in agreement with previous investigations of Pillonel et al. (2003) who have used NIR among other techniques to discriminate 20 Emmental cheeses produced during winter season and originating from different European countries. By applying a series of pretreatment to the spectra (normalization and second derivative) and chemometric tools, the authors pointed out the ability of the NIR to discriminate Emmental cheeses according to their geographic origin.

10.7.2 Meat and meat products NIR spectroscopy among other techniques is used for processing control in the meat industry. Indeed, recently, Nolasco-Perez et al. (2019) utilized a portable NIR spectrometer, NIR hyperspectral imaging, and red, green, and blue imaging to discriminate between different meat species: chicken, beef, and pork. In their study, the authors adulterated chicken breast meat with either pork leg meat or beef round meat from 0% to 50% (w/w). By applying partial leastsquares regression, the NIR hyperspectral imaging gave the best results for chicken adulterated with: (1) pork since with a coefficient of prediction of 0.83, ratio performance to deviation of 1.96, and the ratio of error range of 10.0; and (2) beef with a coefficient of prediction of 0.94, ratio performance to deviation of 3.56, and the ratio of error range of 18.1. One of the main conclusions of their study was that NIR spectroscopy and red, green, and blue imaging could be used as rapid, online inspection of ground meat in the food industry. These findings are in line with those of Zheng, Li, Wei, and Peng (2019), who have used VIS-NIR hyperspectral imaging in the 400 1000 nm range to detect the adulteration minced lamb with duck meat. By applying the partial least-squares regression, R2 of 0.98 was observed indicating the potential use of the hyperspectral technology to rapidly and accurately detect the minced lamb meat with duck meat, in agreement with previous findings of Alamprese, Amigo, Casiraghi, and Engelsen (2016) who studied by FTNIR raw, frozen-thawed, and cooked minced beef meat adulterated with turkey meat. By applying partial least-squaresdiscriminant analysis to the NIR spectra, clear discrimination between meat samples containing less than 20% (adulteration threshold) from those adulterated with a level of more than 20% was observed. The authors concluded that FT-NIR spectroscopy could be used as a reliable tool for the identification and quantification of minced beef adulteration with turkey in raw, frozen-thawed, and cooked meat samples supporting previous investigations of Kamruzzaman, Sun, ElMasry, and Allen (2013) who accomplished to detect the presence of minced pork in minced lamb. A good prediction model was obtained using the whole spectral range in the 910 1700 nm with a coefficient of determination of 0.99 and root-mean-square errors estimated by cross-validation of 1.37%. The authors have then tested four wavelengths (940, 1067, 1144, and 1217 nm) and found a coefficient of determination of 0.98 and a root-mean-square errors estimated by cross-validation of 1.45%, suggesting the replacement of the laborious and time-consuming reference analytical techniques by a rapid analytical technique named NIR for the detection of the adulteration of minced lamb meat.

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On the other hand, the capability of NIR to predict some physicochemical parameters of Iberian pork loins was studied by Ca´ceres-Nevado, Garrido-Varo, De Pedro-Sanz, and Pe´rez-Marı´n (2019) (Table 10.2). The best equations obtained for intact loin following the use of two modes of analysis displayed a standard error of cross-validation of 1.06% and 1.09% and determination coefficient of cross-validation of 0.69 and 0.77 for fat, respectively. Similar results were obtained for moisture and protein contents. The obtained results were in agreement with those of: (1) Wyrwisz et al. (2019) who succeeded to predict the Warner Bratzler shear force on day 1 and day 7 of aging as well as a* color parameter on day 1; and (2) Furtado et al. (2019) who predicted by using NIR the color parameters of 77 porcine longissimus dorsi muscle, since R2 and a residual predictive deviation of 0.67/1.7, 0.86/2, and 0.76/1.9 were estimated for color parameters L*, a*, and b*, respectively. The authors indicated that the NIR failed to predict with high accuracy the pH, since an R2 of 0.67 and an RPD of 1.6 were observed.

10.7.3 Fish and fish products During freezing and thawing of fish, ice crystal growth provokes biochemical and physical changes that impact the final quality of fish and fish products. In this context, Uddin and Okazaki (2004) assessed the feasibility of NIR to differentiate between 162 fresh and frozen-thawed horse mackerel samples. The acquired spectra showed bands B1510, 1700, 1738, 2056, 2176, 2298, and 2346 nm. The spectra indicated the difference between fresh and frozen-thawed samples in the 1920 2350 nm spectral region. A complete (100%) of correct classification was observed suggesting the use of NIR to differentiate between fresh and frozen-thawed fish. By using an FT-NIR benchtop spectrometer and a handled NIR coupled with linear discriminant analysis and soft independent modeling of class analogy, Grassi, Casiraghi, and Alamprese (2018) succeeded to differentiate between fillets and patties of Atlantic cod (n 5 80) from those of haddock (n 5 90). On the other hand, He, Wu, and Sun (2014) used VIS-NIR hyperspectral imaging (400 1720 nm) to determine the tenderness of salmon fillets in replacement to Warner Bratzler shear force that is a widely used as an objective indicator for tenderness evaluation. Four wavelengths located B555, 605, 705, and 930 nm, the most valuable wavelengths for determining the tenderness of fish, were used to determine the tenderness. The authors applied the least-squares support vector machine and the successful result was obtained for the tenderness with a correlation coefficient of 0.9 and root-mean-square error estimated by prediction of 1.1. These obtained results were confirmed recently by Ivorra, Verdu, Sa´nchez, Grau, and Barat (2016), who utilized the hyperspectral short-wave NIR system to determine the freshness state of gilthead sea bream, since R2 of 0.92 was observed in the pupil region. Wang et al. (2019) investigated the ability of NIR to predict total volatile basic nitrogen and texture profile analysis. The results showed that the level of the former parameter was accurately predicted, whereas the latter parameter was determined with high accuracy. In the study conducted by He, Wu, and Sun (2013), the moisture content of farmed Atlantic salmon fillets was determined by hyperspectral imaging technique in the VIS-NIR region (400 1700 nm). Three different spectral regions were considered separately: 400 1000, 900 1700, and 400 1700 nm. The authors have applied partial least-squares regression to the three spectral regions and observed coefficients of determination of 0.893, 0.902, and 0.849 and root-mean-square errors of prediction of 1.513%, 1.450%, and 1.800% for three spectral ranges, respectively. The authors selected eight wavelengths in the 400 1000 nm region: 420, 445, 545, 585, 635, 870, 925, and 955 nm that were considered as the most interesting wavelengths for the establishment of moisture model. The obtained results were confirmed, later, by Chen and Sun (2017), who applied NIR and hyperspectral imaging spectral data for the prediction of moisture content, lipid content, protein content, pH, total volatile basic nitrogen, thiobarbituric acid reactive substances, and K index value. The authors stated that the NIR and hyperspectral imaging spectral data techniques in tandem with partial least-squares regression model method could be suitable for the determination of the quality of fish quality confirming previous findings of Tito, Rodemann, and Powell (2012), who achieved to differentiate between fresh salmon samples from those kept during 9 days at 4 C. By applying partial least-squares regression, the authors determined a calibration model for the prediction of the bacteria load with R2 5 0.95 and root-mean-square error of 0.02 log cfu g21. Less successful results were obtained for the validation model, since R2 of 0.64 and a root mean-square-error of 0.32 log cfu g21 were obtained.

10.8

Nuclear magnetic resonance

10.8.1 Milk and milk products To be labeled as “lactose-free,” milk should contain less than 0.01% (w/w) of lactose. As the analysis of such low levels of lactose is often hampered by other saccharides present or formed during milk processing, highly sensitive, accurate

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and precise techniques are required (Churakova et al., 2019). In this context, different analytical techniques including NMR, enzymatic kits, cryoscopy, lactose biosensors and high-performance anion-exchange chromatography with pulsed amperometric detection, were used. The authors depicted that NMR failed to determine with high accuracy the lactose when it is present at a low level; The Biomilk30 and high-performance anion-exchange chromatography with pulsed amperometric detection gave comparable results. However, the NMR showed its high potential to determine the water mobility in semihard, hard, and extra-hard goats’ cheeses (Tomaszewska-Gras et al., 2019). The authors stated that the lowest amount of bulk water was observed for extra-hard cheese indicating that the water was strongly entrapped in the protein network and bound by the water-soluble substances arising after increased proteolysis. The bulk water fraction is mainly a component of water-in-fat emulsions. One of the main findings of this study was the high correlation observed between the NMR parameter T1 (spin-lattice relaxation time) and the freezable water content determined by differential scanning calorimetry. The adulteration of skim and nonfat dry milk powders by adulterants was investigated by NMR. Adulteration was detected at the lowest concentrations ($0.005% 0.05% w/w) for samples containing nitrogen-rich, small molecules (melamine and dicyandiamide). For urea, a milk metabolite, and for sucrose and maltodextrin, the detection thresholds were higher ($0.5% w/w). Adulteration by soy protein isolate and whey protein concentrate was not detected even at 5% w/w of spiking, which was attributed in part to poor protein solubility. The obtained results are in line with the investigation of Siren, Liang, and Ji (2018), who studied different treated camel milk (65 C, 30 min, 75 C, 15 20 s) adulterated with cow milk, sheep milk, soybean milk, urea and water. By applying the specific chemometric tools, such as principal component analysis, it was found that the adulterated samples were well differentiated from the others.

10.8.2 Meat and fish products Frankfurters, made following the mixing of different meat sources (beef, chicken, turkey, or pork or mixtures of them), are categorized as ready to eat meat products. Thus due to the difference in the price between these meat species, the substitution of beef meat by other meat species of low quality could occur. In this context, Uguz, Ozvural, Beira, Oztop, and Sebastia˜o (2019) used NMR relaxometry to detect the authenticity of the type of mixture in frankfurters. It was found that the NMR relaxometry discriminates clearly frankfurters of different meat origins confirming previous findings of Zang et al. (2017) who by using NMR succeeded to detect the adulterated yellow croaker with carrageen or distilled water from the nonadulterated ones. The authors succeeded to predict from the NMR spectra the water and fat contents with R2 of 0.9877 and 0.9054, respectively. Tan, Huang, Feng, Li, and Cai (2018) studied the freshness state of intact zebrafish by 2D 1H J-resolved NMR spectroscopy conjugated with pattern recognition methods. A total of 21 metabolites were identified and their amounts during the spoilage process were determined. During storage, the authors pointed out that the levels of glycerol, asparagine, sarcosine, tyrosine, acetate, glycine, and serine increased, while that of lipid decreased. One of the main conclusions of this study was that freshness level of fish could be determined by NMR that is confirmed later and recently by Duflot, Sa´nchez-Alonso, Duflos, and Careche (2019), who have used low NMR of proton transverse relaxation signal (T2) to monitor fresh, frozen, and cooked minced hake (Merluccius merluccius) (Table 10.2). The authors pointed out that the relaxation rate of the major component (1/T21) increased significantly upon frozen storage, whereas water or NaCl addition had an opposite effect. In addition, a linear relationship was found between 1/T21 and two other parameters named pH and protein content. On the other hand, Giese, Winkelmann, Rohn, and Fritsche (2017) predicted with success the oxidation state of fish oil during the storage of nine raw fish oils of different fish species up to: (1) 3 months at room temperature at different degrees of sunlight exposure; and (2) 6 days at 40 under constant light exposure that constitute accelerated storage conditions. Fish oil samples were analyzed by 1H NMR and reference methods named peroxide value, anisidine value, TOTOX value, and acid value. By applying the partial least-squares regression, the best regression with R2 of 0.949, 0.962, 0.991, and 0.977 were obtained for peroxide value, anisidine value, TOTOX value, and acid value, respectively, indicating that the 1H NMR could be used as a rapid screening tool for sustainable assessment of fish oil quality with regard to lipid oxidation. Rao et al. (2018), have used the low-field NMR T2b during the drying process of lamb meat. Area of T21 (intramyofibrillar water) decreased, while that of T22 (extra-myofibrillar water) increased when the moisture content decreased from 55% to 45%, indicating the water migration from myofibril to extramyofibril. One of the main conclusions of their study was that the drying rate could be predicted by the area of T2 populations since a high correlation coefficient of 0.99 was observed.

