Evaluation technologies for food quality 9780128142189, 0128142189, 9780128142172

Table of contents :
Content: 1. An introduction to evaluation technologies for food quality / Jian Zhong, Xichang Wang. Part One: Food sensory evaluation technologies for food quality: 2. Electronic nose for food sensory evaluation / Yaoguang Zhong --
3. Electronic tongue for food sensory evaluation / Wenli Wang, Yuan Liu --
4. Electronic eye for food sensory evaluation / Changhua Xu. Part Two: Chemical analysis technologies for food quality: 5. Mature chemical analysis methods for food chemical properties evaluation / Wellington da Silva Oliveira, Daniela Andrade Neves, Cristiano Augusto Ballus --
6. Ultraviolet-visible spectroscopy for food quality analysis / A.C. Power, J. Chapman, S. Chandra, D. Cozzolino --
7. Near-infrared spectroscopy for food quality evaluation / Xichang Wang --
8. Raman instruments for food quality evaluation / Tianxi Yang, Bin Zhao, Lili He --
9. Atomic absorption spectroscopy for food quality evaluation / M.N. Mohd Fairulnizal, B. Vimala, D.N. Rathi, M.N. Mohd Naeem --
10. Determination of food quality using atomic emission spectroscopy / Rohit Thirumdas, Madhura Janve, Kaliramesh Siliveru, Anjineyulu Kothakota --
11. Nuclear magnetic resonance spectroscopy for food quality evaluation / Yongqi Tian, Qingyan He, Xu Chen, Shaoyun Wang --
12. Gas chromatography for food quality evaluation / Tao Feng, Min Sun, Shiqing Song, Haining Zhuang, Lingyun Yao --
13. High-performance liquid chromatography for food quality evaluation / Qixing Nie, Shaoping Nie --
14. High-performance capillary electrophoresis for food quality evaluation / Adele Papetti, Raffaella Colombo --
15. Supercritical fluid chromatography for food quality evaluation / Karamatollah Rezaei, Ali Aghakhani --
16. Mass spectrometry for food quality and safety evaluation / Xinzhong Zhang. Part Three: Physical analysis technologies for food quality: 17. Texture analyzers for food quality evaluation / Yi-Xiang Liu, Min-Jie Cao, Guang-Ming Liu --
18. Rheology instruments for food quality evaluation / Qiang Wang, Aimin Shi, Faisal Shah --
19. Fluorescence spectroscopy and imaging instruments for food quality evaluation / Ewa Sikorska, Igor Khmelinskii, Marek Sikorski --
20. Dynamic light scattering for food quality evaluation / Pingfan Rao, Zhaoshuo Yu, Huan Han, Yang Xu, Lijing Ke --
21. Tribological analyses for the evaluation of food quality / Song Miao, Duanquan Lin --
22. X-ray diffraction for food quality evaluation / Soumya Ranjan Purohit, Lakshmi E. Jayachandran, Anu S. Raj, Debasis Nayak, P. Srinivasa Rao --
23. Measurement techniques of electrical properties for food quality evaluation / Yang Jiao. Part Four: Molecular biology technologies for food quality: 24. Gene chips for food quality evaluation / Bowen Chen, Siqi Wang, Bin Hong, Yong Zhao --
25. Nucleic acid probes for food quality evaluation / Juan Yan, Gang Liu, Yanli Wen, Lanying Li --
26. Immunoassay for food quality evaluation / T. Gomez-Morte, M. Ayala-Hernández, M.J. Yánez-Gascón, A. Gil-Izquierdo, D.A. Auñon-Calles, R. Domínguez-Perles, M.I. Fortea, E. Núñez-Delicado, J.A. Gabaldón. Part Five: Micro/nano technologies for food quality: 27. Microfluidics technique for food quality evaluation / Xiaojun Bian, Qiyue Wu --
28. Atomic force microscopy for food quality evaluation / Mengzhen Ding, Cuiping Shi, Jian Zhong --
29. Scanning electron microscopy (SEM) in food quality evaluation / Vasudha Sharma, Aastha Bhardwaj --
30. Transmission electron microscopies for food quality evaluation / Abdollah Hajalilou, Laleh Saleh Ghadimi --
31. Electrochemical sensor method for food quality evaluation / Neelam Yadav, Annu Mishra, Jagriti Narang --
32. Nanoparticle-based methods for food safety evaluation / Hongcai Zhang, Shunsheng Chen.

Citation preview

Evaluation Technologies for Food Quality

Woodhead Publishing Series in Food Science, Technology and Nutrition

Evaluation Technologies for Food Quality Edited by

Jian Zhong Xichang Wang

An imprint of Elsevier

Woodhead Publishing is an imprint of Elsevier The Officers’ Mess Business Centre, Royston Road, Duxford, CB22 4QH, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, OX5 1GB, United Kingdom © 2019 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. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN: 978-0-12-814217-2 For information on all Woodhead publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Matthew Deans Acquisition Editor: Nina Rosa de Araujo Bandeira Editorial Project Manager: Lindsay Lawrence Production Project Manager: Joy Christel Neumarin Honest Thangiah Cover Designer: Greg Harris Typeset by SPi Global, India

Contributors

Ali Aghakhani Department of Food Science, Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran D.A. Aun˜on-Calles Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain M. Ayala-Herna´ndez Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain Cristiano Augusto Ballus Department of Food Science and Technology, Federal University of Santa Maria, Rio Grande do Sul, Brazil Aastha Bhardwaj Department of Food Technology, Jamia Hamdard (Hamdard University), New Delhi, India Xiaojun Bian College of Food Science and Technology, Shanghai Ocean University, Shanghai, China Min-Jie Cao College of Food and Biological Engineering, Jimei University; Key Laboratory of Marine Functional Food in Xiamen; Marine Functional Food Engineering Technology Center of Fujian Province; National & Local Joint Engineering Research Center for Deep Processing of Aquatic Products, Xiamen, People’s Republic of China S. Chandra Agri-Chemistry Group, School of Medical and Applied Sciences, Central Queensland University (CQU), North Rockhampton, QLD, Australia J. Chapman School of Science, RMIT University, Melbourne, VIC, Australia Xu Chen College of Biological Science and Technology, Fuzhou University, Fuzhou, People’s Republic of China Bowen Chen Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Preservation (Shanghai), Ministry of Agriculture, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China

xvi

Contributors

Shunsheng Chen Laboratory of Aquatic Products Quality & Safety Risk Assessment (Shanghai) at China Ministry of Agriculture; College of Food Science and Technology, Shanghai Ocean University, Shanghai, China Raffaella Colombo University of Pavia, Department of Drug Sciences, Pavia, Italy D. Cozzolino School of Science, RMIT University, Melbourne, VIC, Australia Mengzhen Ding Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China R. Domı´nguez-Perles Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain Tao Feng School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, People’s Republic of China M.I. Fortea Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain J.A. Gabaldo´n Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain Laleh Saleh Ghadimi Research Laboratory of Polymer, Department of Organic and Biochemistry, Faculty of Chemistry, University of Tabriz, Tabriz, Iran A. Gil-Izquierdo Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain T. Gomez-Morte Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain Abdollah Hajalilou Faculty of Mechanical Engineering, Department of Materials Engineering, University of Tabriz, Tabriz, Iran Huan Han CAS.SIBS-ZJGSU Joint Center for Food and Nutrition Research, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, China Lili He Department of Food Science, University of Massachusetts, Amherst, MA, United States

Contributors

xvii

Qingyan He College of Biological Science and Technology, Fuzhou University, Fuzhou, People’s Republic of China Bin Hong Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Preservation (Shanghai), Ministry of Agriculture, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Madhura Janve Department of Food Engineering & Technology, Institute of Chemical Technology, Mumbai, India Lakshmi E. Jayachandran Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India Yang Jiao Shanghai Ocean University, Shanghai, China Lijing Ke CAS.SIBS-ZJGSU Joint Center for Food and Nutrition Research, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, China Igor Khmelinskii Universidade do Algarve, FCT, DQB and CEOT, Faro, Portugal Anjineyulu Kothakota Agro-Processing and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram, India Lanying Li Laboratory of Biometrology, Shanghai Institute of Measurement and Testing Technology, Shanghai, China Duanquan Lin Teagasc Food Research Centre, Moorepark, Fermoy, Ireland Yuan Liu Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China Yi-Xiang Liu College of Food and Biological Engineering, Jimei University; Key Laboratory of Marine Functional Food in Xiamen; Marine Functional Food Engineering Technology Center of Fujian Province; National & Local Joint Engineering Research Center for Deep Processing of Aquatic Products, Xiamen, People’s Republic of China Guang-Ming Liu College of Food and Biological Engineering, Jimei University; Key Laboratory of Marine Functional Food in Xiamen; Marine Functional Food Engineering Technology Center of Fujian Province; National & Local Joint Engineering Research Center for Deep Processing of Aquatic Products, Xiamen, People’s Republic of China

xviii

Contributors

Gang Liu Laboratory of Biometrology, Shanghai Institute of Measurement and Testing Technology, Shanghai, China Song Miao Teagasc Food Research Centre, Moorepark, Fermoy, Ireland Annu Mishra MD University, Rohtak, India; Amity Institute of Nanotechnology, Amity University, Noida, India M.N. Mohd Fairulnizal Cardiovascular, Diabetes and Nutrition Research Centre, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, Malaysia M.N. Mohd Naeem Cardiovascular, Diabetes and Nutrition Research Centre, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, Malaysia Jagriti Narang MD University, Rohtak, India; Amity Institute of Nanotechnology, Amity University, Noida, India Debasis Nayak Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India Daniela Andrade Neves School of Food Engineering, University of Campinas, Sao Paulo, Brazil Qixing Nie State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, People’s Republic of China Shaoping Nie State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, People’s Republic of China E. Nu´n˜ez-Delicado Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain Wellington da Silva Oliveira School of Food Engineering, University of Campinas, Sao Paulo, Brazil Adele Papetti University of Pavia, Department of Drug Sciences, Pavia, Italy A.C. Power Agri-Chemistry Group, School of Medical and Applied Sciences, Central Queensland University (CQU), North Rockhampton, QLD, Australia Soumya Ranjan Purohit Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India Anu S. Raj Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India

Contributors

xix

Pingfan Rao CAS.SIBS-ZJGSU Joint Center for Food and Nutrition Research, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, China P. Srinivasa Rao Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, Kharagpur, India D.N. Rathi Cardiovascular, Diabetes and Nutrition Research Centre, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, Malaysia Karamatollah Rezaei Department of Food Science, Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran Faisal Shah Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, People’s Republic of China Vasudha Sharma Department of Food Technology, Jamia Hamdard (Hamdard University), New Delhi, India Aimin Shi Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, People’s Republic of China Cuiping Shi Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Ewa Sikorska Faculty of Commodity Science, Poznan University of Economics and Business, Pozna n, Poland Marek Sikorski Faculty of Chemistry, Adam Mickiewicz University, Poznan, Poland Kaliramesh Siliveru Department of Grain Science and Industry, Kansas State University, Manhattan, KS, United States Shiqing Song School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, People’s Republic of China Min Sun School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, People’s Republic of China Rohit Thirumdas Department of Food Process Technology, College of Food Science & Technology, PJTSAU, Telangana, India

xx

Contributors

Yongqi Tian College of Biological Science and Technology, Fuzhou University, Fuzhou, People’s Republic of China B. Vimala Cardiovascular, Diabetes and Nutrition Research Centre, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, Malaysia Xichang Wang Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Wenli Wang College of Food Science and Technology, Shanghai Ocean University, Shanghai, People’s Republic of China Shaoyun Wang College of Biological Science and Technology, Fuzhou University, Fuzhou, People’s Republic of China Qiang Wang Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing, People’s Republic of China Siqi Wang Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Preservation (Shanghai), Ministry of Agriculture, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Yanli Wen Laboratory of Biometrology, Shanghai Institute of Measurement and Testing Technology, Shanghai, China Qiyue Wu College of Food Science and Technology, Shanghai Ocean University, Shanghai, China Changhua Xu College of Food Science and Technology, Shanghai Ocean University, Shanghai, China Yang Xu CAS.SIBS-ZJGSU Joint Center for Food and Nutrition Research, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, China Neelam Yadav MD University, Rohtak, India; Amity Institute of Nanotechnology, Amity University, Noida, India

Contributors

xxi

Juan Yan College of Food Science and Technology, Shanghai Ocean University, Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Shanghai, China M.J Ya´nez-Gasco´n Department of Food Technology & Nutrition. UCAM Universidad Cato´lica de Murcia, Murcia, Spain Tianxi Yang Department of Food Science, University of Massachusetts, Amherst, MA, United States Lingyun Yao School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, People’s Republic of China Zhaoshuo Yu CAS.SIBS-ZJGSU Joint Center for Food and Nutrition Research, School of Food Science and Biotechnology, Zhejiang Gongshang University, Hangzhou, China Xinzhong Zhang Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, People’s Republic of China Hongcai Zhang Laboratory of Aquatic Products Quality & Safety Risk Assessment (Shanghai) at China Ministry of Agriculture; College of Food Science and Technology, Shanghai Ocean University, Shanghai, China Bin Zhao Department of Food Science, University of Massachusetts, Amherst, MA, United States Yong Zhao Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Preservation (Shanghai), Ministry of Agriculture, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Jian Zhong Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Shanghai Engineering Research Center of Aquatic-Product Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Yaoguang Zhong National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product

xxii

Contributors

Processing, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China Haining Zhuang Key Laboratory of Edible Fungi Resources and Utilization (South), Ministry of Agriculture, Division of Edible Fungi Fermentation and Processing, National Engineering Research Center of Edible Fungi, Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, People’s Republic of China

An introduction to evaluation technologies for food quality

1

Jian Zhong, Xichang Wang Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture and Rural Affairs of the People’s Republic of China, Shanghai Engineering Research Center of AquaticProduct Processing and Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China

The era of food quality has come, and the price and brands of food depend on their quality. In utilitarian terms, food quality can be defined as “fitness for consumption” [1]. That is to say, food quality is the characteristics of food that are acceptable to consumers. It is an important feature of food and decides food nutrition and food safety. It includes external factors such as appearance (size, shape, color, gloss, and consistency), texture, and flavor (taste and odor), and internal factors such as chemical composition, physical characteristics, and microorganisms. Among these factors, certain features such as appearance are observed or felt, and certain features such as chemical components need to be analyzed with the aid of instruments. Scientists in the field of food science and engineering need to evaluate their foods and food-processing instruments. During this evaluation process, evaluation technologies are necessary to evaluate developed or processed foods using food-processing instruments. Many evaluation technologies are being developed or have been widely developed and applied to comprehensively evaluate foods. These evaluation technologies can be classified into five types: food sensory evaluation technologies, chemical analysis technologies, physical analysis technologies, molecular analysis technologies, and novel micro/nanotechnologies. Some books have been published in these fields. For example, a number of books focus on food sensory evaluation technologies [2, 3]. The book Food Analysis focuses on chemical and physical analysis technologies [4], and the book Handbook of Food Analysis Instruments mainly focuses on chemical analysis technologies [5]. Until now, there are no books that systematically describe all food quality technologies. The purpose of this book is to summarize and assess evaluation technologies for food quality. All chapters are classified into five parts: Part I, “Food sensory evaluation technologies for food quality,” mainly introduces food sensory evaluation technologies using the electronic nose technique, electronic tongue technique, and electronic eye technique. In this section, the food sensory technique using human

Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00001-9 © 2019 Elsevier Inc. All rights reserved.

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Evaluation Technologies for Food Quality

sense is not discussed because it has been reviewed in many other books. Interested readers are referred to the classical books [2, 3]. Part II, “Chemical analysis technologies for food quality,” mainly describes evaluation technologies to analyze the chemical properties of food. Typical examples include the basic chemical analysis methods, ultraviolet–visible technique, infrared technique, Raman technique, atomic absorption spectroscopy, atomic emission spectroscopy, nuclear magnetic resonance spectroscopy, gas chromatography, high performance liquid chromatography, high performance capillary electrophoresis technique, supercritical fluid chromatography, mass spectrometry, etc. Part III, “Physical analysis technologies for food quality,” mainly discusses physical analysis technologies to analyze the physical properties of food. Typical examples include the texture analyzer, rheology technique, fluorescence spectroscopy, dynamic light scattering technique, tribological technique, X-ray diffraction technique, measurement technique of dielectric properties, etc. Part IV, “Molecular biology technologies for food quality,” mainly analyzes the recent application of molecular biology technologies to study food quality. Typical examples include gene chip technique, nucleic acid probe technique, immunoassay technique, etc. Part V, “Micro/nanotechnologies for food quality,” mainly analyzes the recent application of micro/nanotechnologies to study food quality. Typical examples include the microfluidics technique, atomic force microscopy, scanning electron microscopy, transmission electron microscopy, electrochemical sensor methods, nanoparticle-based methods, etc. The unique feature of this book is that all the chapters cover the basic principles, basic operational procedures, advantages and limitations, recent technology developments, and recent application progress in different types of foods, and summarize and forecast evaluation technologies. This unique feature will help readers to rapidly learn and understand the evaluation technologies for food quality. This book provides an understanding of applications of evaluation technologies for food quality in the field of food research and in the food industry. The target audience of this book is broad. This book is especially ideal for scientists in the field of food science and engineering. It is also ideal for undergraduate and postgraduate courses on evaluation technologies for food quality. In addition, it is an invaluable reference for professionals in the food industry. Finally, it is also useful for instrument developers who want to develop instruments for food quality evaluation.

Acknowledgments As the editors of this book, we wish to acknowledge the kind cooperation of the authors who provided such grand writing. We also wish to acknowledge the kind cooperation of those publishers who provided copyrights for the figures to; individual accreditation is given in the relevant figure captions. We wish to thank Elsevier for giving us the opportunity to edit this book, and in particular Lindsay C. Lawrence, Sandhya Narayanan, Nina Bandeira, Brianna Garcia, and Joy Christel Neumarin Honest Thangiah of the editorial staff for their almost limitless patience. Finally, we acknowledge research grants from the National Key R&D Program of China (No. 2016YFD0400202-8) and Shanghai Municipal Education Commission—Gaoyuan Discipline of Food Science & Technology Grant Support (Shanghai Ocean University).

An introduction to evaluation technologies for food quality

3

References [1] C. Peri, The Universe of Food Quality, Food Qual. Prefer. 17 (1), 2006, 3–8. [2] H.T. Lawless, H. Heymann, Sensory Evaluation of Food: Principles and Practices, second ed., Springer Science + Business Media, LLC, New York, USA, 2010. [3] H.T. Lawless, Laboratory Exercises for Sensory Evaluation, first ed., Springer Science + Business Media, LLC, New York, USA, 2013. [4] S. Nielsen, Food Analysis, fifth ed., Springer International Publishing, New York, USA, 2017. [5] S. Otles, Handbook of Food Analysis Instruments, first ed., CRC Press, FL, USA, 2008.

Electronic nose for food sensory evaluation

2

Yaoguang Zhong National R&D Branch Center for Freshwater Aquatic Products Processing Technology (Shanghai), Integrated Scientific Research Base on Comprehensive Utilization Technology for By-Products of Aquatic Product Processing, Ministry of Agriculture and Rural Affairs of the People’s Republic of China, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China

2.1

Introduction

Food quality depends on the color, taste, nutrition, safety, and sensory characteristics of foods. People choose foods according to these aspects. Some methods/techniques such as total volatile basic nitrogen measurement, spectroscopies, and chromatographies are not usually suitable for online quality control of food products [1]. New methods like electronic nose, electronic eye, electronic tongue, and their fusion have been proposed for in-process and real-time evaluation and controlling of food products [2–7]. In 1982, the concept of an electronic nose system was suggested by Dodd and Persuad from the University of Warwick, United Kingdom. The system was engineered to mimic the human olfactory system. It is capable of detecting volatile aromas, which are released from various sources [8]. The electronic nose is widely used in meat, grains, tea and coffee, beer, milk, fish, fruits, vegetables, and so on [9]. The typical electronic nose detection process is shown in Fig. 2.1 [9]. In food research and the food industry, we use it to predict the shelf life and detect the freshness of food products [10]. There are also a number of studies on the identification of aroma compounds [11], discrimination among different species of Chinese herbal medicines [12], and quality control of Lonicera japonica during several months of storage [13]. In this chapter, we first introduce the basic principles and procedures of the electronic nose. Second, we discuss the advantages and limitations and recent technology development. Third, we describe recent application progress of the electronic nose in different types of foods. Finally, we summarize and forecast electronic nose technology for food quality evaluation.

2.2

Basic principles and procedures

The electronic nose is an apparatus that analyzes, recognizes, and examines complex gases. It is an “intelligent” system and is designed to simulate human olfactory senses. The mechanisms of odorous substances identification are similar to those present in the human nose. The human olfactory system is composed of olfactory cells, an Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00002-0 © 2019 Elsevier Inc. All rights reserved.

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Evaluation Technologies for Food Quality

Sensor array

Sensor response

Signal preprocessing

S1 S2 S3 S4 O1

O2

Training

Odorant

Knowledge base

Multivariate pattern analysis techniques

New sample Trained model

Output decision

Fig. 2.1 Schematic diagram of an electronic nose for food quality evaluation. Reprinted with permission from Alireza Sanaeifar, Hassan ZakiDizaji, Abdolabbas Jafari, Miguel de la Guardia, Early detection of contamination and defect in foodstuffs by electronic nose: a review. Trends Anal. Chem. 97 (2017) 257–271. Copyright Elsevier Publisher 2017.

Electronic nose for food sensory evaluation

9

olfactory neural network, and other elements. It consists of three parts: (1) headspace sampler. This is an air sampling device that provides gas through a sealed bottle. Proper sampling of the volatile fraction is a significant challenge in an electronic nose. The common sampler could be classified into static headspace, dynamic headspace, and solid-phase microextraction (Fig. 2.2) [14]. Static headspace is one of the earliest and most basic methods of sampling in an electronic nose. Static headspace is mainly used in electronic noses equipped with sensitive detectors such as fast gas chromatography [15] and mass spectrometry [16]. Dynamic headspace has advantages such as relatively short response time and faster recovery because the analytes are flushed from the system. Solid-phase microextraction is seldom coupled with an electronic nose because it is expensive, complicated, and time consuming. It can be used to enrich analytes such as those from Spanish olive oil [17]. (2) Array of gas sensors. These are gas sensors that detect smells from a sample. The gas sensors assimilate the human olfactory cells, transforming different odor molecules on the surface into

Fig. 2.2 Common samplers used in an electronic nose. (A) Static headspace, (B) dynamic headspace, and (C) solid-phase microextraction. Reprinted with permission from Tomasz Majchrzak, Wojciech Wojnowski, Tomasz Dymerski, Jacek Gębicki, Jacek Namie
snik, Electronic noses in classification and quality control of edible oils: a review. Food Chem. 246 (2018) 192–201. Copyright Elsevier Publisher 2018.

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Evaluation Technologies for Food Quality

a single group that can be measured by physical methods. A variety of different sensor types have been developed based on different materials: metal oxides, conducting polymer composites, and intrinsically conducting polymers. According to different detection principles, the sensors can be classified into conductive sensors, optical sensors, surface acoustic wave sensors, gas-sensitive field effect transistors, and quartz microbalance sensors. In this field, microelectromechanical systems and nanotechnologies are the most promising emerging technologies. (3) Signal processing system. This can process the information obtained and make a feature extraction [18]. It is like the human olfactory nerve system and judges the obtained signals. Once the data from the individual sensor from the array are collected, the signal processing system analyzes and classifies the data. Data processing techniques used in this system for postprocessing of pattern recognition routines include principal component analysis (PCA), linear discriminate analysis (LDA), partial least squares (PLS), functional discriminate analysis (FDA), cluster analysis (CA), fuzzy logic, or an artificial neural network (ANN) such as a probabilistic neural network (PNN). These pattern analysis techniques can be classified into biologically inspired methods and statistical chemometrics (Fig. 2.3) [9]. These techniques can be classified into linear methods (PCA, LDA, PLS, FDA, and CA) and nonlinear methods (fuzzy logic, ANN, and PNN) [19, 20]. Finally, we can obtain the composition and concentration of the mixture gases. Basic operational procedures include three steps: sample preparation, data collection, and data analysis. The sample preparation step is simple. The food of interest is placed in a sealed vial for a time to allow the volatile substances from the foods in the vial to reach a balance. Then, an electronic nose is applied to detect the volatile substances. A standard operational procedure manual can be obtained from a commercial electronic nose company. Because different gas sensors are sensitive to different volatile organic substances, different gas sensors will show different signals and the commercial software will record the important information. After this step, using the commercial software, volatile substances can be analyzed and shown to the operators.

Fig. 2.3 Classification scheme of the multivariate pattern analysis technique applied for an electronic nose. Reprinted with permission from Alireza Sanaeifar, Hassan ZakiDizaji, Abdolabbas Jafari, Miguel de la Guardia, Early detection of contamination and defect in foodstuffs by electronic nose: a review. Trends Anal. Chem. 97 (2017) 257–271. Copyright Elsevier Publisher 2017.

Electronic nose for food sensory evaluation

2.3

11

Advantages, limitations, and recent technology developments

In recent years, electronic nose technology has developed quickly because of its wide advantages such as easy operation, quickness, small sample requirement, low cost, real-time detection, and quality control. Different types of electron nose systems have been developed. Their advantages and limitations and potential applications are presented in Table 2.1. However, it is still in the initial development period. It has many limitations. There is a big difference compared with the human olfactory system. The application of an electronic nose is limited due to sensors and analytical methods. The gas sensor array has limitations such as sensor poisoning, sensor drift, and sensitivity. The sensor arrays are sensitive to environmental factors such as humidity and temperature. The methods of analyzing the data in the signal processing system are not easy for food scientists. In short, further research is needed to develop this technique to make it more adaptable for food evaluation. Several generations of scientists have been studying electronic noses. The demand for an odor measurement technology is constant. There has been much work focused on the development of a novel electronic nose. Kiani et al. [21] developed a portable electronic nose as an expert system for aroma-based classification of saffron. Timsorn et al. [22] developed a briefcase electronic nose to evaluate the bacterial population on chicken meats. The results indicated that the developed electronic nose system could be used as a cheap, quick, portable, nondestructive, and real-time controlled technique to evaluate the samples with high accuracy. Development of sensor materials such as nanomaterials for the electronic nose is also a promising research hotspot. Carmona et al. [23] described a mixed array merging nanowire and thin film metal oxide technologies to develop the electronic nose as a tool to monitor food pathogen microbiota. The proposed electronic nose could be applied to discriminate between different blends of microorganisms and to follow up microbiota growth. Liu et al. [24] developed single-walled carbon nanotubemetalloporphyrin chemiresistive gas sensor arrays for volatile organic compounds. The results demonstrated the great potential of single-walled carbon nanotubes in the development of a cheap, portable electronic nose for the identification of volatile organic compounds. The pattern recognition components of an electronic nose are nontrivial because of nonlinearity of the sensor response. Therefore it is necessary to compensate for sensor drift. In addition, signal processing techniques should be able to recognize specific patterns representing odors. Though there are many linear methods (PCA, LDA, PLS, FDA, and CA) and nonlinear methods (fuzzy logic, ANN, and PNN) for electronic noses, there is still much room for improvement in this field. Many novel algorithms have been developed and explored for the application of electronic noses such as learning vector quantization, support vector machine, and extreme learning machine [25]. An alternative method for the costly panels of human “sniffers” would be of great interest. Primarily, the electronic nose is based on arrays of weak specific gas sensors. Currently, gas chromatography [26], mass spectrometry [27, 28], and ion mobility

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Evaluation Technologies for Food Quality

Table 2.1 Advantages and disadvantages of different electronic nose systems Detection type

Advantages

Disadvantages

Possible application

Semiconductor sensors

Variety of commercially available models Relatively inexpensive

Cheapest solutions Stationary devices Most of the current applications Most common in prototype solutions

Electrochemical sensors

Low power consumption High durability High sensitivity Minimal impact of humidity on the signal Small sized Short response time High sensitivity and selectivity

Sensor drift Susceptible to poisoning High power consumption Humiditydependent signal Little choice of commercially available sensors Bulky

Little choice of commercially available sensors Low signal-tonoise ratio High elaboration cost Low reproducibility of measurement Large sized High power consumption High cost Complex construction

Portable devices Miniaturized systems Electronic noses dedicated to specific solutions

Large sized Complex construction Need for carrier gas Relatively long analysis time

Laboratory screening In solutions where one or a few sensors are used Control of technological processes in industrial plants Analysis of selected part of volatile fraction

Piezoelectric sensors

Mass spectrometry based

Gas chromatography based

High sensitivity Universal application Large quantity of obtained variables Quantitative and qualitative information Universal detector Numerous application possibilities Large amount of data collected Possibility of simple quantitative and qualitative analysis

Few commercially available solutions Portable devices Dedicated solutions

Laboratory screening For complicated matrices Searching for correlation with other mass spectrometry techniques

Electronic nose for food sensory evaluation

13

spectrometry [29] have been applied in electronic noses to distinguish components that are difficult to distinguish using a common electronic nose. These devices are stable and sensitive. However, these combinations are stationary and expensive. Moreover, they generally require appropriate measurement conditions. These factors limit their wide application in the field of food evaluation.

2.4

Recent application progress in different types of foods

The electronic nose plays an important role in the detection of the quality of food products. The electronic nose can make relevant analyses and judgments by gathering, detecting, and analyzing the volatile gases of food products. Moreover, as a novel detecting technology, the electronic nose can also classify food products according to their quality and predict shelf life to ensure the safety of food products. In addition, this instrument is widely used to detect food adulteration. Every food product has its shelf life. Food products cannot be eaten once the shelf life is passed. During food processing, storage, transportation, distribution, and consumption, food products may be corrupted by enzymes, microorganisms, or other factors, which may have detrimental effects on the quality and shelf life of food products. Traditional methods for predicting shelf life may be troublesome, fussy, and time consuming. Volatile gases change during food storage, which may result in a substantial difference compared to the initial product. Therefore an electronic nose is a good choice for predicting the shelf life of food products [30, 31]. Food adulteration harms consumers and influences the development of the whole food industry. Devising a rapid and accurate method to detect food adulteration is important and impending. In the processing of meat, some companies may replace high-cost meat with other materials that are cheaper and easier to obtain to make extra benefits. What is worse, some companies even add illegal materials to products, which heavily affects the quality of products and disrupts the development of the whole food industry. Adulteration of milk, honey, and meat products is more common. Because different raw materials have different flavors and can affect the final flavor of products, the adulteration of products by detecting different smells can be carried out using an electronic nose [32, 33].

2.4.1 Meat An electronic nose can make a quick and accurate detection of meat to ensure its quality and safety [34]. Electronic nose systems can be applied to detect meat freshness (spoilage), and therefore can assess the shelf life of meat [35, 36]. Kodogiannis [37] applied an electronic nose coupled to a fuzzy-wavelet network for the detection of beef fillet spoilage. It can also be applied to classify beef samples in the relevant quality class (i.e., fresh, semifresh, and spoiled). Huang et al. [38] integrated an electronic nose, near infrared spectroscopy, and computer vision to analyze the total volatile basic nitrogen for evaluating pork freshness. The results demonstrated that

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Evaluation Technologies for Food Quality

integration has potential in nondestructive detection of total volatile basic nitrogen content, and data fusion from the three techniques could significantly improve total volatile basic nitrogen prediction performance. Go´rska-Horczyczak et al. [39] developed a technique using an electronic nose based on ultrafast gas chromatography to differentiate chill-stored and frozen pork necks. The results demonstrated that the technique allowed for effective recognition of chilled and frozen pork meats. It provided an effective method for the meat industry to rapidly and reliably assess meat freshness. Electronic nose systems can also be applied to detect the presence of pathogenic microorganisms in meat. There are rich nutrients in meat, such as protein and fat, which provide ideal conditions for microorganisms to survive and breed. Arnold and Senter [40] found that an electronic nose could be used to detect the changes in microorganisms in chicken by comparing the changes in the varieties and quantities of microorganisms obtained from an electronic nose in the processing and microorganism index of a total bacterial count. In addition, electronic nose systems can also be applied to detect the adulteration of meat. Nurjuliana et al. [41] used an electronic nose to detect different pork sausages and compared this result with that obtained from gas chromatography. They found that an electronic nose was able to detect the adulteration of pork in sausage. Tian et al. [42] analyzed pork adulteration in minced mutton using an electronic nose and compared several analysis methods. This work built on a previous method to predict adulteration.

2.4.2 Grains For grains, the electronic nose has been used to classify wheat based on storage age. The first report was the use of electronic nose technology in the classification of grains in 1993 [43]. In addition, it was reported that some Swedish researchers classified a total of 235 samples of wheat, barley, and oats. Moreover, the percentage of moldy barley in mixtures with fresh grains could be determined [44]. Mishra et al. [45] used multivariate chemometrics and a fuzzy logic-based electronic nose to predict Sitophilus granarius infestation in stored wheat grain. This work opened up a convenient, rapid, yet nondestructive approach for quality determination of insect-infested wheat grains at various stages during storage. Lippolis et al. [46] developed an electronic nose-based method to rapidly predict deoxynivalenol contamination in wheat bran. The results demonstrated that the electronic nose-based method could be useful for high-throughput screening of deoxynivalenol-contaminated wheat bran samples. The samples could be classified as acceptable/rejectable at contamination levels according to the European Union maximum limit for deoxynivalenol.

2.4.3 Tea and coffee An electronic nose is one of the best solutions to analyze the quality of tea and coffee. The extract of tea leaves has chemical components, including flavanol, caffeine, phenolic substances, fats, amino acids, and volatile components. These are the sources

Electronic nose for food sensory evaluation

15

that determine the flavors and aromas. The electronic nose can be used to predict the optimum fermentation time of tea. Sharma et al. [47] developed a quartz crystal microbalance sensor-based electronic nose method to monitor the fermentation process of black tea. The results were in good agreement with the estimations of the ultraviolet– visible spectrophotometer-based reference method. Ghosh et al. [48] also developed a recurrent Elman network in conjunction with an electronic nose for fast prediction of optimum fermentation time of black tea. Coffee provides various flavors. Usually, roasted coffee contains more than 600 components. It is very difficult to discriminate the quality of adulterated coffee using human sensory panels. Electronic noses have quantified the concentration levels of the identified aromas in coffee [20]. Moreover, some researchers found that improvements in equipment and design were necessary to obtain consistency of the system [44]. Electronic noses can also be applied to analyze the changes in the aromatic profile of espresso coffee as a function of the grinding grade and extraction time [49]. In addition, electronic noses can be used to discriminate between washed arabica, natural arabica, and robusta coffees [50].

2.4.4 Beer Using an electronic nose, Zimmermann and Leclercq studied 50 samples of malt and concentrated on the difference between malt types. It was expected that the electronic nose could be used to develop new beer products and improve product quality [51]. An electronic nose can be applied for beer recognition [52]. Men et al. [53] used an electronic nose to classify beer and the results showed that the developed method was a reliable tool for accurate identification of beer olfactory information. Electronic nose data can also be applied to build simple classification methods for machine learning for the binary discrimination of beers [54]. It demonstrated the capability of the electronic nose for detecting and differentiating beer aromas.

2.4.5 Milk Milk usually contains few bacteria. The bacteria will increase during processing and storage. Bacterial growth leads to the spoilage of milk. An electronic nose can be used to identify milk spoilage. For example, an electronic nose was used to determine the shelf life of milk. The French electronic nose was also used to determine differences in milk flavorings [34]. Using an electronic nose, Marsili et al. studied the off-flavors in milk. Marsili [55] found that the headspace of milk from a cow bearing a genetic defect contains trimethylamine. Eriksson et al. observed higher levels of ketones and acids in milk from an unhealthy cow. For this experiment, an electronic nose was used to detect milk and the milk data were analyzed. Among these, the dominating components were trimethylamine, ethanol, and acetic acid, and in general the infected milk had higher CO2 content than the healthy milk. Therefore mastic milk can be identified [20].

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Evaluation Technologies for Food Quality

2.4.6 Fish The application of electronic nose technology to fish is one of the largest application areas in the food industry. The main purpose of the electronic nose is quality assurance. In addition, other applications are spoilage identification, detection of offflavors, and classification of bacterial strains. The recognition of fish freshness is tested using an electronic nose. Han et al. [56] combined electronic nose and electronic tongue techniques to nondestructively detect the freshness of fish stored at 4°C. Shi et al. [57] used an electronic nose to predict tilapia fillet freshness during storage at different temperatures. They built a promising method to predict changes in the freshness of fillets stored from 0 to 10°C in the cold chain. Zhang et al. [58] explored the discrimination of marine fish surimi using an electronic nose. The electronic nose can be successfully applied to distinguish four species of marine fish surimi. G€ uney et al. [59] used an electronic nose to discriminate three different fish species.

2.4.7 Fruits and vegetables An electronic nose can also be applied for identification, ripeness, and quality grading of fruits and vegetables [60,61]. Chen et al. [62] confirmed the use of an electronic nose to evaluate the freshness of fresh-cut green bell pepper. Hui et al. [63] built a winter jujube quality forecasting method based on an electronic nose. The method had a forecasting accuracy of 97.35% and showed a promising application prospect. Sanaeifar et al. [64] applied an electronic nose to predict banana quality properties. The results demonstrated that the electronic nose had the potential of becoming a reliable instrument to estimate chemical and physical properties of banana according to the electronic nose signals. Feng et al. [65] explored the determination of postharvest quality of cucumbers using nuclear magnetic resonance and an electronic nose combined with chemometric methods. The results demonstrated that the developed method was a promising technique to monitor cucumber quality. Ezhilan et al. [66] built an electronic nose-based method for royal delicious apple quality assessment. The results demonstrated that the electronic nose can be applied for real-time quality estimation of apple samples.

2.4.8 Edible oil The electronic nose technique may be applied in the classification, geographical origin determination, oil adulterations, and oxidation assessment of edible oils [14]. Compared with other analytical techniques (chemical analysis, gas chromatography, sensory analysis) in the quality evaluation of edible oils, the electronic nose technique has several advantages such as rapid analysis, no sample preparation stage, low cost, ease of use, and capability to be applied in portable devices. Park et al. [67] used an electronic nose to differentiate volatiles of sesame oils prepared with diverse roasting conditions. It demonstrated that it was possible to classify the oils based on the roasting temperature using the electronic nose. Melucci et al. [68] used a flash gas

Electronic nose for food sensory evaluation

17

chromatography electronic nose and chemometrics for rapid and direct analysis of the geographic origin of extra olive oils. The results demonstrated that the developed method was suitable to verify the geographic origin of the oils based on PCA and discriminant analysis of the volatile profiles. Wei et al. [69] used an electronic nose for rapid detection of oil adulterations in peony seed oil. The results demonstrated that the electronic nose was suitable for oil adulteration analysis. Xu et al. [70] developed a qualitative method for the analysis of edible oil oxidation using an electronic nose. The methods were rapid, noninvasive, and sensitive for the quality control of edible oils. Buratti et al. [71] combined electronic nose, electronic tongue, and electronic eye to characterize edible olive oil and assess shelf life. The results demonstrated the combined application of three types of methods that can be applied to assess the decay of oils.

2.5

Summary

The electronic nose is an important tool in many different fields such as protein engineering, electronics, processing methods, and so on. At present, the electronic nose is widely used in many areas, especially the food industry, due to its low cost, high sensitivity, simple operation, and real-time controlling compared with traditional methods, which are destructive, time consuming, fussy, and expensive. We usually use electronic noses to detect the freshness and adulteration of products, as well as set models to predict the shelf life of different products. However, electronic nose technology is still at the developing stage. There are still many problems. We need to perfect the instrument and technology to make greater use of it for food science and technology. In the near future, scientists and academics should try their best to optimize the sensor array by studying sensor material with high specificity and sensitivity according to the physicochemical feature of the sample. We also need to select suitable methods according to the items we want to analyze. It is expected that dynamic improvements to electronic noses will be forthcoming. With the development of sensor technology, the biological chip, and biological information, the function of the electronic nose will be infinitely close to the human olfactory system so that it will be good enough to replace it and be widely used in a wider range of applications. There is no doubt that the electronic nose will be designed and fabricated into smaller and more portable devices at low cost. One day, every person will be able to use an electronic nose instrument to detect food products to ensure quality and safety and to make their lives more convenient, efficient, and healthy.