Targeted and untargeted analytical techniques Chapter | 10

10.9

287

Microscopic methods

The use of microscopic techniques to evaluate food microstructure was developed considerably in recent years (Table 10.3). Ice crystal characteristics impact the microstructure of food products, providing irreversible cell and tissue damages resulting in quality losses. In this context, Mulot, Fatou-Toutie, Benkhelifa, Pathier, and Flick (2019) explored the impact of freezing on the quality of beef meat by X-ray microcomputed tomography (Table 10.3). It was found that crystals become 2.5 times smaller when the freezing temperature passed from 230 C to 2100 C. Fluorescence optical microscopy and electron scanning microscopy were also used to do a qualitative analysis that confirmed the effect of the freezing temperature on ice crystal Physicochemical properties of pomegranate fruit, such as the volume of arils and juice content, are of considerable economic importance for the plant breeding, growing, and food processing industries. Arendse, Fawole, Magwaza, and Opara (2018) used a commercial microfocus X-ray system in combination with image analysis to estimate the volume of juice from intact pomegranate fruit Wonderful. It was found that the calculated juice volume represented 89.8% (142.7 6 16.4 mL) of the total aril volume (162.5 6 16.2 mL). The obtained results were in line with those of Tanaka, Nashiro, Obatake, Tanaka, and Uchino (2018) who characterized and quantified the internal structure of cucumber fruit during storage. The average grayscale value calculated from X-ray computed tomography showed high correlations with the density, porosity, and elastic modulus determined by traditional methods. The X-ray tomography has also been used to characterize the microstructure of Fried Potato (Alam & Takhar, 2016). The potato disks were fried at 190 C for 0, 20, 40, 60, and 80 s. Different parameters including total porosity, pore size distribution, oil content, and air content were determined from resulting 3-D data sets. It was found that oil and air contents determined by analysis of microcomputed tomography images gave similar trends than those determined, respectively, by Soxtec and gas pycnometry methods. It was found that the frying time induced: (1) a significant change in pore size distribution; and (2) some modifications in the tortuosity. The authors depicted a linear inverse relationship between porosity and tortuosity since porosity increased with the decrease of tortuosity. One of the main conclusions of this study was that during frying, oil level increased with the decrease of tortuosity indicating that a lower tortuosity provoked a less complicated and sinuous path resulting in less resistance to oil penetration. The X-ray microtomography was also used to visualize the microstructure of loose-packed and compacted milk powder and to quantify the proportion of both interstitial and occluded air voids in each sample. The X-ray microtomography images illustrated some characteristic details of the spherical morphology of the particles, the size of the particles, and internal air voids of various sizes. Within loose-packed powders, the proportion of air voids was significantly higher in whole milk powder than in skim milk powder that is attributed by the authors to the disparity in the proportion of air voids in both loose-packed and compacted samples of the spray-dried skim milk powder and whole milk powder. Recently, Guo et al. (2019) investigated the impact of P. roqueforti, during ripening on chicken breast meat, on the texture, microstructure, protein structure, water mobility, and volatile flavor compounds (Table 10.3). Regarding

TABLE 10.3 A summary overview of microscopic techniques used for the determination of the quality of food products. Analytical techniques

Objectives

Main results

References

X-ray tomography

Determining the impact of freezing on the quality of beef meat

The crystals become 2.5 times smaller when the freezing temperature goes down from 230 C to 2100 C

Mulot et al. (2019)

Scanning electron microscopy

Effect of Penicillium roqueforti, during ripening on chicken breast meat, on the texture, microstructure, protein structure, water mobility, and volatile flavor compounds of chicken breast meat

The scanning electron microscopy and transmission electron microscope images showed that the granule formed and chicken myofibril fractured after ripening

Guo et al. (2019)

Transmission electron microscope

The diminution in α-helix and increase in β-sheet structure content were accompanied by decrease in hardness and springiness and increase in gumminess

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microscopic analyses, the authors used scanning electron microscopy and transmission electron microscope images, which observed that the granule formed and chicken myofibril fractured after ripening. The authors pointed out a diminution in α-helix and increase in the β-sheet structure that were accompanied by a decrease in hardness and springiness and an increase in gumminess.

10.10 Conclusion During the last years, the quality and authenticity of food products have been determined by different analytical techniques. These techniques allowed us to determine the authenticity of a product at different levels: molecular, microscopic, and macroscopic. The application of specific multivariate statistical tools for determining the quality and authenticity as well as for detecting the adulteration was shown in the present chapter. It was also illustrated that spectroscopic techniques are more and more used for the evaluation of the quality and authenticity of food products due to their relatively low cost and their application in both fundamental research and the factory as on-line sensors.

List of abbreviation FFFS: Front face fluorescence spectroscopy NIR: Near infrared NMR: Nuclear magnetic resonance MIR: Mid infrared VIS: Visible

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LF-NMR to explore water migration and water protein interaction of lamb meat being air-dried at 35 C. Drying Technology, 36(3), 366 373. Rodrigues, B. L., da Costa, M. P., da Silva Frasa˜o, B., et al. (2017). Instrumental texture parameters as freshness indicators in five farmed brazilian freshwater fish species. Food Analytical Methods, 10(11), 3589 3599. Rouissi, H., Dridi, S., Kammoun, M., De Baerdemaeker, J., & Karoui, R. (2008). Front face fluorescence spectroscopy: A rapid tool for determining the effect of replacing soybean meal with scotch bean in the ration on the quality of Sicilo-Sarde ewe’s milk during lactation period. European Food Research and Technology, 226, 1021 1030.

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Chapter 11

Food pathogens Junyan Liu1,2, Yuting Luo1, Zhenbo Xu1,3 and Birthe V. Kjellerup2 1

School of Food Science and Engineering, Guangdong Province Key Laboratory for Green Processing of Natural Products and Product Safety, South

China University of Technology, Guangzhou, China, 2Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA, 3College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA

11.1

Genome sequencing

Understanding the genome contributes to the thorough comprehension of the innate characteristic of a microorganism. Genome sequencing is a basic and initial strategy to investigate the genetic mechanism explaining the phenotypic behavior of food microorganisms, including food spoilage and pathogenesis. Nowadays, the second- and thirdgeneration sequencing platforms are widely applied to analysis genome. Illumina sequencer and PacBio sequencer are the representative second- and third-generation sequencing platforms, respectively. Based on different assembly strategies, genome sequencing is also classified into resequencing and de novo sequencing. The genome sequencing mainly composes of library construction, sequencing, quality examination, and assembly. The detailed analysis steps vary among different sequencers and assembly strategies.

11.1.1 Resequencing Genome resequencing is mainly used for the difference analysis among the genome of strains belonged to a same species with available genome sequence. For genome resequencing performed by Illumina sequencer, genomic DNA was cut into small fragments less than 800 bp randomly through Bioruptor or Covaris method. The ends of DNA fragments were repaired by T4 DNA polymerase or Klenow enzymes with 30 ends added with adaptors. PCR amplification and gel electrophoresis were performed to isolate target segments. Genome sequencing was conducted using the Illumina Hiseq or Miseq platform with pair end library based on the manufacturers’ protocol. The yielded reads were quality examined by software FastQC (v.0.10.1 http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with low-quality reads (length , 100 and average quality , 0.80) filtered before assembly. The qualified reads were mapped with a reference genome by BurrowsWheeler Aligner (BWA) software. Genome mapping resulted in the acquisition of a BWA file. Insertion deletion (InDel) and single nucleotide polymorphism (SNP) were determined by SAM Tools (Wang, Xiao, Guo, An, & Du, 2016). For genome resequencing performed by PacBio sequencer, the genome DNA was cut into fragments by Covaris method and further purified using 0.45 3 AMPure beads. The purified DNA fragments were adapted to DNA repair and end repair. Blunt hairpin adapters were subsequently ligated into blunt-end of DNAs (Bao, Jia, et al., 2017). Two enzymes, ExoIII and ExoVII, were used to digest the DNA fragments without ligated to adapters. Finally, the SMRTbellt library was selected with desired insert size using BluePippin. The quantified library was then sequenced through PacBio RS II platform based on the manufacturers’ protocol. The yielded reads were quality-examined by software FastQC (v.0.10.1, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with low-quality reads (length , 100 and average quality , 0.80) filtered before assembly. The qualified reads were mapped with a reference genome by BWA software. Genome mapping resulted in the acquisition of a BWA file. SNP and InDel were determined by SAM Tools (Wang et al., 2016).

11.1.2 De novo sequencing Genome de novo sequencing is performed without a reference genome and mainly used for the genome analysis of species whose genome sequence is unknow. DNA was cut into small fragments less than 800 bp randomly through Innovative Food Analysis. DOI: https://doi.org/10.1016/B978-0-12-819493-5.00011-X Copyright © 2021 Elsevier Inc. All rights reserved.

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Bioruptor or Covaris method. The ends of DNA fragments were repaired by T4 DNA polymerase or Klenow enzymes with 30 ends added with adaptors. PCR amplification and gel electrophoresis were performed to isolate target segments. Genome sequencing was conducted using the Illumina Hiseq or Miseq platform with pair end library based on the manufacturers’ protocol. The yielded reads were quality-examined by software FastQC (v.0.10.1 http://www.bioinformatics. babraham.ac.uk/projects/fastqc/) with low-quality reads (length , 100 and average quality , 0.80) filtered before assembly. Filtered sequences were adapted to de novo assembly by software Velvet v1.2.08 (Zerbino & Birney, 2008). For genome de novo sequencing performed by PacBio sequencer, the genome DNA was cut into fragments by Covaris method and further purified using 0.45 3 AMPure beads. The purified DNA fragments were adapted to DNA repair and end repair. Blunt hairpin adapters were subsequently ligated into blunt-end of DNAs (Bao, Jia, et al., 2017). Two enzymes, ExoIII and ExoVII, were used to digest the DNA fragments without ligated to adapters. Finally, the SMRTbellt library was selected with desired insert size using BluePippin. The quantified library was then sequenced through PacBio RS II platform based on the manufacturers’ protocol. The yielded reads were quality-examined by software FastQC (v.0.10.1, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with low-quality reads (length , 100 and average quality , 0.80) filtered before assembly. Filtered sequences were adapted to de novo assembly by software HGAP (Chin et al., 2013).

11.2

RNA sequencing

RNA sequencing (RNA-seq), a cost-effective and powerful technology, offers an unbiased tool to determine the transcriptomes of a species under multiple conditions21. Total RNA of food microorganism samples at certain states were isolated using Bacterial Total RNA Extraction kit based on the manufacturer’s instruction and quality examined using Agilent 2100 Bioanalyzer (Agilent Technologies). The rRNA was removed to enrich mRNA using RNase-free DNase I (Ambion Inc.) combined with MICROBExpresst kit (Ambion Inc.) or Ribo-Zerot Magnetic Kit (Epicenter). The enriched mRNA was randomly cut into short fragments using fragmentation buffer, followed by reverse transcript into cDNA using random primers. DNA polymerase I, RNase H, dNTP, and reaction buffer were used to synthesize secondstrand cDNA. The cDNA fragments were purified with QiaQuick PCR extraction kit according to the manufacturers’ protocol, followed by end repaired, poly(A) added, and ligated to adapters for Illumina sequencing. RNA-seq library was constructed by Illumina Paired End Sample Prep kit based on the manufacturers’ protocol. cDNA fragments were sequenced by the Illumina Hiseq or Miseq platform based on the manufacturers’ protocol. Raw reads obtained from the sequencing platform contain adapters or low-quality bases which affect the assembly and analysis. Thus, the raw data were filtered to obtain clean reads, which were further quality-examined by software FastQC (v.0.10.1, http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) with low-quality reads (length , 100 and average quality , 0.80) filtered. Filtered sequences were aligned to the reference genome using TopHat (Trapnell, Pachter, & Salzberg, 2009), with less than two bases mismatch and read gap. Gene coverage was designated to be the percentage of gene sequences covered by reads. Gene function was annotated by software ANNOVAR against various databases (Wang, Li, & Hakonarson, 2010). Gene abundance was quantified by software RSEM primarily composing two steps. Firstly, reference transcript sequence set was generated and preprocessed based on known and new transcripts. Secondly, the reads were realigned to the reference transcripts by Bowtie alignment software to estimate gene abundances. The gene expression level was normalized by fragments per kilobase of transcript per million mapped reads method. It could eliminate the influence of gene length and sequencing data amount on the calculation. Thus, the gene expression level could be directly used for comparing the difference of gene expression among different samples.

11.3

Bioinformatics analysis

After genome sequencing or RNA-seq, only genome sequence or gene expression level is acquired, which barely give us information related to the phenotype behavior of the food pathogens or spoilage strains. Thus, bioinformatics analysis is required to understand the internal characteristic.

11.3.1 Bioinformatics analysis of genomic data Genome sequencing and assembly resulted in the acquisition of genome sequence. Genes in the genome were predicted using software GeneMarkS (Besemer, Lomsadze, & Borodovsky, 2001) and subsequently annotated by local BLAST against Nr and SwissProt databases. Clusters of Orthologous Groups of proteins (COG) categories, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and Gene Ontology (GO) were annotated through COG, KEGG, and GO

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databases (Ashburner et al., 2000; Ogata et al., 1999; Tatusov et al., 2003). The transfer RNA (tRNA), ribosomal RNA (rRNA), and repeat sequences were predicted by software tRNAscan-SE (v 1.21), RNAmmer (v 1.2), and RepeatMasker, respectively (Lagesen et al., 2007; Lowe & Eddy, 1997). Besides, prophages, secretion proteins, pathogen host interactions (PHI), protein domains (Pfam), and carbohydrate-active enzymes (CAZy) were predicted by PHAST 2.1, siganlP 4.1, PHI-base, Pfam_Scan, and CAZy database, respectively (Baldwin et al., 2006; Cantarel et al., 2009; Petersen, Brunak, von Heijne, & Nielsen, 2011; Zhou, Liang, Lynch, Dennis, & Wishart, 2011). Insertion sequences (IS) and repeat sequences were predicted by RepeatMasker and IS finder, respectively. Virulence factors and antibiotic resistance genes were identified by Virulence Factors of Pathogenic Bacteria database (VFDB) and Antibiotic Resistance Genes Database (ARDB), respectively. COG database is an encoding protein system constructed based on phylogenetic classification. It is used to predict protein function of single gene or the whole genome. The encoding proteins are classified into 25 COG categories belonged to four classifications including metabolism, cellular processes and signaling, information storage and processing, and poorly characterized (Table 11.1). The GO database is the largest information source on the gene functions worldwide. The information is both human- and machine-readable. It is a foundation for computational analysis of large-scale biomedical genetics and molecular biology experiments. GO is composed of functional terms in biological process, cellular component, and molecular function. KEGG database is a resource to understand top utilities and

TABLE 11.1 COG category classification (Tatusov et al., 2003). Classification

COG category

Information storage and processing

[J] Translation, ribosomal structure, and biogenesis [A] RNA processing and modification [K] Transcription [L] Replication, recombination, and repair [B] Chromatin structure and dynamics

Cellular processes and signaling

[D] Cell cycle control, cell division, and chromosome partitioning [Y] Nuclear structure [V] Defense mechanisms [T] Signal transduction mechanisms [M] Cell wall/membrane/envelope biogenesis [N] Cell motility [Z] Cytoskeleton [W] Extracellular structures [U] Intracellular trafficking, secretion, and vesicular transport [O] Posttranslational modification, protein turnover, and chaperones

Metabolism

[C] Energy production and conversion [G] Carbohydrate transport and metabolism [E] Amino acid transport and metabolism [F] Nucleotide transport and metabolism [H] Coenzyme transport and metabolism [I] Lipid transport and metabolism [P] Inorganic ion transport and metabolism [Q] Secondary metabolites biosynthesis, transport, and catabolism

Poorly characterized

[R] General function prediction only [S] Function unknown

298

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functions of the biological system from molecular-level information, including the ecosystem, the organism, and the cell. It is especially suitable for the analysis of datasets generated by high-throughput experimental technologies like genome sequencing and RNA-seq. The COG category, GO term, and KEGG pathway annotation give us insight into the basic function of the genes in the genome, aiding in the further analysis on the genes. The ARDB database (newly updated to the Comprehensive Antibiotic Resistance Database, http://arpcard.mcmaster.ca) provides antibiotic resistance information, facilitates the annotation of resistance information in organisms whose genome are newly sequenced, and aids in the new genes identification and characterization. The VFDB is a comprehensive database integrated for information about bacterial virulence genes.

11.3.2 Bioinformatics analysis of RNA sequencing data To identify differentially expressed genes (DEGs) among samples or groups, the DEGseq panormalage based on negative binomial distributions or edge R package was used (Anders, 2010). Genes with an adjusted P value , 0.05 and | log2(fold change)| . 1 were identified as DEGs. COG category, GO term, and KEGG pathway of DEGs were performed based on COG, GO, and KEGG databases, respectively (Ashburner et al., 2000; Kanehisa & Goto, 2000). The significantly enriched COG categories, GO terms, and KEGG pathways were determined by P value , 0.01 in Hypergeometric Distribution and P value , 0.05 in Fisher Exact Test, adjusting by false discovery rates (Rivals, Personnaz, Taing, & Potier, 2007; Storey, 2002)

11.4

The application of innovative analysis on Enterobacter

11.4.1 Food pathogen Escherichia coli E. coli is a representative species of the Enterobacter family and Escherichia genus. It is a spore-free gram-negative bacteria with flagella and motility. E. coli is a facultative anaerobic bacterium with a growth temperature range of 846 C and an optimum growth temperature of 37 C. E. coli was discovered in 1885 and has been regarded as a component of the normal intestinal flora for a long period of time and considered to be a nonpathogenic bacterium (Escherich, 1989). It was not recognized that some special serotypes of E. coli were pathogenic to humans and animals until the middle of the 20th century. These pathogen bacteria have ability to contaminate food, including drinking water, meat products, and milk, posing a serious threat to human. Normally, according to different biological characteristics, pathogenic E. coli is divided into six categories: enteropathogenic E. coli, enterohaemorrhagic E. coli (EHEC), enteroaggregative E. coli, enterotoxigenic E. coli, and diffusely adherent E. coli (Nataro & Kaper, 1998). Among them, EHEC strains are a subset of Shiga toxin-producing E. coli, which are considered to be the important enteropathogens causing outbreaks of diarrheal diseases. The most important factors for E. coli O157 are the production of Shiga toxins encoded by stx1 and stx2 genes. O157:H7 is the main toxin-producing serotype (Law, 2000). However, non-O157 E. coli can also cause illnesses with production of Shiga toxin in the worldwide, such as O26, O111, O103, O121, O45, and O145 (Mathusa, Chen, Enache, & Hontz, 2010). EHEC acquires acid resistance which enhance its survival in food, including fermented sausages, yoghurt, apple juice, and apple cider (Leyer, Wang, & Johnson, 1995). Acid resistance relies on three mechanisms: acid-induced oxidative system, acid-induced arginine-dependent system, and glutamate-dependent system (Law, 2000). However, acidinduced oxidative system is not significant in cells grown in complex medium with glucose, which involve in fermentation metabolization. And the other two systems also protect cells growing at pH 2.5 if the medium is supplemented with arginine and glutamate. Sigma factor RpoS has effect in oxidative system, while adi gene and its regulators cysB and adiY are responsible for acid-induced arginine-dependent system (Lin et al., 1996).

11.4.2 Food pathogen Cronobacter sakazakii C. sakazakii is a type of Enterobacteriaceae and is motile peritrichous and gram-negative. In 1980, it was renamed as C. sakazakii from Enterobacter cloacae. It causes severe neonatal meningitis, enterocolitis, and bacteremia, with a mortality rate of higher than 50%. Currently, researchers are still not aware of the source of contamination of C. sakazakii, but many cases report powdered infant formula (PIF) is the main infection source (Muytjens, Roelofs-Willemse, & Jaspar, 1988; van Acker et al., 2001). Infant and young children are particularly vulnerable to foodborne infections. It is regarded as an emerging foodborne pathogen threatening bacterial infections in infants. Thus, the microbiological safety of infants and follow-up formula is of importance. The first outbreak of C. sakazakii caused two deaths in 1958

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(Drudy, Mullane, Quinn, Wall, & Fanning, 2006). The World Health Organization has recommended infants without breast-fed should be provided with a suitable breast milk substitute, formulated in conformity to Codex Alimentarius Commission standards. Recommendations for PIF preparation and storage have been made to decrease the risk of infants’ infection. For now, no published data are available determining the virulence factors or the pathogenicity of C. sakazakii. C. sakazakii is susceptible to partial antibiotics, including numerous β-lactams, aminoglycosides, tetracyclines, quinolones, chloramphenicol, and antifolates. It is also naturally resistant to antibiotics including lincomycin, streptogramins, macrolides, rifampicin, fosfomycin, clindamycin, and fusidic acid (Stock & Wiedemann, 2002).

11.4.3 Application of genome sequencing and bioinformatics analysis on Cronobacter sakazakii A comprehensive analysis of C. sakazakii was performed through the next generation whole genomic sequencing and bioinformatics analysis (Fig. 11.1). The genome sequencing and finally assembly yielded 1,734 scaffolds, with a total length of 1,943,986 bp and the GC content of 52.43%. The N50 scaffold was 1,293 bp, and largest scaffold was 6684 bp. The whole genome coverage was 98.92%. A total of 2,420 genes with coverage length of 703.03 bp were predicted and annotated by GO term, COG category, and KEGG pathway. Among the COG categories, 220 genes were enriched in conversion and energy production, 212 gene were enriched in carbohydrate transport and metabolism, and 206 genes were enriched in general function prediction only. Among the GO terms, ATP binding, metal binding, and DNA binding enriched in cellular component. DNA-dependent regulation of transcription and oxidation-reduction process enriched in biological process. Cytoplasm, cytosol, and integral to membrane enriched in molecular function. For KEGG pathways, ABC transporters and bacterial chemotaxis enriched in environmental information processing and cellular processes, respectively. Causing high morbidity and mortality rate, C. sakazakii was still opaque. Virulence genes identification might contribute to the understanding the pathogenesis of C. sakazakii. Cronobacter invasion has been reported as the main reason of its pathogenicity (Chandrapala et al., 2014; Ripolles et al., 2017). Before invasive into cells, C. sakazakii always adhere to the cell surface. Genes encoding adhesin AidA involved in the type V secretory pathway were identified as the largest subfamily of autotransporters. Genes encoding collagen-binding surface adhesin SpaP were responsible for the intensification of its adherence ability. The adhesin was first identified encoding antigen I/II in Streptococcus mutans. Six virulence factors potentially contribute to bacterial invasion of epithelial cells includes genes encoding invasion beta-domain of outer membrane, bacterial Ig-like domain, and attaching and effacing protein-like protein. They might be responsible for the infections including necrotizing enterocolitis and meningitis in infants through invading the bloodbrain barrier and epithelial cells. In addition, putative virulence factors including cell wall-associated hydrolases and uncharacterized membrane protein were determined to contribute to the high morbidity and mortality of C. sakazakii. The C. sakazakii strain was identified to possess varieties of genes responsible for its antibiotic resistance. Multidrug resistance efflux pumps, AmpG, and Opp could inhibit the biosynthesis of peptidoglycan and increase muropeptides. DAP-containing peptide fragments, MurF and MraY, and GlcNAc-anhMurNAc muropeptides NagZ involved in synthesis of UDP-MurNAc-L-Ala-D-Glu-LL-dap-D-Ala-D-Lac are associated with synthesis of Und-PPMurNAc[R] [E] 205

[M]

206

220

120

[L]

99

[H]

85 [J]

18 18

[C]

14 14 13 12

Selenocompound metabolism Glutathione metabolism Lipopolysaccharide biosynthesis Thiamine metabolism Biotin metabolism Phenylpropanoid biosynthesis

11 10 2

Dioxin degradation Drug metabolism - cytochrome P450 Retinol metabolism Toluene degradation

115

[S]

1 1

Butanoate metabolism Peptidoglycan biosynthesis Nitrogen metabolism

212

135 [P]

GABAergic synapse Glutamatergic synapse

[G]

[K]

82

2 2

Sphingolipid metabolism Huntington’s disease Tuberculosis Prostate cancer Chemical carcinogenesis

80 66 61 58 43

[O] [T]

[F] [I]

[V]

Cellular Processes Environmental Information Processing Genetic Information Processing Human Diseases Metabolism Organismal Systems

1 1 1 1

Viral carcinogenesis Alzheimer’s disease Homologous recombination RNA degradation

18 13 12

Mismatch repair RNA transport ABC transporters Bacterial chemotaxis Cell cycle - Caulobacter Lysosome

2 101 14 10 1 9.09

FIGURE 11.1 Genome circus and annotation of Cronobacter sakazakii (Bao et al., 2017).

Classification

1 1 1 2 2

4.55 -log10(Pvalue)

50 No.of Genes

100

300

Innovative Food Analysis

(GlcNAc)-A-Ala-D-Glu-L-Lys-D-Ala-D-Lac. It contributed to the strong resistance of C. sakazakii strain to vancomycin. Genes encoding tellurium resistance, mental resistance, auxiliary component, and organic solvents were also identified in the genome.