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[53] H. Men, Y. Shi, Y. Jiao, F. Gong, J. Liu, Electronic nose sensors data feature mining: a synergetic strategy for the classification of beer, Anal. Methods 10 (17) (2018) 2016–2025. [54] M. Ghasemi-Varnamkhasti, S.S. Mohtasebi, M. Siadat, H. Ahmadi, S.H. Razavi, From simple classification methods to machine learning for the binary discrimination of beers using electronic nose data, Eng. Agric. Environ. Food 8 (1) (2015) 44–51. [55] R.T. Marsili, Shelf-life prediction of processed milk by solid-phase microextraction, mass spectrometry, and multivariate analysis, J. Agr. Food Chem. 48 (8) (2000) 3470–3475. [56] F. Han, X. Huang, E. Teye, F. Gu, H. Gu, Nondestructive detection of fish freshness during its preservation by combining electronic nose and electronic tongue techniques in conjunction with chemometric analysis, Anal. Methods 6 (2) (2014) 529–536. [57] C. Shi, X. Yang, S. Han, B. Fan, Z. Zhao, X. Wu, J. Qian, Nondestructive prediction of tilapia fillet freshness during storage at different temperatures by integrating an electronic nose and tongue with radial basis function neural networks, Food Bioprocess Technol. 11 (10) (2018) 1840–1852. [58] X. Zhang, W. Wei, W. Hu, X. Wang, P. Yu, J. Gan, Y. Liu, C. Xu, Accelerated chemotaxonomic discrimination of marine fish surimi based on Tri-step FT-IR spectroscopy and electronic sensory, Food Control 73 ( (2017) 1124–1133. [59] S. G€uney, A. Atasoy, Study of fish species discrimination via electronic nose, Comput. Electron. Agric. 119 (2015) 83–91. [60] M. Baietto, A. Wilson, Electronic-nose applications for fruit identification, ripeness and quality grading, Sensors 15 (1) (2015) 899. [61] J. Brezmes, E. Llobet, Chapter 6—Electronic noses for monitoring the quality of fruit, in: M.L.R. M
endez (Ed.), Electronic Noses and Tongues in Food Science, Academic Press, San Diego, 2016, pp. 49–58. [62] H.-Z. Chen, M. Zhang, B. Bhandari, Z. Guo, Evaluation of the freshness of fresh-cut green bell pepper (Capsicum annuum var. grossum) using electronic nose, LWT Food Sci. Technol. 87 (2018) 77–84. [63] G. Hui, J. Jin, S. Deng, X. Ye, M. Zhao, M. Wang, D. Ye, Winter jujube (Zizyphus Jujuba Mill.) quality forecasting method based on electronic nose, Food Chem. 170 (2015) 484–491. [64] A. Sanaeifar, S.S. Mohtasebi, M. Ghasemi-Varnamkhasti, H. Ahmadi, Application of Mos based electronic nose for the prediction of banana quality properties, Measurement 82 (2016) 105–114. [65] L. Feng, M. Zhang, B. Bhandari, Z. Guo, Determination of postharvest quality of cucumbers using nuclear magnetic resonance and electronic nose combined with chemometric methods, Food Bioprocess Technol. (2018). [66] M. Ezhilan, N. Nesakumar, K. Jayanth Babu, C.S. Srinandan, J.B.B. Rayappan, An electronic nose for royal delicious apple quality assessment—a tri-layer approach, Food Res. Int. 109 (2018) 44–51. [67] M.H. Park, M.K. Jeong, J. Yeo, H.J. Son, C.L. Lim, E.J. Hong, B.S. Noh, J. Lee, Application of solid phase-microextraction (Spme) and electronic nose techniques to differentiate volatiles of sesame oils prepared with diverse roasting conditions, J. Food Sci. 76 (1) (2011) C80–C88. [68] D. Melucci, A. Bendini, F. Tesini, S. Barbieri, A. Zappi, S. Vichi, L. Conte, T. Gallina Toschi, Rapid direct analysis to discriminate geographic origin of extra virgin olive oils by flash gas chromatography electronic nose and chemometrics, Food Chem. 204 (2016) 263–273.

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[69] X. Wei, X. Shao, Y. Wei, L. Cheong, L. Pan, K. Tu, Rapid detection of adulterated peony seed oil by electronic nose, J. Food Sci. Technol. 55 (6) (2018) 2152–2159. [70] L. Xu, X. Yu, L. Liu, R. Zhang, A novel method for qualitative analysis of edible oil oxidation using an electronic nose, Food Chem. 202 (2016) 229–235. [71] S. Buratti, C. Malegori, S. Benedetti, P. Oliveri, G. Giovanelli, E-nose, E-tongue and E-eye for edible olive oil characterization and shelf life assessment: a powerful data fusion approach, Talanta 182 (2018) 131–141.

Electronic tongue for food sensory evaluation

3

Wenli Wang*, Yuan Liu† *College of Food Science and Technology, Shanghai Ocean University, Shanghai, People’s Republic of China, †Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

3.1

Introduction

Taste is an important organoleptic property controlling food acceptance through the mouth in mammalians [1]. The major gustatory receptors from the largest G proteincoupled receptor (GPCR) family in mammalians are responsible for sensing taste molecules. Taste formation is related to an impressive chemical transduction. Sensitivity to five basic tastes shows regional differences, suggesting that there are different transduction mechanisms among them [2]. For taste sensory evaluation, classical threshold theory assumes that taste sensations depend on the intensity and attributes of the stimulus—energy levels below which no sensation would be produced by a stimulus and above which a sensation would reach consciousness [3]. Neurotransmitters also play an important role in determining taste thresholds under circumstances such as anxiety or depression. Sensory evaluation comprises a set of techniques for accurate measurement of human responses to foods and minimizes the potentially biasing effects of brand identity and other influences on consumer perception. Though the primary and better method for taste measurement is human panel tasting, the use of sensory panelists is very difficult and problematic in the food industry because of its disadvantages, such as potential toxicity of food, subjectivity of taste panelists, etc., even if the members of a taste panel are well trained and calibrated [4]. These factors are the reasons why researchers and the food industry have been seeking a reliable, reproducible analytical tool to replace food sensory evaluation by panelists. Electronic taste sensing as an alternative to food taste evaluation, which is useful for quality control and monitoring of various samples in the food industries, has been increasingly used for the classification and identification of similar samples, especially with regard to liquids [5–7]. In this chapter, food taste information can now be carried out using an analytical taste sensory system (chemical taste sensor array) known as an electronic tongue for the rapid assessment of complex liquid systems [8]. Electronic tongue technology, which is a new analytical instrument in diverse types of applied fields, was developed in the mid-1980s [9]. This technique is also known as taste sensor technology or artificial taste recognition technology because of the

Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00003-2 © 2019 Elsevier Inc. All rights reserved.

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Fig. 3.1 Electronic tongue system and its applications.

similarity with the human gustatory system. The electronic tongue has global selectivity like the unique ability of the brain, which groups all the information received from the tongue in distinct patterns of response encoding the taste quality [10]. The electronic tongue has already shown its potential to be used as a complementary tool with human tasters in the sensor technology field because of fast, highly sensitive and selective methods. It is an analytical sensory array unit that can detect specific substances by different artificial membranes and electrochemical techniques [11]. The sensor array responds to the liquid sample and outputs the signal, and the signal is then processed by the computer system and patterned by the pattern recognition system. Subsequently, the result of the sample with taste characteristics is obtained. Compared with the common chemical analysis method, the sensor output of the electronic tongue is not the analytical result of individual compositions in the sample, but a signal pattern related to certain sample characteristics. The signal pattern can be obtained by computer analysis with pattern recognition ability to give an overall evaluation of the taste characteristics of the sample [12]. Two unique properties of the electronic tongue are measurement and characterization for complex liquid matrices. For example, the electronic tongue based on the array of low selective photovoltaic sensors and principal component analysis (PCA) is proposed for alcohol detection in various solutions (Fig. 3.1) [1]. In this chapter, we focus on papers published in the last few years. The chapter includes the following areas: tongue and taste, electrochemical concepts in instrumentation, performance qualification of the electronic tongue, its limitations and difficulties, its data processing and analysis techniques, and its applications in the food field. This chapter provides an overview of significant contributions to the electronic tongue in the food field and their future perspectives.

Electronic tongue system

3.2

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The basic principles

The tongue is a muscular hydrostat on the floor of the mouth that manipulates food mastication [1]. For mammalians, there are 50–100 taste cells in each taste bud on the tongue’s surface. Taste recognition mainly relies on these taste cells with a number of taste nerve fibers, which are distributed on taste cells and transmit information on the peripheral taste cells to the brain [13]. The five taste qualities are detected by specialized taste receptors in the taste cell.

3.2.1 Human tongue structure In addition to phonetic articulation and cleaning of the teeth, the tongue also serves as a natural means of tasting in human beings. The tongue as the primary organ of gustation is covered in a variety of papillae and taste buds. The majority of taste buds are found on the tongue surface embedded in gustatory papillae on the soft palate and the throat [13]. The tiny bumps called papillae give a rough structure. There are many types of papillae on the human tongue, such as circumvallate papillae (located at the back of the tongue) and fungiform papillae (located at the front of the tongue) [14]. Several thousands of taste buds exist on the surfaces of the papillae. Taste buds contribute to our quality of life by parsing food chemicals into the gustatory qualities of sweet, salty, sour, bitter, umami, and perhaps other tastes (e.g., fatty, astringent, metallic) [15]. Nerve cells in taste buds, which help to detect and transmit taste signals to the brain, connect to nerves running into the brain. Taste receptors present in the taste buds of the tongue play a key role in sensing the taste of food materials. This is the reason why we can apperceive food flavor.

3.2.2 Taste The taste system has a positive role in the “effective identification” of food for mammalians, because it helps animals to find the nutritional ingredients and avoid the intake of toxic and harmful substances. The mammalian sense of taste, which plays an important role in the evaluation of the quality of the consumed food, is comprised of five basic qualities: sweet, bitter, salty, sour, and umami. Because the main nutritional components are related to five basic taste qualities. Sweet taste is associated with carbohydrates (monosaccharides, disaccharides, oligosaccharides, and polysaccharides), which are an important energy source for mammalians. Similarly, umami taste from protein-rich foods is associated with L-amino acids (predominantly L-glutamate, ribonucleotide monophosphate, and oligopeptide). The umami taste, which is of significant importance to the food industry, helps humans to ingest high-protein food because of its flavor enhancement properties. Salty taste, which is mainly elicited by inorganic ions, especial sodium ions, helps to keep electrolyte homeostasis of the body. Sour taste is mainly from unripe fruits or food that is potentially spoiled. Finally, bitter taste is elicited by numerous toxic plant metabolites, and is believed to protect animals from the ingestion of poisonous substances.

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3.2.3 Taste mechanism Each of the five basic tastes is an individual taste, which are believed to be detected by specialized taste receptor cells located in the oral cavity. The tasted substance is detected by receptors on the apical surface of the taste cell [16]. Taste molecules activate taste receptors and initiate a series of complex signal transmissions in the papillae, and the signal is sent through the taste nerve to the brain whose intrinsic and extrinsic neuronal circuits are activated and mediated by mediators through a complex network [12, 17]. Taste signals are transduced primarily through GPCR pathways for sweet and bitter, and ion channels for salty and sour. Several taste signal transduction proteins or key channels have recently been discovered and identified by taste researchers, including the T2Rs (a family of bitter-responsive receptors), the T1Rs (heterodimeric sweet-responsive receptors T1R2/T1R3 and umami-responsive receptors T1R1/T1R3), α-gustducin (a G protein α-subunit), Gγ13 (the γ subunit of gustducin), PLCβ2, and Trpm5 (a calcium-activated cation channel), respectively [18]. Taste receptors not only exist in taste buds, but also in the whole body (organs, tissues, and cells) such as in the intestinal tract. The reason why we cannot feel taste receptors in other tissues except the oral cavity, is that the transmitted signal by the taste bud cell is delivered to the brain, and the other signals from the intestinal receptor are mainly transmitted to the hypothalamus or hypophysis cerebra. Those taste receptors in the intestinal receptor regulate nutrient absorption, transport, storage, decomposition, incretion, food intake, fullness, and appetite through metabolism [19]. So, humans and animals can sense environmental variety and nutrition intake by using taste receptors. Though considerable efforts have been made in the last decade, there is still no unanimous consensus on the mechanisms responsible for taste formation. At present, the same concepts of the human taste mechanism have been applied to the electronic tongue. The electronic tongue has been used mainly in commercial equipment for taste measurement in food, pharmacies and the environment field.

3.3

Advantages and limitations

Sensory-based electronic tongue technology has been applied in the food taste field. There is no doubt about its great potential in the application of rapid food screening and measurement. At present, various instruments and devices in taste detection have been put on the market. Electronic tongue technology has the advantages of fulfilling requirements with specific analysis, such as no requiring the pretreatment of taste material and low cost, but its disadvantages are also obvious. The taste sensors of the electronic tongue do not distinguish the individual chemical constituents or selectively detect specific chemical substances like other conventional chemical sensors. In addition, high sensitivity and durability of the taste sensors is inadequate. The electronic tongue system is affected by environmental factors such as temperature, humidity, etc., and these factors can cause sensor drift. It also has the following shortcomings:

Electronic tongue system

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(a) Lower accuracy in the mathematical model. The mathematical model of the signal lacks accurate analysis at an early stage. In this respect, chemical analysis data are much more reliable. The detection ability of the electronic tongue is poorer than high-performance liquid chromatography, gas chromatography–mass spectrometry, capillary electrophoresis, nuclear magnetic resonance, and other technical methods. The mechanism of electrochemical reaction in the electronic tongue is not very clear. The electronic tongue classifies a sample on the basis of taste. Besides this feature, color is another important feature for food sample classification. So, it is expected that with a combined model for an electronic tongue, inclusion of a computer vision module, and chemical analysis better discrimination or decision capability can be achieved. (b) Big size for response signal. Data collection for the electronic tongue is immense, so it is necessary to pretreat these data, compress the signal, remove the redundant information and enhance the signal-to-noise ratio, etc. It is difficult to distinguish among samples that are very similar, especially the signal showing high overlap. (c) Lower repeatability. The surface of the electrode in an electronic tongue is easily stained, and it is difficult to clean it. The resetting degree of an electronic tongue is difficult to achieve when the tested sample is very viscous, so sensitivity is diminished. For the complex composition of liquid samples, the accuracy and repeatability of electronic tongues are also quite low. (d) Poor classification or quality estimation. At present, electronic tongue applications mainly focus on the overall evaluation and distinction of similar samples from a quality perspective. Although it possesses many data processing and data analysis techniques, the quantitative analysis of samples is still insufficient.

For the electronic tongue, the data obtained from different systems are fused in the computer/processor, but the human taste system relies on sensing state information fused in the brain to give judgment. The true sense of the taste nerve and its physiological role are not directly linked. This may be the biggest drawback in the real-time application of the electronic tongue compared to human sensory evaluation. This is because there are a large number of intracellular signaling pathways responsible for transmitting information within the cell when humans taste food. This is a very important factor for human perception to judge the actual state.

3.4

Procedures and recent technology developments

The electronic tongue is based on human taste buds, and its function is similar to that of the human tongue with nonselective and broadly tuned. So the taste sensors of the electronic tongue respond globally to taste compounds (sweet, bitter, salty, sour, umami, and so on) present in the test sample. The electronic tongue is a multichannel taste sensor (more than five basic tastes) with global selectivity and is composed of several kinds of lipid/polymer membranes for transforming information on taste substances [20] into electrical signals, which are fed into a computer. The signals of the electronic tongue are analyzed in a pattern recognition unit to discriminate between similar samples. In a word, the electronic tongue as a powerful analytical tool includes three parts: (1) an array of nonspecific and low selective chemical sensors which have partial specificity (cross-sensitivity) to different components in the liquid

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Evaluation Technologies for Food Quality

Fig. 3.2 Block diagram of an electronic tongue system.

sample; (2) an appropriate method of pattern recognition; (3) multivariate calibration for data processing. A block diagram of an electronic tongue system is shown in Fig. 3.2 [21].

3.4.1 Sensor For sensors, the primarily materials (membranes, electrodes or particles) that respond to taste components, have been developed. From an analytical point of view, a sensor is a device designed to perform selective measurements of physical, chemical or biological parameters and generating a quantifiable physical signal [22]. The structure of the sensor array is achieved by modifying the surface of the sensor with different chemical materials. The selectivity and detection limits of a sensor array depend on composition and properties of the sensing materials [23]. The number of sensors in the array may range from 4 to 40 [24]. Various sensor technologies are employed for the electronic tongue. Sensor arrays are usually categorized in three classes: arrays of redundant sensors, arrays of selective sensors and arrays of cross-sensitive sensors [25]. A summary of sensor types from a review is shown in Fig. 3.3 [21]. Among

Fig. 3.3 Types of sensors commonly employed in the electronic tongue.

Electronic tongue system

29

the various principles of detection, the potentiometric sensor and cyclic voltammetry sensor are the most prominent ones. (1) Potentiometric sensor

The gustatory sensor based on potential was first presented by Iiyama [26]. It is composed of several kinds of lipids/polyvinylchloride membranes for transforming taste quality information (saltiness, bitterness, etc.) into electrical signals. Another type of potential taste sensor, which was designed by several nonspecific sensors based on chalcogenide glass as a transducer, was presented by Vlasov [27]. The involved principle is potentiometric, and the used materials are chalcogenide, oxide glasses and noble metals for amperometric signal detection [27, 28]. Potentiometric sensors are the most widespread type of sensors in electronic tongue systems. Potentiometric measurements must be carried out by using a multichannel voltmeter with high-input impedance. An electronic tongue system based on an array of six metallic potentiometric sensors (metallic wires) was developed and utilized for discrimination of foodstuffs [29]. (2) Voltammetry sensor

In 1997, the concept of an electronic tongue based on voltammetry sensors was first presented by F. Winquist and his group [30]. Voltammetry sensors generally contain an array of working electrodes, reference electrode and auxiliary electrode in three electrode systems. The voltammetric response of a solution strongly depends on the nature of the electrode material [30–32], for example, Au, Pt, and Rh are usually used as working electrodes. Voltammetric techniques have several advantages, such as high sensitivity, stability, simplicity and robustnes [33]. Three types of sensor are used: (1) multifrequency large amplitude pulse voltammetry which has different step lengths; (2) stripping voltammetry; (3) cyclic voltammetry. They are used extensively in analytical chemistry.

3.4.2 Data processing and data analysis techniques For detecting samples before analysis, the raw data from the sensors are preprocessed, so the choice of the appropriate preprocessing technique is crucial for the performance of the pattern classifier and the presentation of experimental results. The techniques of data preprocessing are used to transform a dataset so that this step can provide a better input to the pattern recognition engines [34]. The effects of different preprocessing techniques on electronic tongue data have been studied in the classification of different grades of black tea [35]. For these data preprocessing techniques, it is important to investigate their efficacy in terms of pattern classification accuracy on a case-by-case basis. We generally use different data analysis algorithms to analyze different objects. For qualitative analysis, the commonly used methods include PCA, cluster analysis, linear discriminant analysis and principal component regression (PCR). For quantitative analysis, the commonly used ones are partial least squares-discriminant analysis (PLS-DA), artificial neural networks (ANNs), support vector machine, multilinear

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Evaluation Technologies for Food Quality

regression, partial least squares regression, wavelet neural network, and so on. Among the different methods that can be implemented, PCA, PLS-DA, and ANNs are the most widely employed in electronic tongue applications [36]. (1) Principal component analysis

PCA is the most common method in electronic tongue measurements. In most cases it is used as a preprocessor for sensor array data. Although as many principal components (PCs) as variables in the original data are generated, almost all the variance carried by the original variables can be summarized in a few PCs. PCA decomposes the data matrix into a new set of uncorrelated variables (PCs), which means that it finds new directions in the pattern space, so that they explain the maximum amount of variance in the dataset [37]. Multivariate data can be explored by using PCA, reducing their noise without loss of information, as well as the possibility of assessing the significance of individual components [38]. These new variables may be plotted on a PCA plot or used as inputs for more complex classifiers, e.g., neural networks. Its main advantages include simplicity, providing a visual output of samples (dis)similarities and providing a measure of signal representativeness, but it focuses on the maximum variance between samples. Hence it gives less importance to small differences, which in some cases could be significant. It also cannot be used as a classifier by itself, requiring its coupling with a modeling tool [39]. (2) Artificial neural network

ANNs, a powerful tool for nonlinear approximations, are widely used in artificial senses data analysis because of their ability to imitate human brain behavior by learning the solution to problems from the data by avoiding the necessity for modeling. ANNs are distributed computing systems composed of processing units connected by weighted links that can be assembled in one or more layers, simulating the structure and functioning of the human brain. ANNs can be used in either qualitative or quantitative modeling. One of main advantages of ANNs is their ability to learn from data through training algorithms [29, 40]. They simulate the behavior of real neural systems in a very simplified way [41]. The output of an ANN is the predictor matrix (Ypred), which gives the percent of correct classifications (the same procedure as for PLS-DA) after comparison with the target matrix. ANNs have more flexible modeling methodologies, linear and nonlinear functions can be both used (or combined) in the processing units. However, they also have many disadvantages, such as complexity of their architecture, indispensable preprocessing step for data reduction and the lower accuracy of mode after rebuilding [39]. (3) Partial least squares-discriminant analysis

PLS-DA is a supervised method, which models the relationship between two matrices, i.e., the dataset obtained from sensor array measurements and a class affiliation matrix (a target matrix composed of a vector with true class affiliations). PLSDA determines a set of latent variables corresponding to principal components in PCA, explaining as much as possible the covariance between the two matrices (PLS-DA scores). PLS-DA is usually used in qualitative modeling. This is a

Electronic tongue system

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generalization of multiple linear regression; it can analyze noisier and uncompleted data, and it is able to manage multicollinearity problems, which often occur in sensor array measurements [1]. The output of PLS-DA is PLS-DA scores that can be plotted the same as in PCA and Ypred, which estimates class affiliation. A comparison of particular vectors of the predictor matrix with respondent vectors of the target matrix shows correctness or incorrectness of a particular sample classification. Its main advantage is that convergence of the system into a minimum residual error is often achieved using fewer factors than using PCR, but modeling of nonlinear systems is not handled particularly well [39]. In a word, PCA, PLS-DA, and ANN may be combined to enhance classification capability.

3.5

Recent application progress in different types of foods

From an analytical sense, electronic tongue are composed of various sensors with unique properties and characteristics of partial selectivity or cross-selectivity. Their unique property is the measurement and characterization of complex liquid matrices. Due to these features, they were first used in the food industry. Later, their application has been widely spread in the monitoring environment, medical diagnostics, herbal products, detection of endotoxins and pesticides, etc. A literature search employing the term “electronic tongue” using ISI Web of Science on 27/02/2018 demonstrated a steady increasing in the number of publications from 3 in 1995 to 166 in 2017. Many variants of the electronic tongue have also been reported in the literature after an extensive search. In this chapter, for the application of the electronic tongue in food, a brief classification is summarized in Table 3.1. Wine, beer, juice, and beverages are the most common samples in the application of the electronic tongue. In taste evaluation, chemical analysis, liquid chromatographic analysis, electronic nose, human sensory evaluation, or near infrared spectroscopy are also used with the electronic tongue system. For example, umami taste in mushrooms [42], commercially available umami products and some amino acids [43], the taste characteristics of Chinese rice wine [44], discrimination and characterization of strawberry juice [62], evaluation of oxygen exposure levels and polyphenolic content of red wine [46], the influence of micro-oxygenation and oak chip maceration on wine composition [69], and so on. In a word, the application of the electronic tongue in food is trying to diversify.

3.6

Summary and outlook

For taste analysis of food samples, it is necessary to consider all processes, including the release of flavor chemicals in the mouth, the combination of receptors with taste molecules, the specificity and characteristics of receptors, transduction mechanisms, cell signaling processing and electrochemistry in the gustatory system. The electronic

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Evaluation Technologies for Food Quality

Table 3.1 List of applications using the electronic tongue in food, including type of sensing unit and principle of detection Application

Principle of detection

References

Umami taste Wine Beer Olive oils Honey Edible oils Apple liqueurs Rice Meat taste Hydrolysate Coffee Juice and beverages Taste samples Tea Water Milk Gliadins in foodstuffs Soy sauce

Potentiometry, voltammetry Potentiometry, voltammetry Potentiometry, voltammetry Potentiometry, voltammetry Potentiometry Voltammetry Voltammetry Voltammetry Potentiometry Voltammetry Potentiometry, voltammetry Potentiometry, voltammetry Potentiometry, voltammetry Fluorometry, voltammetry, potentiometry Potentiometry, voltammetry Voltammetry, potentiometry Potentiometry Potentiometry

[42, 43] [5, 6, 44–46] [47] [48–50] [51–53] [54] [55] [56, 57] [58] [59] [60, 61] [7, 62, 63] [64] [65] [31] [66] [67] [68]

tongue as a valuable tool for assessment and prediction of the taste of food and related products is emerging and finding use in promising fields of recent chemical sensor science. It could replace human panels in routine analysis in food production and analysis, and reduce the risk of using human panels to test products. So far, applications of the electronic tongue are found mostly in the area of human and pet food, environment detection, water analysis and a few clinical areas. This may be extended to several other areas of human safety such as pesticide residues, fungal or bacterial contamination detection, etc. Electronic tongue technology is related to computer science, materials science and signals processing science. To the best of the authors’ knowledge, at present, commercialized electronic tongue devices are potentiometry based, and Alpha Mos in France is the best-known company in this field. However, bioelectronic tongues or electronic tongues based on other transduction principles are still at an early stage. At this stage, what are needed are speed, reproducibility, consistency and robustness for commercial applications. In this area, researchers need to make greater efforts to produce numerous commercialized applications in the coming years, and their success will be further enhanced with sensor technology and artificial intelligence development. The detection system of an electronic tongue would need to meet these requirements with specific analysis to achieve optimization algorithms and quantitative analysis. The electronic tongue combined with different sensors, modified electrodes and responses from vision along with suitable pattern recognition systems or hardwarelevel combined systems, would improve the comprehensive level of acquisition of

Electronic tongue system

33

information. A portable electronic tongue for testing could also be developed, so that an electronic tongue with higher intelligence will have better prospects in applications. Instead of using a conventional sensor, development of an application-specific sensor would produce better quality analysis, and this area may be considered as another research direction. All things considered, it may be concluded that combining multiple artificial sensing systems enhances the performance of classification or quality evaluation of the products under consideration, but the combined systems are still at an early stage and more research in this area is needed. Improved feature extraction techniques should be also explored for electronic sensor response analysis.

References [1] R.S. Latha, P.K. Lakshmi, Electronic tongue: an analytical gustatory tool, J. Adv. Pharm. Technol. Res. 3 (1) (2012) 3–8. [2] P. Kovacic, R. Somanathan, Mechanism of taste; electrochemistry, receptors and signal transduction, J. Electrost. 70 (1) (2012) 7–14. [3] G. Jellinek, Sensory evaluation of food, in: Theory and Practice, Ellis Horwood Ltd., 1985. [4] H. Smyth, D. Cozzolino, Instrumental methods (spectroscopy, electronic nose, and tongue) as tools to predict taste and aroma in beverages: advantages and limitations, Chem. Rev. 113 (3) (2013) 1429–1440. [5] A. Rudnitskaya, L.M. Schmidtke, A. Reis, et al., Measurements of the effects of wine maceration with oak chips using an electronic tongue, Food Chem. 229 (2017) 20–27. [6] P. Gimenez-Gomez, R. Escude-Pujol, F. Capdevila, et al., Portable electronic tongue based on microsensors for the analysis of cava wines, Sensors (Basel) 16 (11) (2016). [7] M. Gutierrez-Capitan, J.L. Santiago, J. Vila-Planas, et al., Classification and characterization of different white grape juices by using a hybrid electronic tongue, J. Agric. Food Chem. 61 (39) (2013) 9325–9332. [8] K. Toko, A taste sensor, Meas. Sci. Technol. 9 (12) (1998) 1919. [9] M. Otto, T. JDR, Model studies on multiple channel analysis of free magnesium, calcium, sodium, and potassium at physiological concentration levels with ion-selective electrodes, Anal. Chem. 57 (13) (1985) 2647–2651. [10] K. Toko, Taste sensor with global selectivity, Mater. Sci. Eng. C Biomim. Mater. Sens. Syst. 4 (2) (1996) 69–82. [11] L.A. Garcon, M. Genua, Y. Hou, et al., A versatile electronic tongue based on surface plasmon resonance imaging and cross-reactive sensor arrays—a mini-review, Sensors (Basel) 17 (5) (2017). [12] A. Riul Jr., C.A. Dantas, C.M. Miyazaki, et al., Recent advances in electronic tongues, Analyst 135 (10) (2010) 2481–2495. [13] R.L. Doty, Handbook Of Olfaction And Gustation (Vol 32, PG 1432, 1995), Neurology 45 (10) (1995) 1952. [14] S. Yamaguchi, K. Ninomiya, Umami and food palatability, J. Nutr. 130 (4) (2000) 921S–926S. [15] S.D. Roper, Parallel processing in mammalian taste buds? Physiol. Behav. 97 (5) (2009) 604–608. [16] A.I. Spielman, H. Nagai, G. Sunavala, et al., Rapid kinetics of second messenger production in bitter taste, Am. J. Phys. Cell Phys. 270 (3) (1996) C926–C931.

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[38] A. PHB, D. Volpati, R. Jr, et al., Layer-by-layer technique as a new approach to produce nanostructured films containing phospholipids as transducers in sensing applications, Langmuir 25 (4) (2009) 2331–2338. [39] X. Ceto, N.H. Voelcker, B. Prieto-Simon, Bioelectronic tongues: new trends and applications in water and food analysis, Biosens. Bioelectron. 79 (2016) 608–626. [40] B.L.M. Iliev, L. Robertsson, et al., A fuzzy technique for food-and water quality assessment with an electronic tongue, Fuzzy Sets Syst. 157 (9) (2006) 1155–1168. [41] J.E.K.K. Haugen, Electronic nose and artificial neural network, Meat Sci. 49 (1998) S273–S286. [42] C. Phat, B. Moon, C. Lee, Evaluation of umami taste in mushroom extracts by chemical analysis, sensory evaluation, and an electronic tongue system, Food Chem. 192 (2016) 1068–1077. [43] L. Bagnasco, M.E. Cosulich, G. Speranza, et al., Application of a voltammetric electronic tongue and near infrared spectroscopy for a rapid umami taste assessment, Food Chem. 157 (2014) 421–428. [44] H. Yu, J. Zhao, F. Li, et al., Characterization of Chinese rice wine taste attributes using liquid chromatographic analysis, sensory evaluation, and an electronic tongue, J. Chromatogr. B Analyt. Technol. Biomed Life Sci. 997 (2015) 129–135. [45] A.M. Simoes Da Costa, I. Delgadillo, A. Rudnitskaya, Detection of copper, lead, cadmium and iron in wine using electronic tongue sensor system, Talanta 129 (2014) 63–71. [46] M.L. Rodriguez-Mendez, C. Apetrei, M. Gay, et al., Evaluation of oxygen exposure levels and polyphenolic content of red wines using an electronic panel formed by an electronic nose and an electronic tongue, Food Chem. 155 (2014) 91–97. [47] E.W. Nery, L.T. Kubota, Integrated, paper-based potentiometric electronic tongue for the analysis of beer and wine, Anal. Chim. Acta 918 (2016) 60–68. [48] I. Marx, N. Rodrigues, L.G. Dias, et al., Sensory classification of table olives using an electronic tongue: analysis of aqueous pastes and brines, Talanta 162 (2017) 98–106. [49] L.G. Dias, A. Fernandes, A.C. Veloso, et al., Single-cultivar extra virgin olive oil classification using a potentiometric electronic tongue, Food Chem. 160 (2014) 321–329. [50] A.C. Veloso, L.G. Dias, N. Rodrigues, et al., Sensory intensity assessment of olive oils using an electronic tongue, Talanta 146 (2016) 585–593. [51] M. Juan-Borras, J. Soto, L. Gil-Sanchez, et al., Antioxidant activity and physico-chemical parameters for the differentiation of honey using a potentiometric electronic tongue, J. Sci. Food Agric. 97 (7) (2017) 2215–2222. [52] L.G. Dias, A.C. Veloso, M.E. Sousa, et al., A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools, Anal. Chim. Acta 900 (2015) 36–45. [53] M.E. Sousa, L.G. Dias, A.C. Veloso, et al., Practical procedure for discriminating monofloral honey with a broad pollen profile variability using an electronic tongue, Talanta 128 (2014) 284–292. [54] P. Oliveri, M.A. Baldo, S. Daniele, et al., Development of a voltammetric electronic tongue for discrimination of edible oils, Anal. Bioanal. Chem. 395 (4) (2009) 1135–1143. [55] M. Sliwinska, C. Garcia-Hernandez, M. Koscinski, et al., Discrimination of apple liqueurs (Nalewka) using a voltammetric electronic tongue, UV-Vis and Raman spectroscopy, Sensors (Basel) 16 (10) (2016). [56] L. Lu, X. Hu, S. Tian, et al., Visualized attribute analysis approach for characterization and quantification of rice taste flavor using electronic tongue, Anal. Chim. Acta 919 (2016) 11–19.

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[57] L. Wang, Q. Niu, Y. Hui, et al., Discrimination of rice with different pretreatment methods by using a voltammetric electronic tongue, Sensors (Basel) 15 (7) (2015) 17767–17785. [58] X. Zhang, Y. Zhang, Q. Meng, et al., Evaluation of beef by electronic tongue system TS-5000Z: flavor assessment, recognition and chemical compositions according to its correlation with flavor, PLoS One 10 (9) (2015)e0137807. [59] L. Wang, Q. Niu, Y. Hui, et al., Assessment of taste attributes of peanut meal enzymatichydrolysis hydrolysates using an electronic tongue, Sensors (Basel) 15 (5) (2015) 11169–11188. [60] S. Buratti, N. Sinelli, E. Bertone, et al., Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis, J. Sci. Food Agric. 95 (11) (2015) 2192–2200. [61] R.B. Dominguez, L. Moreno-Baron, R. Munoz, et al., Voltammetric electronic tongue and support vector machines for identification of selected features in Mexican coffee, Sensors (Basel) 14 (9) (2014) 17770–17785. [62] S. Qiu, J. Wang, L. Gao, Discrimination and characterization of strawberry juice based on electronic nose and tongue: comparison of different juice processing approaches by LDA, PLSR, RF, and SVM, J. Agric. Food Chem. 62 (27) (2014) 6426–6434. [63] L.G. Dias, C. Sequeira, A.C. Veloso, et al., Evaluation of healthy and sensory indexes of sweetened beverages using an electronic tongue, Anal. Chim. Acta 848 (2014) 32–42. [64] S. Altan, M. Francois, S. Inghelbrecht, et al., An application of serially balanced designs for the study of known taste samples with the alpha-ASTREE electronic tongue, AAPS PharmSciTech 15 (6) (2014) 1439–1446. [65] L. Xu, S.M. Yan, Z.H. Ye, et al., Combining electronic tongue array and chemometrics for discriminating the specific geographical origins of green tea, J. Anal. Methods Chem. 2013 (2013) 350801. [66] Z. Wei, J. Wang, Detection of antibiotic residues in bovine milk by a voltammetric electronic tongue system, Anal. Chim. Acta 694 (1–2) (2011) 46–56. [67] A.M. Peres, L.G. Dias, A.C. Veloso, et al., An electronic tongue for gliadins semiquantitative detection in foodstuffs, Talanta 83 (3) (2011) 857–864. [68] Q. Ou-Yang, J.W. Zhao, Q.S. Chen, et al., Study on classification of soy sauce by electronic tongue technique combined with artificial neural network, J. Food Sci. 76 (9) (2011) S523–S527. [69] A. Rudnitskaya, L.M. Schmidtke, I. Delgadillo, et al., Study of the influence of microoxygenation and oak chip maceration on wine composition using an electronic tongue and chemical analysis, Anal. Chim. Acta 642 (1–2) (2009) 235–245.

Further reading [70] S.V. Litvinenko, D.O. Bielobrov, V. Lysenko, et al., Optical addressing electronic tongue based on low selective photovoltaic transducer with nanoporous silicon layer, Nanoscale Res. Lett. 11 (1) (2016) 374.

Electronic eye for food sensory evaluation

4

Changhua Xu College of Food Science and Technology, Shanghai Ocean University, Shanghai, China

4.1

Introduction

With the development of economies and progress of technology, there is an increasing demand for high-quality food. When it comes to food quality, nutrition and safety are the two primary concerns. In addition, food flavors and colors are gradually attracting increasing public attention. However, food deterioration and illegitimate operations are continuously taking place, with some food manufacturers seeking illegal profits, and exposing customers to risks of unsafe food. Hence it is necessary to monitor and evaluate food quality variation. Certain methods have been employed in response to these problems, focusing on determination and recognition of food colors. Chemical colorimetry and visible spectrophotometry are the most common detection procedures in food evaluation, which are based on chemical reactions leading to color changes in food. Qualitative and quantitative analyses are achieved with a number of tools such as the colorimetric card, reagent kit, test strip, visible spectrophotometer, and even the naked eye. These methods are rapid, easy to handle, high throughput, and convenient compared to others, and can be applied to all samples having color-change reactions during storage or processing. However, trace analysis is hard to achieve in spite of several advantages in macro- and microanalysis being proposed [1]. Also, some biochemical tests depend on complicated enzyme-related reactions with reliable results but complicated processes. Therefore new methods to measure food appearance and color in a quick and convenient way would be ideal. The computer vision system (Fig. 4.1), a new artificial perception technique, is a promising way of detecting the external characteristics of food. Compared to traditional detection methods, the computer vision system deals with food color, shape, size, and texture effectively. More importantly, the computer vision system is a reliable mimic technique for food detection and is convenient, efficient, nondestructive, easy to operate, and can analyze more details than the human eye.

4.1.1 What is an electronic eye An electronic eye, a computer vision technology converting optical images into digital images, uses an image sensor instead of the human eye to collect images of objects, and employs computer simulation criteria to identify the images with the purpose of avoiding subjective deviation of the human eyes [2, 3]. Its wide application has proved Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00004-4 © 2019 Elsevier Inc. All rights reserved.

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Fig. 4.1 General content of the chapter.

it to be a rapid, precise, and nondestructive detection technology for evaluating product quality in the form of shape, size, as well as color monitoring and texture analysis [4].

4.1.2 Historical development of electronic eye technology The electronic eye is gaining increasing popularity in the food industry because it integrates mechanics, optical instrumentation, electromagnetic sensing, and digital videoand image-processing technology. This novel engineering technology has been promptly put to use but its application can be traced back to the 1960s [5]. In 1982, information (image) processing, including computational theory, representations, algorithms, and hardware implementation, was proposed by Marr [6]. In the following 10 years, researchers were alerted to the analysis of moving objects, so-called tracking, which was applied to most production lines. Typically, in modern vision systems, a charged-coupled device (CCD) camera is frequently used as the image sensor. After image collection, image processing is applied to enhance and improve the acquired images for further analysis [5]. Nowadays, the electronic eye is employed to evaluate food freshness, process monitoring, determination of shelf life, and quality authentication of raw food based on shape, color, and size parameters.

4.2

The basic principles

In the food industry, the electronic eye obtains the digital images of tested substances through optical sensing technology, and then uses image-processing technology to extract characteristic information of images related to the quality of food and establishes the detection model of food quality. Based on high-resolution camera imaging under controlled lighting conditions in a closed cabinet, the electronic eye achieves detailed measurements of a product’s aspects (colors and shapes). The instrument can evaluate the whole product as perceived by the consumer, or focus on selected portions.

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In the case of the human eye, the operation of vision systems depends on the intensity of the lighting, so proper lighting improves the precision and reduces the time of analysis. Fluorescent and incandescent bulbs are the most common light sources. Luminescent electric diodes, quartz halogen lamps, metal halide lamps (applied in microscopy), and high-pressure sodium lamps (best suited for lighting large industrial buildings) are used as well. However, due to more uniform and intensive light at specific wavelengths, a circular lamp system is used with flat samples and a scattered system for lighting ball-shaped samples. An X-ray tube is used to perform a detailed evaluation of the quality and ripeness of food products, and the penetration of X-rays depends on the emitted energy, absorption coefficient, density, and thickness of the analyzed objects. Another part of the image analysis system is a still camera, movie camera, or scanner for recording a photograph of a given object. There are two types of camera: analog and digital cameras equipped with CCD or complementary metaloxide semiconductor sensor arrays. In an analog camera, the recorded image is converted into an analog signal and then transferred to a frame grabber (in the form of a card), which transforms the analog signal into a digital data stream and sends it to a computer memory. In digital cameras, a frame grabber is not needed because the analog signal is sent directly to the computer via a USB or firewire adapter.

4.3

Procedures

The electronic eye undergoes a procedure that includes acquisition, processing, and analysis of images. As we can see in Fig. 4.2, generally, a camera captures the measured object’s reflected light and transfers it into electrical analog signals. A computer processing system then extracts the target characteristic information, selects the regions of interest, and divides them into background and target images. The image segmentation process, which makes it possible to obtain the region of interest containing chemical information, can be done by thresholding, edge-based segmentation, or region-based segmentation. The analytical parameters are built from the color information, and qualitative or quantitative analytical information is extracted by applying single calibration, pattern recognition, and multivariate analysis [8, 9].

4.4

Advantages and limitations

4.4.1 Advantages 1. Objective and reliable visual assessment: Unaffected by product consistency or texture, the electronic eye achieves reproducible color and shape measurements under controlled conditions and assures product traceability through data storage. 2. In-depth analysis: The instrument measures both color and shape parameters in one acquisition of the whole product. It does not deliver a mean value but determines the proportion of each visible color, color distribution, and variations across the surface as well as information such as circularity, area, or surface ratio between minimum and maximum size.

40 Evaluation Technologies for Food Quality

Fig. 4.2 Schematic structure of an image analysis system [7].

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3. Easy and fast method: This nondestructive analysis requires no sample preparation and is suitable for complex and nonuniform areas. Thanks to a large measurement surface, sample size is seldom an issue, which also allows several samples to be assessed in one analysis.

4.4.2 Limitations Specific to individual applications, selection and calibration of system components are the critical factors for obtaining improved efficiency. For instance, poor or inconsistent lighting significantly affects acquired image quality, while a high-quality image offers lower complexity and less time for image processing. This makes measurements more uncertain and the instrumental resolution of a signal worse. Moreover, it is affected by operating or environmental conditions.

4.5

Recent technology developments

A large amount of data has been obtained through the measurement of electronic sensors, so it is necessary to apply data analysis methods to classify samples. Principal component analysis (PCA) is commonly employed to model, compress, and visualize multivariate data (Fig. 4.3). The PCA model can be described by the following equation: X½m, n ¼ T½m, f   P½ f , n T + E½m, n

(1)

The aim is to present the dataset X, with m objects and n variables, in the form of a product of two new matrices T(m  f ) and P(n  f ), where f ≪ n. E is the matrix of residues for the PCA model with f principal components, and matrices T and P contain the object coordinates and parameters that lie on the first new coordinate (first principal component), which is the direction of maximum variance [10]. In addition, other methods of multivariate data processing shown in Table 4.1. Based on the models listed in Table 4.2, some applications combined with a computer vision system have been carried out. Tao et al. [11] leveraged the computer vision system combined with an artificial neural network (ANN) to evaluate the quality of Hanyuan Zanthoxylum bungeanum Maxim. Ouyang et al. [12] used a support vector machine (SVM) in model calibration to evaluate rice wine, and the results showed that the performance of the SVM) was a little better than that of other models because the prediction errors for samples in SVM models were 90%) than the human panel test results. Examples of the application of computer image processing in food analysis are presented in Table 4.2.

4.7

Summary and outlook

Due to the rapid increase in demand for safe and high-quality food, a fast, objective, and nondestructive sensory analysis technique is urgently needed. In recent years, electronic eye technology has been widely applied for quality evaluation of food products based on shape, size, as well as color monitoring, and texture analysis in fruits, vegetables, grains, meat, fish, dairy products, and liquid foods. Because sample preparation is simple and analysis is noninvasive, food process monitoring, evaluation of food freshness, and testing the shelf life of food can be carried out by means of electronic eye technology. Furthermore, to achieve comprehensive analysis, quality evaluation systems combining diverse artificial sensors that encompass the evaluation of appearance, taste, and smell, simulating the sensory analysis of testers, have been developed. Applications of the electronic eye, including nonfusion and fusion technology, have demonstrated great potential for providing objective sensory evaluation of different types of foods. Traditionally, food quality evaluation generally performed by panels of trained human experts is the most common method; however, this approach still suffers from several disadvantages, e.g., it is time consuming, expensive, and subjective. Conventional instrumental standard methods rely on precision laboratory devices and suffer from similar drawbacks, e.g., they are time consuming, labor intensive, and expensive. Therefore the need for an advanced method led to the development of imaging techniques. With the development of analytical methods based on imaging devices, machine vision systems have the potential to replace manual (visual) methods of inspection and gain wide acceptance in industries. As a tool for quality evaluation of numerous agricultural products, it provides rapid and accurate information about external quality aspects of food for further data analysis and result verification flexibly

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under various analytical models. Thus electronic eye technology has been widely applied for evaluating food quality and is a valid alternative to the tedious and time-consuming traditional analytical methods. Moreover, the fusion of artificial sensory technology combining electronic eye, electronic nose, and electronic tongue contributes to a system with complementary and comprehensive information regarding food, which provide promising perspectives and trends for monitoring, controlling, and automating food processing systems in the near future.

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[36] J. Liu, M.R. Paulsen, Corn whiteness measurement and classification using machine vision, Trans. ASAE 43 (3) (2000) 757–763. [37] D. Mery, J.J. Chanona-Perez, A. Soto, J.M. Aguilera, A. Cipriano, N. Velez-Rivera, I. Arzate-Vazquez, G.F. Gutierrez-Lopez, Quality classification of corn tortillas using computer vision, J. Food Eng. 101 (4) (2010) 357–364. [38] V.G. Narendra, K.S. Hareesha, Quality inspection and grading of agricultural and food products by computer vision—a review, Int. J. Comput. Appl. 2 (1) (2010) 43–65. [39] M. O’Sullivan, Evaluation of pork colour: Prediction of visual sensory quality of meat from instrumental and computer vision methods of colour analysis, Meat Sci. 65 (2) (2003) 909–918. [40] L. Huang, J. Zhao, Q. Chen, Y. Zhang, Nondestructive measurement of total volatile basic nitrogen (TVB-N) in pork meat by integrating near infrared spectroscopy, computer vision and electronic nose techniques, Food Chem. 145 (2014) 228–236. [41] R.E. Larraı´n, D.M. Schaefer, J.D. Reed, Use of digital images to estimate CIE color coordinates of beef, Food Res. Int. 41 (4) (2008) 380–385. [42] N.A. Valous, F. Mendoza, D.W. Sun, P. Allen, Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams, Meat Sci. 81 (1) (2009) 132–141. [43] S. Barbieri, F. Soglia, R. Palagano, F. Tesini, A. Bendini, M. Petracci, C. Cavani, T. Gallina Toschi, Sensory and rapid instrumental methods as a combined tool for quality control of cooked ham, Heliyon 2 (11) (2016) e00202. [44] A.C.M. Oliveira, M.O. Balaban, Comparison of a colorimeter with a machine vision system in measuring color of Gulf of Mexico sturgeon fillets, Appl. Eng. Agric. 22 (4) (2006) 583–587. [45] R.A. Quevedo, J.M. Aguilera, F. Pedreschi, Color of Salmon fillets by computer vision and sensory panel, Food Bioprocess Technol. 3 (5) (2010) 637–643. [46] D.A. Luzuriaga, M.O. Balaban, S. Yeralan, Analysis of visual quality attributes of white shrimp by machine vision, J. Food Sci. 62 (1) (1997) 113. [47] M. Mohebbat, M.-R. Akbarzadeh-T, F. Shahidi, M. Moussavi, B.G. Hamid, Computer vision systems (CVS) for moisture content estimation in dehydrated shrimp, Comput. Electr. Agricult. 69 (2) (2009) 128–134. [48] X.Y. Zhang, W. Wei, W. Hu, X.C. Wang, P. Yu, J.H. Gan, Y. Liu, C.H. Xu, Accelerated chemotaxonomic discrimination of marine fish surimi based on tri-step FT-IR spectroscopy and electronic sensory, Food Control 73 (2017) 1124–1133. [49] M.L.G.-M. Martin, J. Wei, R. Luo, J. Hutchings, F.J. Heredia, Measuring colour appearance of red wines, Food Qual. Prefer. 18 (6) (2007) 862–871. [50] S. Kiani, S. Minaei, M. Ghasemi-Varnamkhasti, Fusion of artificial senses as a robust approach to food quality assessment, J. Food Eng. 171 (2016) 230–239. [51] N. Prieto, M. Gay, S. Vidal, O. Aagaard, J.A. de Saja, M.L. Rodriguez-Mendez, Analysis of the influence of the type of closure in the organoleptic characteristics of a red wine by using an electronic panel, Food Chem. 129 (2) (2011) 589–594. [52] I.M. Apetrei, M.L. Rodrı´guez-Mendez, C. Apetrei, I. Nevares, M. del Alamo, J.A. de Saja, Monitoring of evolution during red wine aging in oak barrels and alternative method by means of an electronic panel test, Food Res. Int. 45 (1) (2012) 244–249. [53] S. Sahameh, M. Saeid, M.-C. Mahdi, G.-V. Nasrollah, B. Mohsen, Potential application of machine vision to honey characterization, Trends Food Sci. Technol. 30 (2)(2013) 174–177. [54] B. Susanna, B. Simona, G. Gabriella, Application of electronic senses to characterize espresso coffees brewed with different thermal profiles, Eur. Food Res. Technol. 243 (3) (2017) 511–520.

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[55] C. Apetrei, I.M. Apetrei, S. Villanueva, J.A. de Saja, F. Gutierrez-Rosales, M. L. Rodriguez-Mendez, Combination of an e-nose, an e-tongue and an e-eye for the characterisation of olive oils with different degree of bitterness, Anal. Chim. Acta 663 (1) (2010) 91–97. [56] R. Ferna´ndez-Va´zquez, C.M. Stinco, A.J. Melendez-Martı´nez, F.J. Heredia, I.M. Vicario, Visual and instrumental evaluation of orange juice color: a consumers’ preference study, J. Sensory Stud. 26 (6) (2011) 436–444. [57] H.-H. Wang, D.-W. Sun, Evaluation of the functional properties of Cheddar cheese using a computer vision method, J. Food Eng. 49 (1) (2001) 49–53. [58] H. Ni, S. Gunasekaran, A computer vision system for determining quality of cheese shreds, in: Food Processing Automation IV Proceedings of the FPAC Conference, 1995. [59] T. Pearson, N. Toyofuku, Automated sorting of pistachio nuts with closed shells, Appl. Eng. Agric. 16 (1)(2000) 91–94. [60] N.-A. Hosein, O. Mahmoud, M. Seyed Saeid, S.F. Mahmoud, Design, development and evaluation of an online grading system for peeled pistachios equipped with machine vision technology and support vector machine, Inform. Process. Agricult. 4 (4) (2017) 333–341. [61] M.G. Scanlon, R. Roller, G. Mazza, M.K. Pritchard, Computerized video image analysis to quantify color of potato chips, Am. J. Potato Res. 71 (11) (1994) 717–733. [62] H. Yin, S. Panigrahi, Image processing techniques for internal texture evaluation of French fries, Appl. Eng. Agric. 20 (6) (2004) 803–811. [63] W. Jian, Z. Xianyin, D. ShiPing, Identification and grading of tea using computer vision, Appl. Eng. Agric. 26 (4) (2010) 639–645. [64] L. Amit, P. Neelam Rup, S. Shashi, M. Himanka Sekhar, K. Amod, K. Pawan, Significant physical attributes affecting quality of Indian black (CTC) tea, J. Food Eng. 113 (1) (2012) 69–78. [65] Q. Chen, J. Zhao, J. Cai, Identification of tea varieties using computer vision, Trans. ASABE 51 (2) (2008) 623. [66] Q.S. Chen, D.L. Zhang, W.X. Pan, Q. Ouyang, H.H. Li, K. Urmila, J.W. Zhao, Recent developments of green analytical techniques in analysis of tea’s quality and nutrition, Trends Food Sci. Technol. 43 (1) (2015) 63–82. [67] Q.S. Chen, J.W. Zhao, H.D. Zhang, M. Fang, Identification of tea color by using computer vision, J. Jiangsu Univ. (National Science Edition) 26 (2005) 461–464. [68] W.-L. Wang, C.-Y. Li, A multimodal machine vision system for quality inspection of onions, J. Food Eng. 166 (2015) 291–301. [69] F.A. Mendoza, P. Dejmek, J.M. Aguilera, Colour and image texture analysis in classification of commercial potato chips, Food Res. Int. 40 (9) (2007) 1146–1154. [70] M. Xu, S.L. Yang, W. Peng, Y.J. Liu, D.S. Xie, X.Y. Li, C.J. Wu, A novel method for the discrimination of Semen Arecae and its processed products by using computer vision, electronic nose, and electronic tongue, Evid. Based Complement. Alternat. Med. 2015 (2015) 753942. [71] I.Y. Zayas, C.R. Martin, J.L. Steele, A. Katsevich, Wheat classification using image analysis and crush-force parameters, Trans. ASAE 39 (6) (1996) 2199–2204. [72] M. Nair, D.S. Jayas, Dockage identification in wheat using machine vision, Can. Agric. Eng. 40 (4) (1998) 293–298. [73] C.-Y. Yang, Q.-H. Chu, J.-X. Sun, P. Li, The development trend of meat production, Meat Res. 115 (09) (2008) 3–6. [74] J. Lu, J. Tan, P. Shatadal, D.E. Gerrard, Evaluation of pork color by using computer vision, Meat Sci. 56 (1) (2000) 57–60.

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[75] W. Zhou. Research on the non-destructive testing of pork freshness based on multiinformation fusion. Master, Hua Zhong Agricultural University, 2009. [76] T. Zhou, Y.-K. Peng, Method of information extaction of marbling image of characteristic and automatic classification for beef, Trans. Chin. Soc. Agricult. Eng. 29 (15) (2013) 286–293. [77] L.-Z. Wang, Y.-H. Chen, L.-Y. Tan, K.-L. Leng, X.-C. Li, Quality status and suggestions for improvement of frozen fish and shellfish products, China Aquicult. 11 (11) (2000) 70–71. [78] F. Korel, D.A. Luzuriaga, M.O. Balaban, Objective quality assessment of raw tilapia (Oreochromis niloticus) fillets using electronic nose and machine vision, J. Food Sci. 66 (7) (2001) 1018–1024. [79] R.A. Quevedo, J.M. Aguilera, F. Pedreschi, Color of Salmon fillets by computer vision and sensory panel, Food Bioprocess Technol. 3 (5) (2008) 637–643. [80] H.-H. Wang, D.-W. Sun, Melting characteristics of cheese: analysis of effect of cheese dimensions using computer vision techniques, J. Food Eng. 53 (3) (2002) 279–284. [81] K. Sajad, M. Saeid, G.-V. Mahdi, Integration of computer vision and electronic nose as non-destructive systems for saffron adulteration detection, Comput. Electron. Agric. 141 (2017) 46–53. [82] G. Gagandeep Singh, K. Amod, A. Ravinder, Monitoring and grading of tea by computer vision—a review, J. Food Eng. 106 (1) (2011) 13–19. [83] Q. Ouyang, J.W. Zhao, Q.S. Chen, Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion, Anal. Chim. Acta 841 (2014) 68–76. [84] B. Susanna, B. Simona, G. Gabriella, Application of electronic senses to characterize espresso coffees brewed with different thermal profiles, Eur. Food Res. Technol. 243 (3) (2016) 511–520. [85] M.L. Rodriguez-Mendez, A.A. Arrieta, V. Parra, A. Bernal, A. Vegas, S. Villanueva, R. Gutierrez-Osuna, J.A. de Saja, Fusion of three sensory modalities for the multimodal characterization of red wines, IEEE Sensors J. 4 (3) (2004) 348–354.

Mature chemical analysis methods for food chemical properties evaluation

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Wellington da Silva Oliveira*, Daniela Andrade Neves*, Cristiano Augusto Ballus† *School of Food Engineering, University of Campinas, Sao Paulo, Brazil, †Department of Food Science and Technology, Federal University of Santa Maria, Rio Grande do Sul, Brazil

5.1

Introduction

Problems with the quality and safety of food have existed for centuries. Consequently, the quality of foods for human consumption has become an essential point in discussions of national and international public health. However, dealing with problems related to food security is challenging because our dietary habits are changingand because we are living longer [1]. Consumers have increased their search for safe food with organoleptic and healthy qualities. For this reason, there is continuous research into the detection and identification of adulteration of food, changes in food composition, and ways to monitor them [2]. There is strict legislation in the United States, Europe, Mercosur, Asia, and many other countries to ensure standards of production of foodstuffs and quality. To achieve this, chemical properties of foodstuffs have been constantly measured with tried and tested methods to verify the compliance of raw materials or final products and ensure food security. Many of these methods are cheap, do not need sophisticated instruments, and can be performed simultaneously with food processing or even on the farm, which make them a valuable way to ensure the production of safe food. Given its importance, this chapter discusses the main methods used to evaluate food chemical properties (Fig. 5.1), including pH, titratable total acidity, sweetness, saltiness, water activity (aw), antioxidant activity, enzymes, and lipids characterization.

5.2

Water activity

Water is the major compound of most foodstuffs and is present in bound, entrapped, and free states. Bound water is associated with hydrophilic substances in the food matrix and behaves as part of the solid; it cannot be frozen and is unable to act as a solvent. Free water is weakly or not bound to the matrix, is easily eliminated, and is available for use as a solvent and in the development of microorganisms. Entrapped water is immobilized in capillaries or cells; however, it can act as a solvent Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00005-6 © 2019 Elsevier Inc. All rights reserved.

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Fig. 5.1 Main chemical properties measured in foods.

and in the development of microorganisms, and can be frozen but cannot be easily eliminated [3–5]. An indication of the intensity with which water is associated with the food matrix is aw [3, 4]. aw is defined as the partial vapor pressure of water in a solution (P) divided for the vapor pressure of pure water (P0) at the same temperature (Eq. 5.1). aw can also be related to equilibrium relative humidity (ERH) (Eq. 5.1) [4, 6]: aw ¼

P ERHð%Þ ¼ P0 100

(5.1)

aw is related to food quality since it influences texture, microorganism growth, nonenzymatic browning (Maillard reaction), enzymatic action, and lipid oxidation. For this reason, the measurement of aw can be estimated based on which kind of microbiological and chemical alteration food is susceptible to. Food is susceptible to bacterial and yeast growth at aw above 0.8 and fungi growth above 0.6. The Maillard reaction and enzymatic activity occur at aw from 0.2 to 0.8 and 0.4 to 0.9, respectively. Lipid oxidation is minimum at intermediary aw and maximum at low and high aw. Therefore lower aw indicates that the product presents a higher shelf life, being limited only by lipid oxidation [6–8]. Most food processing is aimed at reducing aw to stabilize and increase shelf life. Thus aw can be used as a parameter in quality control. For aw determination, samples must be at the same temperature as the equipment being used, otherwise water condensation and an increase in equilibrium time may result. It is very important that the measurement is made on the same day that the sample was collected to avoid moisture loss or gain. Samples should be stored in sealed containers until analysis. In case of storage for long periods, the packaging must be sealed with minimum head space and stored in a freezer to avoid evaporation and condensation [8, 9]. Sample preparation depends on the objective of the analysis. Samples may be analyzed intact; however, they are usually broken to fit in the sample cup. Samples may be

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ground, but this is only relevant if that is the form it will be in the product, otherwise it will cause a change in aw. To avoid incorrect aw determination, samples should be representative of the product, for example, a sample of a chocolate bar with raisins and nuts must contain the same proportion of ingredients as the product. Food characteristics should also be taken into consideration to avoid mistakes in analysis. For a glassy sample it is important that before analyses the chamber is dried with a desiccant, since this is a product that presents a low aw. For products with aw near 1 it is essential to determine aw from a dry sample between readings to ensure that the detector is not saturated. In an emulsion, glass beads must be added to reduce the equilibrium time [8, 9]. There are numerous methods for measuring aw, and all of them, with the exception of the technique of freezing point, are based on the thermodynamic equilibrium between the sample and a small volume of air surrounding it [6, 10]. Instruments and techniques that may be used are the hygrometer (hair or polymer, electric and dew point), manometer, freezing point depression, thermocouple, psychrometer, and the isopiestic method. These all vary in complexity, cost, accuracy, reproducibility, speed measurement, calibration, linearity, stability, and ease of use [8, 11–13]. aw can be estimated by direct measurement of partial vapor pressure of water in a food using a pressure manometer [14, 15]. To do this, the system, with the exception of the vial containing the sample, is evacuated to a pressure less than 200 mmHg. Then, the sample bottle is evacuated for 1 or 2 min, so that moisture loss of the sample is minimal. Vapor pressure exerted by the sample during this period promotes an offset of the liquid from the column. Subsequently, water vapor is removed by using a desiccant, and the pressure exerted by the fumes of the gas system and the volatile remnants from the sample can be read by the difference in height of the liquid monomeric. The aw of food is obtained from the difference between the two readings divided by the vapor pressure of pure water at the same temperature [14, 15]. This method provides direct measures, is accurate, and is not subject to contamination of glycols, as can happen with the sensors of some electric hygrometers. However, this method is not suitable for materials containing volatile components, and requires very precise temperature control [11, 13]. The hair hygrometer is based in the absorption property of hair keratinaceous protein, or polyamides, which absorb moisture and elongate. The extension is measured by a lever arm and converted to aw. This technique is low cost; however, it takes a long time to achieve equilibrium, produces a limited accuracy (0.03–0.05), can be contaminated with volatiles, requires good temperature control, and is only capable of measuring aw between 0.4 and 0.9 [8, 11, 12]. The electric hygrometer is based on the measurement of the electrical capacitance, conductivity, or resistance of certain polymers and salts (crystals or solutions) after they reach equilibrium with the sample. This technique can measure aw from 0.22 and 0.90 depending on the sensor used. Also, this method presents good precision, repeatability, and is easy to use. The equilibrium time varies from 15 to 120 min and the accuracy depends on calibration, which needs to be done with five or more standards. Equipment is expensive and the sensors must be constantly repaired to avoid incorrect determinations [8, 11, 12].

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Fig. 5.2 Dew point water activity analyzer with internal temperature control.

The dew point technique consists of the temperature at which the first condensation of a food’s water vapor occurs. During aw determination by this method the water content of the sample reaches equilibrium with water vapor in the empty space of the sample chamber (Fig. 5.2). Then, the vapor comes in contact with the surface of a cooled mirror and condensates. Detection of the exact point at which condensation first appears on the mirror is measured by a change in the mirror’s reflectance by an optical reflectance sensor, and the temperature of the dew point is recorder by a thermocouple attached to the mirror [6, 8, 9, 11, 13]. At the same time, the sample temperature is measured with an infrared thermometer, and both temperatures are used to calculate aw. This technique does not require calibration, presents high accuracy and reproducibility, is simple to use, and is capable of measuring aw from 0.03 to 1.0 at a low equilibrium time, which is approximately 5 min [6, 8, 9, 11, 13]. For aw measurement by psychrometry a thermocouple is allocated above the sample in a small closed environment. Then, the water of a food sample reaches equilibrium with the water vapor in a chamber. Afterward, the chamber is cooled and the vapor water condensates on a thermocouple, which causes the thermocouple temperature to decrease [13, 14]. Therefore the temperature is determined and used to calculate aw according to Eq. 5.2 [8]. This technique is accurate, however the disadvantages of this method are that the equilibrium time ranges from 30 to 120 min and it can only be used in samples with aw higher than 0.93 and [8]. P ¼ P0 ðTw Þ  γPa ðTd  Tw Þ

(5.2)

where P is the vapor water pressure of the air, P0 is the vapor pressure of pure water at the same temperature, Tw is the wet bulb temperature, γ is the psychrometer constant (0.000666C1), Pa is the pressure of air, Td is the dry bulb (ambient) temperature, and P0(Tw) is the saturated vapor pressure at the wet bulb temperature [8].

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Freezing point determination is based in Raoult’s law, which states that the partial vapor pressure of each component of an ideal mixture of liquids is equal to the vapor pressure of the pure component multiplied by its mole fraction in the mixture [14]. Therefore the freezing point corresponds to the temperature at which the vapor pressure of the solid and liquid phase of water is the same. When a solute is added to pure water, water molecules orient themselves on the surface of the solute. As a result, the freezing point decreases, the boiling point increases, and the vapor pressure is reduced. Thus the composition and concentration of solutes of foods determine the temperature at which water freezes [13]. To determine the freezing point, the sample is cooled in an alcohol bath and its temperature is measured by a thermometer. Then, it is possible to calculate the aw according to Prior [14] (Eqs. 5.3 and 5.4): P n1 ¼N¼ ¼ aw n1 + n 2 P0 n2 ¼

GΔTf 100Kf

(5.3)

(5.4)

where P is the pressure of the solvent in solution, P0 is the vapor pressure of the pure solvent, N is the mole fraction of the solvent in solution, n1 is the number of moles of solvent in the medium, n2 is the number of moles of solute, G is the grams of solvent used in preparation, ΔTf is the freezing point depression (°C), and Kf is the molal freezing point depression constant (1.86 for water) [14]. Measurement of the exact freezing point temperature is difficult, demands experience, and requires only a small amount of sample, which many not be representative of the product. Besides that, this method is capable of measuring aw higher than 0.8 [14, 16]. For the isopiestic method a sample with known mass is placed in an environment that provides a relative humidity constant, allowing the sample to reach a balance. Therefore a sample is put in a chamber with a known aw or in an atmosphere with a known aw. Then, the sample reaches equilibrium by losing or gaining weight (water). Afterward, the sample is weighed and aw is calculated by using a sorption isotherm constructed in the same analysis conditions [12, 14]. This method is cheap, accurate, and has good reproducibility; however, it is time consuming (24–48 h), requires space, and may be applicable only for samples with aw higher than 0.85 [8]. Therefore there are different methods for aw determination. When choosing the best method, it is important to consider the price of the equipment, its accuracy, reproducibility, time of analysis, and the aw range intended to be measured.

5.3

pH and titratable acidity

The concepts of pH and titratable acidity are interrelated. Titratable acidity is related to the concentration of acids in food. Normally, it is determined by titrimetric methods using a base standardized for neutralization. This gives a better explanation of the

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impact of acids on the taste of foodstuffs. On the other hand, organic acids from foods can be ionized forming their conjugated base plus active hydrogen (H+ or H3O+). The latter can be expressed as pH and is very important because so many chemical and biological processes are pH dependent [5]. pH is one of the main parameters that can affect food stability. Enzymatic activity, color fixing, microbial growth, texture, hydrolysis reactions, and packaging, among others, can be affected by pH. The pH scale was determined based on the equilibrium constant of water taking into account reversible ionization: H2O $ H+ + OH. Since in 1 L of pure water at 25 C the concentration of water is 55.5 M and the Keq ¼ 1.8  1016 [17], therefore:
Keq ¼ ½H + ½OH =55:5 M;55:5 M  1:8  106 M ¼ ½H +   ½OH  1  1014 M2 ¼ ½H +   ½OH 

(5.5)

In view of this, the pH varies from 0 to 14 in any solution with H+ or OH– concentration up to 1.0 M. In terms of definition, pH ¼  log a[H+], where a is the relative activity of H effectively ionized. The pH is intended to be a measure of the activity of hydrogen ions in solution, not the concentration of hydrogen ions [18]. However, since foods are diluted systems, it can be considered that the pH ¼  log[H+]. Normally, pH measurement is performed using indicator reagents, pH test strips, and metal (hydrogen electrode, quinhydrone electrode, and antimony electrode methods) or glass electrodes [19]. pH indicators, such as phenolphthalein and methyl red, are organic compounds that shift their conformation with the activity of hydrogen ions resulting in a change of color [19]. Changing of the color is variable to each indicator, depends on the range of pH, and can vary with temperature. Moreover, in pigmented or colloidal solutions the change of color might not be easily observed leading to an erroneous measurement. Use of a pH indicator is widely reported in classical titrimetric methods; however, it is not suitable for accurate measurement since it indicates a pH range. pH strips are strips of paper (Fig. 5.3) or other material fixed with indicators, normally litmus or flavin based. Papers strips are immersed in the food sample, changing the color and indicating the range (i.e., litmus paper) or the approximate pH (flavinbased strips). This method is simple but does not show high accuracy due to the errors associated with high salt concentration, temperature, or organic substances from the sample. The hydrogen electrode method is the gold standard used to calibrate standard buffer solutions, against which the pH of all substances can be measured. In this method, the activity of the hydrogen ions is determined by potentiometric measurement in a cell (Harned cell) containing a platinum electrode, which catalyzes the electrode reaction: H+ + e ! 1/2 H2 in solutions saturated with hydrogen gas. The method has good reproducibility and low uncertainty. However, this is not suitable for daily use since it needs a constant supply of hydrogen gas [18].

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Fig. 5.3 Strips for pH measurement.

Quinhydrone and antimony electrodes are currently hardly used. These were used for the potentiometric determination of pH prior to the introduction of the glass electrode. However, they showed low reproducibility, which limited their application [19]. The glass electrode is considered the secondary standard for pH measurement (Fig. 5.4). It consists of a glass tube with a permeable glass bulb at the lower extremity, selective to hydrogen ions. Inside the tube, there is one reference electrode (normally calomel) immersed in KCl solution, and an internal electrode for pH determination immersed in a fixed buffered chloride solution. Both are separated by a thin layer of glass and a wet junction for stable electrical behavior. When immersed in the sample, the bulb exchanges sodium ions for hydrogen ions creating charges inside the membrane, which is proportional to the layer of hydrogen ions in the external solution. Hydrogen ion activity is determined by the voltage that develops between the two electrodes, according to the Nernst equation: E ¼ E0 + 2:303

RT log aH + NF

(5.6)

where E is the electrode potential, E0 is the standard potential of the electrode, R is the gas constant (8.31441 J K1 mol1), T is the temperature (in kelvin), n is the valence (1 for hydrogen ions), F is Faraday’s constant, and aH+ is the activity of hydrogen ions [5, 19]. The response of the glass electrode may vary with time, history of use, and memory effects. Moreover, the potential of the glass electrode is strongly temperature dependent. For this reason, calibrations and measurements should be carried out under temperature-controlled conditions. On the other hand, this electrode is most widely used for pH measurement due to the response independence of redox interferences, short balancing time of electrical potential, high reproducibility, and long lifetime [18, 19].

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Fig. 5.4 Glass electrode for pH measurement with (A) KCl solution; (B) reference electrode; (C) internal electrode; (D) junction; (E) bulb; (F) probe for temperature compensation.

pH determination is widely carried out in foods and can provide information on the freshness or conservation of the product. pH below 7 is frequently associated with the presence of organic acids such as citric, malic, lactic, tartaric, and acetic, which might occur naturally in the food or be formed through degradation processes such as oxidation, hydrolysis, or fermentation [5]. Moreover, inorganic acids such as phosphoric and carbonic (arising from carbon dioxide in solution) can contribute to the acidification of the food. Both organic and inorganic acids can also be added to the food to play a technological role, such as the addition of acid for jam production. However, acid concentration has a greater impact on food taste than does pH. The concentration of organic acids can be determined by titrimetric methods using acid-base indicators or potentiometric measurement using pH meters. The volume of titrant used, along with the normality of the base and volume (or weight) of the sample, is used to calculate the titratable acidity, expressed in terms of the predominant organic acid [5]. The titratable acidity of samples containing carbonic and phosphoric acids should be determined by the potentiometric method since both acids form a buffer in the range of color change of phenolphthalein (8.0–9.6). Furthermore, samples with intense color, such as grape juice, should also be determined by this method since it is impossible to see the color of the indicator and determine the correct equivalence point.

5.4

Antioxidant capacity

Reactive species are molecules that have one or more electrons unpaired in the external orbit, therefore they are highly reactive. To stabilize these molecules, they receive an electron or donate an electron not paired to another molecule, which becomes a free

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radical [20–22]. These reactive oxygen species (ROS) (e.g., OH%, ROO%, RO%, H2O2, and HOCl, O%–2, among others) and reactive nitrogen species (RNS) (e.g., ONOO and ON%) can be generated by external sources such as cigarette smoke, ultraviolet (UV) light, and polluted air, among others. However, they can also be formed during the normal metabolism of cells [23, 24]. The reactive species, in small quantities, are responsible for influencing gene expression, cellular signal transmission, cellular protection against infectious agents, cellular function regulation, and apoptosis [20, 22]. Excessive production of ROS and RNS causes oxidative stress, which can cause damage to proteins, lipids, DNA, and other molecules. Such damage can lead to aging and chronic diseases such as cancer, cardiovascular diseases, diabetes, obesity, Alzheimer’s disease, and joint complications, among others [20, 23, 25]. The balance between production and the elimination of reactive species is essential for the proper functioning of the body. The elimination of these compounds is carried out by means of antioxidants, which become stable after the transfer reaction of a hydrogen atom or an electron to the ROS and RNS. Even in small quantities, antioxidants are able to inhibit or delay the formation of new reactive species by stopping the propagation of free radicals [20, 26]. The hydrogen atom-based transfer (HAT) method measures the capacity of a compound to donate a hydrogen atom to a free radical. Most HAT-based assays determine the antioxidant capacity through a kinetic assay of probe degradation by using a radical. A peroxyl radical (ROO ) is usually employed since it is a radical with biological relevance and a long half-life [27, 28]. In the electron transfer mechanism the antioxidant donates an electron to the reactive species (redox reaction) stabilizing them. The reactive species do not present a biological relevance and the probes used are synthetic. These probes react with the antioxidants by abstracting an electron, and changing their color according to antioxidant concentration. These methods are pH and solvent dependent and present a reaction velocity lower than that of the HAT-based assay. pH is important since at acid conditions the antioxidant is protonated, therefore it presents a lower reduction capacity. However, in basic conditions the antioxidant would be dissociated increasing its reduction capacity [27, 28]. 20 2-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), power of iron reduction (FRAP), oxygen radical absorption capacity (ORAC), and 1.1diphenyl-2-picrylhydrazyl (DPPH) are the most used methods for the measurement of antioxidant capacity. These methods are based on FRAP or the capture of free radicals in the ORAC, ABTS, and DPPH methods [25, 28–30] and can be measured by spectrophotometry. ABTS is a stable radical soluble in water and organic solvents, therefore it is capable of determining the antioxidant capacity of hydrophilic and hydrophobic samples. The ABTS method is based on the reduction of the ABTS%– radical by the antioxidant. The radical is formed by ABTS reaction with potassium persulfate developing a blue/green color with a maximum absorption at 645, 734, and 815 nm. In the presence of an antioxidant this radical is inhibited and causes discoloration and consequent reduction in absorption. Absorption at 734 nm is usually chosen l

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Fig. 5.5 20 2-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) measurement using a microplate and multimode microplate reader.

because the interferences are minimized (Fig. 5.5). Depending on the antioxidant activity there are variations in the discoloration extent and in inhibition time [31–33]. The FRAP method determines Fe reduction. The FRAP reagent is composed of 2,4,6-tris(2-pyridyl)-s-triazine (TPTZ) and iron chloride, which reacts forming an FeIII-TPTZ complex. When an antioxidant is present this complex will be reduced to FeII-TPTZ, which has a blue coloration with a maximum absorption at 593 nm [34]. The pH used in this method is 3.6 to maintain the iron solubility and drive electron transfer. This method is simple, fast, cheap, and does not require specialized equipment [33, 35]. The DPPH method consists of the determination of DPPH radical reduction by the antioxidant. DPPH presents a violet color in ethanol with maximum absorbance at 515 nm. During DPPH inhibition there is a loss of color causing a decrease in absorbance [36, 37]. This method may be influenced by the pH and the amount of solvent used, since the radical is soluble in organic solvents. The biggest disadvantage of this method is that there are many compounds (carotenoids and anthocyanins, among others) that absorb at the same wavelength of the DPPH radical, therefore they interfere with the analysis. This method is simple, fast, does not require specialized equipment, and the radical is commercially available [33, 35]. The ORAC method is based on the inhibition or retardation of the peroxyl radical, preventing fluorescein degradation. The peroxyl radical has a high biological value formed by the thermal decomposition of 2,20 -azobis(2-methylpropionamidine). Antioxidant capacity is measured by comparing the area under the fluorescence decay curve constructed with and without the presence of antioxidants (sample) [38]. The advantage of this method is that it uses a radical with biological value and may be adapted to the determination of the antioxidant capacity in lipophilic samples by changing the solvents and radical source. However, it requires expensive equipment [28, 35]. l

l

l

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Most traditional methodologies use free radical scavenging, which does not mimic the physiological conditions. More recent antioxidant capacity methodologies have been developed using ROS (H2O2, HOCl, and O%–2, among others) and RNS (ONOO and ON%, among others) present in the human body. These methods are more sensitive than results observed in the body. However, they are more expensive and laborious than conventional methods. Among these methods are the hydrogen peroxide (H2O2) scavenging assay, hypochlorous acid (HOCl) scavenging assay, superoxide anion (O%–2) uptake test, nitric oxide (%NO) scavenging activity, and peroxynitrite (ONOO) scavenging activity [39, 40]. The hypochlorous acid scavenging assay is based on the monitoring of the fluorescent compound rhodamine 123 formed during the oxidation of dihydrorhodamine 123 (DHR) by HOCl. In the presence of antioxidants there is an inhibition of DHR oxidation, which is determined by a reduction in fluorescence. The hydrogen peroxide scavenging assay is based on lucigenin oxidation by H2O2 forming 2,7dichlorofluorescein, which is chemiluminescent. The presence of an antioxidant reduces the formation of this compound, therefore the chemiluminescence signal is reduced. The superoxide anion uptake test is based on the formation of a colorful compound (diformazan) after nitrotetrazolium blue chloride reduction by the anion O%–2. The action of an antioxidant inhibits probe reduction and prevents or avoids the formation of diformazan, reducing the absorbance. Singlet oxygen scavenging capacity is measured by monitoring the effect of the antioxidant on the oxidation of nonfluorescent DHR to fluorescent rhodamine 123 by this ROS [39–41]. Nitric oxide scavenging activity is measured by monitoring antioxidant inhibition capacity of the NO-induced oxidation of nonfluorescent DAF2 to the fluorescent triazolofluorescein (DAF–2T). Peroxynitrite scavenging activity is measured by monitoring the antioxidant inhibition capacity of the ONOO-induced oxidation of nonfluorescent DHR to fluorescent rhodamine 123 [39–41].

5.5

Sweetness

The parameters most used to indicate the sweetness of fresh and processed products is total soluble solids (TSS), which is the sugar content with small amounts of vitamins, proteins, pigments, acids, phenolics, and minerals. Determination of this parameter can be done by hydrometer or refractometer and is usually performed on the °Brix scale: 1°Brix corresponds to 1 g of sucrose in 100 g of sample [42, 43]. The hydrometer measures solution density and may be calibrated to be read in different scales, such as °Brix (most used), therefore the TSS is determined by a direct reading, usually at 20°C. This technique may be influenced by the sample temperature, which may change the density, or by the surface tension and surface film formation, which could cause errors in the analysis [8, 42]. Another method for TSS determination is by using a refractometer calibrated at the °Brix scale, which is the standard method. The refractometer (Fig. 5.6) measures the change of direction (refraction) and velocity of light when it passes from one medium to another having different densities. Therefore considering that the solution density

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Fig. 5.6 Abbe refractometer for °Brix determination.

and TSS content are positively correlated, the light index refraction increases with the increase in TSS. The Abbe refractometer (Fig. 5.6) is mostly used for this determination. It consists of the reflection of diffuse light by a mirror through a double prism containing the liquid sample where the index refraction of a specific wavelength (589 nm from white light) is measured. Since the refractometer is calibrated at the °Brix scale, TSS is determined by a direct reading. However, the value must be compensated for by using tables provided with the instrument, since the refraction index changes with the solution temperature. This is an inexpensive, easy-to-use, and accurate technique [5, 44]. Other compounds that constitute TSS, such as acids, proteins, and salts, among others, also contribute to the flavor of the food, mainly in fruits. Thus TSS may not always present a correlation with sweetness. Therefore the ratio TSS to titratable acidity (TSS/TA) has been used to determine fruit maturation degree. However, this index also does not always correlate with the sweetness or acidity of the food [42, 45, 46]. Thus the index BrimA was created, which is the subtraction of TSS by titratable acidity, multiplied by a constant (BrimA ¼ TSS – k TA). This constant varies from 2 to 10 depending on the fruit analyzed and was added because the tongue is more sensible to TA than to sugar. This index is a better indicator for food sweetness than TSS alone or the TSS/TA index and was adopted by the California Department of Food and Agriculture in 2012 [42, 46, 47].

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75

Saltiness

Saltiness is the salt content in a medium. The most used salt in food is sodium chloride (NaCl), which is also present naturally. Moreover, it can be added as sodium nitrate, sodium bicarbonate (baking soda), monosodium glutamate, potassium chloride (KCl), and calcium chloride (CaCl2). The addition of salt to food has a technological function to enhance flavor, improve texture, and inhibit microorganism growth. Determination of saltiness in food can be made by refractometry, ion-selective electrodes, conductivity, and titration [48, 49]. Refractometry measures the refractive index of a solution, which is the change in direction of light (refraction) when it passes through a prism. The more salts and other minerals present in the sample, the greater will be the refraction index. The % of salt is measured directly, but the value must be compensated for using tables provided with the instrument, since the refraction index changes with the solution temperature. This method is simple, rapid, and cheap and demands a small amount of sample. However, it presents limited accuracy and is not specific for salts, therefore compounds such as minerals, sugar, and fat may interfere with the determination [48, 49]. Salt content can also be determined by an ion-selective electrode (ISE), which is a chemical sensor capable of measuring the concentration of a specific ion. In the case of food it is determined by the measurement of the sodium concentration, therefore the electrode used is sensitive to sodium. Analysis is based on a change of sodium in the electrode membrane, which causes potential change. The voltage is then converted to ion concentration according to the Nernst equation (Eq. 5.7). There is little interference in this method of analysis since it is very specific, and also there is no interference by the sample color, viscosity, or turbidity. However, it is moderately costly, demands extensive electrode care, and daily calibration is required [8, 49]. E ¼ E0 + 2:303

RT log A nF

(5.7)

where E is the electrode potential, E0 is the electromotive force or normal potential of the corresponding cell (obtained from the normal potentials of the electrodes), R is a global gas constant (8.313 J/degree/g mole wt), T is the absolute-scale temperature (kelvin), F is the Faraday constant (96,490C/g equiv. wt), n is the number of electrons transferred, and A is the activity of the ion being measured [5]. Another method for saltiness determination is electrical conductivity. This technique measures the sample conductivity by the application of alternating current. Reduction in voltage is measured and converted to electrical conductivity. The higher the content of salt, the higher is the sample electrical current conduction. This is an easy and fast technique and can be used with a wide variety of food samples. However, it is expensive, demands the use of a considerable amount of sample, and is not accurate since it is not specific. Therefore it gives only an estimate of food saltiness and is not an exact salt concentration [48, 49]. The titration method is the official method and is the most used to determine salt in food samples such as cheese, meat, and vegetables. The Morh method is one of the

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oldest methods that is still used. This method is based on sample titration with silver nitrate at pH 7 using potassium chromate as a color indicator. The silver reacts with all chloride present in the sample forming a silver chloride (AgCl) insoluble precipitate. Afterward, the excess silver reacts with the potassium indicator forming a red-colored solution, signaling the end of titration. Then, the chloride concentration is calculated and the sodium content is estimated. This endpoint of titration can also be determined by using an ISE sensitive to silver or chloride ions. The manual method is cheap; however, it demands an experienced analyst for accuracy and precision. On the other hand, the potentiometric method is expensive but is more accurate and precise, since it eliminates human error. Both methods can only determine the chloride content, therefore samples that contain other chloride salts, such as magnesium chloride and calcium chloride, would overestimate the saltiness [5, 8, 48–50].