11.4.4 Application of RNA sequencing and bioinformatics analysis on Cronobacter sakazakii RNA-seq combined with bioinformatics analysis was performed on different C. sakazakii strains (pmrA mutant and wild type) and the same strain grown for different time, respectively. A total of 814 DEGs with 526 and 288 genes showing up- and down-regulation, respectively, were determined by the comparison of gene expression levels between samples (the same strain grown for different time). Seventeen of the up-regulated DEGs with |log2(fold change)| . 4 and 12 of the DEGs with |log2(fold change)| . 4 were identified to be the most DEGs. The 17 up-regulated DEGs encode fimbrial protein, endodeoxyribonuclease, NAD(P)H-dependent FMN reductase, sucrose porin, phosphotransferase system (PTS) system, glycosyl hydrolase family 32, sucrose-specific IIC component, sucrose-specific IIB component, aminoimidazole riboside kinase, oxalate/formate antiport family major Facilitator Superfamily of transporters MFS transporter, 1-phosphofructokinase, bifunctional PTS fructose transporter subunit IIA/HPr protein, EF hand domain protein, PTS fructose transporter subunit EIIBC, RNA helicase, transcriptional regulator, and hypothetical/uncharacterized proteins. The 10 down-regulated DEGs encode hydrogenase 3 membrane subunit, phosphoenolpyruvate synthase, formate hydrogenlyase complex iron-sulfur subunit, hydrogenase 3 maturation endopeptidase HyCI, hydrogenase 3 large subunit, formate hydrogenlyase subunit 7, HNH endonuclease, taurine dioxygenase, glycine dehydrogenase, formate hydrogenlyase maturation protein HycH, peptidyl-prolyl cis-trans isomerase B, and uncharacterized protein. Comparing with the 8 h C. sakazakii biofilm, key DEGs in 16 h biofilm encoded up-regulated virulence factors and fimbrial adhesin and down-regulated colanic acid capsular biosynthesis activation protein A. GO term enrichment analysis was conducted for the 814 DEGs. Given the sequence homology, the DEGs were annotated and categorized into 711, 210, and 388 secondary-level GO terms with 116, 89, and 72 specific GO terms enriched in biological process, cellular component, and molecular function, respectively. Among the enriched GO terms, seven were enriched in biological process, including “response to stimulus,” “locomotion,” “localization,” “cellular process,” “metabolic process,” “single-organism process,” and “biological regulation.” The “macromolecular complex,” “organelle part,” “organelle,” “cell,” “membrane,” “cell part,” and “membrane part” were enriched in cellular component. While “structural molecule activity,” “nucleic acid binding transcription factor activity,” “binding,” “transporter activity,” and “catalytic activity” were significantly enriched in molecular function. Among the annotated KEGG pathways, “Flagellar assembly”; “Ribosome”; “Valine, leucine, and isoleucine biosynthesis”; “C5-Branched dibasic acid metabolism”; “Biosynthesis of amino acids”; “Bacterial chemotaxis”; “Glycine, serine, and threonine metabolism”; “Cysteine and methionine metabolism”; “Citrate cycle”; “Butanoate metabolism”; “Biosynthesis of siderophore group nonribosomal peptides”; “Carbon metabolism”; “2-Oxocarboxylic acid metabolism”; and “Starch and sucrose metabolism” were significantly enriched. A total of 91 DEGs with 24 and 67 genes showing up- and down-regulation, respectively, were determined by the comparison of gene expression levels between samples (pmrA mutant and wild type). Four of the up-regulated DEGs with |log2(fold change)| . 2 and seven of the DEGs with |log2(fold change)| . 2 were identified to be the most DEGs. The four up-regulated DEGs encoded phenol hydroxylase, uncharacterized protein, molecular chaperone DnaK, and beta-phosphoglucomutase, respectively. The seven down-regulated DEGs encoded uncharacterized/hypothetical proteins, large ribosomal subunit ribosomal protein L33P, His-Asn-His (HNH) endonuclease, taurine dioxygenase, ABC transporter ATP-binding protein, and glycine dehydrogenase, respectively. Comparing with the 8 h C. sakazakii biofilm of wild type, key DEGs in pmrA mutant encoded significantly down-regulated flagellar protein FliT, flagellar hook protein FlgE, flagellar hook-basal body complex protein FliE, virulence factor SrfB, flagellar hook-associated protein FlgK, virulence factor, flagellar hook-length control protein FliK, flagellar protein export ATPase FliI, and flagellar rod assembly protein/muramidase FlgJ. GO term enrichment analysis was conducted for the 91 DEGs. Given the sequence homology, the DEGs were annotated and categorized into 156, 50, and 41 secondary-level GO terms with 32, 18, and 9 specific GO terms enriched in biological process, cellular component, and molecular function, respectively. Among the enriched GO terms, seven were enriched in biological process, including “locomotion,” “localization,” “cellular process,” “metabolic process,” “cellular component organization or biogenesis,” “single-organism process,” and “biological regulation.” The “macromolecular complex,” “organelle part,” “organelle,” “cell,” “cell part,” and “membrane” were enriched in cellular component. While “binding” and “catalytic activity” were significantly enriched in molecular function.

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Among the annotated KEGG pathways, “Flagellar assembly,” “Bacterial chemotaxis,” and “Biosynthesis of unsaturated fatty acids” were significantly enriched. According to the RNA-seq and bioinformatics analysis on C. sakazakii biofilm, genes contributed to its pathogenesis were identified, including putative virulence factors srfB, MviM, ESA_01909, ESA_pESA3p05434, regulator of virulence determinants phoQ, invasion response regulator UvrY, activator of invasion genes FliZ, and outer membrane lipoprotein NlpD. UvrY and phoQ are part of NarL and OmpR family two-component systems, respectively (AlvarezOrdonez et al., 2014). FliZ is necessary to achieve full virulence, and NlpD, in similarity with virulence determinant LppB from Haemophilus somnus, is associated with stationary-phase cell survival. In the pmrA mutant, SrfB, phoQ, MviM, and ESA_01909 genes’ expression was down-regulated, while UvrY, NlpD, FliZ, and ESA_pESA3p05434 genes’ expression was up-regulated, indicating the function of virulence factors affected by the pmrA gene deletion. The expression of C. sakazakii resistance genes including MdtE, MdtH, YfiO, MarR, CusR, CusC, CusB, UspE, and MacA was also influenced. MdtE and MdtH is responsible for norfloxacin and enoxacin resistance, while YfiO is related to the resistance to ampicillin and tetracycline. MarR, a part of marRAB operon, is an activator of oxidative stress and antibiotic resistance genes. CusR, coupled with CusS, is a response regulator in two-component regulatory system regulating the copper efflux system. CusB and CusC, as part of a cation efflux system, show resistance to silver and copper (Bao, Yang, et al., 2017; Yan et al., 2013). UspE plays a role in UV irradiation resistance. MacA, responsible for the resistance to macrolide through active drug efflux, is a macrolide transporter subunit. In the pmrA mutant, CusA, CusB, CusR, UspE, and MacA genes’ expression was down-regulated, while MdtH, MdtE, YfiO, and MarR genes’ expression was up-regulated, indicating the function of resistance genes affected by the pmrA gene deletion.

11.5

The application of innovative analysis on Staphylococcus aureus

11.5.1 Food pathogen Staphylococcus aureus Ranking as top five major pathogens responsible for foodborne diseases globally, S. aureus is able to cause staphylococcal food poisoning (SFP; Almeida et al., 2017; Athrrayilkalathil, Ranjini, & Riya, 2011; Gschwendtner et al., 2016; Leriche et al., 2004; Marchand et al., 2009; Shirtliff, Mader, & Camper, 2002; Singh, Paul, & Jain, 2006; Xu, 2017; Xu, Peters, Li, Li, & Shirtliff, 2016). Resistant to antibiotics, S. aureus, such as livestock-associated methicillin-resistant S. aureus, is responsible for various animal infections and diseases and animals or plants origin food contaminations (Xu, 2017). Consequently, S. aureus has been well-documented to be a major pathogen in food safety. According to research and investigation reports in various parts of China, S. aureus mainly infects raw meat, various dairy products, frozen food, and cooked food, accounting for 38%, 20%, 16%, and 14% respectively. It has also been reported in beans, vegetables, and aquatic products. S. aureus produces several extracellular proteins with similar structures and virulence, but different antigenicities including the staphylococcal enterotoxins (SEA, SEB, SEC, SED, SEE, SEG, SEH, and SEI), exfoliative toxins (ETA and ETB), toxic shock syndrome toxin-1, and PantonValentine (Dinges, Orwin, & Schlievert, 2000). Among them, SEs can lead to SFP with common symptoms of nausea, retching, vomiting, stomach cramps, exhaustion, and diarrhea. SEs have high thermal stability. Even when bacteria have been eliminated by heating process, SEs remain active. Among the food poisoning caused by SEs, more than 75% are caused by SEA, followed by SED, SEC, and SEB, and all types of enterotoxin can cause food poisoning (Zhen-bo, Lin, & Bing., 2013). The conditions that generally affect the formation of enterotoxin include the degree of bacterial contamination, preservation temperature, and food ingredients.

11.5.2 Application of RNA sequencing and bioinformatics analysis RNA-seq combined with bioinformatics analysis was performed on S. aureus biofilm grown with and without 1/4 MIC of ampicillin (Fig. 11.2). A total of 530 DEGs with 167 and 363 genes showing up- and down-regulation, respectively, were determined by the comparison of gene expression levels between samples (the same strain grown for different time). Six of the upregulated DEGs with |log2(fold change)| . 2 and 10 of the DEGs with |log2(fold change)| . 4 were identified to be the most DEGs. The six up-regulated DEGs encode PTS system lactose-specific transporter subunit IIBC, riboflavin biosynthesis protein, argininosuccinate synthase, argininosuccinate lyase, 6-phospho-beta-galactosidase, and cation transporter E1-E2 family ATPase. The 10 down-regulated DEGs encode ornithine carbamoyltransferase, 2-isopropylmalate synthase, arginine/ornithine antiporter, 23 S rRNA, carbamate kinase, phage head protein, and hypothetical proteins.

302

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metal ion binding

15

nickel cation binding

6

15

ATP binding

47

RNA binding

Threonine biosynthesis:

3

rRNA binding

1

DNA binding

Betaine biosynthesis:

4.3.1.19 2-Oxobutanoate

27

extracellular region

10

GLYCINE, SERINE AND THREONINE METABOLISM

2

structural constituent of ribosome

O-PhosphoL-homoserine

−log10(pval)

Classification Biological Process

Iysine biosynthetic process via diaminopimelate

5

Cellular Component

6

Homoserine 1.1.1.3

carbohydrate metabolic process carbohydrate transmembrane transport 1

threonine biosynthetic process

0

−2

0

2

4

4.55

2.27

-log10(Pvalue)

50

No of Genes

Betaine

L-Aspartate 4-semialdehyde

44

Lysine biosynthesis

8 8

Phosphotransferase system (PTS)

1.2.1.11

17

Nitrogen metabolism

Staphylococcus aureus infection

L-4-Aspartylphosphate

1.2.1.8 1.1.3.17

100

Glycine, serine and threonine metabolism

Arginine biosynthesis

GbsB

3

Micorbial metabolism in diverse environments

Monobactam biosynthesis

2.7.2.4

Betaine aldehyde

15

translation

log2FoldChange

1.1.3.17

DoeC

6

regulation of transcription, DNA-templated

−4

L-Aspartate

15

cell division

1.14.15.7 1.1.99.1

2.7.1.39

Molecular Function 15

2

phosphorelay signal transduction system

Choline

4.2.3.1

31

integral component of membrane

Phosphoenolpyruvate-dependent sugar phosphotransferase system

Threonine

Classification Environmental Information Processing Human Diseases

5

Metabolism

11 16 14

FIGURE 11.2 RNA sequencing and enriched functions of Staphylococcus aureus (Liu et al., 2018a).

GO term enrichment analysis was conducted for the 530 DEGs. Given the sequence homology, the DEGs were annotated and categorized into 535, 277, and 536 secondary-level GO terms with 183, 21, and 252 specific GO terms enriched in biological process, cellular component, and molecular function, respectively. Among the enriched GO terms, nine were enriched in biological process, including “carbohydrate transmembrane transport,” “translation,” “threonine biosynthetic process,” “phosphorelay signal transduction system,” “carbohydrate metabolic process,” “cell division,” “phosphoenolpyruvate-dependent sugar phosphotransferase system,” “regulation of transcription, DNAtemplated,” and “lysine biosynthetic process via diaminopimelate.” The “extracellular region” and “integral component of membrane” were in cellular component. While “DNA binding,” “RNA binding,” “rRNA binding,” “ATP binding,” “metal ion binding,” “nickel cation binding,” and “structural constituent of ribosome” were significantly enriched in molecular function. Among the annotated KEGG pathways, “S. aureus infection”; “Glycine, serine, and threonine metabolism”; “Arginine biosynthesis”; “Lysine biosynthesis”; “Monobactam biosynthesis”; “Phosphotransferase system”; “Nitrogen metabolism”; and “Microbial metabolism in diverse environments” were significantly enriched. Concerning the biofilm formation of S. aureus, DEGs in ampicillin-treated biofilm sample encode adhesins, surface proteins, proteases, capsular polysaccharide proteins, and virulence factors (Wang & Wood, 2011). The genes encoding adhesins Fib and SdrD, and genes encoding proteases ClpB and ClpC, required for biofilm formation, intracellular replication, and stress tolerance, were significantly down-regulated (Frees et al., 2004). Significantly up-regulated cap5B and cap5C genes mediate the capsular polysaccharide biosynthesis. With the treatment of low concentration of ampicillin, S. aureus strain might induce biofilm formation through capsular polysaccharides synthesis. The master regulator of cysteine metabolism, cymR, contributing to biofilm formation of S. aureus, and a novel virulence protein, spdC, controlling the activity of histidine kinase, were not DEGs (Poupel, Proux, Jagla, Msadek, & Dubrac, 2018; Soutourina et al., 2009).

11.6

The application of innovative analysis on Pseudomonas

11.6.1 Food pathogen Pseudomonas Pseudomonas aeruginosa is an underestimated bacterium in the safety of food industry. Due to its low growth requirement, rapid reproducible capability, and strong adaptability, P. aeruginosa is commonly carried by human beings and distributed widely in environment, consequently causing food safety problems in various sides. For foodborne contamination including food poisoning and food spoilage, P. aeruginosa was frequently identified in dairy products, meat and plant origin food, and water. In dairy products, Pseudomonas comprised more than 50% of the total bacterial strains in milk (Champagne, Laing, Roy, Mafu, & Griffiths, 1994; Gunasekera, Dorsch, Slade, & Veal, 2003) and considered as the key bacteria causing milk spoilage by producing proteolytic and lipolytic enzymes (Eneroth, Ahrne´, & Molin, 2000; Gunasekera et al., 2003; Wiedmann, Weilmeier, Dineen, Ralyea, & Boor, 2000). Among the Pseudomonas, P.

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aeruginosa is the predominant species that has been commonly reported in milk (Arslan, Eyi, & Ozdemir, 2011; Athrrayilkalathil et al., 2011; Benie et al., 2017; Chen, Wei, & Chen, 2011; Cousin, 1982; Davis & Conner, 2007; Dogan & Boor, 2003; Juffs, 1976; Munsch-Alatossava & Alatossava, 2006), with the composition reaching 45% (Chen et al., 2011). In water, according to Centers for Disease Control and Prevention (CDC), P. aeruginosa commonly exists in moist environments, including water containers, pools, sinks, and tubs (“P. aeruginosa in Healthcare Settings”). Capable of surviving in distilled water and disinfectants, this pathogen is therefore well known as a contamination source in tap water and becomes one of the most dangerous bacteria in water, with an identification rate of 90% in sewage water (Geldreich, 1996; Raposo, Pe`rez, Faria, Ferru´s, & Carrascosa Iruzubieta, 2017), 18.8% in bottled water (Papapetropoulou, Iliopoulou, Rodopoulou, Detorakis, & Paniara, 1994), 9.0% in tap water, and 3.0% in drinking water samples (Allen & Geldreich, 1975). For vegetables and fruits, P. aeruginosa had been frequently identified in raw and fresh vegetables and fruits including lettuces, eggplants, tomatoes, spinaches, endives, minitomatoes, carrots, onions, cucumbers, celeries, radishes, and various types of salads (Correa, Tibana, & Gontijo Filho, 1991; Oie et al., 2008; Remington & Schimpff, 1981; Rodrigues et al., 2014). As a consequence, it was suggested to eliminate salads from the diets for high-risk patients (Remington & Schimpff, 1981). Furthermore, for meat, P. aeruginosa had been frequently determined in fish, bovine, and poultry meat (Benie et al., 2017; Davis & Conner, 2007). It also contributed to the scombroid poisoning due to the ingestion of fresh fish, smoked fish, or canned fish under inappropriate conditions (Liscum, 1999). Primarily isolated from the intestines and cutis of fish, P. aeruginosa is capable of expressing enzymes to induce decarboxylation process and produces high level of histamine in the muscle of fish, leading to scombroid poisoning (Geornaras, Kunene, von Holy, & Hastings, 1999; Jantschitsch, Kinaciyan, Manafi, Safer, & Tanew, 2011; Liscum, 1999). P. aeruginosa had also been identified in food from canteens, schools, and hospitals (Shooter, Cooke, Faiers, Breaden, & O’Farrell, 1971). Indiscriminate application of currently available antibiotics on animals or humans results in the spreading of bacterial antibiotic resistance capability. It is an emerging threat for food safety and public health globally (Xu et al., 2012). The estimated annual consumption in China is 140 g/person, 10 times higher than that in the United States and the United Kingdom. China is still one of the worst areas, where abuse antibiotics in animals reached 85,000 tons in 2013 (You et al., 2012; Zhou et al., 2011). According to the U.S. Food and Drug Administration and CDC, antibioticresistant microorganisms are capable of contaminating animal products like meat during the slaughtering and processing of animal food. It would further spread into products such as vegetables and fruits, which were irrigated with contaminated water, and transfer into human bodies when intaking animal products like meat, vegetables, and fruits and other contaminated products (Jaspe, Oviedo, Fernandez, Palacios, & Sanjose, 1995). As a typical “Superbug,” P. aeruginosa is considered a potentially zoonotic pathogen, which could infect animals such as bovines, dairy cows, horses, canines, felines, calves, fowls, and pigs. High prevalence and occurrence of P. aeruginosa in animals remain a leading concern in food safety, as various animal diseases caused by this pathogen have been frequently reported, including mastitis, otitis, urinary tract and gastrointestinal infections, endometritis, hemorrhagic pneumoniae, acute septicaemia, chronic wounds, and abscess (Al Bayssari, Dabboussi, Hamze, & Rolain, 2015; Haenni et al., 2015; Haenni et al., 2017; Hossain, Saha, Rahman, Singha, & Mamun, 2013; Kidd et al., 2011; Mushin & Ziv, 1973; Poonsuk & Chuanchuen, 2012). We have previously suggested carriage of bacteria in food might not be limited to food hazard. It also posed another significant concern in food safety as the occupational risk for the industrial staff including asymptomatic carriers, uncolonized individuals, and handlers (Benie et al., 2017). According to CDC, carriage of P. aeruginosa often occurs even in healthy people especially after exposure to water, which could lead to mild illnesses including skin and ear infection. Additionally, infection transmission by P. aeruginosa in food themselves or food handling was also previously reported (Kominos, Copeland, Grosiak, & Postic, 1972; Remington & Schimpff, 1981; Wright, Kominos, & Yee, 1976) Commonly identified to thrive in complex polymicrobial communities in the biofilm state attached to biotic/abiotic sites, most microorganisms rarely exist as mono-species in the planktonic state (Peters, Jabra-Rizk, O’May, Costerton, & Shirtliff, 2012). Biofilm is defined to be a microbial cell community embedded in an extracellular polymeric matrix and attached to each other, to an interface, or a substrate (Archer et al., 2011). Over 99% bacterial population, with 80% causing persistent bacterial infections in food processing plants and 65% causing human infections stay as biofilms (Antonio & Milena S, 2017; Costerton et al., 1987; Costerton, Lewandowski, Caldwell, Korber, & Lappin-Scott, 1995; Costerton, Stewart, & Greenberg, 1999; McLean, Lam, & Graham, 2012; Williams & Costerton, 2012). It has been well considered as a major concern and risk in food safety (Page, Baksh, Duveiller, & Waddington, 2009; Shi & Zhu, 2009; Srey, Jahid, & Ha, 2013). Polymicrobial interaction among foodborne microorganisms was rarely reported, although multiple species were frequently isolated from the same food sample. P. aeruginosa and S. aureus are typical biofilm former frequently isolated from dairy products, fruits and vegetables, and meats (Carmichael et al., 1998;

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Chmielewski & Frank, 2006; Iii & Demirci, 2015; Marchand et al., 2012). Both species are widely distributed in environment and are major pathogens causing a large amount of diseases and infections. The coexistence of both species poses a high risk and concern for food contamination and consumers’ or industrial staff’s infection. Considered as the representative “Superbugs” in gram-positive and -negative bacteria, S. aureus and P. aeruginosa caused high rate of animal diseases and infections. P. aeruginosa produces a variety of toxin-causing factors, including ADP-glycosyltransferase, fluorescent pigment, and β-lactamase, leading to acute intestinal diseases, skin inflammation, and other diseases and even severe acute infections such as pneumonia, meningitis, and sepsis. Quorum sensing (QS) system is a communication system between microbial cells, which can regulate bacteria to produce resistance to antibiotics, form biofilms, produce virulence factors, and attenuate host immune responses. It is currently found that the QS system in P. aeruginosa consists of four subsystems: las system, rhl system, pqs system, and iqs system (Rasamiravaka & El Jaziri, 2016). The signal molecules of each system are N-(3-oxododecanoyl)-L-homoserine lactone, N-butanoyl-L-homoserine lactone, 2-heptyl-hydroxy-4quinolone, and 2-(2-hydroxyphenyl)-thiazole-4-carbaldehyde, respectively (Lee & Zhang, 2015). The formation and spread of biofilms are important mechanism for multidrug resistance of P. aeruginosa. Considerable studies indicated that the QS system regulates all stages of biofilm formation (adhesion, maturation, aggregation, and dispersion). The las system determines the difference in bacterial biofilm structure. The signaling molecules of the system can also regulate bacterial adhesion, swimming, and formation of bacterial biofilm (Kjelleberg & Molin, 2002). The rhl system maintains the basic structure of the biofilm by regulating the production of rhamnolipids (Davey, Caiazza, & O’Toole, 2003). The pqs system may promote biofilm formation by increasing rhamnolipid production (Reen et al., 2011).

11.6.2 Food spoilage bacteria Pseudomonas Four Pseudomonas species (P. fragi, P. lundensis, P. fluorescens, and P. viridiflava) are the main food spoilage Pseudomonas. Forty percent of fruits and vegetables decay on surface are contributed by P. fluorescens and P. viridiflava. Pseudomonas spp. are important bacteria that lead to high-protein food spoilage due to their ability to degrade proteins and fats, such as pasteurized milk, and psychrotolerant property. P. fragi, P. lundensis, and P. fluorescens cause the formation of sulfides and trimethyl carbamates by breaking down proteins and fats, causing the decay of animal foods such as meat, eggs, and dairy foods with off-flavor (Holl, Behr, & Vogel, 2016; Marchand et al., 2009). They mainly cause milk spoilage in two ways: to secrete heat-stable lipohydrolase and proteolytic enzymes that can survive postpasteurization or even hyperthermia in raw milk and cause corruption during the cold storage of pasteurized milk (Dogan & Boor, 2003; Walker, 1988; Wiedmann et al., 2000). Enzymatic activities lead to degradation of milk components, which decrease the shelf-life of processed milk. Decomposition of casein by proteases cause a bitter flavor and clotting of milk. Free fatty acids are produced by hydrolysis of tributyrin and milk fat through lipases with rancid, bitter, unclean, and soapy smell. Digestion of milk fat globule membranes by lecithinase enhances the susceptibility of milk fat to the effect of lipases. Overall, the decreased organoleptic quality of fluid dairy products are contributed by the hydrolytic products of milk fats and proteins (Dogan & Boor, 2003). P. fluorescens was identified as the predominant microbe in unique spoilage of cheese with blue, nondiffusible pigment on the surface (Martin, Murphy, Ralyea, Wiedmann, & Boor, 2011). It has been determined that Pseudomonas is the leading spoilage organism in high-oxygen modified atmosphere packaging meat products. The primary virulence factors acquired by Pseudomonas are classified into adherence, antiphagocytosis, biosurfactant, iron uptake, pigment, protease, regulation, secretion system, and toxin. For adherence related virulence factors, lipopolysaccharide (LPS), flagella, and type IV pili are functional. Alginate, rhamnolipid, pyocyanin, and QS are the representative of antiphagocytosis, biosurfactant, pigment, and regulation, respectively. Concerning iron uptake, pyochelin and pyoverdine are responsible. Three proteases including alkaline protease, lasA, and lasB are common in Pseudomonas. Secretion systems mainly include type III secretion system, HcpI secretion island I, and xcp secretion system. Concerning the toxins contributing to the pathogenesis, exoASTUY and PLC are the key factors. To elucidate the innate cause of food poisoning capability, genome sequencing using PacBio sequencer and de novo assembly in combination with bioinformatics analysis were applied on two representative Pseudomonas strains, P. aeruginosa and P. putida.

11.6.3 Application of genome sequencing and bioinformatics analysis on Pseudomonas aeruginosa A comprehensive analysis of P. aeruginosa was performed through the next generation whole genomic sequencing and bioinformatics analysis (Fig. 11.3). The genome sequencing and finally assembly yielded two scaffolds including a

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megaplasmid. The chromosome has a total length of 6,430,493 bp and the GC content of 66.43%. The largest and smallest scaffold were 16,899 and 90 bp, respectively. The chromosome sequence showed 99% identity and 97% coverage with the sequence of P. aeruginosa strain H27930 and F22031. The two P. aeruginosa strains were isolated from different region but in the same year as the sequenced strain (Xu et al., 2009; Xu et al., 2007). With 5 S rRNA, 16 S rRNA, 23 S rRNA, 62 tRNAs, four other ncRNAs, and 48 repeat sequences, a total of 5,881 genes were predicted and annotated by COG category, GO term, and KEGG pathway. Among, 4,168, 4,778, and 5,210 genes were annotated with COG categories, GO terms, and KEGG pathways, respectively. The 86 KEGG pathways were classified into five types, including “Metabolism” (n 5 66), “Genetic Information Processing” (n 5 15), “Human Diseases” (n 5 3), “Environmental Information Processing” (n 5 1), and “Cellular Processes” (n 5 1). The three genes involved in “Human Diseases” encode peptidase inhibitor I42, peptidase S9, and peroxiredoxin. In addition, two genes involved in the infectious disease pathways encode alkyl hydroperoxide reductases (Xu et al., 2010; Xu et al., 2017). Two large prophage sequences and 618 secretion proteins were also identified in the chromosome. Concerning CAZy, 186, 606, 953, 1564, and 1723 genes were involved in the “Auxiliary Activities,” “Carbohydrate Esterases,” “Carbohydrate-Binding Modules” (CBM), “Glycosyl Transferases,” and “Glycoside Hydrolases”, respectively. 335 virulence factors were predicted to be acquired by the genome (Deng, Liu, Peters, et al., 2015; Xu, Li, Shi, & Shirtliff, 2011; Zhou et al., 2011). Among the virulence factors, 22 alg, 20 fle, 36 pil, 18 xcp, 18 psc, 17 pch, 16 fli, 14 che, 13 tsr, 11 bvg, 10 flg, and 9 pcr genes were identified. The alg genes compose of algU, alg44, algE, algR, algK, and algX genes (encoding alginate biosynthesis proteins); algJ, algI, and algF genes (encoding alginate o-acetyltransferases); and algQ and algP genes (encoding alginate regulatory proteins). The fle genes compose of 16 fleQ (encoding transcriptional regulators), 3 fleR (encoding two-component response regulator), and a fleN (encoding flagellar synthesis regulator). The array of pil genes is a type IV pil operon similar to that in PA96 (Deraspe et al., 2014). Psc genes encode type III export proteins, while xcp genes encode general secretion pathway proteins.