5.7

Fat characterization

Lipids are a class of compounds comprised of several components, which diverge chemically, but they are all soluble in organic solvents. Food lipids can be classified as fats (solids) or oils (liquids), with regard to their physical state at room temperature. They can also be referred to as nonpolar (for example, triacylglycerols and cholesterol) or polar (for example, phospholipids) lipids. The latter present surfactant properties, because their structure has a hydrophilic “head” group and a hydrophobic “tail” group [51]. Fats and oils are the most common way of storing energy in several organisms. Phospholipids and sterols are the main structural components of biological membranes. There are other lipids that, even in lower contents, present special roles as enzyme cofactors, electron carriers, photosensitive pigments, emulsifying agents in the digestive tract, hormones, and intracellular messengers [17]. They also serve as a source of energy and carriers of lipophilic bioactive compounds, such as some vitamins and antioxidants, for example. In foods, lipids are related to several desirable sensory properties, affecting mouth feel, color, rheological properties, and flavor [52]. Apart from their beneficial properties, lipids in biological systems, including foods, can be targets of oxidation reactions, generating free radicals that will sustain a chain reaction event and lead to lipid deterioration (oxidative rancidity). This deterioration will decrease the quality of the product and also generate compounds that are detrimental to health [53]. Chemical characterization of fats and oils in foods normally starts with an extraction step to separate the lipidic fraction from other food constituents. This step, of course, is not necessary when working directly with fats or oils as samples.

5.7.1 Lipids extraction Considering the diversity of lipids in foods, there are several methods available for lipids extraction. Choosing the most appropriate method will depend on the type of analyzed material and nature of the subsequent analytical problem. It is necessary to break the bonds between lipids and other components, and then solubilize the

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released compounds. Solubilization must take into account the polarities of the lipid and the solvent, which must be similar. Some examples of solvents used for lipids extraction are hexane, ethyl ether, petroleum ether, alcohols, and chloroform. It is important to remove moisture from the sample beforehand, because water content will interfere with lipids extraction [54]. One of the most used extraction methods with organic solvents uses the Soxhlet apparatus (Fig. 5.7). In fact, the Soxhlet configuration was originally used to determine fat in milk. In this system, the sample remains in a thimble-holder that is gradually filled with condensed fresh extraction solvent from a distillation flask. After the solvent reaches the overflow level, it is aspirated by means of a siphon and replaced in the distillation flask, carrying the extracted lipids into the bulk liquid. All these steps are repeated exhaustively, until all the lipids are removed from the sample. It can be considered a semicontinuous extraction system. The most important disadvantages of Soxhlet extraction are the long time taken to finish the extraction, the large quantity of solvent wasted, and the possibility of thermal degradation [55].

Fig. 5.7 Soxhlet apparatus for lipids extraction.

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Another technique using organic solvent, but this time in a continuous system, is known as Goldfish. In this system, solvent from a boiling flask continuously flows over the sample maintained in a ceramic thimble. After solvent evaporation, the lipid content is obtained by gravimetry. The main disadvantage is the possibility of channeling throughout the sample, which can result in incomplete extraction [5]. There are also direct or discontinuous methods with solvent extraction. In these methods, there is no continuous flow of solvent, and the lipids are extracted with a fixed volume of solvent. After reaching the extraction endpoint, the solvent layer is recovered, and the dissolved lipids are isolated through evaporation of the organic solvent. Rose-Gottlieb and modified Mojonnier are examples of these techniques, and they always include acid or base dissolution of proteins to release lipids from the matrix, such as in milk and dairy products, for example [56]. In milk, fat globules are present in the form of an emulsion of oil in water with a thin protein film around them. It is necessary to break the emulsion and remove the protein film before lipids extraction. In addition to Rose-Gottlieb and Mojonnier, there is other technique, the Gerber method, where the milk sample is mixed with isoamyl alcohol and sulfuric acid in a Gerber glass butyrometer. After agitation and centrifugation, the fat content can be read directly on the volumetric scale at the top of the butyrometer extraction [54]. Another method with several applications in lipids extraction from food samples makes use of a mixture of chloroform and methanol. The most recognized protocol with this mixture of solvents is known by the name of its developers, Bligh and Dyer. As described in their original paper [57], the sample is homogenized with a mixture of chloroform and methanol in such proportions that a miscible system is formed with the water in the sample. The addition of more chloroform and methanol separates the homogenate into two layers: the chloroform containing all the lipids and the methanolic layer with the interferents. The chloroform layer can then be isolated, and the solvent evaporated, to allow determination of the total lipid content by gravimetry. This method does not apply heating, so there is virtually no thermal degradation.

5.7.2 Fat and oil characterization 5.7.2.1 Refractive index Refractive index measures the degree of deflection of a light beam when it passes from one transparent medium to another. To perform this measure, it is necessary to use a refractometer, usually at a temperature of 25°C. The value of the refractive index relates to molecular weight, fatty acid, chain length, degree of unsaturation, and degree of conjugation. It is used for controlling the endpoint of hydrogenation reactions, for example [58].

5.7.2.2 Smoke, flash, and fire points These parameters are extremely important to evaluate deep-fat frying processes. Smoke point can be associated with the content of free fatty acid and partial glycerides, and depends on the oil’s acidity [59].

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All three values can be related to thermal stability. The smoke point consists of the temperature at which a significant decomposition begins, when the sample is heated in contact with the atmosphere. At this temperature, the products of volatile decomposition are formed in sufficient quantity and become visible. The flash point is the temperature at which the decomposition products are able to sustain ignition, but not continued combustion. The fire point is the temperature at which the content of decomposition products formed is able to support continued combustion [60]. The procedure to determine these parameters starts by filling a cup with oil or melted fat, and then submitting it to heating in a well-lit container. The smoke point is the temperature at which it is possible to visualize a thin and continuous stream of smoke being produced from the sample. To determine the flash and fire points, the sample continues to be heated, and a test flame is passed over the sample at 5°C intervals [5].

5.7.2.3 Iodine value Iodine value can be used to measure the degree of unsaturation of oils and fats. The results are normally expressed as the number of grams of iodine absorbed by 100 g of oil or fat, considering the conditions of the test. This parameter is extremely important in the palm oil industry, where it is used to follow the fractionation process. The protocol for iodine value determination usually comprises a titration procedure, such as the Wijs method [61]. In this procedure, iodine chloride is used for double-bond saturation analysis, and the content of consumed iodine is measured by titration with 0.1 mol L1 sodium thiosulfate solution [62].

5.7.2.4 Melting point (slip melting point) This parameter is defined as the temperature at which the solidified oil becomes fluid enough to slip in an open capillary tube. It can be an important indicator of the general behavior of fat- and oil-derived products at various environments, including cool, ambient, and elevated temperatures. It can also be very useful in the development of new products and in the prediction of the consistency of a finished product [63].

5.7.2.5 Free fatty acids and acid value Free fatty acids content can be defined as the percentage, by weight, of free fatty acid groups present in oils and fats. The procedure is accomplished by a neutralization volumetric technique. About 1–2 g of sample and 25 mL of neutral ethanol are boiled for a few minutes in a water bath, and then titrated with 0.1 mol L1 NaOH solution, using phenolphthalein as indicator, until it reaches the pink color endpoint [64]. This measure is normally correlated with the amount of fatty acids hydrolyzed from triacylglycerols. While the free fatty acid is the percentage by weight of a specified fatty acid (oleic acid, for example), the acid value is defined as the mg of KOH necessary to neutralize the free fatty acids present in 1 g of fat or oil [5].

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5.7.2.6 Saponification value This value is correlated to the average molecular weight of the triacylglycerols in the mixture and can be determined by the amount of KOH necessary to saponify a certain quantity of oil or fat. A high saponification value indicates small average molecular weight and vice versa [65].

5.7.2.7 Methods for measuring lipid oxidation Peroxides, or hydroperoxides, are the primary products of lipid oxidation. This makes peroxide determination one of the most used methods to measure the extent of oxidation. Iodometric methods are extensively used and are based on the measurement of the iodine produced from potassium iodide by the peroxides present in the oil. The excess of iodine is titrated with sodium thiosulfate. Another important technique uses thiobarbituric acid (TBA) through a colorimetric reaction. The colored product is formed after the condensation of two molecules of TBA with one molecule of malonaldehyde, a secondary product in the oxidation of polyunsaturated fatty acids. The colored compound is measured spectrophotometrically at 530 nm [66]. The UV spectrum can provide repeatable and reproducible information on the oxidation state of crude or pure fats. Peroxides produced from the oxidation of linolenic acid absorb at 232 nm. Other nonvolatile decomposition products are also conjugated dienes and are absorbed at 232 or 270 nm. The E232/E270 ratio decreases when the oil contains more secondary oxidation products [67].

5.8

Enzymes

Enzymes are proteins with catalytic activity that are capable of very great specificity and reactivity under physiological conditions. Enzymes are found in all living systems and are responsible for mediating vital processes such as synthesis, turnover, signalization, and metabolism [51]. Michaelis and Menten showed that the enzyme–substrate saturation curve can be expressed mathematically by Eq. (5.8): ν0 ¼

Vmax ½S0  Km + ½S0 

(5.8)

where ν0 is the observed initial velocity, Vmax is the maximum velocity when the enzyme is fully saturated with substrate S0, and Km is [S0] at which ν0 ¼ 0.5 Vmax [68]. In foodstuffs, enzymes can be from an exogenous or endogenous source. Enzymes from exogenous sources are added to cause a desirable change in the food, such as chymosin used in cheese production or papain used to improve beef meat tenderness. On the other hand, endogenous enzymes can be responsible for the reactions that affect the quality of foods.

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The enzyme levels in food might be used to determine the degree of freshness (as in the case of oxidative enzymes in vegetables) and to detect particular treatments, such as pasteurization, or microbial growth [69]. Endogenous enzymes are a great control challenge. Moreover, food processing might be restricted to limit their activity and to assure the quality of the final product. A typical example is vegetable processing, which most often requires a bleaching step for inactivation of endogenous enzymes. For analysis of enzymes in foods, it is necessary to be familiar with the methods of measurement of the reaction. Any physical or chemical property of the system involving the substrate or product concentration can be used for monitoring the reaction. Absorbance spectrometry, fluorimetry, manometric methods, titration, isotope measurement, chromatography, mass spectrometry, and viscosity have been used to monitor the enzymatic reaction. Nonetheless, only mature methods for detecting enzyme activity will be discussed.

5.8.1 Enzymes that act on carbohydrate Most enzymes that act on carbohydrates are hydrolytic (glycosyl-hydrolases) and can catalyze the transfer of glycosyl groups or reverse hydrolytic reactions. These have been used as processing aids in the food industry for the production of sweeteners and thickeners (dextrins) from starch, and for modifying carbohydrates used in bakery products. However, the action of these enzymes can negatively affect vegetables mainly by decreasing texture [51]. In foodstuffs, glycosyl-hydrolases might be from endogenous or exogenous sources.

5.8.1.1 α- and β-amylases α-Amylases (EC 3.2.1.1) are endoglucanases that catalyze the cleavage of internal glucosidic bonds in starch and related polysaccharides to yield dextrins and oligosaccharides with the anomeric C1-OH in the α-configuration. On the other hand, β-amylase (EC 3.2.1.2) is an exoenzyme that catalyzes the hydrolysis of 1,4-α-D-glucosidic linkages in polysaccharides to remove successively β-anomeric maltose units from the nonreducing end of α-1,4-glucans such as starch and glycogen. For the baking, brewing, and fermentation industries, α- and β-amylases play a central role since they can act to decrease viscosity and improve bread volume, besides releasing carbohydrates for fermentation. Methods for measuring amylase activity are characterized by the type of substrate employed. The detection of reducing sugar, the use of dyed starch, or a low molecular weight oligosaccharide with a defined structure have been used for α-amylase activity measurement. The first method is the most popular and is used for α-amylase activity measurement. In this method the starch is hydrolyzed by α-amylases and the hemiacetal groups can be determined using 3,5-dinitrisalicyclic acid (DNS) under alkaline conditions. The concentration of reducing sugar can be measured at 540 nm using glucose as standard. One unit of amylase activity was defined as the amount of enzyme

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that releases 1 μmol of reducing sugars per minute and is expressed as units per gram of dry substrate (U/g). The measurement of α-amylase activity by this method is accurate only if the sample is not contaminated with other amylolytic enzymes [70, 71]. Modified starch (starch azure) is also used as a substrate for α-amylase activity quantification. Starch azure is an insoluble corn starch, covalently linked to Remazol Brilliant Blue, a reactive anthraquinone dye. For analysis, the insoluble starch is suspended in a buffer with the α-amylase, which results in the solubilization of colored fragments. The unreacted substrate might be removed by filtering or by centrifugation, and the color intensity of the solution can be measured by spectrophotometry at 620 nm [72, 73]. Taking into account that products of β-amylases or α-glucosidases directly interfere with other assay methods, the use of starch azure to measure α-amylase activity is very advantageous because exoenzymes are not active on this substrate. Moreover, starch azure is available commercially. Starch hydrolyzed with 1,4-butandioldiglycidether and labeled with Cibachron Blue was also used as a substrate for the α-amylase test. The method works basically in the same way as described previously: hydrolysis, dye release, and determination by spectrophotometry at 620 nm. However, this substrate is susceptible to β-amylase action, and therefore this assay is nonspecific for α-amylase. The substrate is sold in kit form and is used in the Harmonized Methods of the European Commission of Honey for measurement of diastase in honey [74]. The last method uses synthetic oligosaccharides with a low molecular weight, decreasing the substrate variability. Among them, blocked p-nitrophenyl-αD-maltoheptaoside (BPNPG7) and 4-ethyliden-p-nitrophenyl-maltoheptaosid (EPSG7) have been widely used and both are commercially available in kit form. In these subtracts the α-amylase acts by cleaving a bond within the blocked p-nitrophenyl maltosaccharide. The nonblocked reaction product containing the p-nitrophenyl substituent is instantly cleaved to glucose and free p-nitrophenol by α-glucosidase present in the mixture. The p-nitrophenol increases the absorbance at 400 nm, which enables the determination of enzymatic activity. The use of EPSG7 as a substrate for the determination of α-amylase activity in biological fluids is recommended by the International Federation of Clinical Chemistry and Laboratory Medicine. On the other hand, official methods for foods using BPNPG7 have been published by AOAC (Method 2002.01), AACC (Method 22–02.01), ICC (Standard No. 303), RACI (Standard Method), and CCFRA (Flour Testing Working Group Method 0018). Regarding β-amylases, use of methods such as DNS to determine reduction group is nonspecific, and the measurement of β-amylase activity by these methods is accurate only if the sample is devoid of α-amylase and other amylolytic enzymes [68]. Notwithstanding, synthetic saccharides have been used to determine β-amylase activity, such as p-nitrophenyl α-maltopentaoside (PNPα-G5) or p-nitrophenyl β-maltotrioside (PNPβ-G3). In cereals, these substrates are too short to be cleaved by α-amylase, but are readily cleaved by β-amylase releasing p-nitrophenyl maltotriose (PNPα-G3) or nitrophenyl β-glucose (PNPβ-G1). Next, PNPα-G3 or PNPβ-G1 is cleaved releasing the p-nitrophenol group, which increases absorbance at 410 or 400 nm. One enzyme

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unit is defined as the amount of enzyme that catalyzes the release of 1 mol of p-nitrophenol per minute under the assay condition [68]. The use of PNPβ-G3 was developed from the PNPα-G5 method. PNPα-G5 is still used for β-amylase analysis but will be replaced by PNPβ-G3 as soon as it is available in kit form. Moreover, PNPα-G5 can be readily attacked by other α-amylases, such as Aspergillus niger α-amylase [68, 75].

5.8.1.2 Pectic enzymes Pectic enzymes are depolymerases and esterases active on methyl- and acetylesters of galacturonosyl uronic acid residues in the galacturonan and rhamnogalacturonan structures. Pectic enzymes occur naturally in many vegetables (endogenous enzymes), but they are also added as processing aids (exogenous enzymes) for vegetable juice manufacturing, such as fruit juice clarification, enzymic pulp treatment for juice extraction, liquefaction, and maceration [68]. Three types of pectic enzymes are well described in the literature: pectin methylesterase (PME) (EC 3.1.1.11), polygalacturonase (EC 3.2.1.15, EC 3.2.1.67, EC 3.2.1.82), and pectate lyases (EC 4.2.2.2). The first one hydrolyzes the methyl ester bond of pectin to give pectic acid and methanol. Otherwise, exo or endo polygalacturonase hydrolyzes the α-1,4-glycosidic bond between the anhydrogalacturonic acid units. This is the main enzyme involved in the reduction of texture in vegetables. Lastly, the pectate lyases cleave the glycosidic bond of both pectin and pectic acid by β-elimination, releasing a product with a reducing group and another product with a double bond. This last enzyme is found in microorganisms, but not in higher plants [51]. Many methods have been described in the literature to determine PME activity. However, the titrimetric method is a good alternative due to simplicity and low cost [76]. In this method, carboxyl groups formed by PME action are determined using methyl red as pH indicator or with the aid of automatic titrators. For this, a pectin solution (0.25%–1%) is prepared in NaCl (0.15–1.0 M) under constant conditions of pH (usually 7–7.5 for plant material) and temperature (30°C). During hydrolysis at room temperature, pH was maintained at 7.5 by adding NaOH. The amount of NaOH added for 30 min was recorded. One PME activity unit (UPE) is defined as the micromoles of carboxylic groups produced per minute and milliliter of sample at pH 7.5 and room temperature (Eq. 5.9). Buffers used in enzyme purification and organic acids from nonpurified samples could interfere with the method due to changes in pH measurements [76]. PMEðunits=mLÞ ¼

ðmL NaOHÞðnormality of NaOHÞð1000Þ ðmL sampleÞðtimeÞ

(5.9)

Methanol determination with or without derivatization, as well as the colorimetric method, can be used for monitoring PME activity. However, they are more expensive and laborious than the titrimetric method [68, 76].

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The determination of reducing groups is the main way to measure polygalacturonase activity. For this, polygalacturonic acid is used as a substrate and the reducing groups are determined spectrophotometrically at 535 nm using DNS [77]. One unit (U) of endo-polygalacturonase activity was defined as the amount of enzyme that released reducing sugars equivalent to 1 mol of D-(+)-galacturonic acid per minute under standard conditions [78]. Polygalacturonase activity can also be determined by the cup plate method. In the cup plate method solidified agar containing the substrate is filled with the enzyme solution. After incubation time, the zones of degraded substrate are identified with iodine and quantified. Regarding pectate lyase activity measurement, the use of spectrophotometric methods has been widely reported due to the formation of Δ4,5-unsaturated products from their specific substract, which can be easily monitored at 235 nm [68].

5.8.2 Enzymes that act on lipids Lipase and lipoxygenase are major endogenous enzymes that act on lipids. Lipid enzymes are more apt to act at the water-lipid interface. Although they are associated with degradative processes, these enzymes can be used to improve food sensory characteristics, such as color and taste [51].

5.8.2.1 Lipase Lipase occurrence is widely reported in grains and dairy products. The lipase (EC 3.1.1.3) hydrolyze triglycerides into diglycerides, monoglycerides, glycerol, and fatty acids. The fatty acid produced by lipase is more susceptible to oxidation and consequently contributes to loss of product quality [79, 80]. Furthermore, lipase has shown specificity to the acyl-ester group regarding the position as well as the size of the chain and amount of esterification in the glycerol, among others. For example, a 1,3-specific lipase catalyzes transesterification of fatty acids at the 1- and 3-positions of glycerol, while a nonspecific lipase catalyzes transesterification at the 1-, 2-, and 3-positions of glycerol [51]. Numerous methods are available to measure lipase activity [81]. However, the titrimetric method using olive oil as the substrate has been the most used, followed by the colorimetric method with p-nitrophenyl palmitate as substrate. In the first one, fatty acid released from the triglyceride is determined by titration with sodium hydroxide. Since the lipases are water soluble and the substrate is water insoluble or partially water insoluble, it is necessary to add emulsifiers or surfactants (Triton, Tween, etc.) to maintain the homogeneity of the reaction medium. Moreover, the method works in a restricted pH range and has low sensitivity (up to 1 mmol/min) [82]. The last one measures the release of p-nitrophenol as a yellow chromophore. The main disadvantage of this method is the turbidity generated when the palmitate is released to the aqueous medium, or the need to add emulsifiers or organic solvents (ethanol or propanol) to maintain the homogeneity of the reaction medium.

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Recently, the method to measure the determination of lipase activity at high temperatures has improved, which is not possible with other organic solvents due to evaporation and without the interference of buffers or Ca2+ ions [82].

5.8.2.2 Lipoxygenase Lipoxygenases (LOXs) (EC 1.13.11.12) are dioxygenases that catalyze the conversion of unsaturated fatty acids into corresponding hydroperoxides. With few exceptions, plant LOX oxidizes linoleic and linolenic acids regiospecifically at either the ω6 (C-13) or ω10 (C-9) position with (S)-stereospecificity. In the animal kingdom, LOXs also oxidize arachidonic acid and other C-20 polyenoic acids [68, 83]. The action of LOX in food decreases the amount of essential fatty acids. Furthermore, the free radicals formed by LOX act on carotenoids (vitamin A precursors), tocopherols (vitamin E), vitamin C, and folate decreasing the nutritional quality of food and being responsible for off-flavor formation. On the other hand, LOX has also shown a desirable effect in foods such as in the bleaching of wheat and soybean flours or in the formation of disulfide bonds in gluten during dough formation [68, 83]. The spectrophotometric method proposed by Axelrod [84] has been widely used to measure LOX activity. For the assay of LOX-1, sodium linoleate is used as a substrate in borate buffer (pH 9). The conjugated diene formed is monitored at 234 nm at 25°C. Activity is defined in units (mole of product formed per minute). Specific activity is defined as units/mg or g protein. The mole of product is calculated from the molar extinction coefficient (2.5  104 M1 cm1) of the conjugated hydroperoxydiene measured at 234 nm. The method is very satisfactory for highly purified LOXs. However, protein and other UV absorbing materials may absorb at 234 nm, and if the hydroperoxideutilizing enzymes are present, the conjugated diene chromophores formed generally are destroyed. Moreover, surfactants are included to clarify the solutions and avoid scattering of UV light; however, this can change the absorbance in pH values below 7.5 leading to errors in enzyme activity measurement [68].

5.8.3 Other enzymes present in foods 5.8.3.1 Peroxidase Peroxidases (POXs) are abundant in all domains of life, including microorganisms, plants, and animals. This enzyme catalyzes the oxidoreduction between hydrogen peroxide and reductants (H2O2 + AH2 ! 2H2O + A) [69, 85]. Three superfamilies of POX have been reported based on their structural and catalytic properties. The first one consists of enzymes from animals, such as lactoperoxidase (EC 1.11.1.7) found in milk. The second POX superfamily is the catalases (EC 1.11.1.6), found mainly in animals. The last group consists of POXs from plants (EC 1.11.1.6) [85]. Plant POXs (EC 1.11.1.7) are capable of oxidizing a vast array of endogenous compounds such as lipid and phenolic oxidations with consequent deterioration of flavor and discoloration due to surface browning [86]. On the other hand, in view of the

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higher temperatures required to inactivate the enzyme, POX activity might be used as an indicator of adequate heat treatment in foods. POX activity is easily monitored by spectrophotometry. Pyrogallol, ABTS, 4-methoxy-α-naphthol, and phenol plus aminoantipyrine can be used as hydrogen donors. However, guaiacol is the main hydrogen donor employed since it is widely available and simple to handle [68]. The oxidation of guaiacol is monitored at 470 nm. One unit of POX activity is the amount of enzyme that results in the oxidation of 1 mol of guaiacol per minute at 25°C (pH 7.0, molar extinction coefficient ¼ 2.75  103 M1 cm1). POX specific activity is obtained by Eq. (5.10): POXAct ¼

V  AΔ470 min 1 2:75  103 M1 cm1  ½E

(5.10)

where V is the total reaction volume in mL, [E] is the enzyme concentration in mg/mL, and ΔA/min is the change of absorbance at 470 nm min1 [68]. Qualitative POX assay is used to evaluate the heat treatment of milk. Since POX is denatured if heated at 75°C for 20 s, the absence of this enzyme in milk indicates overheating. For this test, 10 mL of milk is heated in a water bath at 43°C for 5 min. Next, 2 mL of guaiacol is added followed by three drops of H2O2. The pink color indicates POX activity [87].

5.8.3.2 Polyphenol oxidase Polyphenol oxidases (PPOs) are involved in the enzymatic browning of many edible plant products, especially fruits, vegetables, mushrooms, as well as crustaceans such as shrimp, lobster, and crab. PPO enzymes are able to catalyze the oxidation of phenols in o-quinones. They have catechol oxidase activity (oxidation of o-diphenols to their corresponding o-quinones, EC 1.10.3.1) and many also have the ability to hydroxylate monophenols to o-diphenols (tyrosinase, EC 1.14.18.1) [68, 88]. This reaction is responsible for the deterioration of color in juices and fresh vegetables such as lettuce, and in taste and nutritional quality. Therefore much effort has gone into developing methods for control of PPO activity [51, 69]. Spectrophotometry and polarography are the main techniques used to determine PPO activity. The results of the two techniques are similar. However, spectrophotometry has been employed more for this assay. In this method, it is necessary to know the initial velocity of reaction since the benzoquinone is converted to other products that do not absorb maximally at the wavelength required by benzoquinone. Consequently, the reaction of the enzyme with o-diphenols is linear in too short a time [68].

5.9

Summary and outlook

The use of mature methods for food chemical properties evaluation is due to simplicity because they do not require sophisticated instruments. Recent advances have been reported to improve the methods’ sensitivity and decrease the use of solvents.

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Currently, miniaturization is the main change made in mature methods. Moreover, the use of cheaper devices and easy access, such as a smartphone instead of spectrophotometric determination, is a trend for improving the detection of mature methods over the next few years.

References [1] S.J. Forsythe, Microbiologia da Seguranc¸a dos Alimentos, second ed., Artmed Editora, 2013. [2] C. Nerı´n, M. Aznar, D. Carrizo, Food contamination during food process, Trends Food Sci. Technol. 48 (2016) 63–68. [3] V.A. Vaclavik, E.W. Christian, Essentials of Food Science, Springer New York, New York, 2013. [4] D. Reid, O.R. Fennema, Water and ice, in: S. Damodaran, K.L. Parkin, O.R. Fennema (Eds.), Fennema’s Food Chemstry, CRC Press, Boca Raton, 2008, pp. 17–82. [5] S.S. Nielsen, Food Analysis, fourth ed., Springer US, New York, 2010. [6] W. R€odel, Water activity and its measurement in food, in: E. Kress-Rogers, C.J. B. Brimelow (Eds.), Instrumentation and Sensors for the Food Industry, Woodhead, Cambridge, 2001, pp. 453–483. [7] Belitz, H.D., W. Grosch, and P. Schieberle, Food Chemistry. 2009, Berlin: Springer Berlin Heidelberg. [8] L.M.L. Nollet, Handbook of Food Analysis: Physical Characterization and Nutrient Analysis, CRC PressI Llc, New York, 2004. [9] METER, AquaLab Pre, Water Activity Meter - Operator’s manual, 87 METER Group, Inc, Washington, DC, 2017. [10] J.F. Le Page, P.S. Mirade, J.D. Daudin, Development of a device and method for the timecourse estimation of low water fluxes and mean surface water activity of food products during ripening and storage, Food Res. Int. 43 (4) (2010) 1180–1186. [11] J. Troller, Methods to measure water activity, J. Food Prot. 46 (2) (1983) 129–134. [12] R.E. Wrolstad, et al., Handbook of Food Analytical Chemistry: Water, Proteins, Enzymes, Lipids, and Carbohydrates, Vol. 1, Wiley, New Jersey, 2005. [13] J.A. Fontana, Measurement of water activity, moisture sorption isotherms, and moisture content of foods, in: G.V. Barbosa-Ca´novas et al., (Ed.), Water Activity in Foods: Fundamentals and Applications, Blackweel Publishing, Iowa, 2007, pp. 155–171. [14] B.A. Prior, Measurement of water activity in foods: a review, J. Food Prot. 42 (8) (1979) 668–674. [15] M.S. Rahman, et al., Direct manometric determination of vapor pressure, in: Current Protocols in Food Analytical Chemistry, John Wiley & Sons, Inc., 2001. [16] M.S. Rahman, Food Properties Handbook, second ed., CRC Press, 2009. [17] D.L. Nelson, M.M. Cox, Lehninger Principles of Biochemistry, sixth ed., Macmillan Learning, 2012. [18] R.P. Buck, et al., Measurement of pH. Definition, standards, and procedures (IUPAC Recommendations 2002), Pure Appl. Chem. 74 (11) (2003) 2169–2200. [19] M. Yuqing, C. Jianrong, F. Keming, New technology for the detection of pH, J. Biochem. Biophys. Methods 63 (1) (2005) 1–9. [20] V. Afonso, et al., Reactive oxygen species and superoxide dismutases: role in joint diseases, Joint Bone Spine 74 (4) (2007) 324–329. [21] C.M. Bergamini, et al., Oxygen, reactive oxygen species and tissue damage, Curr. Pharm. Des. 10 (14) (2004) 1611–1626.

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[22] M. Valko, et al., Free radicals and antioxidants in normal physiological functions and human disease, Int. J. Biochem. Cell Biol. 39 (1) (2007) 44–84. [23] B. Uttara, et al., Oxidative stress and neurodegenerative diseases: a review of upstream and downstream antioxidant therapeutic options, Curr. Neuropharmacol. 7 (1) (2009) 65–74. [24] W.A. Yehye, et al., Understanding the chemistry behind the antioxidant activities of butylated hydroxytoluene (BHT): a review, Eur. J. Med. Chem. 101 (2015) 295–312. [25] S. Zang, et al., Determination of antioxidant capacity of diverse fruits by electron spin resonance (ESR) and UV–vis spectrometries, Food Chem. 221 (2017) 1221–1225. [26] R.L. Prior, X. Wu, K. Schaich, Standardized methods for the determination of antioxidant capacity and phenolics in foods and dietary supplements, J. Agric. Food Chem. 53 (10) (2005) 4290–4302. [27] R. Apak, et al., Antioxidant activity/capacity measurement. 1. Classification, physicochemical principles, mechanisms, and electron transfer (ET)-based assays, J. Agric. Food Chem. 64 (5) (2016) 997–1027. [28] D. Huang, B. Ou, R.L. Prior, The chemistry behind antioxidant capacity assays, J. Agric. Food Chem. 53 (6) (2005) 1841–1856. [29] J.B. Tan, Y.Y. Lim, Critical analysis of current methods for assessing the in vitro antioxidant and antibacterial activity of plant extracts, Food Chem. 172 (2015) 814–822. [30] N. Paixa˜o, et al., Relationship between antioxidant capacity and total phenolic content of red, rose and white wines, Food Chem. 105 (1) (2007) 204–214. [31] R. Re, et al., Antioxidant activity applying an improved ABTS radical cation decolorization assay, Free Radic. Biol. Med. 26 (9–10) (1999) 1231–1237. [32] M.B. Arnao, Some methodological problems in the determination of antioxidant activity using chromogen radicals: a practical case, Trends Food Sci. Technol. 11 (11) (2000) 419–421. [33] L.M. Magalhaes, et al., Methodological aspects about in vitro evaluation of antioxidant properties, Anal. Chim. Acta 613 (1) (2008) 1–19. [34] I.F. Benzie, J.J. Strain, Ferric reducing/antioxidant power assay: direct measure of total antioxidant activity of biological fluids and modified version for simultaneous measurement of total antioxidant power and ascorbic acid concentration, Methods Enzymol. 299 (1999) 15–27. [35] F. Shahidi, Y. Zhong, Measurement of antioxidant activity, J. Funct. Foods 18 (2015) 757–781. [36] L.L. Mensor, et al., Screening of Brazilian plant extracts for antioxidant activity by the use of DPPH free radical method, Phytother. Res. 15 (2) (2001) 127–130. [37] W. Brand-Williams, M.E. Cuvelier, C. Berset, Use of a free radical method to evaluate antioxidant activity, LWT Food Sci. Technol. 28 (1) (1995) 25–30. [38] A. Da´valos, C. Go´mez-Cordoves, B. Bartolome, Extending applicability of the oxygen radical absorbance capacity (ORAC fluorescein) assay, J. Agric. Food Chem. 52 (1) (2004) 48–54. [39] A. Berto, et al., Bioactive compounds and scavenging capacity of pulp, peel and seed extracts of the Amazonian fruit Quararibea cordata against ROS and RNS, Food Res. Int. 77 (2015) 236–243. [40] R.C. Chiste, et al., In vitro scavenging capacity of annatto seed extracts against reactive oxygen and nitrogen species, Food Chem. 127 (2) (2011) 419–426. [41] M.D. Defago´, et al., Food composition data in Argentina: A systematic review of the literature, J. Food Compos. Anal. 43 (2015) 39–48. [42] L.S. Magwaza, U.L. Opara, Analytical methods for determination of sugars and sweetness of horticultural products—a review, Sci. Hortic. 184 (2015) 179–192.

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[43] A.A. Kader, Flavor quality of fruits and vegetables, J. Sci. Food Agric. 88 (11) (2008) 1863–1868. [44] J. Rheims, J. K€ oser, T. Wriedt, Refractive-index measurements in the near-IR using an Abbe refractometer, Meas. Sci. Technol. 8 (6) (1997) 601. [45] E.A. Baldwin, et al., Relationship between sensory and instrumental analysis for tomato flavor, J. Am. Soc. Hortic. Sci. 123 (5) (1998) 906–915. [46] D. Obenland, et al., Determinants of flavor acceptability during the maturation of navel oranges, Postharvest Biol. Technol. 52 (2) (2009) 156–163. [47] R.B. Jordan, R.J. Seelye, V.A. McGlone, A sensory-based alternative to brix/acid ratio, Food Technol. 55 (6) (2001) 36–44. [48] F. ˙Ic¸ier, T. Baysal, Dielectrical properties of food materials—2: measurement techniques, Crit. Rev. Food Sci. Nutr. 44 (6) (2004) 473–478. [49] T. King, et al., Food safety for food security: relationship between global megatrends and developments in food safety, Trends Food Sci. Technol. 68 (2017) 160–175. [50] AOAC, AOAC official method 937.09 salt (chlorine as sodium chloride) in seafood: Volumetric method. Sec. 35.1.18. In: P. Cunniff (Ed.), Official Methods of Analysis of AOAC International, 16th ed., AOAC International, Gaithersburg, MD, p. 7, [51] S. Damodaran, K.L. Parkin, O.R. Fennema, Fennema’s Food Chemistry, fourth ed., CRC Press, 2007. [52] Z.Z.E. Sikorski, A. Kolakowska, Chemical and Functional Properties of Food Lipids, CRC Press, 2010. [53] E.N. Frankel, Lipid Oxidation, Oily Press, 2005. [54] Y. Pomeranz, C.E. Meloan, Food Analysis: Theory and Practice, Van Nostrand Reinhold, 1994. [55] M.D. Luque de Castro, F. Priego-Capote, Soxhlet extraction: past and present panacea, J. Chromatogr. A 1217 (16) (2010) 2383–2389. [56] C.C. Akoh, D.B. Min, Food Lipids: Chemistry, Nutrition, and Biotechnology, third ed., Taylor & Francis, 2008. [57] E.G. Bligh, W.J. Dyer, A rapid method of total lipid extraction and purification, Can. J. Biochem. Physiol. 37 (1) (1959) 911–917. [58] R.D. O’Brien, Fats and Oils: Formulating and Processing for Applications, third ed., CRC Press, 2008. [59] F. Gunstone, Vegetable Oils in Food Technology: Composition, Properties and Uses, Wiley, 2011. [60] D.A. Morgan, Smoke, fire, and flash points of cottonseed, peanut, and other vegetable oils, Oil and Soap 19 (11) (1942) 193–198. [61] Y.B. Che Man, G. Setiowaty, F.R. van de Voort, Determination of iodine value of palm oil by Fourier transform infrared spectroscopy, J. Am. Oil Chem. Soc. 76 (6) (1999) 693–699. [62] N.B. Kyriakidis, T. Katsiloulis, Calculation of iodine value from measurements of fatty acid methyl esters of some oils: comparison with the relevant American oil chemists society method, J. Am. Oil Chem. Soc. 77 (12) (2000) 1235–1238. [63] N.A.S. Ramli, et al., Stability evaluation of quality parameters for palm oil products at low temperature storage, J. Sci. Food Agric. 98 (2017) 3351–3362. [64] M. Ghosh, et al., Preparation of human milk fat analogue by enzymatic interesterification reaction using palm stearin and fish oil, J. Food Sci. Technol. 53 (4) (2016) 2017–2024. [65] M.D. Guillen, N. Cabo, Infrared spectroscopy in the study of edible oils and fats, J. Sci. Food Agric. 75 (1) (1997) 1–11. [66] J.I. Gray, Measurement of lipid oxidation: a review, J. Am. Oil Chem. Soc. 55 (6) (1978) 539–546.

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[67] J.L. Multon, W.J. Stadelman, B.A. Watkins, Analysis of Food Constituents, Wiley-VCH, 1997. [68] J.R. Whitaker, A.G.J. Voragen, D.W.S. Wong, Handbook of Food Enzymology, Taylor & Francis, 2002. [69] R.B. Mun˜oz, Enzymes, in: L.M.L. Nollet (Ed.), Handbook of Food Analysis: Physical Characterization and Nutrient Analysis, CRC Press LLC, Belgium, 2004, pp. 204–220. [70] G.L. Miller, Use of dinitrosalicylic acid reagent for determination of reducing sugar, Anal. Chem. 31 (3) (1959) 426–428. [71] B. Escaramboni, et al., Ethanol biosynthesis by fast hydrolysis of cassava bagasse using fungal amylases produced in optimized conditions, Ind. Crop. Prod. 112 (2018) 368–377. [72] G. Lehoczki, L. Kandra, G. Gyema´nt, The use of starch azure for measurement of alphaamylase activity, Carbohydr. Polym. 183 (2018) 263–266. [73] H. Rinderknecht, P. Wilding, B.J. Haverback, A new method for the determination of α-amylase, Experientia 23 (10) (1967) 805. [74] M. Sak-Bosnar, N. Sakacˇ, Direct potentiometric determination of diastase activity in honey, Food Chem. 135 (2) (2012) 827–831. [75] H.Y. Jeon, et al., Characterization of a novel maltose-forming α-amylase from Lactobacillus plantarum subsp. plantarum ST-III, J. Agric. Food Chem. 64 (11) (2016) 2307–2314. [76] J.A. Salas-Tovar, et al., Analytical methods for pectin methylesterase activity determination: a review, Food Anal. Methods 10 (11) (2017) 3634–3646. [77] F. Amin, et al., Improvement of activity, thermo-stability and fruit juice clarification characteristics of fungal exo-polygalacturonase, Int. J. Biol. Macromol. 95 (2017) 974–984. [78] T. Tu, et al., High-yield production of a low-temperature-active polygalacturonase for papaya juice clarification, Food Chem. 141 (3) (2013) 2974–2981. [79] M. Obadi, et al., Characterization of oil extracted from whole grain flour treated with ozone gas, J. Cereal Sci. 79 (2018) 527–533. [80] N.N. Gandhi, Applications of lipase, J. Am. Oil Chem. Soc. 74 (6) (1997) 621–634. [81] F. Beisson, et al., Methods for lipase detection and assay: a critical review, Eur. J. Lipid Sci. Technol. 102 (2) (2000) 133–153. [82] S. Herna´ndez-Garcı´a, M.I. Garcı´a-Garcı´a, F. Garcı´a-Carmona, An improved method to measure lipase activity in aqueous media, Anal. Biochem. 530 (2017) 104–106. [83] R. Heshof, et al., Industrial potential of lipoxygenases, Crit. Rev. Biotechnol. 36 (4) (2016) 665–674. [84] B. Axelrod, T.M. Cheesbrough, S. Laakso, [53] Lipoxygenase from soybeans: EC 1.13.11.12 Linoleate:oxygen oxidoreductase, in: Methods in Enzymology, Academic Press, 1981, pp. 441–451. [85] S. Hiraga, et al., A large family of class III plant peroxidases, Plant Cell Physiol. 42 (5) (2001) 462–468. [86] N.N. Misra, et al., Cold plasma interactions with enzymes in foods and model systems, Trends Food Sci. Technol. 55 (2016) 39–47. [87] Adolfo Lutz Institute, Metodos fı´sico-quı´micos para ana´lise de alimentos, Brazilian Ministry of Health, 2008. [88] M.L. Sullivan, Beyond brown: polyphenol oxidases as enzymes of plant specialized metabolism, Front. Plant Sci. 5 (2014) 783.

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A.C. Power*, J. Chapman†, S. Chandra*, D. Cozzolino† *Agri-Chemistry Group, School of Medical and Applied Sciences, Central Queensland University (CQU), North Rockhampton, QLD, Australia, †School of Science, RMIT University, Melbourne, VIC, Australia

6.1

Introduction

Spectroscopic analysis deals with the interaction of electromagnetic waves and organic molecules. The use of sensors based on molecular spectroscopy is well recognized within the analytical community because they allow the real-time and simultaneous monitoring of multiple chemical variables or compounds during routine and process analysis, in particular, food samples [1]. Both atomic and molecular spectroscopy are the predominant spectroscopic techniques used in food analysis. These techniques are based on the interaction between light and matter, which result in either the absorption, emission, or scattering of incident electromagnetic radiation [2]. These interactions can be detected by a variety of spectroscopic methods, including ultraviolet-visible (UV-Vis), near-infrared (NIR), mid-infrared (MIR), far infrared (FIR), and Raman spectroscopy, using terahertz waves, microwaves, radio waves, and nuclear magnetic resonance (NMR) over different wavelength ranges of the electromagnetic spectrum [2–6]. As stated, methods and techniques based on molecular spectroscopy are very popular in several stages of food production, including routine analysis, quality control, and bioprocess monitoring. These techniques offer multiple advantages over traditional methods such as the ability to monitor several variables or compounds simultaneously, minimal or no preprocessing of the sample before analysis, low cost, no reagents, and the potential use of fiber optics allowing remote control of the process, among others [7–10]. Fig. 6.1 shows the relationship between spectroscopy and chemometrics for the analysis of food quality. This chapter reviews the basic concepts of UV-Vis spectroscopy and provides examples of this technique applied to the analysis of different food matrices (e.g., meat, milk, coffee, wine, and olive oil).

6.2

The basic principles

6.2.1 Origin of UV-Vis spectra UV-Vis spectroscopy is a method that can monitor and measure the interactions of UV and visible light with different chemical compounds in the wavelength range between 200 and 780 nm. The technique exploits different physical responses of light and Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00006-8 © 2019 Elsevier Inc. All rights reserved.

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Fig. 6.1 The relationship between spectroscopy and chemometrics for the analysis of food quality.

analytes within the sample such as absorption, scattering, diffraction, refraction, and reflection [1]. The phenomenon of UV and visible light absorption is restricted to specific chromophores and several chemical species with defined molecular functional groups [1]. Consequently, the characteristic absorption spectra may be obtained for single molecules because electrons within these chromophores are excited [1]. Quantitative analysis based on UV-Vis spectroscopy is ultimately described by the BeerLambert law and is the correlation between the quantity of the incident light absorbed by the molecule, the sample, the light path length, and the concentration of the absorbing compound or molecule in the matrix [1]. The method allows for the determination and quantification of target molecule concentration within the food matrix [1, 11, 12]. With the continuous development of instrumentation comes improved analytical capabilities, for example, modern fiber optic UV-Vis spectrophotometers with linear photodiode arrays or charge-coupled devices, because detectors are portable due to their compact geometry, highly sensitive, capable of low analyte concentration detection in complex matrices (including aqueous solutions), and efficient (instantaneously reporting a sample’s full spectra) [1, 11, 12].

6.2.2 Sample presentation Solid, liquid, and gaseous samples can be analyzed using UV-Vis spectroscopy. However, the nature of the sample can present challenges for the analyst, because often the sample will be unsuitable for real-world quantitative spectroscopic analysis. Analytical measurements may be hindered by different factors such as the complex nature of the sample, because inaccurate measurements due to interferences or masking agents

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may occur and because of the inability of the analyst to analyze the material as a whole due to the sample’s dimensions; some of these issues can often present in food samples [13–15]. The vast majority of modern commercially available UV-Vis spectrometers are capable of quantitative analysis of chemical and biological samples designed for liquid samples. Solid samples often require a significant level of sample preparation, such as dissolution in an appropriate solvent. Often solid samples are not easily dissolved; if this is the case a more aggressive approach may be necessary, such as acid digestion [13–15]. Analysis of UV absorption solutes can only be performed in homogeneous solutions. In nonhomogeneous samples, particularly where solid particles are present, significant interference is observed within spectra due to the absorption and light scattering effects of the individual particles. In the biotechnology and food industries, this phenomenon is exploited in optical density (OD) measurements that can then be used to determine the biomass concentration in turbid samples. It should be noted that for proteins and other similarly large molecules, incident light is absorbed by multiple functional groups within the compound, which results in nonspecific UV-Vis spectra. Consequently, although the quantification of the total protein concentration of a sample can and often is performed using the Beer-Lambert law via UV absorption at defined single wavelengths, the differentiation of proteins via UV spectra is seldom possible. Many optical density sensors have been developed for the food industry that allow for in-line applications; these sensors are based on transmission or turbidimetry measurements [16–18]. Here the transmission reading of the sample is determined by light absorption over a constant path length and the turbidity is calculated by measuring the light scatter at 90 or 180 degrees [16–18]. However, it must be acknowledged that the OD method is generally considered limited because OD measurements only correlate linearly with cell mass at low concentrations [16–18]. This limitation is further compounded because the technique often only recognizes the presence of particles and cannot distinguish between viable and dead cells, or between intact cells and cell debris or other solid particles [16–18]. Fig. 6.2 shows the effect of path length on the UV-Vis spectra of a series of wine samples. The basic principles of UV-Vis instrumentation are described in work by other authors [19–21]. Recent reviews also highlighted different uses of UV-Vis spectroscopy in bioprocessing and other specific food applications [22–27]. The analysis of solid samples (e.g., meat and flesh foods) by UV-Vis spectroscopy is shown in Fig. 6.3.

6.2.3 Data analysis Food analysis, particularly if using food “stuffs,” is highly complex, variable, and offers significant analytical challenges. Moreover, foodstuffs exist in numerous physical states, such as solids, solutions, emulsions, foams, as well as complicated, heterogeneous systems [28–33]. The many components of food systems, while mainly composed of water, also include carbohydrates, proteins, fats, and other trace constituents such as vitamins, minerals, etc. all contributing to the absorbance spectrum

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Fig. 6.2 Effect of path length on the ultraviolet and visible spectra of alcoholic beverages.

0.0015

Second derivative

0.001

0.0005

0

–0.0005

–0.001

–0.0015

–0.002

UV

Vis Wavelengths (nm)

Fig. 6.3 Second derivative of the ultraviolet and visible spectra of fresh muscles scanned in reflectance.

obtained [28–33]. Food heterogeneity results in considerable spectral complexity, particularly because of the major components (water, carbohydrates, proteins, fats) that dominate the spectra; consequently, conventional approaches regarding the use of spectra are not appropriate and should not be applied. Historically, much of the research in food analysis has been conducted and described as “univariate” in nature, because only the response of a single variable within the overall matrix is examined [28–33]. Because the nature of technology

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has changed, today, relatively speaking, samples are expensive and measurements are cheap. Thus, while analyzing the effect of one variable at a time by analysis of variance techniques does provide useful descriptive information, it is cost prohibitive. Moreover, such analysis is not capable of providing specific information about relationships between multiple variables and other components within the matrix as a whole [34–39]. Chemometrics, multivariate analysis, was developed in the late 1960s, and was introduced by a number of analytical, physical, and organic chemistry-focused research groups. The researchers highlighted that the advancement of instrumentation coupled with the greater availability of computers allowed the measurement of multivariate responses for each sample analyzed [36, 40–43]. Computers and the advancement of modern chemical measurements, where analysts are generally confronted with an abundance of data points, have led to critical information not being readily observable [36, 40–43]. This is particularly evident with spectral data with many different observations (peaks or wavelengths) being collected and where each individual response could be considered resulting from a different dimension of the overall sample. Traditionally, analysts have endeavored to simplify and consolidate analysis measurement by isolating or extracting the analyte of interest to ultimately eliminate potential matrix interference in methods [36, 40–43]. However, using methods to isolate or extract the analyte of interest fails to account for the potential chemical and physical interactions between total components of the sample—this is especially evident for complex foodstuffs such as grapes and wine. Because univariate models do not consider the contributions of multiple variable sources the models on which the analysis is based can be oversimplified and as a result be limited. Therefore it is necessary that the sample in its entirety is analyzed and not just at a single component to ultimately untangle all the complicated interactions between the constituents and understand their combined effects on the whole matrix. Multivariate methods provide the means to move beyond the one-dimensional (univariate) approach. In the majority of many cases reported, multivariate analysis highlights the constituents that are genuinely important/influential by investigating the various interferences and interactions within the sample that univariate analysis ignores [12, 36–39, 41–44]. Today, food quality measurement techniques tend to be multivariate and are based on indirect measurements of the chemical and physical properties of the sample [35, 44–46]. A common characteristic of the most useful of the instrumental techniques utilized is that paradoxically the measurement variable rarely possesses a direct relationship with the property of interest, for example, the particular concentration of an analyte in the sample—so the majority of techniques are correlative methods [35, 44–46]. This is best explained when the chemical and physical interferences of the analysis are considered. Spectroscopic techniques provide an incredible amount of information from a single measurement, because they have the capability to record the response at multiple wavelengths simultaneously [35, 44–46]. As a result, it is necessary to use multivariate analysis to properly extract the information of interest from the total data. There is a growing portion of the literature where further information on the numerous algorithms, formulas, and procedures exploited by multivariate analysis can be sourced [34–39, 41–46].

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Table 6.1 Advantages and limitations of ultraviolet and visible spectroscopy to analyze foods Advantages Sensitivity of the application Cost of instruments Remote sampling Sample average Solid samples Slurry samples

High sensitivity Relatively low cost Available using fiber optics Good to very good depending on the optics Analyzed using reflectance Analyzed using transflectance or fiber optics

High chemical resolution Effect of path length Qualitative analysis (multiple wavelengths) Quantitative analysis

6.3

Limitations

Linear regression

Effect of scattering and path length No Sensitive to changes in path length Need for chemometric tools (principal components) Multivariable calibration needs of chemometric tools

Advantages and limitations of UV-Vis spectroscopy

Table 6.1 summarizes some of the advantages and limitations of the use of UV and VIS spectroscopy to analyze foods. These advantages and limitations include sensitivity of the application, cost of instruments, potential use on remote sampling, analysis of liquid, solid and slurry samples, chemical resolution, and the effect of path length, among others.

6.4

Recent applications and progress of UV-Vis spectroscopy in different types of foods

The usefulness of UV-Vis spectroscopy (often in combination with other spectroscopic or chromatic techniques) coupled with chemometrics for food analysis is evident by the range of foodstuffs that have been analyzed by the method reported in the literature. The following are some examples of applications of UV-Vis spectroscopy in different food materials and commodities.

6.4.1 Coffee Dankowska and colleagues [47] reported the use of UV-Vis spectroscopy in combination with fluorescence to quantify the concentrations of roasted Coffea arabica and

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Coffea canephora var. robusta in 33 different coffee blends from 15 different countries. The authors applied chemometric techniques such as principal component analysis (PCA) to reduce data multidimensionality [47]. Linear discriminant analysis (LDA) was also used to determine the percentage of bean type in each blend. The classification rate obtained based on the UV-Vis spectra was over 96% [47]. The results reported by these authors determined that such analysis would contribute significantly to the reduction of food fraud and better protect the interest of consumers [47]. In a similar study, Souto and collaborators [48, 49] conducted studies where UV-Vis spectroscopy combined with chemometrics was used to analyze coffee. In their 2010 work [48], the authors compared two methodologies, successive projections algorithm (SPA)-LDA and soft independent modeling of class analogy (SIMCA), as a means to classify between caffeinated and decaffeinated roast coffee [48]. The SPA-LDA model demonstrated a greater ability to discriminate the conservation states of the 43 samples than SIMCA and more promisingly retained a high classification accuracy (96%) despite the introduction of artificial noise into the spectra [48]. The authors further investigated the potential of UV-Vis paired with chemometrics in a work reported in 2015 [49], where they demonstrated the technique’s ability to identify the adulteration of roasted ground coffee with husks and other organic matter. These authors stated that SPA associated with LDA is the most appropriate method for classification and that the group’s proposed protocol is advantageous because it is simple and rapid with very little sample preparation—extraction with hot water alone [48].

6.4.2 Milk A number of studies in the literature detail the use of UV-Vis spectroscopy to monitor melamine content in milk products using gold nanoparticles as probes [50–54]. The method is generally based on the aggregation of gold nanoparticles in the presence of melamine resulting in a color change of the nanoparticle solution from wine red to purple [50–54]. The authors of these studies highlight the practicality, speed, simplicity, and reliability of the technique, in comparison to other published assay approaches that require high cost equipment and complicated pretreatments [50–54].

6.4.3 Olive oil UV-Vis spectroscopy is widely used due to its great versatility, easy handling, high sample turnover, and automation feasibility. However, the low selectivity of this technique makes its application, without a previous pretreatment, an almost impossible task. In fact, its direct application on VOO and EVOO analysis is limited to a few cases (e.g., anisidine value, peroxide value, general color, carotenoids, and chlorophylls). Nevertheless, chemometrics has proven to be very useful to deal with the major issues of this methodology. A similar scenario appears in the application of fluorimetric techniques, which find selectivity and improve their performance in combination with chemometric techniques. A good example of this tendency is the work of Torrecilla and collaborators [55], who utilized UV-Vis spectroscopy to quantify the adulteration

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of extra virgin olive oil with refined olive oil and olive-pomace oil [55]. This work reported that the technique was capable of estimating the adulteration agent concentration with a mean square error of less than 1%, with the authors determining that the method is appropriate not only for adulteration detection but also for the measurement of impurities within higher grade olive oils [55]. Mignani and collaborators combined UV-Vis spectroscopy with chemometrics to produce multiple specific defect models for extra virgin olive oil [56]. To generate the models, the UV-Vis spectrum was preprocessed using first derivatives and second-order smoothing polynomials through seven points [56]. Prior to derivatization, standard normal variate, offset, and baseline (third-order) corrections were applied before the resulting spectrum was mean centered [56]. The spectral range of 580–1000 nm provided the best results to classify the specific defects and the whole spectrum (300–1000 nm) was suitable for general nondefective and nonedible oils. The first region corresponded to the Vis range, which showed the presence of dyes and pigments [56]. A range from 380 to 450 nm belonged to carotenoid pigments that had high stability. However, chlorophylls and pheophytins, with an exclusive absorption band at 650–700 nm, had a high influence on the model for all of the defects. According to the authors, this may be attributed to the defective samples that have undergone a degradation process for different reasons, which may have affected the composition of these substances [57, 58]. Another band near 935 nm had significant influence on the classification model for winey and rancid samples. In general, peaks at 610 and 670 nm are reduced for all of the degenerated or defective olive oils. The adulteration of olive oil with inferior substitutes using lowfield (LF) proton (1H) NMR relaxometry and UV-Vis spectroscopy was conducted by Ok [59]. In his investigation, three different olive oils with different oleoyl acyl contents were mixed with almond, castor, corn, and sesame oils. The author determined that both LF 1H NMR relaxometry and UV-Vis spectroscopy were required to quantitatively detect the adulteration concentration. In another study by Casale and collaborators [60], olive oil samples were analyzed using a combination of spectroscopic (UV-Vis, NIR, and MIR) and chemometric techniques [60]. The authors found that the spectra and composition of the olive oil were influenced by multiple factors, including agricultural and harvesting methods, transport and storage conditions, and climate. The work also determined that the characterization of Chianti Classico PDO olive oils was best achieved by combining NIR and UV-Vis analysis [60].

6.4.4 Tea Diniz and colleagues reported the use of chemometrics and UV-Vis to simultaneously classify both the geographic origin and variety of teas using water extraction [61]. The authors reported that SPA-LDA and PCA-LDA was significantly better for tea classification of the five studied classes (Argentinean green tea, Brazilian green tea, Argentinean black tea, Brazilian black tea, and Sri Lankan black tea) with the reported methodology being superior to traditional tea quality evaluation methods because it is a simpler, faster, and more affordable classification [61]. In another study, Wang and collaborators [62] combined chemometrics, UV-Vis, and NIR spectroscopy to classify five different green tea varieties. These authors reported that the developed method affords a useful low-cost means for the rapid classification of green teas [62].

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Similarly, Pauli and collaborators combined chemometrics and UV-Vis spectroscopy to discriminate between Camellia sinensis tea leaves [63]. The authors highlighted the advantages of the proposed technique in terms of simplicity, data acquisition, time, and cost [63].

6.4.5 Vinegar Torrecilla and colleagues combined UV-Vis spectroscopy and chemometric modeling to identify vinegar blends via their raw materials (red or white wine, cider, apples, rice, and molasses) [64, 65]. In their work, the authors utilized both partial least squares discriminant analysis and artificial neural networks [64, 65]. The average correct classification rate of a series of comparable internal validations was around 55% and 90% for the PLS-DA and ANN models, respectively [64, 65], with the authors reporting the design of an accurate chemometric tool for the detection of specific vinegars in mixtures in an inexpensive and straightforward manner [64, 65].

6.4.6 Wine The use of UV-Vis spectroscopy by the wine industry is not new. This technique has been used routinely by the industry to measure phenolic compounds and color. UV-Vis spectroscopy has been applied to the classification of white wines because the relevant wavelengths fall in the spectral range of 240–400 nm, which relates to esters and hydroxycinnamic acids, and it is highlighted that the technique requires coupling with pattern recognition methods [66]. However, the anthrocyanin and other phenolic concentrations of red wines are reflected over the 250–800 nm range [67–71]. The technique was evaluated to discriminate between wines from different geographical regions in terms of monitoring quality [72–74] as well as a potential method to detect wine adulteration [75].

6.4.7 Meat and fish The potential use of UV-Vis, NIR, and MIR spectroscopy, combined with chemometric techniques, to measure the adulteration of minced beef with turkey meat was evaluated [76]. The spectral data was processed and then analyzed using PCA, LDA, and PLS regression. According to the authors of this study, the best results were obtained with NIR and MIR spectroscopy, whereas the UV-Vis results were less satisfactory [76]. However, a combination of information from UV-Vis with NIR and MIR spectroscopy improved the overall results [76]. UV-Vis spectroscopy was used to classify Japanese dace fish into fresh or spoiled samples using support vector machine (SVM), LDA, PCA, and SIMCA as classification techniques [77]. The authors reported that classification models based on UV-Vis spectra (250–600 nm) and SVM correctly classified 100% of the fresh fish samples analyzed. Similar results were reported by the same authors comparing different classification methods [78, 79]. The so-called artificial fish swarm algorithm (AF) for the synchronous selection of wavelengths and different pretreatment methods was evaluated to quantify the level of

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beef adulteration with spoiled beef [80]. The authors of this study evaluated different vibrational spectroscopy techniques such as UV-Vis, NIR, and UV-Vis-NIR [80]. The best classification model was based on the combination of Vis-NIR [80]. The authors demonstrated that AF was a useful tool for model optimization as compared with other techniques such as a genetic algorithm [80].

6.4.8 Saffron A stepwise approach was used as a screening tool to identify and monitor the origin of adulteration of saffron during trade with carminic acid [81]. This natural dye is of insect origin and should not be present in Kosher and Halal foods such as saffron [81]. The authors reported the use of UV-Vis spectroscopy to detect gross adulteration levels (>25.0%, w/w) based on the reaction with diphenylamine-sulfuric acid [81]. According to the authors, UV-Vis spectroscopy was able to detect adulteration down to the level of 2.0% (w/w) [81].

6.4.9 Propolis Propolis is a beneficial natural product and has been used in the food and pharmaceutical industries as a food preservative [82]. In this study, UV-Vis spectroscopy and cyclic voltammetry were combined with PCA to confirm the presence of two botanical subtypes of propolis [82]. The results reported by these authors confirmed that UV-Vis spectroscopy combined with chemometrics has the potential to discriminate complex natural products such as propolis [82].

6.5

Summary and outlook

In the literature, there are numerous reports of the use of UV-Vis spectroscopy (both individually and combined with other spectroscopic methods) in food analysis (qualitative and quantitative analysis). Technique popularity is due to the common availability of instruments, simplicity of use, speed, precision, accuracy of the analysis, and the relatively low cost. In recent years, UV-Vis spectroscopy techniques have also been evaluated as useful techniques for monitoring several compounds simultaneously during different processes (e.g., in line, at line). However, the development of such applications requires the use of multivariate data analysis methods or chemometrics. Such methods based on UV-Vis spectroscopy will offer the possibility to provide noninvasive and remote analysis of foods in industrial settings. However, various barriers still hinder the growth and development of these applications by the food industry. Among them, the hesitancy of the food industry to accept the integration of chemistry and mathematics (the benefits of chemometrics are often ignored by those who prefer to employ classical statistics) and the lack of academic education and skills in the use and application of instrumental methods based in UV-Vis spectroscopy as high-throughput tools for process monitoring.

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Xichang Wang Laboratory of Quality & Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), Ministry of Agriculture, Shanghai Engineering Research Center of Aquatic-Product Processing & Preservation, College of Food Science & Technology, Shanghai Ocean University, Shanghai, China

7.1

Introduction

Nowadays, food safety is gaining increasing public attention because it is directly related to people’s health and social progress. With the development of economies and progress of technology, there is an increasing demand for high-quality food, which requires timely and objective quality evaluation. Therefore the supervision of rapid detection methods for hazardous substances in food is urgently needed. Near-infrared spectroscopy (NIRS) is a nondestructive and rapid method that has been applied increasingly to food quality evaluation over the past decade (Fig. 7.1) [1]. The common NIR spectrometer has seven important parts: light source, beam splitter, reflector, sample, diffuse reflection detector, transmission detector, and computer. Until now, NIRS has been widely used in the field of food quality evaluation because of its characteristics of fast analysis, good reproducibility, low cost, no sample consumption, online analysis realization, etc. [2, 3]. Moreover, based on chemometrics methods, NIRS could be employed not only for rapid detection of food contaminants, but also for food quality evaluation to guarantee food safety and to provide technical support in food development.

7.2

Basic principle

Light is electromagnetic radiation, which could be considered to be two mutually perpendicular electric and magnetic fields being propagated as a sine wave. According to the American Society for Testing and Materials, the near-infrared region of an electromagnetic spectrum is defined as an electromagnetic wave between visible light and middle infrared light, which spans the wavelength range 780–2526 nm [3, 4]. The NIR spectrum is a molecular vibration spectrum, in which absorption bands mainly correspond to overtones and combinations of fundamental vibrations. The intensity of NIR bands depends on the change in dipole moment and the anharmonicity of the bond. The hydrogen atom is the lightest, and therefore it exhibits the largest vibrations and the greatest deviations from harmonic behavior. The main bands typically observed in the NIR region correspond to bonds containing this and other light atoms Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00007-X © 2019 Elsevier Inc. All rights reserved.

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Dairy products

3 2

Beverages and liquor

1

Meat products

5

Fish and shellfish Grains and oil product

4

Fruits and vegetables

6

7

Fig. 7.1 Schematics of an NIR spectrometer and its application in different types of foods. 1—light source. 2—beam splitter, 3—reflector, 4—sample, 5—diffuse reflection detector, 6—transmission detector, 7—computer. Combination band region

Second overtone region Third overtone region

First overtone region

ArOH RNH 2

ArOH CONH2 RNH2

ArOH RNH2

ArCH RNHR’ ArCH

ArCH

CONHR

CH

CH

CH

CH2

CH2

CH2

CH3

700

CH3

900

RCO2H

ROH

ROH

ROH

H2O

H2O

H2O

H2O

1100

POH

ArCH

CH

CONH2

RNH2

CONH2(H)

CHO

CH2

CH3

1300

SH

ROH

CH

CH2

CH3

RCO2R’

CC

1700

CH2

CH3

CH3

1500

CH

1900

2100

2300

2500

Wavelength (nm)

Fig. 7.2 Main absorption bands in NIR spectrum.

(namely CdH, NdH, OdH, and SdH). By contrast, the bands for bonds such as C]O, CdC, and CdCl are much weaker or even absent [5]. The main absorption bands in NIR spectra are shown in Fig. 7.2. Various hydrogen groups could generate different absorption bands. Furthermore, according to the Lambert-Beer law,

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chemical compositions in food and intensity of the absorption band have positive correlations. Most chemical and biological products exhibit unique absorptions that can be used for qualitative and quantitative analysis.

7.3

Basic operational procedure

The common procedures of NIRS are described as follows. First, chemical compositions in food are analyzed by an external standard detection method. Second, NIRS of samples are acquired to rapidly analyze the chemical profile variances. Finally, a prediction model is constructed to predict and analyze unknown samples based on the chemometrics method. The analytical information contained in the typically broad bands of NIR spectra is hardly selective and is influenced by a number of physical, chemical, and structural variables. NIRS requires chemometrics to extract as much relevant information as possible from the analytical data [6]. With the development of technology, chemometrics methods have been improved and developed to enhance their ability to analyze the chemical profile variances and predict the count of target composition, indicating that NIRS could be better used in the detection of food contaminants and food quality evaluation.

7.3.1 Pretreatment of spectra Spectral pretreatment should be used to minimize influence of the physical properties incorporating irrelevant information into spectra before the development of simple and robust models for data analysis [7]. Some frequent pretreatments for NIR spectra are normalization, smooth, multiplicative scatter correction (MSC), and standard normal variate (SNV). Normalization refers to a process that makes something more normal or regular to reduce spectral variance of similar samples. Smooth could trim abnormal value in spectra. Based on the spectral matrix manipulation of a set of samples, MSC could separate scattering signals and the chemical absorption information [8]. When an NIR instrument acquires diffuse reflection spectra, spectral information could be influenced by surface scattering and solid powder particle size [9]. In this case, SNV could be applied to reduce spectral error.

7.3.2 Chemometrics methods Chemometrics is the science of extracting information from chemical systems via mathematical and statistical methods [10]. NIRS requires chemometrics to extract as much relevant information as possible to construct classification and calibration models. With the invention of the computer and its subsequent development, chemometrics has developed into a research field in its own right, which promotes the development and application of NIRS significantly. According to their purpose and the algorithms or computational procedures, chemometrics methods could be classified into quantitative analysis and qualitative analysis (Fig. 7.3). The best known and most widely used method for the reduction of variables is principal component

108

Whole spectrum Discrete wavelengths

Original spectra

Reduced variables

Euclidean Correlation

Distance Mahalanobis

Data

• Cluster analysis

Similarity

• ANN (Kohonen)

• ANN • Nonlinear PLS

Unsupervised methods

Nonlinear methods Quantitative analysis

Multivariate methods

Qualitative analysis Supervised methods

Linear methods Original variables

Modeling

Reduced variables • PCR

Correlation

Distance

• PLS • Mahalanobis

Supervised ANNs

Discriminant Residual variance

• KNN • LDA

• SIMCA

• Euclidean

Fig. 7.3 Classification of the major chemometrics methods used in NIRS. ANN, Artificial neural networks; PCA, principal component analysis; PLS, partial least squares; PCR, principal component regression; MLR, multiple linear regression; SIMCA, soft independent modeling of class analogy; KNN, K-nearest neighbor; LDA, linear discriminant analysis. Reprinted with permission from M. Blanco, I. Villarroya, NIR spectroscopy: a rapid-response analytical tool, Trends Analyt. Chem. 21 (4) (2002) 240–250 (Elsevier Publisher, 2007).

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• MLR

• PCA

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analysis (PCA), which allows dimensions of the original data to be reduced to a few uncorrelated variables [11, 12]. Soft independent modeling of class analogy (SIMCA) is the best known and most widely used method in qualitative multivariate analysis, whose model the space occupied by a class and determine whether a sample belongs to it on the basis of distance measurements or residual variance [13]. Quantitative analyses in NIRS usually rely on the use of constructed spectral libraries. The simplest quantitative analysis method is multiple linear regression (MLR) [14], which usually uses fewer than five spectral wavelengths. MLR assumes concentration is a function of absorbance, which entails knowledge of the concentrations of not only the target analyses, but also all other components contributing to the overall signal. The most frequently used multivariate-regression methods in NIR spectroscopy are principal component regression [15] and partial least-squares (PLS) regression [16]. However, the spectral data and the target property are not linearly related as a result of instrumental factors or the physical-chemical characteristics of the sample. These cases could be addressed using nonlinear calibration methods, particularly prominent among which are artificial neural networks [17]. Hence there is no best chemomtrics model for all applications. Appropriate combination of these models is needed for relevant analysis in various applications.

7.4

Advantages and limitations

NIRS has been widely used because of its many advantages. First, the detection time (several minutes or seconds) of NIRS for evaluating chemical compositions is less than conventional detection methods. Second, NIRS is a nondestructive detection method because sample consumption is minimal. Third, the cost of detection using NIRS is less than conventional detection methods because of simple sample pretreatment processes. Fourth, NIRS could be employed for online analysis in the food industry with good reproducibility. Every coin has two sides. There are four limitations to hinder the application of NIRS to the food field. First, it could not be used to analyze trace composition in samples because the infrared absorption of target composition is weaker compared with other compositions. Obviously, the analytical information is influenced by a number of other chemical compositions. Hence NIRS is used to detect samples whose counts are more than 0.1% mass ratio. Second, NIRS is less precise without a sample separation process. Third, heavy analytical work is needed to analyze the chemical profile variances. Fourth, NIRS usually needs to build an IR prediction model with chemometrics methods.

7.5

Recent technology development

The main recent technology development of NIRS is its integration with hyperspectral imaging systems. Hyperspectral imaging techniques integrate both NIRS and imaging techniques into one system to provide simultaneous spectral and spatial information of

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the sample [18]. Typical application functions include contaminant detection, defect identification, constituent analysis, and quality evaluation. It was originally developed for remote sensing applications [19]. Then, it caught the attention of many research and industrial fields such as astronomy [20], agriculture [21], pharmaceutical science [22], medicine [23], and food [24, 25]. Hyperspectral images consist of hundreds of contiguous wavebands for each spatial position of interest (Fig. 7.4). They are three-dimensional blocks of data: two spatial and one wavelength dimension. Each pixel in a hyperspectral image contains the spectrum of that specific position. Like a fingerprint technique, hyperspectral images could be used to characterize the composition of that particular pixel. This technique could be used to analyze the biochemical constituents of a sample because similar chemical compositions have similar spectral properties. In recent years, the near-infrared hyperspectral image technique has been explored in food research activities such as monitoring of oxidative damage of pork myofibrils during frozen storage [26], discrimination of gluten-free oats from contaminants [27], determination and visualization of the caffeine content of coffee beans [28], evaluation of extractable polyphenols released to wine from cooperage by-products [29], etc.

l

y

x

Pixel spectrum at (xi, yj) Reflectance

Image plane at li

(xi, yj)

li Wavelength (l)

Fig. 7.4 Schematic representation of hyperspectral imaging hypercube showing the relationship between spectral and spatial dimensions. Reprinted with permission from A.A. Gowen, C.P. O’Donnell, P.J. Cullen, G. Downey, J.M. Frias, Hyperspectral imaging—an emerging process analytical tool for food quality and safety control, Trends Food Sci. Technol. 18 (12) (2007) 590–598 (Elsevier Publisher, 2007).

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7.6

111

Recent application progress in different types of foods

The applications of NIRS are widespread, particularly in the food industry. It has been widely used for the rapid detection of food contaminants and food quality evaluation.

7.6.1 Dairy products Dairy foods are important sources of many nutrients for humans. As a complete nutrient source, milk is widely marketed and consumed around the world. Because food compositions in the dairy industry have similar homogeneity, NIRS has been reported to be a rapid, consistent, and economic tool to determine fat content in milk and milk powder in the dairy industry for over 30 years. At present, the conventional methods for determination of fat content and fatty acids composition are generally based on solvent extraction and gas chromatography techniques. These methods are time consuming, laborious, and destructive to the tested samples. Laporte and Paquin [30] explored the determination of the fat and fatty acids contents in cow’s milk by NIRS with the PLSR method, the modeling results with SEC and SEP of 0.08% and 0.05%, 0.12% and 0.07%, respectively, which indicated the high capacity of NIRS in milk fat analysis. Furthermore, a rapid method for the quantification of cholesterol in dairy powders was developed using NIRS coupled with PLS regression [31]. The results showed that the second derivative PLS model in the spectral region of 6101–5446 cm
1 was the most robust with the best performance indicators (r2 ¼ 0.9998, RMSECV ¼ 1.05 mg cholesterol/100 g), indicating that the developed NIRS method has good reproducibility and satisfactory accuracy profile. Moreover, NIRS could be also employed to evaluate food quality. During the productive process of cheeses, proteolysis, glycolysis, and lipolysis led to significant changes in cheese composition and induced the development of the sensory properties of cheese [32]. The information could be used to analyze the maturity of cheese and pursue origin traceability. The combination of NIRS with chemometrics methods was applied to discriminate between Emmental cheeses from different European geographic origins, with 89% and 86.8% of the calibration and validation spectral datasets, respectively [33]. A total of 91 Emmental cheeses produced during winter in Austria (n ¼ 4), Finland (n ¼ 6), Germany (n ¼ 13), France (n ¼ 30), and Switzerland (n ¼ 38) were investigated. The results indicated that NIRS might provide useful fingerprints and allow the identification of Emmental cheeses according to geographic origin and production conditions.

7.6.2 Beverages and liquor Due to good homogeneity of beverages and liquor, NIRS could be widely used to detect and evaluate these products. The value of amino acid nitrogen is an important indicator to evaluate food quality of Chinese rice wine during its fermentation process [34]. Common chemical analytical methods have complicated operations, high

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expense, and time-consuming pretreatment processes, and therefore they are not conducive to fast monitoring of the value of amino acid nitrogen in Chinese rice wine. The combination of NIRS with PLS for rapid detection of amino acid nitrogen in Chinese rice wine was constructed (R ¼ 0.9603, RMSECV ¼ 0.0414, and RPD ¼ 3.58), whose model could provide an effective way for the establishment of a Chinese rice wine safety quality control system [35]. NIRS could also be used to nondestructively detect the quality of Chinese rice wine and establish a predictable model of wine age [36, 37]. To rapidly discriminate adulterated soymilk, NIRS combined with PLS was used to obtain spectra for unadulterated and adulterated soymilk samples; the correlation coefficients of predicted and measured values in the NIRS model were 0.9756 and 0.9489, indicating that this detection method could support the healthy and durable development of the soymilk industry [38]. Tea is the most popular beverage worldwide and is of great interest due to its beneficial medicinal properties. With the increasing consumption of tea, quality control of tea has become increasingly important nowadays. NIRS was applied to measure caffeine content in green tea within an near-infrared region of 4000–12,000 cm
1, and NIRS using the PLS-first derivative plus straight line subtraction method could determine the caffeine content in tea samples accurately up to an R2 value greater than 0.98 and a standard error of prediction value less than 2.0 [39]. These methods have exhibited great potential for NIRS application in beverages and liquor products.

7.6.3 Meat products Meat, a nutrient food, is the main source of animal protein. Meat consumption increases continuously with the rapid improvement of modern living standards. Therefore this increasingly high-quality requirement shows promise for the meat market [40]. Nowadays, meat quality becomes a great concern when it is consumed without timely and complete identification. Intramuscular fat content and composition influence consumer selection of meat products. A study using NIRS, as a rapid and easy tool, was conducted to provide useful information on the contents of conjugated linoleic acids in beef [41]. NIRS coupled with suitable chemometrics strategies was applied for the identification and quantification of turkey meat adulteration in fresh, frozen-thawed, and cooked minced beef [42]. PLS regression models with R2 predicted to be higher than 0.884 and RMSEP lower than 10.8% were developed, and PLS-DA was applied to discriminate each type of sample in two classes showing the values of sensitivity and specificity in prediction to be higher than 0.84 and 0.76, respectively. They demonstrated that NIRS is a reliable tool for the identification and quantification of minced beef adulteration with turkey meat.

7.6.4 Fish and shellfish Fish, shrimp, and shellfish have high moisture content, weak muscle tissue, low fat content, fewer natural immune substances, and high levels of unsaturated fatty acids and soluble proteins; all of these features lead to both easier spoilage than other animal meat and shorter storage duration [43]. Fish and shellfish meat and their products have

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always been popular high-protein and nutrient foods. TVB-N was used as an index to evaluate freshness in fish and shellfish products. NIRS combined with PLS was applied to evaluate the freshness of ice-stored large yellow croaker during different storage periods, whose calibration model gave the correlation coefficient of 0.999, with a standard error of prediction of 0.990 [44]. Surimi is a semifinished product with good homogeneity, which is acquired from fish meat. Wang et al. [45] developed a rapid nondestructive determination of moisture and protein contents in Alaska pollock surimi using NIRS and PLS regression. The correlation coefficients of the calibration models for predicting moisture and protein contents in Alaska pollock surimi were above 0.96 and 0.98, respectively. The RSD values were both less than 10% [45]. NIRS could also be employed to evaluate food quality in fish and shellfish products. Our group established a rapid and nondestructive grade estimation model on white croaker surimi with different grades using NIRS [46]. Different spectra at seven kinds of physicochemical indexes (moisture, protein, crude fat, salt-soluble protein, gel strength, water-holding ability, and whiteness) were acquired. Furthermore, a grade estimation model was structured by PCA, which has better performance on frozen white croaker surimi with a comprehensive accuracy of 96.3% [46].

7.6.5 Grains and oil products During grain seed development, different contents of moisture, carbohydrate, protein, and natural hydration processes have changed the food compositions of grains. It is crucial to rapidly and nondestructively detect various nutrient contents of grain foods to guarantee food safety and evaluate food quality. Protein content in wheat is also an important parameter to distinguish different ranks of wheat flour. NIRS has been applied to construct a model based on PLS regression to nondestructively predict the protein content in wheat with a coefficient of determination of 0.9986 and an SEP of 0.0528 [47]. Furthermore, NIRS has also been used to evaluate food quality in grain foods. The thickness of grain pericarp is an important breeding criterion for sorghum. Diarah et al. [48] developed an NIRS method to accurately predict pericarp thickness of sorghum whole grain for grain quality parameters. Oil is often used in the preparation process to enhance the taste of food due to its unique taste and texture combination. There are many types of oil adulteration. Typical examples are shown in Table 7.1. NIRS could be used to classify and quantitatively detect oil adulteration for food safety assurance. NIRS could also be applied to detect the presence of lard adulteration in palm oil [49]. Pure and adulterated palm oil samples were classified using the SIMCA algorithm with model accuracy more than 0.95. Additionally, by employing PLS regression, the coefficient of determination was 0.9987 with root mean square error of calibration 0.5931. The results showed that NIRS could be applied to distinguish pure and adulterated palm oil. Furthermore, to rapidly detect adulteration of butter fats with cheaper vegetable fats, NIRS and multivariate models were explored, in which PCA and PLS discriminant analysis were used to build classification and quantification models, respectively [50]. The performance of NIRS in the assessment of C4:0 fatty acid levels is as low as in GC, but the disadvantage of GC is outweighed by shorter measurement times and lower skill

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Table 7.1 Study of the application of near-infrared spectroscopy in edible oil adulteration detection

Products

Adulteration

Palm oil Butter

Lard Vegetable oil Soybean oil

Camellia oil

Chemometrics

Spectral region (nm)

Classification

Calibration

References

950–1650 869–2222

SIMCA PCA

PLS PLS

[49] [50]

400–2500

SIMCA

PLS

[51]

levels required. Moreover, camellia oil is often the target for adulteration or mislabeling in China because it is a high-priced product with high nutritional and medicinal values. As a rapid and cost-efficient classification and quantification technique, NIRS has been preliminarily investigated for the authentication of camellia oils, whose R and RMSEC values for the PLS model are 0.992 and 0.70, respectively [51].

7.6.6 Fruits and vegetables Recent advances have shown good potentials of NIRS in real-time monitoring and modeling of different food processes of vegetables to improve process efficiency and final product quality by enhancing understanding and control of the manufacturing processes. Viegas et al. [52] developed an analytical method to accurately predict total anthocyanins content and total phenolic compounds in intact wax jambu fruit using NIRS and PLS algorithms. The spectra of NIRS contain much information in various compositions, which could be used to evaluate food quality. NIRS through the application of PLS was investigated to assess the fruit structure effect (passion fruit, tomato, and apricot) on prediction performance of soluble solids content and titratable acidity to evaluate internal food quality [53].