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Concerning the antibiotic resistance genes predicted in the genome of P. aeruginosa strain, bl1_PAO, bacA, mexABDEFHIWXY, opmD, and oprJM were identified. Bl1_PAO gene encodes class C β-lactamase contributes to the resistance to cephalosporin by breaking the β-lactam ring (Zhou et al., 2011). Baca gene encodes undecaprenyl pyrophosphate phosphatase responsible for bacitracin resistance based on the undecaprenyl pyrophosphate sequestration (Deng, Liu, Peters, et al., 2015). MexABDEFHIWXY genes encode resistance-nodulation-cell division transporter system responsible for resistance to β_lactam, aminoglycoside, glycylcycline, fluoroquinolone, tetracycline, roxithromycin, chloramphenicol, erythromycin, and tigecycline, OprJM genes encode resistance-nodulation-cell division transporter system responsible for resistance to β_lactam, tetracycline, roxithromycin, aminoglycoside, erythromycin tigecycline, glycylcycline, and fluoroquinolone.

11.6.4 Application of genome sequencing and bioinformatics analysis on Pseudomonas putida A comprehensive analysis of P. putida was performed through the next generation whole genomic sequencing and bioinformatics analysis. The genome sequencing and finally assembly yielded one scaffold. The genome has a total length of 6,031,212 bp and the GC content of 62.01%. The genome sequence showed 99% identity and 93% coverage with the sequence of P. putida strain GB-1. With 8 5 S rRNA, 7 16 S rRNA, and 7 23 S rRNA; 77 tRNA genes; 74 repeat sequences; and IS including three ISCR1 and two integrons (In528 and In1348), a total of 5,421 genes were predicted and annotated by COG category, GO term, and KEGG pathway. A total of 134 genes were assigned as virulence factors (Liu et al., 2018b). Type IV pili (fimV and pilADGHIJQRT identified) is capable of attach to host cells, contributing to the twitching ability and biofilm formation (Hahn, 1997; Mattick, 2002; O’Toole & Kolter, 1998). Flagella encoding genes responsible for the biofilm formation, swimming ability, and other pathogenic adaptations (Adamo, Sokol, Soong, Gomez, & Prince, 2004; Feldman et al., 1998; O’Toole & Kolter, 1998) were identified. LPS related genes waaACFGP were identified mediating the resistance to serum killing and phagocytosis (Lyczak, Cannon, & Pier, 2000; Rocchetta, Burrows, & Lam, 1999; Schroeder et al., 2002). In the genome, six antibiotic resistance genes were identified, with three (aacA4 and strAB) encoding aminoglycoside modifying enzymes including aminoglycoside N-acetyltransferase and aminoglycoside O-nucleotidylyltransferase/ phosphotransferase. They are capable of modifying aminoglycosides through acetylation, adenylation, and phosphorylation. Thus, the three genes confer resistance to amikacin, dibekacin, isepamicin, netilmicin, sisomicin, tobramycin, and streptomycin. Encoding class B β-lactamase, blaVIM-2 gene contributes to the resistance to penicillin, cephamycin, carbapenem, and cephalosporin by breaking the β-lactam ring. Two copies of qnrVC6 gene conferring resistance to fluoroquinolone were also identified.

11.7

The application of innovative analysis on Bacillus

11.7.1 Food spoilage bacteria Bacillus Bacilli are spore-forming bacteria which are usually associated with spoilage of sterilized food due to the survival of their spores in the heating process. When spores exposed to a warm, moist environment, they germinate and cause decay of baked food. Bacilli including B. licheniformis and B. subtilis are able to survive at 55 C, so they would present in some heat-treated food. B. subtilis and occasionally B. licheniformis, B. pumilus, and B. cereus cause baked food soften and sticky due to the production of extracellular slimy polysaccharides together with unpleasant fruit odor and patchy discoloration (De Bellis et al., 2015; Fernandez-No et al., 2011; Pepe, Blaiotta, Moschetti, Greco, & Villani, 2003). They are capable of growth with or without oxygen producing enzymes resulting in “sweet curdling” and “bitty cream” in milk (Huis in ‘t Veld, 1996). Bacillus is a classification of the spoilage bacteria in raw and pasteurized milk, supporting by numerical phenotypic analysis. B. polymyxa and B. cereus are responsible for 77% of food samples spoiled by the gram-positive microorganisms (Ternstrom, Lindberg, & Molin, 1993). In addition, acidophilic Bacillus, such as B. acidocaldarius, is capable of growing at low pH between 3.0 and 5.3 and spoiling juices by producing offflavors (DF, Churey, & Lee, 1994). However, producing food poisoning toxin is more dangerous for Bacillus. B. cereus and B. thuringiensis cause diarrheal and the emetic type of food poisoning by producing enterotoxin and emetic toxin (Granum & Lund, 1997). Two B. cereus strains and one B. thuringiensis were adapted to genome resequencing and de novo sequencing, respectively, in combination with bioinformatics analysis.

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11.7.2 Application of genome sequencing and bioinformatics analysis on Bacillus cereus The genome of the two B. cereus strains both have a length of 5.41 Mb (Fig. 11.4). After filtering, 2,305,276 and 4,295,924 clean reads (aligned rate as 70.26% and 68.47%) were matched to the reference genome sequence of B. cereus ATCC14579, respectively. The depth of resequencing was 90 3 , with the genomic coverage of more than 89%. The genomic reads were quality checked with the Q20 . 90.7% and Q30 . 84.5% and qualified for the further analysis. A total of 30,343 and 16,532 SNPs were identified, respectively. SNPs occurred in the intronic, exon, and intergenic regions. InDel include frameshift/nonframeshift insertion/deletion and stopgain/stoploss. In the genome, 63/43 insertions and 132/85 deletions were identified. A total of 409, 67, and 552 GO terms were identified in biological process, cellular component, and molecular function, respectively. Among the 409 GO terms in biological process, transmembrane transport, proteolysis, oxidationreduction process, DNA dependent, acyl carrier protein biosynthetic process, and regulation of transcription were significantly enriched. Among the 67 GO terms in cellular component, membrane, cytoplasm, integral to membrane, and plasma membrane were significantly enriched. Among the 552 GO terms in molecular function, hydrolase activity, ATP binding, sequence-specific DNA binding transcription factor activity, and DNA binding were significantly enriched. A total of 918 COG categories classified into 21 subcategories were identified. The COG category enriched in [R] General function prediction only (125/14.05%), [E] Amino acid transport and metabolism (107/11.66%), and [M] Cell wall/membrane/envelope biogenesis (72/7.84%). A total of 53 KEGG pathways were identified, enriching in Metabolic pathways (34 unigenes), biosynthesis of secondary metabolites (15 unigenes), and ABC transporters (12 unigenes). 49 genes were identified to associate with the response to high osmotic pressure (Granum & Lund, 1997), including stress response, Na/H, K transporter, dipeptide or tripeptide transporter related genes.

11.7.3 Application of genome sequencing and bioinformatics analysis on Bacillus thuringiensis A comprehensive analysis of B. thuringiensis was performed through the next generation whole genomic sequencing (PacBio) and bioinformatics analysis. The genome sequencing and finally assembly yielded one scaffold. The genome has a total length of 5,246,329 bp and the GC content of 35.40%. The chromosome sequence showed 99% identity and 90% coverage with the sequence of B. thuringiensis strain HD682. However, the genome was approximately 45,000 bp shorter than HD682 (Johnson et al., 2015), indicating the missing of some sequences during evolution (S. Lin et al., 2016; Xu et al., 2016). With 5 S rRNA, 16 S rRNA, 23 S rRNA, 106 tRNA, and 74 repeat sequences, a total of 5,409 genes were predicted and annotated by COG category, GO term, and KEGG pathway. Among, 5,110, 3,556, and 2,626 genes were annotated with COG categories, GO terms, and KEGG pathways, respectively. The 82 KEGG pathways were classified into five types, including “Metabolism” (n 5 66), “Genetic Information Processing” (n 5 13), “Environmental Information Processing” (n 5 1), “Cellular Processes” (n 5 1), and “Human Diseases” (n 5 1). “Infectious diseases: Amoebiasis” pathway was the only pathway related to “Human Diseases” (Xu et al., 2009; Xu et al., 2007). The three genes (rocF, ahpC, and an unnamed gene) involved in “Human Diseases” encode arginase,

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peroxiredoxin, and aldehyde dehydrogenase family protein, respectively. One medium prophage sequence and 172 secretion proteins were also identified in the chromosome. Concerning CAZy, the predicted genes were involved in the “Auxiliary Activities,” “Carbohydrate Esterases,” “CBM,” “Glycosyl Transferases,” and “Glycoside Hydrolases”. Among the 21 virulence factors identified (Xu, Li, Shi, & Shirtliff, 2011; Xu et al., 2010; Xu et al., 2017), 4 clp genes (clpC, clpE, and 2 clpP) encode endopeptidase Clp ATP-binding chain C, ATP-dependent protease, and ATPdependent Clp protease proteolytic subunit. Two each of colA and hasC genes encode collagenase and UDP-glucose pyrophosphorylase, respectively. Cap8D and cap8M encode capsular polysaccharide synthesis enzyme. acfB, essC, htpB, insF, lmo0931, pvdD, pfoA, yscN, and encode accessory colonization factor AcfB, hypothetical protein, heat shock protein HtpB, pyoverdine synthetase D, putative Yops secretion ATP synthase, hypothetical protein, and lipoate protein ligase, respectively. Among the nine identified antibiotic resistance genes, four of baca genes encoding undecaprenyl pyrophosphate phosphatase contribute to bacitracin resistance (Deng, Liu, Peters, et al., 2015). Bcra gene encoding bacitracin efflux pump and ABC transporter system also confer resistance to bacitracin (Zhou et al., 2011). Bl2a_1 and bl2a_III genes encoding class A β-lactamase are responsible for penicillin resistance (Yu et al., 2016). Fosb gene encoding glutathione/metalloglutathione transferase contributes to the resistance of fosfomycin (Xu, Shi, Alam, Li, & Yamasaki, 2008). Vanrb (2,988,555 to 2,989,292 bp) is a part of the vanB type vancomycin resistance operon (Z. Xu, Li, et al., 2008).