7.7

Summary and outlook

Due to the rapid increase in demand for safe and high-quality food, a rapid, objective, and nondestructive analysis technique is urgently needed. In recent years, NIRS with good reproducibility, low cost, and without the preseparation step has been widely applied for the fast detection and quality evaluation of food products. However, some food products have poor homogeneity, which hiner NIRS application in the food field. With the development of statistical analysis and technology, chemometrics methods and near-infrared instruments could be developed to enhance spectral resolution and prediction ability to reduce detection error in these food products. The prediction model of NIRS and chemometrics methods need to analyze heavy spectral data.

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However, with the development of chemometrics methods, more advanced prediction models have enhanced operational and analytical ability with similar spectral data. The need for advanced instruments has led to the development of NIRS imaging techniques, which have the potential to macroscopically detect food compositions for food evaluations in real time. NIRS could be widely used to detect food compositions and evaluate food quality to guarantee food safety and provide technical support in food development.

Acknowledgments This work has been supported from the Shanghai Municipal Education Commission—Project of Gaoyuan Gaofeng Discipline Food Science and Engineering Grant Support—Gainers from Shanghai Ocean University, National Natural Science Foundation of China (31471608), Shanghai Eriocheir sinensis Modern Agriculture Industrial Technology System Construction Project.

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[29] B. Baca-Bocanegra, J. Nogales-Bueno, J.M. Herna´ndez-Hierro, F.J. Heredia, Evaluation of extractable polyphenols released to wine from cooperage byproduct by near infrared hyperspectral imaging, Food Chem. 244 (2018) 206–212. [30] M.F. Laporte, P. Paquin, Near-infrared analysis of fat, protein, and casein in cow’s milk, J. Agric. Food Chem. 47 (7) (1999) 2600–2605. [31] J. Chitra, M. Ghosh, H.N. Mishra, Rapid quantification of cholesterol in dairy powders using Fourier transform near infrared spectroscopy and chemometrics, Food Control 78 (2017) 342–349. [32] S.T. Martı´n-del-Campo, D. Picque, R. Cosı´o-Ramı´rez, G. Corrieu, Middle infrared spectroscopy characterization of ripening stages of Camembert-type cheeses, Int. Dairy J. 17 (7) (2007) 835–845.  Dufour, L. Pillonel, E. Schaller, D. Picque, T. Cattenoz, J.-O. Bosset, The [33] R. Karoui, E. potential of combined infrared and fluorescence spectroscopies as a method of determination of the geographic origin of emmental cheeses, Int. Dairy J. 15 (3) (2005) 287–298. [34] X.R. Zhao, H.J. Zou, G.C. Du, J. Chen, J.W. Zhou, Effects of nitrogen catabolite repression-related amino acids on the flavour of rice wine, J. Inst. Brew. 121 (4) (2015) 581–588. [35] F. Shuangxi, Z. Qingding, L. Guohui, J. Wang, X. Zhenghe, Z. Huijun, Optimization of the model for rapid determination of amino acid nitrogen in yellow rice wine by near infrared spectroscopy, Chin. Liquor-Making Sci. Technol. 251 (05) (2015) 11–14. [36] H.Y. Yu, Y.B. Ying, X.P. Fu, H.S. Lu, Quality determination of chinese rice wine based on Fourier transform near infrared spectroscopy, J. Near Infrared Spectrosc. 14 (1) (2006) 37–44. [37] H.Y. Yu, Y.B. Ying, X.P. Fu, H.S. Lu, Classification of Chinese rice wine with different marked ages based on near infrared spectroscopy, J. Food Qual. 29 (4) (2006) 339–352. [38] H.-d. Li, Y.-y. Pan, H. Zhang, Adulteration detection of soymilk based on near-infrared spectroscopy, Trans. Chin. Soc. Agric. Eng. (03) (2014) 238–242. [39] V.R. Sinija, H.N. Mishra, FT-NIR spectroscopy for caffeine estimation in instant green tea powder and granules, LWT Food Sci. Technol. 42 (5) (2009) 998–1002. [40] C.-Y. Yang, Q.-H. Chu, J.-X. Sun, P. Li, The development trend of meat production, Meat Res. (09) (2008) 3–6. [41] V. Sierra, N. Aldai, P. Castro, K. Osoro, A. Coto-Montes, M. Oliva´n, Prediction of the fatty acid composition of beef by near infrared transmittance spectroscopy, Meat Sci. 78 (3) (2008) 248–255. [42] C. Alamprese, J.M. Amigo, E. Casiraghi, S.B. Engelsen, Identification and quantification of turkey meat adulteration in fresh, frozen-thawed and cooked minced beef by FT-NIR spectroscopy and chemometrics, Meat Sci. 121 (2016) 175–181. [43] L.-Z. Wang, Y.-H. Chen, L.-Y. Tan, K.-L. Leng, X.-C. Li, Quality status and suggestions for improvement of frozen fish and shellfish products, China Fish (11) (2000) 70–71. [44] Y. Liu, W.-h. Chen, X.-c. Wang, Q.-j. Wang, H. Wang, Study on freshness evaluation of ice-stored large yellow croaker (Pseudosciaena crocea) using near infrared spectroscopy, Spectrosc. Spectr. Anal. (04) (2014) 937–941. [45] X.-c. Wang, Y. Lu, Y. Liu, Rapid nondestructive determination of moisture and protein contents in Alaska Pollock Surimi by near infrared reflectance spectroscopy (NIRS), Chinese Food Sci. 16 (2010) 168–171. [46] H. Wu, X.-c. Wang, Y. Liu, W.-h. Chen, Applied research in grade estimation of Surimi by near infrared spectroscopy, Spectrosc. Spectr. Anal. (05) (2015) 1239–1242.

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[47] D. Ye, L. Sun, B. Zou, Q. Zhang, W. Tan, W. Che, Non-destructive prediction of protein content in wheat using NIRS, Spectrochim. Acta A Mol. Biomol. Spectrosc. 189 (2018) 463–472. [48] D. Guindo, F. Davrieux, N. Teme, M. Vaksmann, M. Doumbia, G. Fliedel, D. Bastianelli, J.-L. Verdeil, C. Mestres, M. Kouressy, B. Courtois, J.-F. Rami, Pericarp thickness of sorghum whole grain is accurately predicted by NIRS and can affect the prediction of other grain quality parameters, J. Cereal Sci. 69 (2016) 218–227. [49] K.N. Basri, M.N. Hussain, J. Bakar, Z. Sharif, M.F.A. Khir, A.S. Zoolfakar, Classification and quantification of palm oil adulteration via portable NIR spectroscopy, Spectrochim. Acta A Mol. Biomol. Spectrosc. 173 (2017) 335–342. [50] P.C.M. Heussen, H.-G. Janssen, I.B.M. Samwel, J.P.M. van Duynhoven, The use of multivariate modelling of near infra-red spectra to predict the butter fat content of spreads, Anal. Chim. Acta 595 (1) (2007) 176–181. [51] L. Wang, F.S.C. Lee, X. Wang, Y. He, Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR, Food Chem. 95 (3) (2006) 529–536. [52] T.R. Viegas, A.L.M.L. Mata, M.M.L. Duarte, K.M.G. Lima, Determination of quality attributes in wax jambu fruit using NIRS and PLS, Food Chem. 190 (2016) 1–4. [53] G.A. de Oliveira, S. Bureau, C.M.-G.C. Renard, A.B. Pereira-Netto, F. de Castilhos, Comparison of Nirs approach for prediction of internal quality traits in three fruit species, Food Chem. 143 (2014) 223–230.

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8

Tianxi Yang, Bin Zhao, Lili He Department of Food Science, University of Massachusetts, Amherst, MA, United States

8.1

Introduction

Food quality is the quality characteristics of food that are significant to governments, the food industry, and consumers [1–6]. Food quality consists of attributes, including external factors such as appearance (e.g., size, shape, color, gloss, and consistency), internal factors (e.g., chemical, physical, microbial), as well as texture and flavor. In recent years, food quality-related issues have attracted a great deal of attention. A wide range of food quality studies has been reported, for example, investigation of food compositions, including nutritional value, healthy additives, as well as the determination of unwanted substances such as bacteria, pesticides, and adulterants [7–9]. The implementation of advanced methods, which allow for efficient and reliable assessment, is crucial to ensure food quality. Evaluation of food quality is traditionally carried out by using chromatographic techniques such as gas chromatography (GC) [10] or liquid chromatography-based methods [11] and other techniques (e.g., sensory analysis, culture-based method, polymerase chain reaction, and enzyme-linked immunosorbent assay). However, these methods are costly and need laborious manipulation, time-consuming processes, complex procedures of sample treatment, and well-trained personnel to perform them. Raman techniques overcome the disadvantages of traditional methods and have become a powerful analytical tool for food quality evaluation. The focus of the chapter is to discuss the applicability of Raman spectroscopy as a nondestructive and molecule-specific tool for monitoring food quality in an efficient and reliable way. This objective is achieved through a discussion of the basic principles and procedures of Raman scattering and recent application progress of Raman techniques, as well as variations of Raman instrument development for food quality evaluation (Fig. 8.1).

8.2

Basic principles and procedures

Raman spectroscopy is becoming one of the more promising analytical tools for evaluation of food quality [12–17]. Raman spectroscopy is a scattering technique caused by a substance radiated with a monochromatic light, usually from a laser in the visible, near-infrared (IR), or near-ultraviolet (UV) range [18, 19]. It relies on discrete vibrational transitions that take place in the ground electronic state of molecules. The laser light interacts with molecular vibrations, phonons, or other excitations in the system, Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00008-1 © 2019 Elsevier Inc. All rights reserved.

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Fig. 8.1 Raman instruments for food quality evaluation.

resulting in the energy of the laser photons being shifted. Most of the scattered light has the same frequency or energy as that of incident light. Only a slight fraction of the incident light donates or receives energy to contribute to a change in the vibrational and rotational state of molecules and this constitutes Raman scattering. If the frequency of incident radiation is higher than the frequency of scattered radiation, it is called Stokes Raman scattering. However, when the frequency of incident radiation is lower than the frequency of scattered radiation, it is called anti-Stokes Raman scattering. The difference in frequency of the laser and that of the scattered photon is called the Raman shift (ν in cm 1) and is a function of wave numbers. Conventional Raman spectroscopy generally means Stokes Raman spectroscopy because Stokesshifted Raman bands involve the transitions from lower to higher energy vibrational levels and therefore Stokes bands are more intense than anti-Stokes bands. For a Raman active molecule, it must have distorted electron densities or polarizabilities due to energy exchange [20–22]. A change in polarizability during molecular vibration is an essential requirement to obtain Raman spectra of the sample. Raman scattering intensity is proportional to the change of polarizability. Therefore Raman spectroscopy is considered to be selective in detecting apolar molecules, double- or triple-bonded structures, and ring structures. Raman instruments generally consist of several parts [15, 16]: (1) single or multiple lasers, from UV (244 nm) to IR (1064 nm); (2) lenses that are used to focus light onto the sample and to collect the scattered light; (3) filters to purify the reflected and scattered light so that only the Raman light is collected; (4) a means of splitting the light into its constituent colors; (5) a detector; and (6) a device such as a computer to control the whole system. On the basis of the excitation source used in Raman instrumentation, Raman spectroscopy can be classified into three categories: UV resonance Raman spectroscopy, visible excitation Raman spectroscopy, and Fourier transform Raman (FT-Raman) spectroscopy [23]. When a UV laser is equipped with the instrument, the Raman spectrometer is known as UV resonance Raman spectroscopy. An excited electronic state can be formed by the UV light. When the UV excitation wavelength is overlapped with or is very close to an electronic transition, the overlap can result in increased scattering intensities by a factor of 102–106 times compared to a dispersive Raman system [24]. The use of UV excitation is a desirable way to avoid fluorescence effects. However, UV resonance excitation may cause photodecomposition of the samples. Raman spectrometers equipped with visible excitation are called visible excitation Raman

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spectroscopy, which is usually integrated with a charge-coupled device (CCD) detector. A confocal microscope can be coupled to the spectrometer so that the microstructure of food products can be studied. However, this may cause a strong fluorescence background from samples, especially from biological samples. To overcome the drawbacks of CCD and other detectors, a longer wavelength can be used to eliminate both fluorescence background and photodecomposition of samples due to low laser powers. FT-Raman was employed to collect Raman scattering signals using 1064 nm laser excitation in the late 1980s [25, 26]. An FT-Raman spectrophotometer uses a Michelson interferometer as the common wavelength stabilizing system. Indium– Gallium–Arsenic (InGaAs) and germanium (Ge) detectors are operated at cryogenic temperatures to reduce noise and therefore increase the signal-to-noise ratio. Aqueous samples cannot be analyzed by an FT-Raman spectrophotometer because water absorbs in the 1000 nm region. On the basis of the area of use, Raman instruments can be classified into lab-based instruments and portable or hand-held devices or stand-off systems, which are available for in-field analysis [27–31]. The versatility and size of an instrument as well as relative cost of its component units are different, but the basic principles are the same. Benchtop Raman spectrometers are usually used for research, while portable or handheld devices or stand-off systems can be applied for on-site analysis. In a typical Raman evaluation of food quality, Raman scattering signals are associated with the corresponding vibrational modes of molecules [32–34]. Fingerprint information can be used to identify a target molecule in a complex matrix or give valuable information about molecular interaction in a food system [7, 35–38]. In addition, the intensity or frequency shifts of specific vibrational modes reflect the change of chemical composition or molecular structure in the food. When Raman spectroscopy was applied to assess the quality of a complex food matrix, some important information may be hidden or overlapped in the Raman spectra. The combination of chemometric and multivariate statistical methods along with Raman spectroscopy is an alternative and powerful way to study and extract the useful information that is present but hidden in the food product [14, 34, 39]. Among chemometrics, one of the common multivariate tools is discriminant analysis, which is used to determine the identity and quality of an unknown food sample. Other discriminant analysis protocols that can be used to discriminate between groups of observation are commonly principal component analysis (PCA) [40–42] and partial least squares (PLS) [43–45]. Scores are applied in PCA methods to maximize the data variance both in groups and between groups. The overall principle behind the PCA method is to make a favorable coordinate transformation with one- or two-dimensional representations, which represents the significant intensity variations in the Raman spectra. PLS is a technique that combines the features of PCA and multiple linear regressions. It is significant to show a set of dependent variables from a large set of independent variables. For Raman analysis of samples, various sample-handling techniques are employed in Raman instruments [23]. Both solid and liquid samples are usually put on appropriate sample holders, also called substrates. Different sample-holder sizes and geometries are available commercially or can be constructed according to need. The requirement for the substrate is that it will not impact the results. For example, there

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is no chemical reaction between the substrate and the liquid sample or by introducing any Raman features that may interfere with the Raman signature of the samples. In terms of Raman analysis of solid food samples, the most common substrate is the glass slide. However, the Raman spectra of the glass slide may confuse the Raman features of the samples. Therefore scientists used flat aluminum foil to cover the glass slides [46–48]. Besides glass slides, Si slides with only one sharp peak at around 520 cm 1 and gold slides are often used as substrates. For liquid food sample detection, a capillary and a nuclear magnetic resonance glass tube are commonly used since only small quantities of the sample are required and the glass does not absorb Raman scattered light in the visible region, therefore it does not interfere with the Raman spectra.

8.3

Recent application progress in different types of foods

The attractive characteristics of Raman spectroscopic techniques have made it a versatile tool for food quality analysis [14, 32, 34]. Food quality includes attributes, e.g., external factors such as appearance (size, shape, color, gloss, and consistency), texture, and flavor as well as internal factors (chemical, physical, and microbial). In this part, we summarize the recent application progress of quality analysis with Raman techniques in different types of foods, including animal based (e.g., meat and dairy products) and plant based (e.g., vegetables, fruits, beverages, crops, and oil) [6, 49–54].

8.3.1 Meat A large number of investigations have been carried out in sensory analysis such as the tenderness, juiciness, and chewiness of meat. One interesting study measured and predicted quality traits (pH, color, and drip loss) of intact pork muscles at the slaughtering process using a portable Raman instrument and a partial least squares regression (PLSR) model [55]. Raman spectroscopy can provide a fingerprint of the early postmortem metabolism in meat. Researchers successfully correlated Raman signals of glycogen, lactate, creatine, phosphocreatine, ATP, ADP, and the phosphate group with the prediction of the early postmortem pH and drip loss. Another study used the PLSR model to predicate the value of sensory tenderness, chewiness, and juiciness based on Raman spectroscopic characteristics of pork loins (Fig. 8.2) [56]. In this report, sensory attributes of pork loins were moderately correlated to their Raman spectroscopic characteristics. In addition, a new Raman spectroscopic binary barcoding model was created based on spectroscopic characteristics of the pork loins, which differentiate and classify pork loins into quality grades (superior and inferior in terms of tenderness and chewiness). Similarly, correlation of Raman signals with sensory attributes (e.g., juiciness, texture, and overall acceptability) with PLSR has been demonstrated in cooked beef silversides [57]. In this study, shear force was poorly correlated with tenderness. The results also showed that the α-helix to β-sheet ratio

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Fig. 8.2 Partial least squares regression models and testing plots (inlets) for the prediction of sensory attributes of pork loins using Raman spectroscopy: (A) tenderness, (B) chewiness, and (C) juiciness. Adapted from Q. Wang, S.M. Lonergan, C. Yu, Rapid determination of pork sensory quality using Raman spectroscopy, Meat Sci. 91 (3) (2012) 232–239.

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of the proteins and the hydrophobicity of the myofibrillar environment are important factors contributing to shear force, tenderness, texture, and overall acceptability of the beef. Raman spectroscopy can also be used to analyze components such as protein, lipid, and water in the muscle food to evaluate food quality. For example, the fatty acid composition of adipose tissue from beef, lamb, pork, and chicken has been studied by Raman spectroscopy [58]. PLS analysis was carried out between the Raman spectra from unextracted adipose tissue from meat in the region of 800–1800 cm 1. The results showed that unsaturation level was well predicted, which contained the sum of both cis and trans. However, it was difficult to find a good prediction model for trans unsaturation because trans unsaturated bonds have a very low range of relative abundance compared with bonds per fatty acid for cis unsaturated bonds. It also indicated that Raman spectroscopy can accurately predict individual fatty acid in neat adipose tissue. Another study assessed the quality and viability of fish egg from the Japanese medaka (Oryzias latipes) by Raman spectroscopy based on biomolecular information (e.g., fatty acid, amino acid, and carotenoid) [59]. PCA and linear discrimination analysis were used to obtain the classification results from the yolk. The results showed that the supply of oil energy starts just after fertilization and embryogenesis can then be initiated. Fatty acid Raman bands can be used to evaluate the presence or absence of fertilization with 95.7% accuracy. The dominant factors determining the viability of fish eggs were amino acid production and carotenoid pigment deposition. On the basis of Raman bands, these substances show whether the development is normal or abnormal with 80.3% accuracy. Muscle food authenticity is an important quality issue and Raman spectroscopy can be applied as a powerful tool. Raman spectroscopy was reported to be able to differentiate between the origin of meat and meat products based on their extracted fat samples with the aid of a chemometrics method (e.g., PCA). A Raman instrument was explored to test fat samples that were extracted from different meat species (cattle, sheep, pig, fish, poultry, goat, and buffalo) and their salami products [60]. According to the origin, seven meat species and their salami products were successfully differentiated from each other based on the collected Raman data with the PCA method. This research showed good potentials of using Raman spectroscopy for analysis of meat species authenticity. In addition, Ellis et al. aimed to study the problem of authenticity in muscle food products [61]. They applied Raman spectroscopy to discriminate between important poultry species (turkey and chicken), as well as distinguish between muscle groups (breast and leg) within these species. Cluster analysis, which classifies a sample into a small number of exclusive groups based on the similarities among them, was applied to the qualitative interpretation of Raman spectra (0–3000 cm 1) at species and muscle group levels. The results showed that there is a large discrimination between legs and the most expensive breast muscle. A small discrimination between chicken and turkey can be seen. The results indicated that the differentiation between leg and breast muscle is more evident than discrimination at the species level (chicken and turkey). Meat spoilage is annoying during meat production. The detection of meat spoilage is necessary to control meat quality as well as to protect consumers’ health [62–64].

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Fig. 8.3 Scores of principal components analysis of the Raman data for (A) porcine musculus longissimus dorsi and (B) musculus semimembranosus plotted for PC 1 and PC 3. Meat samples stored at 5°C in polyethylene bags; ellipses mark distinct stages of bacterial growth kinetics. The different symbols correspond to the three muscles. Adapted with permission from T. Yang, X. Guo, H. Wang, S. Fu, J. Yu, Y. Wen, H. Yang, Au dotted magnetic network nanostructure and its application for on-site monitoring femtomolar level pesticide, Small 10(7) (2014) 1325–1331. Copyright (2012) Elsevier.

Sowoidnich et al. reported a portable Raman sensor system for the rapid in situ detection of meat spoilage [65] Meat spoilage porcine musculus longissimus dorsi and musculus semimembranosus were used as test samples. The meat cuts refrigerated at 5°C were investigated in time-dependent measurement series up to 3 weeks after slaughter. PCA was applied to discriminate the Raman spectral data during storage. The fresh, incipient spoilage, and microbial spoilage of the meat can be divided as shown in Fig. 8.3. The results indicate a storage time-dependent separation of the meat samples around day 7 postmortem. This time point shows bacterial surface load exceeding 106 cfu/cm2 indicating increased spoilage.

8.3.2 Dairy products The nutritional value of dairy products (e.g., fat, protein, and carbohydrate contents) is of great importance for human health [66–68] and therefore Raman spectroscopy can be applied as a means to determine the nutritional parameters and provide valuable information. The FT-Raman method was used for the simultaneous determination of the important nutritional parameters (e.g., energetic value, carbohydrate, protein, and fat contents) of infant formula and powdered milk samples [69]. Hierarchical cluster analysis was performed after collecting the Raman spectra, and PLSR analysis was used to evaluate their prediction capabilities for the validation set. The results indicated that good estimations were obtained for all nutritional parameters. Among all

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of the nutritional components of milk, fat content plays a significant role for consumers and the dairy industry, e.g., in butter production. Therefore the analysis of fat content in milk with Raman spectroscopy is a hot topic. Meurens et al. first reported the FT-Raman method to determine conjugated linoleic acid (CLA) in cow’s milk fat [70]. The results showed that the examination of the Raman spectra identified three specific Raman signals of the chemical bonds associated with the cis, trans conjugated C]C in the rumenic and trans-10, cis-12-octodecadienoic acids at 1652, 1438, and 3006 cm 1. In addition, after calibration of the Raman spectrometer for CLA determination, three specific signals were well suited for the accurate and reliable measurement of CLA concentration in milk fat. McGoverin et al. predicted the solid fat content of anhydrous milk fat using Raman spectroscopy coupled with PLS analysis [71]. Similarly, El-Abassy et al. reported the rapid and direct milk fat determination in liquid milk by Raman spectroscopy in combination with PLS [72]. In this study, three different methods of sample preparations were applied: liquid milk contained in an open dish, dried milk droplets on glass plates covered with Al foil, and liquid milk contained in quartz cuvettes. The authors found the first two methods showed a good PLS model for milk fat prediction with low root mean square errors and high correlation coefficients. However, Raman spectroscopy of milk samples in glass or quartz cuvettes was not very useful due to the formation of an ill-defined milk layer on the inner surface of the cuvette. This study showed that Raman spectroscopy is suited for in-line monitoring purposes. Dairy product adulteration is a worldwide problem and adulterated foods have adverse effects on human health [73–75]. Raman spectroscopy has been widely applied to monitor dairy adulteration and regulate product quality. Animal feed and formula milk are frequently adulterated with melamine to increase the protein content and causes illnesses and even deaths of pets and infants. Cheng et al. reported using a portable compact Raman spectrometric system to screen melamine adulterant in milk powder [76]. Two characteristic vibration modes at 673 and 982 cm 1 were identified with good reproducibility in melamine-fortified milk powder. A good PLS analysis model was obtained with a detection limit of 0.13%. Besides melamine, researchers also discriminated dairy creams and their analogs with sunflower oil, coconut oil, and palm oil in different milk fat/vegetable fat ratios using Raman spectroscopy and chemometrics analysis [4]. Samples were well separated and displayed distinguishing linear arrangement along the principal component that expressed the variation in lipid unsaturation. However, it was difficult to classify those cream analog samples with similar levels of unsaturated structures. The calibrated model was extremely sensitive (100%) for dairy cream, which indicated that it is possible to use Raman spectroscopy coupled with chemometrics analysis for the detection of dairy cream adulteration with sunflower, coconut, and palm oils. FT-Raman spectroscopy coupled with chemometrics tools has also been explored as a quick screening method to evaluate the presence of lactose and identify milk powder samples adulterated with maltodextrin (2.5%–50%, w/w) [77]. Almeida et al. reported the use of FT-Raman spectroscopy for the evaluation of milk powder quality and the identification of samples adulterated with whey [78]. The FT-Raman spectra of the whole, low-fat, and skimmed milk powder

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samples can be distinguished from commercial milk powder samples. PCA and PLS-discriminant analysis were developed to classify the adulterated milk powder samples.

8.3.3 Oil Raman spectroscopy has been employed in oil authentication [79–83]. The identification of different oils and the detection of oil adulteration are of great importance from both industry and health perspectives. Research has been conducted to classify oils in different categories and analyze adulterated oils. By combining Raman spectroscopy and chemometric analysis, the detection of adulteration of extra virgin olive oil samples with different levels of hazelnut oil was achieved [84]. The composition of hazelnut and extra virgin olive oil mixtures could be accurately predicted using PLS and genetic programming. Zou et al. developed a Raman method based on the intensity ratio of the Raman spectroscopy vibration bands on the intensity ratio of the cis (]CdH) at 1265 cm 1 and cis (C]C) bonds at 1657 cm 1 normalized by the band at 1441 cm 1 (CH2) to authenticate genuine/fake olive oil (Fig. 8.4) [81]. Genuine olive oils can be distinguished from olive oils containing 5% (volume percentage) or more of other edible oils, such as soybean oil, rapeseed oil, sunflower seed oil, or corn oil. This method was suitable for on-site testing in-field applications with a portable Raman spectrometer. Other researchers employed a dispersive Raman spectroscopy with excitation in the visible spectral region in combination with a chemometrics method (e.g., PCA) to identify plant oils in a rapid and nondestructive way [85]. 1.2

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Fig. 8.4 Normalized Raman spectra of olive oil, soybean oil, sunflower seed oil, rapeseed oil, and corn oil. Adapted from M.Q. Zou, X.F. Zhang, Q.I. Xiao-Hua, M. Han-Lu, Y. Dong, L.I.U. Chun-Wei, X.U.N. Guo, H. Wang, Rapid authentication of olive oil adulteration by Raman spectrometry, J. Agric. Food Chem. 57 (14) (2009) 6001–6006.

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The adulteration of olive oil with sunflower oil can easily be detected for a low concentration of adulterant (e.g., 1%). The amount of sunflower oil in adulterated extra virgin olive oil could be quantitatively determined by applying the PLS regression method to the Raman spectra and a reliable trace analysis was achieved with a detection limit of approximately 500 ppm (0.05%). Besides the analysis of oil authentication being a nonnegligible food quality issue, determination of the total degree of unsaturation of oils is significant to the oil and food industry from the viewpoint of the nutritional value of fats. For example, Barthus and Poppi used FT-Raman spectroscopy coupled with PLS methods for determination of the total degree of unsaturation in vegetable oils with iodine values ranging from 17 to 130 [86]. A high correlation coefficient (0.996) indicated good agreement between data from the PLS model with the data obtained from the conventional method.

8.3.4 Beverages Raman spectroscopy can be applied in the quality control of beverages, including soft drinks, alcoholic beverages, honey, and other liquid mixtures due to their significant role in people’s daily lives. Component determination is important because each component could contribute to nutritional values of the food product. Raman spectroscopy was used to determine the quality of different sports supplements [6]. Comparison of Raman spectra of the analyzed supplements can identify the supplement type through the characteristic Raman features of carbohydrates and proteins. Raman bands at 1650, 1250, and 1004 cm 1 were attributed to protein, while the carbohydrate supplements had featured patterns between 1200 and 800 cm 1. The PCA method was used to help the interpretation of Raman spectra, assisting the identification of compounds present in sports supplements. Sugar content, including glucose, fructose, sucrose, and maltose in honey, can be processed by Raman spectrum using chemometrics methods (e.g., PCA and PLS) and artificial neural networks [87]. In Fig. 8.5A, the Raman spectra of the aqueous solutions of glucose, fructose, sucrose, maltose, and honey are collected, and they show different Raman patterns. A PCA plot for the classification of sugars in standard solutions obtained from Raman spectra in the region 200–2000 cm 1 shows each sugar is separated and can be clearly distinguished in Fig. 8.5B. The good correlation coefficient values between actual and predicted values of glucose, fructose, sucrose, and maltose were determined as 0.964, 0.965, 0.968, and 0.949 for PLS. Batsoulis et al. also employed FT-Raman spectroscopy combined with the chemometrics method for simultaneous mass percentage determination of fructose and glucose in honey [88]. Different types of Raman spectrometers have been used for determining alcoholic content. For example, Song et al. reported online detection of distilled spirit quality based on a new type of laser Raman spectroscopy [89]. On the basis of a quantitative relationship between alcohol concentration and its Raman characteristic peak area by Gaussian fitting of Raman spectroscopy and the least squares method, alcohol degree, and methanol content can be accurately determined in a distilled spirit. Raman spectroscopic techniques are applied not only in component determination but also in the detection of adulterants and microorganisms in beverage. Adulteration

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Fig. 8.5 (A) The Raman spectra of glucose, fructose, sucrose, maltose, and honey. (B) PCA plot for classification of sugars in standard solutions obtained from Raman spectra in the region 200–2000 cm 1. Adapted with permission from C.M. McGoverin, A.S.S. Clark, S.E. Holroyd, K.C. Gordon, Raman spectroscopic prediction of the solid fat content of New Zealand anhydrous milk fat, Anal. Methods 1(1) (2009) 29. Copyright (2013) Elsevier.

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of honey is a critical issue in the honey industry [90, 91]. FT-Raman spectroscopy was used to detect adulterants such as cane and beet invert in honey [92]. The spectrum of adulterated samples was characterized. The region between 200 and 1600 cm 1 representing carbohydrates and amino acid fractions was used for quantitative and discriminant analysis. Principal component regression analysis was used for quantitative analysis, while linear discriminant analysis and canonical variate analysis were used for discriminant analysis. The results showed that FT-Raman spectroscopy was efficient in predicting beet and cane invert adulterants with R2 > 0.91. Canonical variate analysis was used to classify the adulterants in honey with a minimum classification accuracy of about 96%. Identification and strain discrimination of the wine spoilage yeasts (e.g., Zygosaccharomyces bailii, Brettanomyces bruxellensis, and Saccharomyces cerevisiae) can be studied by Raman spectroscopy and chemometrics [93]. The results showed that the yeasts were classified with high sensitivity at the species level: 93.8% for Z. bailii, 92.3% for B. bruxellensis, and 98.6% for S. cerevisiae, and these yeasts were classified at the strain level with an overall accuracy of 81.8%.

8.3.5 Crops Raman spectroscopy shows great potential for structural and qualitative analysis of crops, and biochemical information within intact cells, tissues, and even plants can be obtained by analysis of Raman spectroscopic information. The application of Raman spectroscopy has spread in several kinds of crops, such as flour, wheat, coffee, rice, grain, etc. Because some people are intolerant to gluten, there is a health risk for those people if they eat wheat flour with gluten. Czaja et al. determined the gluten in wheat flour based on PLS treatment of FT-Raman data [49]. In the coffee industry, different species have different qualities. The arabica coffees have a more pronounced and finer flavor than the robusta coffees, therefore they are considered of better quality. FT-Raman spectroscopy with chemometric techniques can be applied to discriminate between Coffea arabica (arabica) and Coffea canephora (robusta) with the difference in their lipid fraction, especially in the content of the diterpene kahweol. This study can distinguish coffees with high contents of kahweol from coffees with low contents based on two specific scattering bands of kahweol at 1567 and 1478 cm 1. Wong et al. reported the quality control of two Pueraria species (Puerariae Lobatae Radix and Puerariae Thomsonii Radix) using Raman spectroscopy coupled with PLS analysis [52]. Raman spectroscopy indicated that spectral features of starch and polyphenols differentiated the two species, with the PLS-discriminant analysis model with 100% classification accuracy for the tested samples. The quality of rice and its cooking were assessed through Raman analyses by monitoring the changes in protein-related bands due to denaturation [94].

8.3.6 Vegetables and fruits It is important to maintain the internal and external quality of fruits and vegetables in modern agricultural and food industries. A lot of effort has been made to apply Raman spectroscopic techniques in the quality control of vegetables and fruits, including structural analysis, classification, and quantification.

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The natural wax and related surface quality of apple fruit was evaluated by FT-Raman [95]. Discrimination between cultivars and between storage duration based on wax layer properties was achieved with reasonable accuracy. The spectra of apples from different picking dates and shelf life periods was not significant, while differences between cultivars and storage periods in this analysis were mostly related to differences in the number of aliphatic chains (e.g., alkanes and esters) and the presence of R-farnesene. A dispersive Raman instrument was employed to study tomato fruit quality [96]. Raman spectra showed three characteristic bands of carotenoids, lycopene, and carotene and several bands were recognized as related to carbohydrates. Multivariate calibration models were developed to depict that Raman spectra could make a favorable distinction between the samples based on their maturity stages. Some researchers employed Raman spectroscopy to evaluate citrus fruit freshness assessment based on the Raman intensity of the carotenoid [97]. This is because researchers founded that the intensity of the carotenoid resonance Raman signal is an excellent indicator of the intact citrus freshness, thus introducing objective criteria of appreciation and quality control. Classification of olives is an important first step in improving production of extra virgin olive oil. Guzma´n et al. reported the use of Raman spectroscopy for discriminating between olives to see whether they have been collected directly from the healthy fruit or not, and PCA was used to find natural clusters in the data [98]. The developed method could be applied to contribute to an overall oil improvement in quality control by detecting good quality olives before the oil processing stage. The presence of chemical contaminants (e.g., pesticides) and microorganisms (e.g., bacteria) in fruits and vegetables has become an issue endangering food quality. A lot of research has been conducted to determine pesticides and bacteria with Raman spectroscopy [99–101]. For example, Dhakal et al. reported the nondestructive detection of pesticide (chlorpyrifos) residues in apple surface using Raman technology. This method allowed detection within less than 4 s with a minimum limit of 6.69 mg/kg in apple surface [102].

8.4

Advantages and limitations

A large number indisputable advantages (e.g., nondestructive detection, fingerprint information, rapid data acquisition, etc.) of Raman spectroscopy allow it to become a powerful tool for food quality analysis [103–105]. However, this technique still has some drawbacks that limit its further applications. Raman spectroscopy provides fingerprint information of compounds and can also be applicable to a wide range of substances, including liquids, solids without concern about size, shape, or thickness, or even gas [15, 17, 21, 36, 106–109]. This characteristic makes it a robust analytical method in the food area because food products could be liquid (e.g., beverages) or solid (e.g., fresh produce, milk powder). In addition, Raman scattering is not interfered with by water. This is a great benefit in the analysis of food products (e.g., fresh produce or beverages) because the presence of moisture or water is unavoidable. Raman analysis is very convenient because it can achieve rapid data collection and nondestructive detection, making in situ and online assessment of food products possible. After Raman analysis, samples can be recycled without any damage and consequently

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treated by other procedures. A quantitative study is possible for food component determination because the Raman intensity of spectral signatures is directly proportional to the specific species concentration. Moreover, remote analysis can be realized and there is no need to be in contact with the sample during detection, which is safe and convenient. For example, toxic food samples or those with an unpleasant smell, substances with unknown composition, and properties that are unstable to air or moisture can be measured through protective or cover layers or packages from other materials like glass. Spectra of these protective layers can be later subtracted using software designed for the manipulation of data. Raman spectroscopy therefore appears to be an easy and strikingly useful technique for analyzing and identifying food products even in packaging. In spite of the many advantages of Raman spectroscopy, we have to consider the limitations of this technique. A well-known competing process that can appear alongside Raman scattering is fluorescence [110–114]. A mechanism controlling Raman and fluorescence effects is similar and determines that if one phenomenon occurs, the other one will also occur. For example, Raman features of a certain biological sample (e.g., leafy green vegetables) are often hidden by fluorescence, especially when using visible laser excitation. There are a number of ways to avoid or minimize the adverse effects of fluorescence. Generally, the selection of suitable laser excitations, for instance, a Raman instrument equipped with a 1064 nm laser, can reduce the fluorescence effects. Low sensitivity due to weak Raman scattering is the major problem with this technique. However, sensitivity can be enhanced by using variations of Raman spectroscopy such as resonance Raman spectroscopy [115–117] and surface-enhanced Raman spectroscopy (SERS) [38, 118–120].

8.5

Recent technology development

The limitation of Raman spectroscopy promotes the development of variations of Raman spectroscopy to achieve more efficient food quality evaluation. A wide range of Raman instruments and corresponding advanced spectroscopic techniques such as SERS, resonance Raman spectroscopy, Raman microscopy, spatially offset Raman spectroscopy (SORS), and stand-off Raman spectroscopy are developed and discussed next.

8.5.1 Surface-enhanced Raman spectroscopy The SERS technique overcomes the traditional drawbacks of Raman scattering, which are its inherent weakness [36, 38, 46, 48, 121]. SERS phenomena occur when molecules are adsorbed onto a metal surface with nanoscale roughness (e.g., nanoparticles). Typical metals are gold or silver. The enhancement factor of SERS can be as high as 1014–15, which allows even single molecule detection. The enhancement mechanism lies in electromagnetic field enhancement attributed localized surface plasmon resonance, as well as chemical enhancement due to charge transfer between analyte and substrate [122–125]. The advantages of SERS can be applied to any Raman

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instrument. SERS features sometimes differ from normal Raman patterns and therefore the interpretation of SERS data should be considered. SERS has a wide range of applications in the food quality area [126–128]. For example, SERS can be used as a powerful tool to determine adulterant melamine in milk powder. A portable Raman spectrometer equipped with a 785 nm excitation laser was used to determine trace melamine (1.26 ppm) in real milk samples [8]. Melamine signals were enhanced by the SERS substrate phytic acid (IP6) dual-functionalized gold nanoparticles, which were modified filter paper. This study showed a potential for online analysis. Betz et al. reported direct SERS detection of melamine as low as 5 ppm in liquid infant formula with the help of silver nanostructures [129]. Similarly, Lee et al. also reported the SERS method for the determination of melamine in powdered milk at an excitation wavelength of 632.8 nm with roughened gold substrate, and the limit of detection could be as low as 200 ppb (ng/g) [130]. In addition, in the beverage industry, tomato juice quality can be evaluated by the SERS method [131]. Three major bands at 738, 1333, and 2930 cm 1 assigned to carbohydrates in tomato were detected and analyzed. Moreover, a medium band related to proteins could be identified. SERS can also be applied to analyze food contaminants such as pesticides. Yang et al. applied SERS to monitor multiple classes of pesticides both on the surface and inside fresh produce (e.g., apple, grape, and spinach) [35].

8.5.2 Resonance Raman spectroscopy The resonance Raman technique is another method to increase signal intensities. The incident laser frequency is close in energy to an electronic transition of a compound. The frequency resonance can lead to the greatly enhanced intensity of the Raman scattering, which facilitates the study of compounds present at low concentrations [115, 117, 132]. The instrumentation used for resonance Raman spectroscopy is identical to that used for Raman spectroscopy; specifically, a highly monochromatic light source (a laser), with an emission wavelength in either the near-IR, visible, or near-UV region of the spectrum. The essential point is that the wavelength of the laser emission is coincident with an electronic absorption band of the compound of interest.

8.5.3 Raman microscopy Raman microscopy is a combination of Raman spectroscopy and optical microscopy [133–135]. It can be used along with various Raman spectroscopic techniques such as UV resonance Raman spectroscopy, visible excitation Raman spectroscopy, and FT-Raman spectroscopy. It allows high magnification visualization of a sample and Raman analysis with a microscopic laser spot. There have been several structural changes in Raman spectrometers, with variable exciting laser sources. Various features of Raman spectroscopy have also been altered (e.g., rejection filters for laser, slit or pinhole apertures, and the type of detector). Confocal Raman microspectroscopy also has analytical characteristics similar to Raman spectroscopy and visualization abilities similar to a high-quality microscope [136–139]. Confocal Raman microspectroscopy has a wide range of applications in food quality evaluation

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[12, 140, 141]. For example, it can be used to study the spatial distribution of protein and phenolic constituents in wheat grain to evaluate milling quality during dry milling of wheat [142]. Protein content, composition of the starchy endosperm, and composition of the aleurone cells walls in arabinoxylan as well as ferulic acid derivatives were investigated. Confocal Raman microscopy was also used to follow the evolution of protein content and structure during grain development of various wheat varieties selected on the basis of hardness level and aptitude to the separation of peripheral layers during milling.

8.5.4 Spatially offset Raman spectroscopy The normal Raman techniques are widely applied in the analysis of the quality of the surface of food products. Subsurface food inspection is also important since interesting quality attributes can be at different locations on the samples. Therefore a recent technique called SORS has become an important tool to study the quality issues not only on the surface but also below the surface [50, 51]. Unlike conventional Raman spectroscopy, SORS intends to obtain layered subsurface information by collecting Raman scattering signals from a series of surface positions laterally offset from the excitation laser point. The offset spectra provide different sensitivities to the Raman signals from the surface and subsurface layers. Raman signals from each layer obtained from SORS contribute to mixed spectra. When the distance of laser and detector is gradually increased, surface contribution is steadily decreased, while subsurface contribution is steadily amplified. Pure Raman spectra of each layer of the samples can be analyzed by extracting from an array of the SORS data, which is processed by a spectral mixture analysis algorithm. The spectral mixture analysis algorithm can be used to extract the pure Raman spectra of each layer from the mixture spectra of SORS. The resolved Raman spectra give chemical information from both surface and subsurface by comparing with the reference spectra. Qin et al. reported that the internal maturity of tomatoes can be determined by SORS based on Raman signal changes that occur during tomato ripening and establish their relationship with the ripeness degree of the tomatoes [143].

8.5.5 Stand-off Raman spectroscopy Stand-off Raman spectroscopy is a kind of Raman spectroscopy where the spectrometer is some distance from the sample under inspection [144, 145]. This technique has great benefits when the samples are dangerous (e.g., toxic food sample or samples with unpleasant smells) because the operator is distanced from potential danger. Stand-off Raman analysis not only removes the need for contact but also allows samples to be measured in situ. Like conventional Raman spectroscopy, the choice of laser wavelength affects the performance of a Raman spectrometer. As for stand-off Raman spectroscopy based on the previous studies [144], the use of a UV laser below 300 nm is the best choice for high spectral intensity and low fluorescence influence, as well as less ambient light interference. However, the risk of photodegradation and the difficulties of achieving high-resolution measurements are increased. There are two ways

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to position the laser and telescope relative to the samples: coaxial and biaxial. Coaxial design allows measurements from samples at different distances without the need for realignment, while biaxial geometry allows all the laser power to reach the sample by carefully aligning the laser and telescope. Among the two ways, the biaxial pattern shows a better signal intensity than coaxial, but coaxial allows deeper detection than biaxial on samples. In terms of the coupling system in the instrument, optical fiber couplings are common, which can be used to collect light from the telescope, via a holographic filter, and then direct the light into the spectrograph. Direct coupling between telescope and spectrometer is possible when the size of the spectrograph is very small, and it achieves a higher performance than optical fiber couplings. To obtain better performance of the stand-off Raman instrument, most people use spectrographs and CCD detectors.

8.6

Summary and outlook

Raman spectroscopy shows great potential in food quality analysis. However, there is still a long way to go, such as features of low sensitivity and strong fluorescence from the food samples, as well as signal interference due to complex food matrices. Future studies should focus on developing more efficient spectrometers with advanced optics and units as well as applying more advanced variations of Raman spectroscopy to enhance efficiency (e.g., development of suitable SERS substrates to increase the signal or integration of imaging techniques with the Raman method). The use of different and advanced chemometric algorithms is expected to improve the evaluation of quality parameters in food products. In addition, more innovative portable devices need to be developed for on-site food quality applications in real life. Advances in Raman spectroscopy instrumentation assist the development of this spectroscopic technique for applications in quality evaluation of food products. Raman spectroscopy is a powerful tool in both quantitative and qualitative investigation of food samples because of its nondestructive nature and ease of sampling. Raman spectroscopy has great potential for quality measurements since the Raman effect provides valuable information on chemical composition and molecular interactions of food components. Combined with multivariate qualitative methods, Raman spectroscopy can be developed into models to classify food products in relation to quality-related properties. Future development of the Raman spectrometer is expected to produce more efficient spectrometers, advances in optics, and more sensitive and portable units.

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[76] Y. Cheng, Y. Dong, J. Wu, X. Yang, H. Bai, H. Zheng, D. Ren, Y. Zou, M. Li, Screening melamine adulterant in milk powder with laser Raman spectrometry, J. Food Compos. Anal. 23 (2) (2010) 199–202. [77] P.H. Rodrigues Ju´nior, K. De Sa´ Oliveira, C.E.R. De Almeida, L.F.C. De Oliveira, R. Stephani, M.D.S. Pinto, A.F. De Carvalho, I´.T. Perrone, FT-Raman and chemometric tools for rapid determination of quality parameters in milk powder: classification of samples for the presence of lactose and fraud detection by addition of maltodextrin, Food Chem. 196 (2016) 584–588. [78] M.R. Almeida, K.D.S. Oliveira, R. Stephani, L.F.C. De Oliveira, Fourier-transform Raman analysis of milk powder: a potential method for rapid quality screening, J. Raman Spectrosc. 42 (7) (2011) 1548–1552. [79] X. Zhang, X. Qi, M. Zou, F. Liu, Rapid authentication of olive oil by Raman spectroscopy using principal component analysis, Anal. Lett. 44 (12) (2011) 2209–2220. [80] D. Tuschel, Raman Spectroscopy of oil shale, Spectroscopy 28 (3) (2013) 1–5. [81] M.Q. Zou, X.F. Zhang, Q.I. Xiao-Hua, M. Han-Lu, Y. Dong, L.I.U. Chun-Wei, X.U. N. Guo, H. Wang, Rapid authentication of olive oil adulteration by Raman spectrometry, J. Agric. Food Chem. 57 (14) (2009) 6001–6006. [82] V. Baeten, R. Aparicio, Edible oils and fats authentication by Fourier transform Raman spectrometry, Rev. Lit. Arts Am. 4 (4) (2000) 196–203. [83] V. Baeten, M. Meurens, M.T. Morales, R. Aparicio, Detection of virgin olive oil adulteration by Fourier transform Raman spectroscopy, J. Agric. Food Chem. 44 (8) (1996) 2225–2230. [84] E.C. Lo´pez-Dı´ez, G. Bianchi, R. Goodacre, Rapid quantitative assessment of the adulteration of virgin olive oils with hazelnut oils using Raman spectroscopy and chemometrics, J. Agric. Food Chem. 51 (21) (2003) 6145–6150. [85] R.M. El-Abassy, P. Donfack, A. Materny, Visible Raman spectroscopy for the discrimination of olive oils from different vegetable oils and the detection of adulteration, J. Raman Spectrosc. 40 (9) (2009) 1284–1289. [86] R.C. Barthus, R.J. Poppi, Determination of the total unsaturation in vegetable oils by Fourier transform Raman spectroscopy and multivariate calibration, Vib. Spectrosc. 26 (1) (2001) 99–105. € [87] B. Ozbalci, I.H. Boyaci, A. Topcu, C. Kadilar, U. Tamer, Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks, Food Chem. 136 (3–4) (2013) 1444–1452. [88] A.N. Batsoulis, N.G. Siatis, A.C. Kimbaris, E.K. Alissandrakis, C.S. Pappas, P. A. Tarantilis, P.C. Harizanis, M.G. Polissiou, FT-Raman spectroscopic simultaneous determination of fructose and glucose in honey, J. Agric. Food Chem. 53 (2) (2005) 207–210. [89] L. Song, L. Liu, Y. Yang, J. Xi, Q. Guo, X. Zhu, Online detection of distilled spirit quality based on laser Raman spectroscopy, J. Inst. Brew. 123 (1) (2017) 121–129. [90] L. Mehryar, M. Esmaiili, Honey & honey adulteration detection: a review, in: Int. Congr. Eng. Food 11th, 2011, pp. 1–6. [91] B. Za´brodska´, L. Vorlova´, Adulteration of honey and available methods for detection—a review, Acta Vet. Brno 83 (2014) S85–S102. [92] M.M. Paradkar, J. Irudayaraj, Discrimination and classification of beet and cane inverts in honey by FT-Raman spectroscopy, Food Chem. 76 (2) (2002) 231–239. [93] S.B. Rodriguez, M.A. Thornton, R.J. Thornton, Raman spectroscopy and chemometrics for identification and strain discrimination of the wine spoilage yeasts Saccharomyces

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[134] R.L. McCreery, Raman Microscopy and Imaging, 2000, pp. 293–332. [135] G.I. Petrov, V.V. Yakovlev, J. Squier, Raman microscopy analysis of phase transformation mechanisms in vanadium dioxide, Appl. Phys. Lett. 81 (6) (2002) 1023–1025. [136] C. Sandt, T. Smith-Palmer, J. Pink, L. Brennan, D. Pink, Confocal Raman microspectroscopy as a tool for studying the chemical heterogeneities of biofilms in situ, J. Appl. Microbiol. 103 (5) (2007) 1808–1820. [137] K.J. Baldwin, D.N. Batchelder, Confocal Raman microspectroscopy through a planar interface, Appl. Spectrosc. 55 (5) (2001) 517–524. [138] K. Meister, D.A. Schmidt, E. Br€undermann, M. Havenith, Confocal Raman microspectroscopy as an analytical tool to assess the mitochondrial status in human spermatozoa, Analyst 135 (6) (2010) 1370. [139] R. Tabaksblat, R.J. Meier, B.J. Kip, Confocal Raman microspectroscopy: theory and application to thin polymer samples, Appl. Spectrosc. 46 (1) (1992) 60–68. [140] J. Huen, C. Weikusat, M. Bayer-Giraldi, I. Weikusat, L. Ringer, K. L€ osche, Confocal Raman microscopy of frozen bread dough, J. Cereal Sci. 60 (3) (2014) 555–560. [141] K. Takeuchi, J.F. Frank, Confocal microscopy and microbial viability detection for food research, J. Food Prot. 64 (12) (2001) 2088–2102. [142] O. Piot, J.C. Autran, M. Manfait, Spatial distribution of protein and phenolic constituents in wheat grain as probed by confocal Raman microspectroscopy, J. Cereal Sci. 32 (1) (2000) 57–71. [143] J. Qin, K. Chao, M.S. Kim, Nondestructive evaluation of internal maturity of tomatoes using spatially offset Raman spectroscopy, Postharvest Biol. Technol. 71 (2012) 21–31. [144] A.J. Hobro, B. Lendl, Stand-off Raman spectroscopy, Trends Anal. Chem. 28 (11) (2009) 1235–1242. [145] J. Moros, J.A. Lorenzo, K. Novotny´, J.J. Laserna, Fundamentals of stand-off Raman scattering spectroscopy for explosive fingerprinting, J. Raman Spectrosc. 44 (1) (2013) 121–130.

Further reading [146] Y. Zhang, H. Hong, W. Cai, Imaging with Raman spectroscopy, Curr. Pharm. Biotechnol. 11 (6) (2010) 654–661.

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M.N. Mohd Fairulnizal, B. Vimala, D.N. Rathi, M.N. Mohd Naeem Cardiovascular, Diabetes and Nutrition Research Centre, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, Malaysia

9.1

Introduction to atomic absorption spectroscopy

Food quality is essential to ensure sufficient and important nutrients are consumed for the maintenance of good health. Food quality includes external and internal factors such as physical appearance (shape, color, gloss, consistency), texture, flavor, grade standards, as well as chemical and microbial content. It also deals with product traceability and labeling to ensure there is correct ingredient and nutritional information. This will directly improve productivity and eventually contribute to the economic development of a country. There are many nutrients in foods essential for maintaining good health in humans such as protein, carbohydrate, vitamins, amino acids, fatty acids, and minerals. These nutrients are available in foods in different concentrations significantly depending on the environment (feed, soil, climate), genetic resources and food biodiversity (varieties, cultivar, breeds), storage conditions, processing, fortification, market share, country-specific food, recipe, and brand-name foods [1]. As shown in Fig. 9.1, nutrient analysis is highly recommended in ensuring quality of the various foods consumed. Trends and demands in food science and technology, whether by the consumer, food industry, government agencies, or national and international regulations for the purpose of labeling and safety of the food supply, would often require determination of food composition and characteristics [2]. Currently, there is also a huge interest in the consumption of health foods such as functional foods, functional ingredients, and nutraceuticals. Thus there is no doubt about the importance and continuous development of more robust, efficient, sensitive, and cost-effective analytical techniques to guarantee the safety, quality, and traceability of foods in compliance with legislation and consumers’ demands [3]. Analysis of nutrients content in food will require complex steps and is usually done in the laboratory. Different nutrients require different approaches in preparing and extracting the food prior to being analyzed using specific instrumentation in the laboratory. The nature of the sample such as liquid or solid and the specific reason for the analysis commonly dictate the choice of analytical methods. Establishment and validation of the method for analysis of a specific food matrix is necessary to ensure usefulness of the method. Making an appropriate choice of the analytical technique for a specific application requires a good knowledge of the various techniques and instrumentation. The success of any analytical method relies on the proper selection and Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00009-3 © 2019 Elsevier Inc. All rights reserved.

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Fig. 9.1 Graphical representation of nutrient analysis in the determination of food quality and its importance to human health.

preparation of the food sample, staff training, carefully performing quality control and samples analysis, and doing the appropriate calculations and interpretation of the data [3, 4]. Minerals are one of the essential nutrients and can be analyzed using various technologies such as atomic absorption spectroscopy (AAS), inductively coupled plasmaoptical emission spectrometry (ICP-OES), and inductively coupled plasma-mass spectrometry (ICP-MS). AAS technologies can offer single element analysis, while ICP-OES and ICP-MS offer multielement analysis simultaneously. This chapter will discuss in detail the use of AAS for the analysis of metals in food samples.

9.2

Basic principle of AAS

AAS is a sensitive analytical technique to quantitate the concentration of multielements (both metals and metalloids) in all types of samples (environmental, biological, industrial, etc.) using the absorption of optical radiation (light) by free atoms in the gaseous state. This method has a wide range of applications across multiple fields, including industries. AAS was developed during the 1950s by an Australian scientific team led by Sir Alan Walsh at the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Division of Chemical Physics, Melbourne, Australia. The technique is a quantitative tool that targets the absorption of specific wavelengths of light that are unique to the element [5]. A typical atomic absorption spectrometer is made up of four major components, i.e., light source, atomization system, spectrometer, and detection system (Fig. 9.2). Two basic types of light source are normally used: hollow cathode lamp (HCL) and electrodeless discharge lamp (EDL). Among these, HCL is the most widely used light source for AAS, which is composed of a tungsten anode and a hollow cylindrical-shaped cathode made of the specific element of interest.

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Focusing lens

Atomizer Detector Light source

Monochromator

Fig. 9.2 Schematic diagram of a basic atomic absorption spectrometer.

Both components (anode and cathode) are sealed in a tube perfused with an inert gas (neon or argon) at a very low pressure. Once the inert gas bombards the cathode, this excites the atom that subsequently leads to emission of the analyte of interest. The lamp has a metal cathode that is specific and unique to each element for analysis, although in some cases a few elements may be combined in a multielement lamp. EDLs are typically more intense than HCLs and therefore may offer better precision and lower detection limits for some elements. EDLs are made up of a bulb that contains metal/salt of the element of interest in an argon atmosphere. Radiofrequency energy ionizes the argon and excites the atoms, and results in emission of the characteristic spectrum. EDL sources were known to possess longer lifetime compared to HCL sources [6]. AAS instrumentation includes either flame or graphite furnace atomizers. Flame atomizers commonly use air–acetylene for atomization of many analytes, whereas nitrous oxide–acetylene is used for selected analytes that require hotter flames. In contrast, graphite furnace atomizers use a flameless technique where the graphite tubes are heated electrically. A monochromator receives light passed through the atomizer and produces monochromatic light by excluding unwanted wavelengths. The monochromator is a vital part of AAS due to its critical role in isolating a specific wavelength of light, which allows for the determination of the specific element of interest in the presence of large amounts of wavelengths generated by other elements. The light selected by the monochromator is directed onto a detector, typically a photomultiplier tube that intensifies, amplifies, and converts the obtained light signal into an electrical signal, which is proportional to the light intensity. However, in most modern instruments, solid-state detectors are now widely used. These detectors provide an improved signal-to-noise performance and great flexibility of application [7]. The general function of the AAS instrument is illustrated in Table 9.1. In flame AAS, air and nitrous oxide in combination with acetylene are the two most commonly used oxidant–fuel combinations. The air–acetylene flame requires a temperature of about 2300°C, compatible with all burner heads, and could be widely applied for the determination of various elements. On the other hand, a nitrous oxide–acetylene flame is only compatible with a nitrous oxide burner head for the determination of elements that form refractory oxides at higher temperatures up to 2900°C [8]. A summary of different flames and their requirements is shown in Table 9.2.

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Table 9.1 Key features of atomic absorption spectroscopy instrumentation and its usage Parts

Use

Lamp Atomizer Monochromator

Generates light at specific wavelengths of a particular element Creates a cloud of free atoms from the sample Receives light from the source and emits radiation at specified elements of interest Assists in focusing radiation Converts the light signal into an electrical signal proportional to the light intensity

Focusing lenses Detector

Table 9.2 Specific requirements of different oxidant–fuel combinations in flame atomic absorption spectroscopy Flames (oxidant–fuel)

Temperature (°C)

Burner head

Air–acetylene Nitrous oxide–acetylene

2300 2900

All Only nitrous oxide

Samples are usually introduced in the form of liquids or solids. In flame AAS, the sample is introduced as an aerosol into the flame by a sample introduction system consisting of a nebulizer and spray chamber. The burner head is aligned so that the light beam passes through the flame, where the light is absorbed. However, in graphite furnace AAS, the sample is introduced directly into a graphite tube, which is then heated in a programmed series, eliminating solvent and major matrix components followed by atomization of the remaining sample. All of the analyte is atomized, and the atoms are retained within the tube (and the light path, which passes through the tube) for an extended period of time [7]. For most elements, heating is normally required to break the bonds combining atoms into molecules. However, this is exceptional for mercury since free mercury atoms can exist at room temperature allowing for their measurement in the absence of a heated sample cell. Mercury is usually analyzed by the cold vapor technique, where it is reduced to a free atomic state by the action of strong reducing agents (e.g., stannous chloride, sodium borohydride) in a closed reaction system. Volatilefree mercury atoms are carried in the gas stream through tubing connected to an absorption cell, which remains unheated, excluding situations when heating is necessary to eliminate water condensation [6]. Hydride generation sampling systems for atomic absorption bear some similarities to the cold vapor mercury technique. The difference between these two systems lies in the form of gaseous reaction products, whereby these molecules exist as volatile hydrides and not free analyte atoms. A hydride generation technique is largely used in the determination of certain groups of elements, which are detected at low levels and the salts readily form hydrides with sodium borohydride. This hydride gas will undergo dissociation into free atoms on

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heating of the sample cell. The cell is heated by an air–acetylene flame in some systems, whereas several other systems are heated electrically [6, 9]. In summary, AAS measures the absorption of light as it passes through a cloud of atoms at a discrete wavelength. The amount of light absorbed increases as the number of atoms in the light path increases. The atoms absorb ultraviolet or visible light at specific frequencies, which increase the energy level of the atom. The analyte concentration is determined from the amount of absorption of light. Quantitation of individual elements can be precise with the use of specific light sources and selection of suitable wavelengths. The atom cloud formed by the atomizer is produced by supplying enough thermal energy to the sample to dissociate the chemical compounds into free atoms. The basic principles of atomic absorption spectroscopy can be simplified in two steps: 1. Free atoms (gas) generated in an atomizer can absorb radiation at a specific frequency. 2. The wavelength at which light is absorbed is specific for each element. The amount of light absorbed is proportional to the concentration of absorbing atoms.

9.3

General procedure for analysis using AAS

In general, the procedure for the analysis of food samples using AAS will involve several steps: sample extraction and preparation, setting up the equipment, calibration and standard curves, and analysis of samples.

9.3.1 Sample extraction and preparation Sample preparation is one of the most critical steps and could account for almost 60% of the whole analytical process. Sample preparation imparts a fundamental impact on laboratory throughput and analytical performance. Hence any errors within the sample preparation process will undermine the quality of data at all subsequent stages of the analysis. Depending on the food matrix, the samples are first homogenized into a representative sample form. If the samples were solids, grinding, blending, or other procedures might be necessary to ensure a more homogeneous sample to be measured. Preparing replicate samples will allow us to evaluate if the homogeneity assumption is accurate. To eliminate matrix effects and other interference factors, it is necessary to select and optimize a digestion method to digest organic materials and convert the analyte into a suitable form for determination. Acid digestion would transform the samples from solid to liquid form to remove organic compounds that will interfere during the spectroscopic analysis. Basically, there are two ways of performing digestion, either via open or closed system. Open acid digestions are performed either with a reflux system or in a beaker on a laboratory hotplate. The samples will be left for several hours in the fume hood for the digestion to take place. Wet digestion procedures can be carried out by adding strong oxidizing acids into the sample and heating so that the organic materials in the sample can be decomposed. Digestion of biological

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samples, which contain large amounts of organic materials, is usually carried out using mixed acids such as a combination of nitric acid (HNO3) solution with perchloric acid (HClO4), sulfuric acid (H2SO4), hydrochloric acid (HCl), and hydrogen peroxide (H2O2), respectively. Wet ashing digestion procedures are easy to operate and can digest a relatively large number of samples, but it consumes large amounts of acid, which may introduce interference and can cause environmental pollution. It may also leave undigested matter that requires further filtration or centrifugation prior to introduction into the instrument, which can result in reduced recovery with corresponding poor accuracy. The dry ashing method can also be performed by placing the sample in an open inert vessel and destroying the combustible (organic) portion of the sample by thermal decomposition using a muffle furnace at high temperatures up to 500°C followed by dissolving the ash in appropriate solvent. Dry ashing digestion only requires a few reagents and has low blank value; however, it may cause some elements to volatilize or react with the vessel, which leads to a low recovery rate [10]. In recent years, many researchers have started to use a microwave digestion system. Microwave digestion is a common technique used by elemental scientists to dissolve heavy metals in the presence of organic molecules prior to analysis with atomic spectroscopy. This technique is usually accomplished by exposing the food samples to a strong acid in a closed vessel and raising the pressure and temperature through microwave irradiation. The increase in temperature and pressure of the low pH sample medium increases both the speed of thermal decomposition of the sample and the solubility of metals in solution. Once these metals are in solution, it is possible to quantify the sample through an elemental technique like AAS. The application of microwave enhances the chemistry for sample preparation and allows for shorter reaction times, reduction in the number of discrete sample preparation steps, greater sample homogeneity after digestion, increased sample throughput, and better precision. The processes are also very well suited for standardization and automation during method development [11, 12]. However, during digestion, there is always some vessel-to-vessel temperature variation, the result of microwave field flux variabilities, differing microwave absorption rates among the samples, and chemical reactions during digestion of the sample. In general, during method development, it is beneficial to design the method to use the lowest sample weight that will deliver the detection limits needed during subsequent instrumental analysis. Sample amounts larger than necessary can negatively affect the method by developing excessive pressure and increasing the potential for unwanted temperature variability, as well as shortening consumable lifetime. Successful digestion of a sample is highly dependent on a few factors, which include chemistry, heating temperature, as well as time. Raising the temperature of the sample and reagents increases the rate of reaction and therefore shortens the digestion time. High temperatures are also needed to crest the reaction threshold and begin the digestion process, and enough time is needed for the reaction to run to completion. In situations where a sample is not digested completely, the temperature and digestion time are raised in 10°C and/or 10 min increments and reevaluated. If the hottest digestion step exceeds 60 min or an increase of 30°C is still not successful, the chemistry of

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the sample should be reevaluated and the acids should be modified. Generally, 0.2–0.5 g of each food sample (in duplicate) are digested with 5 mL of HNO3 (analytical grade) and 2 mL of H2O2 (analytical grade) in precleaned polytetrafluoroethylene vessels. After all samples are completely digested, they are diluted to a final volume of 50 mL with deionized water (
18 MΩ). Samples are further diluted as necessary in the same manner. Preparation blanks, consisting of the acid mixture, are taken through the same digestion and preparation process as the samples and analyzed accordingly [13].

9.3.2 Setting up equipment In the analysis of complex samples like food samples with either flame or graphite furnace AAS, obtaining reproducible results can be a challenging task because the analyst has to deal with analytes present at different levels and the potential for matrix interference. The flame and graphite furnace sample introduction system is of paramount importance in optimizing the short-term stability of signals. Measuring multiple elements by flame AAS requires each sample to be analyzed individually for each element, which impacts the speed advantage of flame AAS. To address the speed issue, a fast high-throughput sample automation system can be used. Although samples still need to be analyzed multiple times, the analysis time per sample is significantly reduced, thus increasing sample throughput compared to manual sample introduction. In addition, an automated sample introduction system increases the precision of the analysis and allows the analyst to perform other tasks. The in-line dilution capability of the autosampler allows the analyst to create a single intermediate standard that is then utilized by the flame autosampler for automated generation of all calibration standards as required. In addition, the instrument can be programmed to identify quality control over-range samples and then utilize the in-line dilution capability to automatically rerun a sample that falls outside the calibration range at an increased dilution factor, bringing the signal within the calibration range and providing accurate measurement along with a successful quality control check [11]. A leading company in graphite furnace AAS instrumentation uses a unique built-in camera to monitor sampler tip alignment and sample introduction into the graphite tube [14]. The same company also uses a transversely heated graphite atomizer (THGA), which provides uniform temperature distribution across the entire length of the graphite tube. The THGA features an integrated L’vov platform, which is useful in overcoming potential chemical interference effects common to the graphite furnace AAS technique [14]. Normally, every supplier of AAS equipment will provide standard conditions for the determination of individual elements that the analyst needs to follow to ensure the results obtained are as expected [8].

9.3.3 Calibration and standard curves Quantitative measurements in atomic absorption are based on Beer’s law, which states that concentration (C) is proportional to absorbance (A) (C ¼ kA). However, for most elements, particularly at high concentrations, the relationship between concentration and absorbance deviates from Beer’s law and is not linear. There are several reasons

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for this, including stray light, nonhomogeneities of temperature and space in the absorbing cell, line broadening, and in some cases absorption at nearby lines. When a computerized system was incorporated into atomic absorption instruments, automatic curve correction became a reality. Modern atomic absorption instruments have the ability to calibrate and compute concentrations using absorbance data from linear and nonlinear curves [15]. Selecting the number and concentrations of calibration standards is very important. If the analyte concentration of all the samples to be analyzed falls within the linear range, one calibration standard should be used. The top of the linear range for most elements is between 0.20 and 0.30 absorbance units. The majority of AAS users purchase prepared and certified stock standards for calibration. Alternatively, stock standards can be prepared directly from reagent-grade chemicals. To ensure accuracy, the concentration of these standard solutions should be verified using another analytical technique. The concentration of solutions, particularly very dilute solutions, will change with time in certain cases. If 1% accuracy is required, it is good practice to prepare working standard solutions daily from stock solutions of 500–1000 mg/L [8].

9.4

Advantages and limitations of AAS

AAS techniques that are widely applied for the determination of most metals and metalloids have various advantages as well as several limitations [16]. In general, AAS portrays high sensitivity, good precision, low cost, and relative simplicity. AAS techniques could potentially be utilized for cross-checking purposes with other instrumentation results. Despite its advantages, the potential of AAS is limited to analysis of a single element. The advantages and limitations for all available AAS techniques are discussed and summarized in Table 9.3.

Table 9.3 Several forms of chemical interference Type of interference

Example

Formation of less volatile compounds

Determination of calcium in the presence of phosphate. Calcium phosphate is not fully dissociated in an air–acetylene flame; hence absorbance of calcium atoms decreases with increased phosphate level Formation of chlorides

Formation of more volatile compounds Occlusion into refractory compounds Occlusion into volatile compounds

Refers to small amount of analyte that might be trapped in a refractory substance and hence not atomized efficiently Some compounds sublime explosively and as a consequence this may lead to further enhancement of the atomization process

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9.4.1 Flame AAS The flame technique, as presented in Fig. 9.3, is considered advantageous because it is a simple and rapid measurement technique that is normally used when sufficient analytes are present [16]. Detection limits for most elements analyzed with the flame AAS technique fall in the range of mg/L or parts per million (ppm) [6]. Although flame AAS is regarded as a rapid and precise analytical method that is free from interferences, some distinct interferences have been identified. These interferences could be divided into two groups: spectral and nonspectral [6, 16]. Spectral interferences refer to an inaccurate measurement of light absorption that is caused due to absorption of species other than analyte element. Background absorption is the most common type of spectral interference in flame AAS [6]. Narrow absorption lines among atoms may cause overlapping wavelength absorption interference. However, absorption only takes place if the light source is also present at the absorbing wavelength of another element. Nondissociated matrix materials may have broad absorption spectra, causing light scattering of the tiny solid particles in flame over a wide wavelength region and leading to background absorption [6]. Nonspectral interferences refer to certain aspects that affect analyte formation, which is specific to matrix, chemical, and ionization interference [6, 16].

9.4.1.1 Matrix interference Matrix interference is interrelated with the nebulization process, which is the first step of flame AAS. This type of interference is attributed to the difference in surface tension of standard and sample. For example, samples that are more viscous lead to different nebulization efficiencies between sample and standard. This dissimilarity in the sample uptake rate indicates variation in absorbance as a consequence of the different

Fig. 9.3 Schematic diagram of flame atomic absorption spectroscopy.

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number of atoms in the light beam. All of these discrepancies contribute to matrix interference [6].

9.4.1.2 Chemical interference This is another type of interference that could be introduced via the atomization process. During atomization, a molecular form of the analyte undergoes dissociation to form free atoms provided that energy levels are sufficient. However, chemical interference can occur if any sample component forms a thermally stable compound with the analyte leading to incomplete decomposition with the available energy in the flame [6, 16]. This type of interference may exist in several forms, as illustrated in Table 9.4.

9.4.1.3 Ionization interference Ionization interference is commonly encountered with hot nitrous oxide–acetylene flames, whereas for air–acetylene flame it only arises in the presence of easily ionized elements such as alkali metals [6]. This interference occurs when the energy is in Table 9.4 Summary of the advantages and limitations of different atomic absorption spectroscopy (AAS) techniques AAS techniques Flame AAS

Advantages

Limitations

Simple and rapid measurement Speedy

Spectral interference (background absorption) Nonspectral interference (matrix, chemical, ionization) Sensitivity is interrelated with nebulization process and premix burner design Limited to mercury determination because no other element exists as volatile-free atoms at room temperature Accuracy dependent on analyte valence state, reaction time Restricted to analysis of specific elements Spectral interference (emission interference, background absorption) Nonspectral interference

Moderate sensitivity

Cold vapor

Best technique for mercury analysis

Hydride generation

Low detection limits in the presence of high sampling efficiency Automated technique that allows unattended operation Only requires small sample size Determines elements from various matrices Direct analysis of solid samples About 100–1000 times more sensitive than flame AAS

Graphite furnace AAS

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excess and as a consequence results in depletion of the ground state atoms. The dissociation process is not necessarily inhibited at the ground state, whereby in the presence of additional energy, ion molecules are created via thermal excitation or electron removal from the atoms. Reduction in the number of ground state atoms leads to less atomic absorption, hence causing ionization interference [6]. Other than these interferences, another aspect that is considered vital is the sensitivity of the technique. A few factors were identified as the underlying factors that limit the sensitivity of the flame AAS method. The nebulization process draws sample solution into the burner chamber at approximately 3–8 mL/min; this limits the rate of sample introduction and correlatively fewer sample amounts are available for transport to the flame [6]. In addition, the premix burner design is undesirable and highly associated with sample wastage. The feature of the burner is such that only some nebulized samples reach the flame, while the rest is mostly drained. The sample that is introduced into the flame stays in the light path for a brief moment as it is transported upward [6]. Since the absorbance process in flame AAS depends on the number of atoms in the optical path of the spectrometer, improved sampling efficiency and constraining analyte atoms to the light path for a longer duration are useful aspects that lead to better sensitivity of the flame AAS technique [6].

9.4.2 Cold vapor AAS Higher sensitivity could be attained with the cold vapor technique in comparison to conventional flame AAS. Large sample volumes further increase the sensitivity since a greater amount of mercury atoms are available [6]. The limit of detection (LOD) for mercury by this technique is approximately 0.02 μg/L. The amalgamation technique is an alternative option when even lower detection limits are required [6]. To date, the cold vapor system is the most sensitive and reliable technique for determination of very low concentrations of mercury by AAS. However, this technique is limited to mercury determination due to the incapability of other elements to exist in a volatile-free atomic state at room temperature via chemical reduction [6]. Fig. 9.4 depicts schematic representation of the cold vapor AAS system.

9.4.3 Hydride generation AAS Similar to the cold vapor technique, hydride generation AAS is able to achieve low detection limits as a result of high sampling efficiency. Hydride generation provides a concentration step and transports the analyte to the atomizer efficiently compared to nebulization systems. In addition, the usage of a heated quartz tube atomizer in a hydride generation AAS system results in an increase in sensitivity over flame as well as furnace. Separation of the analyte element from the sample matrix could be utilized to eliminate matrix-related interference and allow for high efficiency of analyte introduction into AAS [6, 9]. The accuracy of hydride generation analysis is dependent on various factors ranging from analyte valence state, reaction time, gas pressures, acid concentration, and cell temperature. In addition, the formation of analyte hydrides is also suppressed by a number of common matrix components that possibly lead to

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Inert gas Detector

NaBH4 Acid Sample

Mixing coil Gas–liquid separator Peristaltic pump

Waste

Fig. 9.4 Schematic diagram of cold vapor atomic absorption spectroscopy.

chemical interference. The hydride technique, however, is restricted to the analysis of specific elements such as arsenic (As), bismuth (Bi), germanium (Ge), lead (Pb), antimony (Sb), selenium (Se), tin (Sn), and tellurium (Te) [6]. A simple hydride generator system is shown in Fig. 9.5.

9.4.4 Graphite furnace AAS The graphite furnace technique is one technique that is mostly automated compared to other AAS methods. Automation is advantageous for unattended operations of graphite furnaces. LOD of most elements in this technique falls within the μg/L or parts per billion (ppb) range. A graphite furnace only requires small sample size (μL); however, comparable detection limits could be attained in contrast to other AAS techniques that need larger sample size [6]. Although initial studies indicated various interferences, instrument improvisation has transformed graphite furnace AAS as a highly reliable routine technique for trace metal analysis. A graphite furnace is also beneficial for determination of elements from various matrices [6]. The attractive feature of graphite furnace AAS also includes its ability for direct analysis of solid samples. This is accountable with the absence of a nebulizer system that simplifies introduction of solid material into the atomizer [17]. In addition, a graphite furnace is generally about 100–1000 times more sensitive than flame AAS under any given radiation sources [18]. Fig. 9.6 depicts a schematic representation of a graphite furnace AAS system. Graphite furnace AAS is well known to be highly prone to interference. Background absorption and physical or chemical effects are some of the most common interferences identified in this technique [16]. Graphite furnace AAS was reported

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Fig. 9.5 Schematic diagram of hydride generation atomic absorption spectroscopy.

Sample

Optical path

Optical path

Light source Monochromator

Graphite tube Detector

Fig. 9.6 Schematic diagram of graphite furnace atomic absorption spectroscopy.

to be incapable of analyzing multielements simultaneously. It is also noted that samples subjected to graphite furnace AAS result in slower analysis time compared to flame AAS [19]. Interference associated with a graphite furnace could be discussed as spectral and nonspectral [6]. Spectral interferences refer to those that result from light absorption by molecules or atoms apart from the analyte. Two types of spectral interference exist.

9.4.4.1 Emission interference This interference arises due to the emission of intense light from the hot graphite tube that reaches the photomultiplier tube detector. Emission interference could possibly lead to erratic results because it might cause dysfunction of the detector [6].

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9.4.4.2 Background absorption Background absorption is apparently the most severe spectral interference with graphite furnace AAS. This absorption is described as a nonspecific attenuation of light at the analyte wavelength caused by matrix components in the sample. In contrast to atomic absorption, background absorption is broadband, which is normally caused by molecular absorption or light scattering that arises from undissociated sample matrix components [6]. Nonspectral interferences include those that affect the production or analyte availability. A general rule of thumb for atomic absorption is that free atoms are available in the light path; however, when diverse components in the sample matrix inhibit the formation of free analyte atoms, nonspectral interference occurs [6].

9.5

Recent technology development of AAS

The latest atomic absorption spectrometer uses fiber optic technology that produces a fully enclosed optical system. The optical system improves light throughput for better detection limits. The new light path also helps to reduce the size of the instrument. It also uses a stacked design where both flame and graphite furnace can be used on the same instrument. This involves a titanium burner that can be easily removed for different analyses. It features a double beam design for quick start-up and long-term stability without the need for recalibration. The instrument can configure either deuterium or longitudinal Zeeman background correction to suit any particular analysis. Deuterium background ensures the best sensitivity and accuracy over a wide wavelength range [20]. The longitudinal Zeeman design allows the traverse heating of a graphite tube, which can significantly reduce matrix effects [21]. It is also equipped with a color furnace camera that allows better sample monitoring. In addition, no gas line connections are required due to a new mixing chamber design. It can also utilize an air purge to clean the inside of the instrument to eliminate corrosive vapors. The instrument can also save running costs by determining the concentration of all elements from a single aspiration sample. The matrix modification technique is a very important feature in the concept of trace metal determinations without interference. The chemical forms and therefore the physical properties of the element studied or the matrix can be changed by addition of a suitable reagent (matrix modifier) in excess to the sample and standard reference solutions [14, 22, 23]. Another notable development of AAS technology is a portable atomic absorption spectrometer, which is suitable for fast field determination of important heavy metal elements. It features a tungsten coil electrothermal atomizer with much lower energy consumption compared to a traditional graphite furnace atomizer; a miniature linear charge-coupled charge device (CCD) array detector spectrometer with much smaller size and weight; no moving parts inside; and no need for adjustment after transportation. It can be operated by battery power supply [24]. A high-resolution continuum source (HR-CS) atomic absorption spectrometer, a technology developed by a Germany-based company, is an instrument that combines

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flame, hydride, and graphite furnace on one platform. This technique uses a highintensity xenon short-arc lamp as the primary radiation source, a high-resolution double monochromator, and a CCD array detector, which can achieve better LODs and an extended linear working range [25]. It offers new possibilities for background correction and three-dimensional imaging of the environment of the analytical line, which is not possible in traditional AAS. The HR-CS atomic absorption spectrometer is also able to analyze molecular bands and thus detect nonmetals such as phosphorus, sulfur, and halogens using molecular absorption “lines” that cannot be analyzed using conventional AAS technology [26].

9.6

Recent application progress in different foods

Metals are the most abundant chemical elements and present at low concentrations in food products. The nutritional and toxicological significance of these elements is known to differ based on its grouping and available amounts [27, 28]. Approximately 30 elements have been recognized as essential and some are required in larger amounts such as calcium (Ca), potassium (K), magnesium (Mg), and sodium (Na), while other elements are present in trace amounts. Among these trace elements, copper (Cu), iron (Fe), zinc (Zn), and manganese (Mn) are most essential for its role in biochemical processes of the human body. At the same time, the presence of these metals at higher levels is toxic and may lead to dangerous health effects. On the other hand, elements such as lead (Pb), cadmium (Cd), nickel (Ni), and arsenic (As) do not portray biological importance and are toxic even at low levels [27, 29–31]. Environmental heavy metals contamination is regarded as an important aspect of public health hazards, and as such their regular monitoring of food products is vital as a measure and guarantee of food safety [27, 32]. In recent years, the development of an analytical method specifically for the mineral content of food samples has gained much insight from various sectors, ranging from researchers, regulating agencies, producers, as well as consumers. This was believed to be very much correlated with concern over food quality available in the market. Attractiveness and easy application are determined by distinct traits that include its high sensitivity and selectivity, robustness, low cost, and environmental friendliness [33]. Traditional sample pretreatment techniques commonly applied in food matrices such as microwave-assisted digestion, alkaline digestion, and sample combustion with subsequent ash solubilization have been in conflict due to lengthy analysis, high cost, and the possibility of analyte loss [34, 35]. As such, a technique known as slurry sampling has been proposed and is known to be capable of minimizing the reported drawbacks. However, this technique might not be easily applied to all food matrices. This argument demands method optimization of slurry samples preparation prior to being subjected to analytical determination [33]. Graphite furnace AAS happens to be the most suitable analytical technique that can be applied for trace metal determination of slurry samples. In graphite furnace AAS, a sample undergoes the pyrolysis stage where almost all interferences would be eliminated prior to atomization. However, the use of chemical modifiers as well as the addition of diluted acids and oxidants were necessary to smoothen pyrolysis and

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atomization to minimize matrix effects and lengthen usage of the pyrolytic graphite tube. Some of the commonly used modifiers include palladium (Pd), palladiummagnesium (Pd-Mg), and ammonium dihydrogen phosphate (NH4H2PO4) specifically for chromium (Cr), lead (Pb), and cadmium (Cd) analysis in foods [36, 37]. The development and progress of several AAS technique applications in various food matrices is discussed and summarized in Table 9.5.