11.8

The application of innovative analysis on lactic acid bacteria

11.8.1 Food spoilage lactic acid bacteria Lactic acid bacteria are facultative anaerobic bacteria. Due to their utilization of fermentation of carbon hydrated to produce lactic acid, they are useful in producing yogurt and pickles. However, these bacteria become the predominant spoilage microorganism under acidic, low temperature, and low oxygen conditions. Lactic acid bacteria can be divided into at least 18 genera, with more than 200 species. But only a few of them have ability to cause spoilage of food. Lactobacillus and Pediococcus are considered to be the most hazardous bacteria in brewing industry, since they are responsible for nearly 70% of the microbial beer-spoilage events. They cause turbidity and buttery odor and sourness in alcohol beverage mainly due to the formation of diacetyl, acetic acid, lactic acid, and extracellular polysaccharide (Back, 1994; Bevilacqua, Corbo, & Sinigaglia, 2016). In wine industry, Pediococcus produce lactic acid through malolactic fermentation. Their secondary metabolites are mainly volatile substances, so if they are rapidly propagated in the wrong time period, they will affect the flavor of the wine and become spoilage microorganism (Bevilacqua et al., 2016). Currently, there are 12 species of Pediococcus, of which only P. damnosus can survive in condition of 10% NaCl even above and low-pH(4.5) (Holland, Crow, & Curry, 2011). Among the components of beer, hop compounds protect beer from spoilage by most bacteria. Most of the bacteria are inhibited by the low acidity of the beer and the antiseptic effect of the hops, but P. damnosus, L. brevis, and L. lindneri have antihop properties and can adapt to the beer environment, resulting in prolonged fermentation time and high levels of diacetyl accumulation, bringing butter odor or sour taste to the beer accompanied with sticky silk and turbidity (Back, 1994). As one of the most popular drinks, beer is considered to be safe as the high microbiological stability. Beer poses stresses to microorganisms, including hop bitter compounds, high carbon dioxide concentration, low pH, high ethanol concentration, low oxygen, and limited nutrient (Sakamoto & Konings, 2003; Suzuki, Iijima, Sakamoto, Sami, & Yamashita, 2006). Most microbial species are unable to survive, grow, and metabolite in beer. However, bacterial species designated as beer-spoilage microorganisms primarily including Lactobacilli and Pediococci could survive and cause spoilage. Nowadays, routine beer-spoilage bacteria detecting culture media even unable to identify specific lactic acid bacteria species, leading to spoilage, food safety concerns, and profit loss (Deng et al., 2014b). It is considered to be contributed by their capability to enter into the viable but/putative non-culturable (VBNC/VPNC) state. VBNC cells have been identified to acquire various phenotypes, including decreased metabolic activity, reduced size, and changed cell wall structure (Nowakowska & Oliver, 2013). Lactic acid bacteria entered into the VBNC state in wine and beer causing spoilage cases. Thus, the VBNC state of foodborne bacteria is being a challenge in food safety. Beyond beer beverage, lactic acid bacteria are also commonly found in vacuum packaging and modified atmosphere packaging meat products knowing as dominant spoilage flora under anaerobic conditions. They are resistant to the bacteriostatic action of nitrite and smoke in processed meat products, and tolerate higher concentrations of salt. Lactic acid bacteria utilize the carbohydrates in the fermented meat to produce sour taste, cheese flavor, and the smell of the animal liver, sometimes accompanied by the production of carbon dioxide. According to the current reports, meat-related lactic

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acid bacteria are Lactobacillus, Lactococcus, Leuconostoc, Carnivora, and Weissella (Y Zhang, W Zhang, Dong, HAO, & Luo, 2018). Among, Lactobacillus spp. are most common spoilage bacteria in packaging meat food with characteristics of sour taste due to the production of lactic acid, acetic acid, and formic acid. Also, this off-odors are related to the formation of acetylene vinyl and 3-methylbutanol (Egan, Shay, & Rogers, 1989). It has been showed that L. sakei, L. curvatus, L. algidus, L. fuchuensis, and L. oligofermentans are related to the decay of poultry, cured meat, and ground pork in vacuum packaging and modified atmosphere packaging with unpleasant smell, acidification, and the presence of mucus (Dainty & Mackey, 1992; Pothakos, Devlieghere, Villani, Bjorkroth, & Ercolini, 2015). L. sakei strains isolated from all the 17 low-acid fermented sausages were determined to be a dominant spoilage bacterium in vacuum packaging processing of meat products. They are capable of producing greenish changes in meat products caused by hydrogen sulfide, resulting in muscle pigmentation converted to green vulgaris myoglobin (Comi & Iacumin, 2012; Egan et al., 1989). Formation of organic acid by Leuconostoc gelidum, Leu. carnosum, and Leu. mesenteroides also causes “cheese flavor,” forming of mucus, production of gas, and greening in some meat products (Aymerich, Martin, Garriga, & Hugas, 2003; Y Zhang et al., 2018). Spoilage induced by lactic acid bacteria is common in dairy food like milk. Lactic acid bacteria contribute flavor defects, “sour” off-flavors by producing acetic, and propionic acids as a by-product of metabolic reactions (Bevilacqua et al., 2016). In the late 1940’s, high incidence of raw milk spoilage with malty flavor was contributed by Lactococcus lactis. And in 1947, various aldehydes and alcohols such as 2-methylbutanal and 3-methylbutanal were concluded to create a “malty flavor” in milk by L. lactis (Morgan, 1976). Milk spoilage caused by lactic acid bacteria is due to the inappropriate storage condition, especially the temperature. It has been demonstrated that the relative proportions of L. lactis which are the major raw milk bacterial component decreases after stored at 4 C for 24 h (Lafarge et al., 2004). The phenomenon of cheese spoilage is related to lactic acid bacteria. Deterioration of cheese produces unpleased odors, gases, and the formation of white calcium lactate crystals on the surface, which are contributed by nonstarter lactic acid bacteria (NSlactic acid bacteria). The surface of the device for producing a stirred curd-type cheddar cheese form an erythrocyte-resistant lactic acid bacteria (L. curvatus or L. fermentum) biofilm. During the processing, the biofilm peel off and contaminated products, and the formation of the biofilm allows them to survive the sterilization process and eventually lead to deterioration of the cheese (Somers, Johnson, & Wong, 2001). Lactobacillus spp. also cause spoilage of other products. L. alimentarius was the dominant gas-borne spoilage causing pickled herring. Unlike “carbohydrate swell,” it causes a slight increase in the pH of canned fish products. Meyer et al. refer to this form of decay as “protein swell” (Lyhs, Korkeala, Vandamme, & Bjorkroth, 2001). L. acetotolerans was able to survive in an environment with acetic acid concentrations above 4% and cause vinegar spoilage (Entani, Masai, & Suzuki, 1986). The food spoilage capability of lactic acid bacteria was analyzed by genomic sequencing using Illumina Hiseq 2500 sequencer in combination with bioinformatics analysis on three strains including L. acetotolerans, L. casei, and L. harbinensis, as well as RNA-seq on the VBNC state of L. acetotolerans.

11.8.2 Application of genome sequencing and bioinformatics analysis on Lactobacillus acetotolerans As a member of the lactic acid bacteria, L. acetotolerans is capable of producing diacetyl and organic acids as end products during carbohydrate fermentation. High concentration of diacetyl imparts an undesirable and unpalatable “buttery” taste and oily mouthfeel to beer. The spoilage capability is complicated by the problem that L. acetotolerans strains are difficult to detect by traditional culture-based techniques, partly due to their capability of entering into the VBNC state under microbial stress conditions (Deng, Liu, Li, et al., 2015). Inability to identify L. acetotolerans by routine culturing methods leading to false-negative detection and subsequent beer spoilage has been considered to be the major concern in the beer brewery industry, resulting in significant profit loss (Deng et al., 2014a). The genome sequence of L. acetotolerans strain was established to provide the genomic basis for further functional analysis regarding beer spoilage and regulation of the VBNC state. A comprehensive analysis of L. acetotolerans was performed through the next generation whole genomic sequencing (Illumina Hiseq 2500) and bioinformatics analysis. The genome sequencing and finally assembly yielded 100 scaffolds. The genome has a total length of 1,542,492 bp and the GC content of 36.35%. The largest scaffold has a length of 114,884 bp with the length of N50 scaffold reaches 32,098 bp. With 4 rRNA and 23 tRNA genes, a total of 1,824 genes were predicted and annotated by COG category, GO term, and KEGG pathway. Numerous genes possibly contributing to the VBNC state were found in the L. acetotolerans genome. The adaptation of bacteria to varying environmental conditions for better survival is a prior factor for entering into the VBNC state. Thus, genes associated with stress adaptation were identified, including those involved in defense (n 5 4), resistance (n 5 7), SOS response (n 5 10), and stress response (n 5 17) pathways. The acquisition of stress adaptation related

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genes indicated the ability of L. acetotolerans strain to export harmful substances to remain viable (Piddock, 2006). Oxidative stress response related gene has been reported to be responsible to bacterial cold stress response (Chattopadhyay et al., 2011). The missing of oxidative stress response related gene in the genome of L. acetotolerans strain explained its nonculturability under cold stress condition. Genes related to the formation of VBNC state also included those encoding transcriptional regulators (n 5 27), predicted membrane protein (n 5 16), signal transduction (n 5 9), and amino acid transporters (n 5 8). Amino acids, which are important growth substrates in bacteria, have been reported to be essential to maintain the balance of carbon-nitrogen concentration in bacterial VBNC cells (Postnikova, Shao, Mock, Baker, & Nemchinov, 2015). Genes acting as signal transducers and transcriptional regulators could induce the adaption of bacteria to varying environmental conditions by quickly modifying cellular physiological function and progression (Skerker, Prasol, Perchuk, Biondi, & Laub, 2005). In a recent report, the sigma factor encoded by rpoS genes played key roles in enhancing stress resistance (Nowakowska & Oliver, 2013) and was associated with the VBNC state (Gonzalez-Escalona, Fey, Hofle, Espejo, & C, 2006), transcriptional control of rpoS involves the accumulation of guanosine 30 ,50 -bispyrophosphate (ppGpp; Cashel M, Hernandez, & Vinella, 1996). However, rpoS gene was absent in the genome of L. acetotolerans. Sigma factor RpoD and RpoE which may be alternative sigma factors and three genes encoded ppGpp involved in the VBNC state of L. acetotolerans. In addition, genes may be associated with beer spoilage acquired by the genome. As for bacterial persistence, the genes related to hop resistance may contribute to its ability to survive and grow in beer. The acquisition of horA, horB, and horC genes has been identified to be responsible for hop resistance (DiMichele & Lewis, 1993; Suzuki, Asano, Iijima, & Kitamoto, 2008). A homolog of the horA gene in L. paracollinoides (Suzuki, Sami, Ozaki, & Yamashita, 2004) was found in the L. acetotolerans genome, potentially explaining its capability to grow in beer. HorA gene acts as a genetic marker to varied beer-spoilage capability of various Lactobacillus species (Sami et al., 1997). HorA, encoded by horA gene, is a multidrug transporter and extrudes toxic hop compounds out of the cells, which contributes to the hop resistance of lactic acid bacteria (Sakamoto, Margolles, van Veen, & Konings, 2001). It has been reported that the presence of horA definitively correlates with lactic acid bacteria growth in beer (Haakensen, Schubert, & Ziola, 2008). Genes encoding enzymes involved in metabolic pathways for lactic acid, acetic acid, and diacetyl byproducts from various sugars by heterofermentative lactic acid bacteria (Abdel-Rahman, Tashiro, & Sonomoto, 2013) were also found in the genome of L. acetotolerans, providing rationale for its ability to impart unpleasant characteristics to beer.

11.8.3 Application of genome sequencing and bioinformatics analysis on Lactobacillus casei As a member of the lactic acid bacteria, L. casei is capable of producing diacetyl and organic acids as end products during carbohydrate fermentation. High concentration of diacetyl imparts an undesirable and unpalatable “buttery” taste and oily mouthfeel to beer. The spoilage capability is complicated by the problem that L. casei strains are difficult to detect by traditional culture-based techniques, partly due to their capability of entering into the VBNC state under microbial stress conditions (Deng, Liu, Li, et al., 2015). Inability to identify L. casei by routine culturing methods leading to false-negative detection and subsequent beer spoilage has been considered to be the major concern in the beer brewery industry, resulting in significant profit loss (Deng et al., 2014a). The genome sequence of L. casei strain was established to provide the genomic basis for further functional analysis regarding beer spoilage and regulation of the VBNC state. A comprehensive analysis of L. casei was performed through the next generation whole genomic sequencing (Illumina Hiseq 2500) and bioinformatics analysis. The genome sequencing and finally assembly yielded 196 scaffolds. The genome has a total length of 3,045,511 bp and the GC content of 46.12%. The largest scaffold has a length of 128,069 bp with the length of N50 scaffold reaches 26,583 bp. With 23 S rRNA and 16 S rRNA and 22 tRNA genes, a total of 3,964 genes were predicted and annotated by COG category, GO term, and KEGG pathway. Numerous genes possibly contributing to the VBNC state were found in the L. casei genome. The adaptation of bacteria to varying environmental conditions for better survival is a prior factor for entering into the VBNC state. Thus, genes associated with stress adaptation were identified, including those involved in defense (n 5 44), detoxification mechanism (n 5 2), and associated with defense and resistance (antibiotics resistance, metal resistance, etc.; n 5 85), and stress response (n 5 16) pathways. The acquisition of stress adaptation related genes indicated the ability of L. casei strain to export harmful substances to remain viable (Piddock, 2006). Oxidative stress response related gene has been reported to be responsible to bacterial cold stress response (Chattopadhyay et al., 2011). The nine oxidative stress response related gene in the genome of L. casei strain explained its viability and nonculturability under cold stress condition. ABC transporters (n 5 112) are necessary for nutrient and toxic molecule metabolism (Davidson, Dassa, Orelle, & Chen, 2008), playing a key role in the adaptation to varying environment conditions, thus survive by the formation

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of the VBNC state. Genes related to the formation of VBNC state also included those encoding signal transduction (n 5 44), transcriptional regulators (n 5 138), amino acid transporters (n 5 163), and predicted membrane protein (n 5 167). Amino acids, which are important growth substrates in bacteria, have been reported to be essential to maintain the balance of carbon-nitrogen concentration in bacterial VBNC cells (Postnikova et al., 2015). Genes acting as signal transducers and transcriptional regulators could induce the adaption of bacteria to varying environmental conditions by quickly modifying cellular physiological function and progression (Skerker et al., 2005). Seven MarR family transcriptional factors are responsible for the oxidative stress resistance, harmful chemical exportation, and toxic compounds and virulence degradation (Grove, 2013). Four LysR family transcriptional factors are ortholog of OxyR in E. coli, which is sensitive to oxidation and activates the antioxidant genes expression responding to H2O2 (Zheng, Aslund, & Storz, 1998). In addition, genes may be associated with beer spoilage were also acquired by the genome. As for bacterial persistence, the genes related to hop resistance and alcohol-tolerance may contribute to its ability to survive and grow in beer. The acquisition of horA, horB, and horC genes has been identified to be responsible for hop resistance (DiMichele & Lewis, 1993; Suzuki et al., 2008). A homolog of the horA gene in L. brevis plasmid was found in the L. casei genome, potentially explaining its capability to grow in beer. Genes encoding enzymes involved in metabolic pathways for lactic acid, acetic acid, and diacetyl byproducts from various sugars by heterofermentative lactic acid bacteria (Abdel-Rahman et al., 2013) were also found in the genome of L. casei, providing rationale for its ability to impart unpleasant characteristics to beer.

11.8.4 Application of genome sequencing and bioinformatics analysis on Lactobacillus harbinensis As a new member of the beer-spoilage lactic acid bacteria, L. harbinensis is capable of producing diacetyl and organic acids as end products during carbohydrate fermentation. A comprehensive analysis of L. harbinensis was performed through the next generation whole genomic sequencing (Illumina Hiseq 2500) and bioinformatics analysis. The genome has a total length of 3,017,769 bp and the GC content of 53.36%. A total of 4,378 genes were predicted and annotated by COG category, GO term, and KEGG pathway. Numerous genes possibly contributing to the VBNC state were found in the L. harbinensis genome. The adaptation of bacteria to varying environmental conditions for better survival is a prior factor for entering into the VBNC state. Thus, genes associated with stress adaptation (n 5 22) were identified. The acquisition of stress adaptation related genes indicated the ability of L. harbinensis strain to export harmful substances to remain viable (Piddock, 2006). Oxidative stress response related gene has been reported to be responsible to bacterial cold stress response (Chattopadhyay et al., 2011). The acquisition of oxidative stress response related gene (n 5 6) in the genome of L. harbinensis strain explained its viability and nonculturability under cold stress condition. In a recent report, the sigma factor encoded by rpoS genes played key roles in enhancing stress resistance (Nowakowska & Oliver, 2013) and was associated with the VBNC state (Gonzalez-Escalona et al., 2006); transcriptional control of rpoS involves the accumulation of guanosine 3’,5’-bispyrophosphate (ppGpp; Cashel M et al., 1996). However, rpoS gene was absent in the genome of L. harbinensis. Sigma factor RpoD and six other RNA polymerase genes which may be alternative sigma factors were involved in the VBNC state of L. harbinensis.

11.8.5 Application of RNA sequencing and bioinformatics analysis on Lactobacillus acetotolerans RNA-seq combined with bioinformatics analysis was performed on normal, mid-term and VBNC states of 3 L. acetotolerans strains. 66/56 (normal vs mid-term), 35/74 (normal vs VBNC), and 72/101 (mid-term vs VBNC) up/down-regulated DEGs were identified. In normal versus mid-term and normal versus VBNC groups, 32 DEGs were in common. In mid-term versus VBNC and normal versus VBNC groups, 53 DEGs were in common. In normal versus mid-term, normal versus VBNC, and mid-term versus VBNC groups, 11 DEGs were in common. However, hop resistance gene horA was not differentially expressed. GO term enrichment analysis was conducted for the DEGs. Among the enriched GO terms of total DEGs, organic phosphonate transport was significantly enriched in biological process, outer membrane-bounded periplasmic space was significantly enriched in cellular component, and organic phosphonate transmembrane transporter activity was significantly enriched in molecular function. Among the enriched GO terms of up-regulated DEGs, carbohydrate metabolic process was significantly enriched in biological process, and 1-phosphofructokinase activity was significantly enriched in molecular function. Among the enriched GO terms of down-regulated DEGs, organic phosphonate transport was

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significantly enriched in biological process, and organic phosphonate transmembrane transporter activity was significantly enriched in molecular function. It has been reported that lactobacilli could change their carbohydrate transportation and metabolism capability with adaption to varied carbon source to withstand stress conditions (Bove et al., 2012; Siragusa et al., 2014; Wang et al., 2013; Wu et al., 2011). During the VBNC state formation, the up-regulation of the carbohydrate metabolic process might be a stress and nutrient depletion adaption strategy of L. acetotolerans. NADPH-generating system activity has been regarded as key factor to maintain the viability of bacterial cells in the VBNC state (Postnikova et al., 2015). Some up-regulated genes annotated with GO terms of response to stress, stress resistance, and regulation of transcription, DNA-dependent might contribute to the formation the VBNC state (Postnikova et al., 2015). The transcriptomics study on the VBNC state of Rhodococcus showed coenzyme binding as the key up-regulated GO term in molecular function (Su et al., 2015; Su, Guo, Ding, Qu, & Shen, 2016). Combined with our data, it indicated coenzyme binding might play an important role in the VBNC state formation. In similarity with the morphological change, the downregulation of DEGs involved in the GO terms of biosynthetic process, enzyme activity, transport process, translation, and transporter activity might minimize the nutrient dependence or biochemical activity in VBNC cells to survive in stress conditions. COG category enrichment analysis was conducted for the DEGs. Among the enriched COG categories of total DEGs, [P] Inorganic ion transport and metabolism and [V] Defense mechanisms were significantly enriched. Among the enriched COG categories of down-regulated DEGs, [P] Inorganic ion transport and metabolism were significantly enriched. Amino acids, which are important growth substrates in bacteria, have been reported to be essential to maintain the balance of carbon-nitrogen concentration in bacterial VBNC cells (Postnikova et al., 2015). The up-regulated COG categories enriched in stress-responsive transcriptional regulator and defense mechanisms might suggest that L. acetotolerans activated defense and stress response systems to export harmful substances to remain viable (Piddock, 2006). KEGG pathway enrichment analysis was conducted for the DEGs. Among the enriched KEGG pathways, ABC transporters were significantly enriched. The ABC transporters pathway plays important roles in the essential nutrient importation and toxic molecule exportation (Davidson et al., 2008). The ABC transporters might induce the adaption of bacteria to varying environmental conditions by quickly modifying cellular physiological function and progression (Postnikova et al., 2015). Enzymatic increase to modify pyruvate metabolism has been confirmed for bacterial survival in starvation or acid stress conditions for lactobacilli (Al-Naseri, 2013; Broadbent, Walsh, & Upton, 2010; Fernandez et al., 2008; Siragusa et al., 2014; Zotta et al., 2012). The up-regulation of DEGs in the pyruvate metabolism pathway indicated that L. acetotolerans might up-regulate relevant enzymes to survive in the acid and starved environment of beer.

11.9

Analysis strategy for food pathogens

Genome sequencing, RNA-seq, and bioinformatics analysis are useful, efficient, and innovative tools for the study on food pathogens and spoilage microorganisms. However, different analysis methods and their combination result in diverse consequences. The combination of genome de novo sequencing using both second-and third-generation platforms, RNA-seq, and bioinformatics analysis is an effective strategy for our understanding of the innate genetic characteristics and the explanation of phenotypic behavior of food pathogens and spoilage bacteria. The genome is the foundation to the thorough comprehension of the innate and internal characterization of a microorganism. Genome sequencing is the initial strategy to investigate the innate mechanism explaining the phenotypic behavior of food microorganisms, including food spoilage and pathogenesis. The second- and third-generation sequencing platforms are widely applied to analyze genome and have their advantages and disadvantages. For second-generation sequencing platform, the genomic DNA should be cut to small fragments. This results in the difficulty in sequence assembly. Thus, the genome sequence yielded by the second-generation sequencing platform is always composed of contigs or scaffolds, which means sequence overlaps or deletion happen. In addition, plasmid cannot be determined. For example, in 9.8.2, L. acetotolerans strain genome yielded 100 scaffolds by second-generation genome sequencing. However, the sequences assembled from the second-generation sequencing reads are accurate and reliable. For third-generation sequencing platform, the genomic DNA is cut to relative larger fragment or does not need to be cut. The assembly of reads is easier and always yields complete genome and plasmids. However, errors would occur with a higher possibility during the sequencing than the second-generation sequencing. Thus, the combination of second- and third-generation sequencing is an effective strategy to get complete genome and plasmids and lower the error rate. Genome sequencing gives us background information about the intrinsic genes and functions of food pathogens or spoilage microorganisms. The explanation of their phenotypic behavior is to be revealed by RNA-seq. Upon RNA-seq, the transcriptome of a strain under certain state is clearly illustrated. The transcriptome is the complete set of transcripts

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in a cell, and their quantity, for a specific developmental stage or physiologic condition (Wang & Wood, 2011). Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues and also for understanding development and disease. The comparison between different transcriptomes gives us insight on the specific phenotypic behavior between two samples. For example, in 9.4.4, RNA-seq and bioinformatics analysis were performed on different C. sakazakii strains (wild type and pmrA mutant) and the same strain grown for different time, respectively. The transcriptomes of the two strains is a comparative group, while the transcriptomes of the same strain grown for different time is a comparative group. The DEGs and enriched GO terms and KEGG pathways differ between the two comparative groups. However, the reads from RNA-seq should be mapping onto a reference genome. If the genome of the strain for RNA-seq is not available, a genome sequence from a relatively similar strain could be used. But the sequence coverage would reduce which decreasing the amount of information acquired and its reliability. To better understand the phenotypic behavior of food pathogens or spoilage microorganisms, such as poisoning, the combination of genome de novo sequencing using both second- and third-generation platforms, RNA-seq, and bioinformatics analysis is served as an effective solution. For example, in 9.5.2, to analyze foodborne pathogen S. aureus, RNA-seq and bioinformatics analysis were performed on three biological replicates of S. aureus biofilm grown with and without 1/4 MIC of ampicillin. The S. aureus genome was previously sequenced and served as the reference genome during RNA-seq data analysis. RNA-seq combined with bioinformatics analysis was performed on S. aureus biofilm grown with and without 1/4 MIC of ampicillin. A total of 530 DEGs with 167 and 363 genes showing up- and down-regulation, respectively, were determined by the comparison of gene expression levels between samples (the same strain grown for different time). Six of the up-regulated DEGs with |log2(fold change)| . 2 and 10 of the DEGs with |log2(fold change)| . 4 were identified to be the most DEGs. The six up-regulated DEGs encode PTS system lactose-specific transporter subunit IIBC, riboflavin biosynthesis protein, argininosuccinate synthase, argininosuccinate lyase, 6-phospho-beta-galactosidase, and cation transporter E1-E2 family ATPase. The 10 down-regulated DEGs encode ornithine carbamoyltransferase, 2-isopropylmalate synthase, arginine/ornithine antiporter, 23 S rRNA, carbamate kinase, phage head protein, and hypothetical proteins. Concerning the biofilm formation of S. aureus, DEGs in ampicillintreated biofilm sample encode adhesins, surface proteins, proteases, capsular polysaccharide proteins, and virulence factors (Wang & Wood, 2011). The genes encoding adhesins Fib and SdrD, and genes encoding proteases ClpB and ClpC, required for biofilm formation, intracellular replication, and stress tolerance, were significantly down-regulated (Frees et al., 2004). Significantly up-regulated cap5B and cap5C genes mediate the capsular polysaccharide biosynthesis. With the treatment of low concentration of ampicillin, S. aureus strain might induce biofilm formation through capsular polysaccharides synthesis. The master regulator of cysteine metabolism, cymR, contributing to biofilm formation of S. aureus, and a novel virulence protein, spdC, controlling the activity of histidine kinase, were not DEGs (Poupel et al., 2018; Soutourina et al., 2009). In this example, the up-regulation of related genes and pathways resulted in the higher biofilm formation capability with the addition of 1/4 ampicillin.

11.10 Conclusion Genome sequencing, RNA-seq, and bioinformatics analysis are useful, efficient, and innovative tools for the study on food pathogens and spoilage microorganisms. Genome sequencing is a basic and initial strategy to investigate the genetic mechanism explaining the phenotypic behavior of food microorganisms. It gives us background information about the intrinsic genes and functions of food pathogens or spoilage microorganisms. RNA-seq offers an unbiased tool to determine the transcriptomes of a species under multiple conditions. Understanding the transcriptome is essential for interpreting the functional elements of the genome and revealing the molecular constituents of cells and tissues and also for understanding development and disease. After genome sequencing or RNA-seq, bioinformatics analysis is required to understand the internal characteristic. The application of these innovative analysis strategies on different food pathogens and spoilage microorganisms, including Enterobacter, Staphylococcus, Pseudomonas, Bacillus, and lactic acid bacteria, with their characteristics were introduced, suggesting the combination of these tools is served as an effective solution for better understanding their phenotypic behavior.

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