9.6.1 Application of slurry sampling and graphite furnace AAS in brown sugar A new technique incorporating slurry sampling and graphite furnace AAS for the determination of Cd, Cr, and Pb in brown sugar was recently reported [33]. Brown sugar is a product derived from sugarcane and recognized by several names such as “jaggery,” “panela,” or “noncentrifugal sugar” [33, 46]. The perspective of higher Table 9.5 Summary of recent application progress in different foods Food type

Elements

Application

References

Detection of Cd, Cr, and Pb

Slurry sampling

[33]

Detection of Pb Detection of Zn Detection of Ni, Mn, Cd, Cr, and Pb Detection of As

Direct sampling Direct injection Microwave digestion

[38] [39] [40]

Lyophilized and milled direct solid sample Slurry sampling

[41]

Solid phase extraction

[42]

Slurry sampling

[43]

Microwave digestion

[40]

Graphite furnace AAS Brown sugar Milk Wine Fruit juice Fish and seafoods Honey

Detection of Cd, Pd, and Cr

[36]

Flame AAS Meat and baby foods Chocolate

Detection of Cd(II), Co(II), Cr(III), Cu(II), Fe(III), Pb(II), and Zn(II) Detection of Cu

Hydride generation AAS Fruit juice

Detection of As

High-resolution continuum source graphite furnace AAS Vegetables

Detection of Ni and Fe

Flour

Detection of Pb

Infant formula

Detection of Cu and Mn

Ground and homogenized direct solid sample Dried direct solid sample Homogenized direct solid sample

[44]

[45] [26]

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nutritional value in organic and minimally processed foods has led to better consumption of brown sugar that contains considerably higher vitamin and mineral contents compared to refined sugar [47, 48]. Brown sugar is naturally comprised of sucrose, glucose, fructose, proteins, insoluble solids, and minerals (K, Ca, P, Mg, Fe, Na, Mn, Zn, and Cu). On the other hand, the presence of Cd, Cr, and Pb is highly attributed to external factors ranging from agricultural practices, industrial processes, minerals exploitation, or the dumping of toxic residues [49]. Pb and Cd are both extremely toxic, whereas Cr can be essential or toxic depending on its oxidation state. As such, knowledge of the presence of these toxic elements in brown sugar samples is vital for its safe consumption [33]. The application of slurry sampling in brown sugar matrices is known to be tedious due to its high level of organic compounds that possibly interfere with its quantification. As such, addition of surfactants, acids, or diluted oxidative solutions is necessary for a more homogeneous and stable solution [50]. Based on series of optimization and validation studies, this developed analytical method was highlighted to be a fast and accurate approach in the direct analysis of these trace elements in brown sugar samples [33].

9.6.2 Application of direct sampling and graphite furnace AAS in milk Milk and its derivatives are essential components of the human diet because of their rich content of proteins, fats, sugars, vitamins, and minerals. Although the majority of milk widely used is obtained from healthy animals and is free from contamination, there is still the possibility of toxic elements present in pasteurized milk or low-quality feeds and supplements [38, 51]. Milk can be contaminated with heavy metals through either manufacturing or packaging processes [52]. Although lead does not portray any biological functions, it is among the most toxic and dangerous of heavy metals, whereby its exposure is known to cause severe pathologies, enzyme inhibition, and even death [53]. Hence it was deemed necessary to determine trace metal levels especially on Pb content as an important quality control measure of dairy products [38]. Previous studies have reported on Pb analysis in different milk varieties (raw, processed, and breast); however, it is worthwhile noting that those studies lacked the application of direct sampling [38]. In line with this scarcity, improvisation efforts in the development of direct analysis methods are highly required. A recent study developed a method for Pb quantification using graphite furnace AAS via direct analysis without pretreatment of bovine raw milk samples. This study showcased the suitability of this application with minimal matrix effects and fast and low-cost analysis that is highly accurate, precise, and reliable [38].

9.6.3 Application of direct injection and graphite furnace AAS in wine Wine, a complex beverage composed of ethanol, sugars, organic acids, tannins, microelements, and aromatic and coloring substances, is a widely consumed beverage throughout the world. A number of studies showed that moderate consumption of red wine improves good health and longevity when it is combined with a balanced

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diet [54]. Several trace elements such as Cd, Pb, Sn, Hg, and As are known to be potentially toxic at certain levels. These elements are naturally occurring in wine grapes. Concentrations of these elements can vary from region to region and from variety to variety due to the presence of nutrients in the soil in which the grapes are grown, the uptake of these nutrients by the vine itself, and the process by which the wine is made. In addition, the content of some trace elements can be used as a marker for the provenance of the wine. Trace elements composition can be detected using both flame AAS and graphite furnace AAS [55–57]. The latest development highlights a novel method of zinc determination in Tannat wine via direct injection graphite furnace AAS [39]. In contrast to the commonly applied reference method of flame AAS, this technique is deemed advantageous because it does not require sample preparation. A lengthy sample pretreatment process could be avoided with direct injection of wine samples into the graphite tube that allows for a fully automated process [39].

9.6.4 Application of microwave digestion with graphite furnace and hydride generation AAS in fruit juice Fruit juice is loaded with many nutrients, including minerals, vitamins, trace elements, and phytochemicals, that possess extensive health benefits. Along with a balanced diet, moderate intake of fruit juices can prove to be beneficial. However, they may also contain toxic elements that lead to detrimental effects [27]. Environmental contamination with heavy metals gained attention through analysis of trace elements in fruits and fruit products [29]. A number of factors were expected to be influential on trace element levels in fruit juices. These include the nature of the fruit, mineral composition of the original soil and irrigation water, weather conditions, agricultural practices such as fertilizer usage, atmospheric metal depositions, addition of other ingredients like sugar during juice processing, as well as packing and storage [27, 31, 58]. Therefore regular monitoring of heavy metals in various fruit juices was necessary to ensure food safety and health benefits [27, 32]. In this study, quantitation of trace elements in several varieties of fruit juices was carried out by microwave digestion followed by hydride generation AAS specifically for arsenic (As), while other elements of Ni, Mn, Cd, Cr, and Pb were analyzed with graphite furnace AAS. The microwave-assisted digestion process allows for shorter digestion, which prevents loss of metals by volatilization and minimizes acid addition [40].

9.6.5 Application of direct solid sample and graphite furnace AAS in fish and seafoods Seafoods are an enriched source of omega-3, fatty acids, proteins, vitamins, and minerals [59]. Despite the benefits of seafood, toxic metals are also absorbed by fish through the membrane surface, tissues, or even by food and water ingestion [60]. Among these, As compounds are among the widely accumulated toxic elements [61]. Toxicity of As compounds are interrelated and dependent on their oxidation

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state, chemical form, and solubility. Generally, inorganic species are more toxic than organic species [61]. The deleterious effects of As consumption to human health indicate the requirement of a highly sensitive, fast, and reliable technique for efficient monitoring [41]. Arsenobetaine (AB) is one of the most dominant organic arsenics in seafoods and is reported to be nontoxic [62, 63]. However, the presence of AB interferes and affects the determination of total arsenic since it is very stable and its chemical decomposition is complicated [64, 65]. Based on previous findings, it was shown that the best technique for routine arsenic analysis is still complicated. An effort was made to evaluate the potential of direct solid sample analysis with graphite furnace AAS for routine fast analysis of total As and inorganic As [41]. The samples were lyophilized and milled prior to being used as direct solid samples. This study showed the suitability of this technique as a simple, fast, and inexpensive method for determining inorganic As in fish and seafoods [41].

9.6.6 Application of slurry sampling and graphite furnace AAS in honey Honey is produced by honeybees from flower nectars, plant secretions, or plant-sucking insect secretions. It is a sweet and viscous product in nature and used widely as a basic food or as preservatives [66–68]. Honey is a complex mixture with fructose and glucose being the major components along with flavonoids, organic acids, vitamins, hormones, enzymes, and minerals as the minor components [67–69]. Trace concentrations of heavy metals are also observed in these products with elements of Na, Mg, Ca, K, Cu, Ni, Pb, Zn, Cd, and several others [66]. Botanical and geographical origins as well as anthropogenic factors were believed to influence its nutritional composition [66, 69–71]. The interrelation of these factors makes it relevant for evaluation of trace metals that become toxic at higher concentrations. Commonly, prior sample treatment is required before being subjected to analysis due to its complex matrix [66, 70, 72]. However, there are plenty of drawbacks that are highlighted despite the widespread usage of sample pretreatments. These include the potential of analyte loss, sample contamination, and lengthy process [66, 73, 74]. Several studies in recent years have reported on the direct analysis of honey using flame or graphite furnace AAS without a sample treatment procedure [66, 75, 76]. These techniques incorporate usage of various acid mixtures and oxidizing agents to break down the organic matrix [76–78]. The latest advancement on the determination of Cd, Pd, and Cr in honey was attempted with slurry sampling and graphite furnace AAS using pyrolytic graphite-coated tubes. The study presented an alternative option for direct analysis of honey samples without tedious pretreatment procedures that also yields reliable and accurate results [36].

9.6.7 Application of slurry sampling and flame AAS in chocolate Chocolate originates from cocoa beans of Theobroma cacao widely found in Amazon and Orinoco valleys [79]. However, the cocoa tree is often affected by infections, and copper compounds are usually proposed as pesticides. Although copper is an essential

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element, it could still pose a health threat beyond its allowed level. Thus copper content analysis in chocolate and cocoa products was strongly emphasized as a quality control for their safe consumption [43]. Chocolate contains high organic compounds content, which makes its digestion process tedious and difficult, leading to the possibility of contamination and analyte loss. The preference for a slurry sampling technique has evolved mainly due to its vast advantages. Most studies have incorporated slurry sampling with graphite furnace AAS or with inductively coupled plasma optical-emission spectrometry techniques; however, application with flame AAS was not extensively reported [43, 80–82]. Determination of copper in chocolate powder samples for routine analysis was attempted with flame AAS and the slurry sampling technique. This proposed technique was deemed feasible, fast, and simple for direct analysis of copper in powdered chocolate samples. In addition, this study also indicated the possibility of applying this technique for copper analysis in rice flour samples and other materials that could be powdered to fine particles [43].

9.6.8 Application of solid phase extraction and flame AAS in meat and baby foods Meat is composed of water, amino acids, fats, fatty acid, carbohydrates, and inorganic compounds. Meat and meat products are an essential part of dietary requirements that contain valuable nutrients, proteins, minerals, vitamins, and other micronutrients contributing significantly to human growth and development [83]. Baby foods (also known as commercial infant foods) are usually introduced after 6 months of exclusive breastfeeding as a complementary food source. These foods provide additional macroand micronutrients that are responsible for child development [84, 85]. Baby foods are made up of a complex matrix that includes proteins, oils, minerals, carbohydrates, and other minor ingredients [86]. Lifestyle changes have increased the dependency on commercial baby foods and sometimes make up the main dietary source for babies between 6 and 12 months old. Under such circumstances, it is equally important to ensure that the safety of these food sources is not compromised [87, 88]. Throughout the processing stages, there are risks of toxic element contamination that justify the need for routine and easy monitoring of heavy metals content in baby foods and meat products. Heavy metal content analysis in food samples is known to be tedious due to its matrix effect and low concentration, which usually demands a separation/ preconcentration process. One of the most prevalently applied separation/ preconcentration procedures is solid phase extraction (SPE) that could achieve high recoveries, reduced disposal costs, and easy recovery of solid phase. However, matrix interference and trace levels of metal ions could possibly result in insufficient solute [89, 90]. A recent study by Dasbasi and coresearchers attempted to analyze trace levels of metal ions in meat and baby products via the SPE technique using poly [N-(3-methyl-1H-indole-1-yl)]-2-methacrylamide-co-2acrylamido-2-methyl-1-propane sulfonic acid-co-divinylbenzene as a chelating resin, which is then quantified using flame AAS [42]. This study demonstrated the

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suitability of chelating resin in the detection of Cd(II), Co(II), Cr(III), Cu(II), Fe(III), Pb(II), and Zn(II) ions in meat products and baby foods by flame AAS [42].

9.6.9 Application of direct solid sample and high-resolution continuum source graphite furnace AAS in vegetables Vegetables are one of the major parts of the human diet, and consist of macrominerals and trace minerals [91]. Potatoes, tomatoes, eggplants, bell pepper, and peppers are some of the vegetables categorized into the Solanaceae family that are mainly consumed as food [92]. The possibility of toxic element absorption into vegetables through the action of pesticides, fertilizers, contaminated waters, or industries in close proximity demands improved continuous monitoring of these metal concentrations [44, 93–95]. HR-CS AAS is equipped with a high-pressure xenon short-arc lamp, high-resolution double monochromator, and linear-CCD array detector. This technique highlights many advantages that include effective interference correction, and the possibility for simultaneous multielement detection provided that their absorption lines are within the spectral window [44]. The application of direct solid sample and HR-CS graphite furnace AAS for simultaneous determination of Ni and Fe have been previously analyzed using pine samples, epiphytic lichen specimens, and fluoropolymers [96, 97]. These findings have suggested that this technique should be expanded and established as a simple regular monitoring procedure for the detection of similar elements in vegetable products. The samples are finely ground and homogenized before being subjected to HR-CS AAS analysis [44].

9.6.10 Application of direct solid sample and high-resolution continuum source graphite furnace AAS in flour Flours are one of the most commonly processed products from wheat and include allpurpose flour, self-raising wheat flour, whole wheat flour, and many others [98]. Contamination hazard is imposed via the consequence of the milling process that could arise from various factors, including metallic fragments, mineral dust, pests, microorganisms, and heavy metals [99]. In line with the requirement for routine monitoring of flour samples, direct solid sampling and HR-CS graphite furnace were applied. Solid flour samples were dried and directly introduced into a graphite tube using a manual solid sampler. This technique showed the possibility of determining the microscale distribution of lead in flour samples without undergoing digestion [45].

9.6.11 Application of direct solid sample and high-resolution continuum source graphite furnace AAS in infant formula Infant formula is usually given as a complementary food to meet the requirements of young children and infants. Industrial production of infant formula caters for the addition of essential micronutrients at a level that is higher than usually found in breast milk, such as Cu and Mn. One study has reported the application of direct solid sample

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and HR-CS graphite furnace AAS for the detection of Cu and Mn in infant formula. This study showed that the applied technique is simple, sensitive, and eliminates the necessity for sample pretreatment as well as sample milling and homogenization [26].

9.7

Summary and outlook

AAS is a widely used analytical technique that embraces a multitude approach to elemental analysis, which facilitates an extended range of applications across diverse industries. The technique is increasingly being used in the food and beverage industries to comply with stringent global legislation requirements to control and monitor the level of metals in food products. Meanwhile, growing concern and demand for healthier and safer food quality from consumers and manufacturers witnessed prodigious growth of global atomic absorption. There are many sources of potential toxic elements in the environment that can penetrate the food chain and cause detrimental effects to consumers. Such elements incur from usage of pesticides, fertilizers, contaminated water, and polluted surroundings. Several analytical techniques have been proven to be very useful for accurate determination of metals in different samples. It is important to determine the concentration levels of the metals in various matrices in foods which any deficiency or excess may cause harmful effects in human. AAS has been used for many years for the analysis of metals and metalloids in a variety of sample types. The various techniques that encompass the field of atomic absorption spectroscopy described in this chapter are flame AAS, graphite furnace AAS, cold vapor AAS, and hydride generation AAS based on the Beer–Lambert law along with principle and instrument. The versatility of the mentioned techniques makes it possible to detect and measure a number of elements in samples at extremely low concentration levels as well as address laborious sample matrices in both product safety and quality issues. AAS also caters for a large number of analyses of toxic trace elements across a wide analytical range, from ppm down to ppb with maximum ease of use, sensitivity, and accuracy. Advances in automation through online sample dilution and automated standard preparation simplify routine tasks and offer increased productivity and sample throughput. These characteristics make AAS an ideal choice for the analysis of trace elements in foodstuffs. The different types of analytical methods discussed in this chapter have been shown to be effective with the consistent increase in demand for determination of metal concentrations in food products. In the future, it is imminent that acceptable limits for elements in food products will be reduced, thereby leading to the conclusion that more sensitive techniques will be required. With further improvements in the available techniques, AAS will begin to play a greater role in the analysis of elements in food industries. Such information is of value to healthcare professionals, researchers, and food manufacturers in preparing nutritious products. Metal content in some food products may still be underreported and hence continuous research and improvisation are deemed necessary.

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Acknowledgments Utmost appreciation is conveyed to the Director General of Health Malaysia and the Director of Institute for Medical Research (IMR) for giving the permission for this manuscript to be published as a book chapter. Special thanks to all staff of the Nutrition Unit, IMR for their continuous support.

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es, A. Gonza´lez-Maso´, M.L. Cervera, M. de la Guardia, Mineral profile of Spanish commercial baby food, Food Chem. 172 (2015) 238–244. S. Sacmaci, S. Kartal, M. Sacmaci, C. Soykan, Novel solid phase extraction procedure for some trace elements in various samples prior to their determinations by FAAS, Bull. Kor. Chem. Soc. 32 (2) (2011) 444–450. € T. Daşbaşı, Ş. Sac¸macı, A. Ulgen, Ş. Kartal, A solid phase extraction procedure for the determination of Cd (II) and Pb (II) ions in food and water samples by flame atomic absorption spectrometry, Food Chem. 174 (2015) 591–596. M. Butnariu, A. Butu, Chemical composition of vegetables and their products, in: Handbook of Food Chemistry, Springer, 2015, pp. 627–692. S. Knapp, L. Bohs, M. Nee, D.M. Spooner, Solanaceae—a model for linking genomics with biodiversity, Int. J. Genomics 5 (3) (2004) 285–291. A.R. Borges, E.M. Becker, M.B. Dessuy, M.G.R. Vale, B. Welz, Investigation of chemical modifiers for the determination of lead in fertilizers and limestone using graphite furnace atomic absorption spectrometry with Zeeman-effect background correction and slurry sampling, Spectrochim. Acta B At. Spectrosc. 92 (2014) 1–8. A.R. Borges, E.M. Becker, C. Lequeux, M.G.R. Vale, S.L. Ferreira, B. Welz, Method development for the determination of cadmium in fertilizer samples using high-resolution continuum source graphite furnace atomic absorption spectrometry and slurry sampling, Spectrochim. Acta B At. Spectrosc. 66 (7) (2011) 529–535. A.R. Borges, L.L. Francois, E.M. Becker, M.G.R. Vale, B. Welz, Method development for the determination of chromium and thallium in fertilizer samples using graphite furnace atomic absorption spectrometry and direct solid sample analysis, Microchem. J. 119 (2015) 169–175. B. Go´mez-Nieto, M.J. Gismera, M.T. Sevilla, J.R. Procopio, Simultaneous and direct determination of iron and nickel in biological solid samples by high-resolution continuum source graphite furnace atomic absorption spectrometry, Talanta 116 (2013) 860–865.

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[97] B.M. Soares, R.F. Santos, R.C. Bolzan, E.I. Muller, E.G. Primel, F.A. Duarte, Simultaneous determination of iron and nickel in fluoropolymers by solid sampling highresolution continuum source graphite furnace atomic absorption spectrometry, Talanta 160 (2016) 454–460. [98] P. Kumar, R. Yadava, B. Gollen, S. Kumar, R. Verma, S. Yadav, Nutritional contents and medicinal properties of wheat: a review, Life Sci. Med. Res. 22 (2011) 1–10. [99] B. McKevith, Nutritional aspects of cereals, Nutr. Bull. 29 (2) (2004) 111–142.

Determination of food quality using atomic emission spectroscopy

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Rohit Thirumdas*, Madhura Janve†, Kaliramesh Siliveru‡, Anjineyulu Kothakota§ *Department of Food Process Technology, College of Food Science & Technology, PJTSAU, Telangana, India, †Department of Food Engineering & Technology, Institute of Chemical Technology, Mumbai, India, ‡Department of Grain Science and Industry, Kansas State University, Manhattan, KS, United States, §Agro-Processing and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (NIIST), Thiruvananthapuram, India

10.1

Introduction

Nutritional data and quality of the food matrix are critical for consumers and producers to make healthy choices and recommendations. Along with the daily dietary intake of protein, carbohydrate, and fat the intake of minerals is necessary because they significantly contribute to human health. The National Research Council (USA) has established a safe and adequate range of daily dietary intakes, which includes nine mineral elements (Cu, Mn, F, Cr, Se, Mo, Na, K, and Cl) and three vitamins (K, B5, and B7). Along with the determination of quality of the food matrix, it is now mandatory in the United States to label food packaging with nutritional composition as per the Nutrition Labeling and Education Act (1990). For analyses of these trace elements the food industries generally rely on atomic spectroscopic techniques. Markiewicz et al. [1] reported that the common methods used to analyze the mineral content and inorganic impurities in foods are atomic absorption spectroscopy, atomic emission spectroscopy (AES), and inductively coupled plasma optical emission spectrometry/mass spectrometry. Microwave plasma atomic absorption spectroscopy (MP-AES) is one of the multipurpose analytical methods [2]. In the 1900s, Max Planck stated that energy could be either emitted or absorbed discontinuously in the form of energy packets called quanta. An atom emits energy when it jumps from a higher state to a lower stable state. Emission radiation is recorded in the atomic spectra and is called AES. Like all other atomic spectroscopic techniques, AES is the most often used technique in the food industries to generate food compositional data. It is also of interest for researchers due to its capability to detect low-level trace elements that are present in smaller concentrations. AES is commonly used to measure the concentration of metals and nonmetals in a wide variety of food matrices providing rapid and accurate results. The sample is first prepared using the different methods of ashing and it is then excited using a source (Fig. 10.1). Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00010-X © 2019 Elsevier Inc. All rights reserved.

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Inductively coupled plasma Radiofrequency generator

Sample solution

Drain Inert gas

Fig. 10.1 Schematic diagram of atomic emission spectroscopy.

The data are collected by the data acquisition system; they are then interpreted and analyzed, and the final output is observed in the form of AES spectra.

10.2

Basic principle

AES is based on the principle that when energy is applied to a molecule in the form of light or heat, molecules are excited and move from a lower energy level state to a higher energy level state. At the higher energy level state, the molecules are unstable and jump back to the lower energy level state on emitting radiations in the form of photons. The wavelengths of emitted photons are recorded in the emission spectrometer. The level of emissions for a molecule is the energy differential between the excited energy and lower stable energy. Each element has its own level of emission frequencies, which helps to detect the elements. The frequencies of the emissions are recorded in the emission spectrometer. According to Bohr, this frequency (υ) occurs when the excited element undergoes a transition between two discrete states with energies E1 and E2. Energy conservation leads to the well-known relationship between the energy of the photon and the energy difference between these states: ΔE ¼ E2  E1 ¼ hυ ¼ h

C γ

where h is Planck’s constant.

10.3

Instrumentation

The methodology and instrumentation comprise (1) sample preparation and introduction, (2) excitation source, (3) spectrometer, (4) detector, and (5) signal processing and instrumentation control. Fig. 10.2 shows the AES instrumentation.

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Carrier gas

177

Ai

Sample Injection port

Detecto

Excitation

Computer data station

Fig. 10.2 Instrumentation of atomic emission spectroscopy.

10.3.1 Sample introduction Sample preparation and introduction of the sample into the plasma is the critical part of the analytical process of AES. A process flow diagram for sample preparation is given in Fig. 10.3. The sample that needs to be analyzed should be first converted into highly excited free atoms. To transport the liquid samples to the source of excitation an inert gas is introduced, typically argon flowing at 0.3–1.5 L/min. The most convenient method for the introduction of liquids into the gas stream is as an aerosol from a nebulizer. The aerosol could be formed from the action of a high-speed jet across the tip of the small orifice or by other means, e.g., by using an ultrasonic transducer. The aerosol droplets produced from the nebulizer have different sizes (Fig. 10.4). Stability Sample Nebulizer

Mist Desolvation Aerosols Volatilization Excitation

Excited atoms

Excited ions

Fig. 10.3 Process diagram for sample preparation.

Excited molecules

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Capillary Shell

Nozzle

Gas input

Liquid sample

Concentric tube nebulizer

(A)

End cap Liquid sample Spray chamber

Gas

(B)

Cross-flow nebulizer Sample

High-pressure gas flow Drain

(C)

Fritted disk nebulizer

Fig. 10.4 Different types of nebulizers.

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of the spectral emission is highly dependent on these droplet sizes. Hence it is critical to select the appropriate nebulizer type for the production of uniform droplet sizes. Appropriate nebulizer selection depends on the characteristics of the sample such as density, viscosity, organic content, total dissolved solids, and total sample volume. There are different types of nebulizers and their applications are explained in detail in Table 10.1. The liquid samples can also be introduced by electrothermal vaporization and hydride generation processes. The solid samples are introduced into the excitation source by slurry or laser ablation in a stream of gas. The solid samples can also be introduced by directly vaporizing the sample. Table 10.1 Types of nebulizers for the introduction of liquid samples Nebulizer type

Applications

Advantages

Disadvantages

Concentric

Used for applications that require low nebulizer gas flows

1. Cost effective 2. Simplicity of application

1. Produces only approximately 1% of droplets of the correct size to pass into the plasma 2. Low analyte transport efficiency 3. Prone to clogging 4. Low tolerance for total dissolved solids

Cross-flow

Used for applications that require low nebulizer gas flows

1. Less prone to clogging and salting effects

1. Produces only approximately 1% of droplets of the correct size to pass into the plasma 2. Blockage may occur when the sample passes through a capillary

Babington

Used to deliver slurries into the system

1. More tolerant to highly dissolved solids 2. More resistant to blockage

1. An extensive memory effect because the solution is allowed to wet the entire face of the sphere 2. Produces aerosol less efficiently

Frit

Used to deliver slurries with highly dissolved solids into the system

1. Droplet size is more uniform with a mean size of 1 μm 2. Provides an excellent fine aerosol

1. Clogs over time

Continued

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Table 10.1 Continued Ultrasonic

Used to deliver slurries with highly dissolved solids into the system

1. Highly efficient in aerosol production independent of the carrier gas flow rate

1. Produces a cooling effect because it allows more water 2. High-cost systems

2. More analytes can be transported at a lower nebulizer gas flow rate

10.3.2 Excitation sources An excitation source is used to dissolve, atomize, and excite the atoms of the sample. The ideal excitation source will allow the excitation of all the elements in the sample and does it repeatedly until it encompasses the entire elemental excitation in the sample. A number of excitation sources can be used for these purposes, which include but are not limited to the following.

10.3.2.1 Direct-current plasma This excitation process involves using two electrodes to produce an electrical discharge to heat the plasma gas, typically argon. This method of excitation is more suitable for samples that contain a high portion of solids.

10.3.2.2 Inductively coupled plasma This is the most commonly used excitation process and it requires a plasma torch made up of concentric quartz tubes to induce excitation in the sample. The inner tube contains argon and the sample, and argon gas flows through the outer tube and acts as a cooling agent. A radiofrequency generator having a range of 1–5 kW at 27 or 41 MHz creates an oscillating current within an induction coil that surrounds the tubes. An oscillating magnetic field is produced by this induction coil and this induces a change in the electric field. The flowing gas seeded with electrons undergoes acceleration and gains energy that is required to excite and ionize the gaseous atoms by collision. This produces the plasma and the sample particles entering the plasma then undergo desolvation, dissociation, atomization, and excitation.

10.3.2.3 Flame The flame is a high-temperature source that is used to desolvate and vaporize the sample to generate free atoms for spectroscopic study.

10.3.2.4 Laser-induced breakdown A high-energy laser pulse is utilized in this method to provoke the elemental excitation in the sample.

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10.3.2.5 Microwave-induced plasma In this method, typically a 2.45 GHz microwave generator (a magnetron) is required to produce a microwave that travels through a cable and is focused via a tuning system where a torch sits in the center of the cavity. The torch has a carrier gas that flows in the outer portion of the torch, and plasma is ignited by a spark. Ozbek and Akman [3] reported that MP-AES is the most versatile new generation analytical method, which operates at a 2.45 GHz magnetic field with nitrogen as the carrier gas.

10.3.2.6 Laser-induced plasma In this method, heated plasma is maintained by a support gas, typically argon, which is focused by a high-energy CO2 laser source.

10.3.2.7 Spark or arc Spark and arc excitation sources employ a spark or an electric pulse or an arc of continuous electrical discharge between two electrodes for vaporizing and exciting the atoms of the sample.

10.3.3 Spectrometer The spectrometer is used to view and analyze a range of given characteristics for a sample. The atomic emission source will excite the atoms or ions from its lower energy stable state to a higher energy state. These excited atoms or ions will then spontaneously return to their stable or lower energy state. During this transition an emission spectrum is produced when a photon of energy is generated. This emitted energy is directly proportional to the concentration of atoms or ions in the sample. The spectrometer is used to measure this energy by using optics to separate the characteristic elemental wavelengths from the plasma background. The spectra of samples containing many elements can be very congested, and spectral separation of nearby atomic transitions requires a high-resolution spectrometer. The spectrometer consists of a dispersive element and image transfer assembly. In the AES spectrometer, the gratings are used as a dispersive element to disperse the incident light into component wavelengths. This grating works by reflecting the light off the angled grating surface, causing the wavelengths to be dispersed through constructive interference at wavelength-dependent diffraction angles. Since all atoms from multiple elements in a sample are excited simultaneously, they can be detected sequentially by using a monochromator or simultaneously by using a polychromator with multiple detectors. The image transfer assembly of the spectrometer consists of entrance and exit slits through which light enters and exits, producing a line separated from the rest of the spectrum, and concave mirrors or lenses.

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10.3.4 Detector Detectors are transducers that transform the analog output of the spectrometer into an electric signal that is viewed and analyzed using a computer. Photon detectors work generally by either causing electrons to be emitted or developing a current when photons strike the detector surface and measuring the intensity of the emission line. Examples include a photomultiplier tube (PMT), charge-coupled device (CCD), and chargeinjection device (CID). PMTs are the most widely used detector in inductively coupled plasma atomic emission spectroscopy (ICP-AES). The PMT detector consists of a photocathode in a vacuum tube and ejects electrons when struck by light. These electrons travel to a dynode that produces secondary electrons that strike another diode and produce new secondary electrons, and so on. The anode is situated in the last end of the last dynode that collects the electrons. One photon produces about a million secondary electrons on striking the photocathode in the tube. At the phototube the electrical current at the anode is measured as elemental line intensity per unit time. The advent of multichannel solid-state detectors provides more flexibility to carry out multiple elemental analyses. PMT detectors are durable and extremely reliable when carrying out elemental analyses. However, they limit the number of elements that can be determined simultaneously, because a separate detector is required for each wavelength. To overcome this challenge, modern AES instruments are equipped with solidstate detectors. These solid-state detectors can measure the continuous emitting spectra. There are two types of solid-state detectors: CID and CCD. These detectors have multiple pixels rows, which are sensitive to light. When struck by radiation, both these detectors generate and store the charge. The magnitude of the charge generated in the detectors is directly proportional to the intensity of the incident radiation. The major difference between these two detectors is how the signal is read from the chip. In CCD detectors, the charge is measured by moving the charge from the detector element, where it is collected by a charge-sending amplifier. However, in CID detectors the charge is measured in terms of voltage change induced by the movement of the charge within the detector element. CID detectors have the advantage of collecting signals at their optimal signal-to-noise ratio. CCDs are used to measure very sensitive and low-level light applications and have the capacity to monitor any wavelength between 170 and 780 nm. CIDs can monitor any wavelength between 165 and 800 nm.

10.3.5 Data processing and instrumentation control The electrical current measured at the anode of the photomultiplier tube is converted into some form of signal that can be passed onto a computer and accessed immediately for analysis. The current generation of AES instruments use a computer to control the spectrometer and to collect, manipulate, and report the analytical data. The amount of computer control over all these functions varies from model to model.

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10.4

183

Advantages and disadvantages

The advantages of various types of AES include: 1. Multiple elemental analyses can be made from a very small sample size. 2. It is a rapid elemental determination technique. 3. The emission spectra are obtained under a single set of excitation conditions and several elements can be determined simultaneously. 4. Refractory samples that are of lower concentration can also be determined. 5. By using plasma sources, nonmetals can also be determined.

The disadvantages of AES include: 1. The initial cost of instrumentation is higher. 2. When compared to atomic absorption techniques, the procedures are more complicated and also the operation costs are higher. 3. The presence of several emission lines or spectral lines can sometimes be disadvantageous because it leads to complications during analysis.

10.5

Recent technology development and applications

There is a need for rapid, automated, solventless, higher extraction rates, and inexpensive procedures for analysis. The technology is focused on the minimal manipulation of samples. One such advancement in sample preparation is the microwave-assisted digestion of the food matrix. The flames used for ionization are replaced by plasma for the excitation of atoms due to its limitations such as low temperature, low sensitivity, and poor efficiency of excitations. Nowadays, microwave plasma and inductively coupled argon plasma with a higher temperature range are excellent hotter sources for atom excitation. Using the nitrogen microwave plasma, temperatures can reach more than 5000 K, which is hotter compared to acetylene flames, which are flammable. The temperature of inductively coupled argon plasma reaches nearly 10,000 K where complete ionization takes place and all the ions emitted are available for detection.

10.5.1 Determination of mineral content, quality, and unintentional adulteration of food samples 10.5.1.1 Sample preparation Sample preparation is an integral part of analysis when it is carried out by spectroscopic methods. The method of sample preparation can be simple or complex, involving its partial or total dissolution. Sample preparation involves separation and/or preconcentration of the analytes. Sample preparation in AES includes three steps: (1) digestion, (2) extraction, and (3) preparation of analytes prior to analysis. Sample preparation is a time-consuming process and requires about 61% of total analysis time.

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The probability of error during analysis due to sample preparation is around 30%. Sample preparation can be carried out manually or by using automated or mechanized processes. Manual methods are tedious and require expensive reagents in large amounts, generate hazardous waste in large amounts, and there are greater chances of analyte contamination. Automated or mechanized procedures are more reproducible than manual methods because they involve a minimum number of stages resulting in reduced uncertainty. Advances in automated techniques of sample preparation such as microwave-assisted acid digestion, ultrasound-assisted extraction and slurry preparation, and direct solid sampling analysis have propelled the ease of sample analysis [4, 5]. Samples involved in analysis may be segregated into different categories: (1) samples in aqueous phase such as beverages, urine, serum, blood, and water, (2) other liquid forms such as oils, fuels, and organic solvents, and (3) solid forms such as soils, sediments, plants, and animal tissues [6]. The principles to be followed prior to the preparation of samples are as follows: 1. Vessels that are in contact with the sample must be soaked in an acid of appropriate concentration, followed by thorough rinsing with deionized water. 2. Special care must be taken during sample preparation in terms of appropriate temperature, pressure, and time of contact with the vessel because uneven conditions result in either decomposition or contamination of the analytes. 3. Deionized water must be used for all operations involved in sample preparation, including reagent preparation. 4. Equipment made from appropriate materials must be used for sample grinding, milling, and homogenization to avoid sample contamination. 5. Limiting the requirement of larger vessels, unnecessary filtrations, and transfer of solutions must be avoided. Also, recoveries for the whole procedure must be checked using reference materials having a composition similar to that of the samples analyzed. 6. Blank procedures must be performed to evaluate contamination and to rectify the results.

10.5.1.2 Sample preparation for solids Solid food samples must be converted into an aqueous solution for analysis. Food matrix dissolution is carried out by adding reagents or by applying energy to break the crystalline structure of solids.

Dry ashing Dry ashing of homogenized and finely ground food samples is carried out at atmospheric pressure in a muffle furnace. Samples are kept in porcelain crucibles at a temperature of 450–600°C for 5–6 h. Dry ashing results in the removal of organic matter and conversion of a mineral part associated with the matrix into a carbonate or oxide that can be dissolved by acid. Additives such as magnesium nitrate and sulfuric acid may be incorporated to avoid the loss of volatile elements (As, Hg, Pb) and volatile chlorides (Zn, Sn). Dry ashing is advantageous when preconcentration of the sample is required. A large number of samples result in ash that can be dissolved in a small volume of acid [7].

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Wet ashing Wet ashing involves the use of powerful oxidizing agents such as a combination of strong acids (nitric, sulfuric, hydrochloric, hydrofluoric, and perchloric) and hydrogen peroxide to extract the metals by decomposing the food matrix to which they are attached. Nitric acid is widely used for the destruction of organic matter and as a primary oxidant since commercially available nitric acid has a high degree of purity and it reacts with metals to form water-soluble nitrates. Boiling of samples at atmospheric pressure in nitric acid does not lead to complete mineralization, and hence closed polytetrafluoroethylene (PTFE) pressure vessels must be used. Hydrofluoric acid is highly corrosive and PTFE-lined vessels or other plastic containers must be used during digestion of samples. Perchloric acid with a concentration of not more than 72% should be used for decomposition of organic matter to avoid the risk of explosion. Permonosulfuric acid is produced during oxidation of hydrogen peroxide with sulfuric acid. This mixture degrades organic materials rapidly. ICP-AES analyses have shown that the presence of sulfuric acid is not desirable. The efficiency of extraction of minerals from the matrix can be increased using the following methods [7].

Microwave-assisted wet ashing The digestion of organic matter with acids (nitric and/or hydrochloric acid) with/without hydrogen peroxide is done in the presence of microwave processing. Vessel systems available for digestion are pressurized in closed vessel systems and open vessel systems. Pressurized closed vessel microwave-assisted extraction systems are advantageous because they require shorter digestion times, a minimum amount of less aggressive reagents for complete digestion, reduced risk of contamination, and minimum loss of volatile elements. The only disadvantage is that longer cooling periods are required before opening the vessel [8].

Ultrasound-assisted wet ashing This is used to extract metallic species from various solid samples in the presence of dilute acids. Acoustic waves speed up the sample degradation process by the collapse of the cavitation bubble, reduction of solvent gradient concentration, and increase in the surface area of the sample due to solid erosion. Ultrasound-assisted extraction has a disadvantage since the collapse of the cavitation bubble results in an increase in local temperature and free radical production, which provokes analytes loss and gross analytical errors [4].

10.5.1.3 Sample preparation for liquids Liquid samples generally have a low concentration of analytes and are prone to contamination. Hence the grade and impurity percentage of reagents, demineralization of water, and type of vessel used for storage are important. Adsorption of analytes on the walls of the vessel can be minimized by acidification. Freezing of sample solution needs to be carried out if the liquid samples are to be stored for a longer time. Liquid samples can be analyzed without sample preparation, provided the sample matrix does not interfere with the analysis. Dilution of liquid samples must be carried out using

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appropriate solvents and considering the quantity of analytes. Organic liquid samples can be analyzed directly in ICP-AES but an increase in radiofrequency power is required to maintain the stability of plasma [6].

10.6

Applications

There is a constant need for emerging technologies and new tools for monitoring food quality to meet consumer expectations for safe, healthy, and high-quality foods. Food labeling legislation made it compulsory to provide the elemental composition of a food, which is considered important for both consumers and health professionals [9]. The evaluation of elements in foods includes nutrients, trace elements, and toxic contents, and these are essential when considering human health, which governs food quality and safety. Many of the applications of AES in the determination of food quality include the estimation of minerals, trace elements, heavy metals, and toxic contaminants. AES is used to determine the leaching of harmful/toxic elements into food from packaging material, which has been reported to decrease food quality. AES is also primarily used for multielement determination to study the elemental fingerprinting of the foods [10]. Tin is used for packaging foods or beverages, and the leaching of tin from cans is toxic and can accumulate in the body [11]. Bakircioglu et al. [12] reported that the consumption of milk and dairy products contaminated with heavy metals might be a serious risk to human health depending on the contamination levels. To determine the essential elements and toxic contents, one of the widely used methods is AES because of its high detection capacity, speed of analysis, ease of use, multielement analysis, and cost. Baker et al. [13] reported that the accurate and precise analysis of food composition enables consumers to make their food choices based on Dietary Reference Intakes. The database of several nutrients required for humans is reported in the book Essential Guide to Nutrient Requirements [14]. Table 10.2 gives different elements analyzed by AES in different food samples. AES is one of the oldest analytical instruments and was used as far back as the early 1800s [33]. Pustjens et al. [34] reported that before 2008, ICP-AES was mostly used to determine the geographical origin of honey and wines. Mineral analysis of honey is carried out to determine the nature, quality, type, and origin [35]. To date, AES has been used to analyze the elements and distinguish the geographical location of different food products such as cumin [36], vinegar [37], and shrimp [38]. Consumers’ acceptability of a particular food is pertinent to the geographical region. Based on nickel content, Czech and Polish honey is differentiated from honey from other parts of the world [35]. Multielement analysis using AES was used to identify the geographical origin of wheat [38] and cereals [39]. The authors have investigated elements such as Mn, Zn, Fe, Cu, Rb, Mo, Ba, Sr, and Ni in rice samples from Japan to determine the geographical origin. In an investigation carried out by Pustjens et al. [34] on the quality of honey using AES, the honey was divided into two groups: honeydew and nectar honeys based on their elemental composition. The determination of elements in honey specifies the environmental contamination of a particular region. Martın et al. [40] reported that the quality of coffee beans can be characterized from the metals present

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Table 10.2 Multielemental analysis, heavy metals, and minerals determination in different food samples using atomic emission spectroscopy Food

Sample preparation

Analytes

References

Almond kernels Tea leaves Coffee

Microwave assisted

[15]

Cereals

Wet ashing

Mussel tissues

Microwave assisted, ultrasonic extraction

Mushrooms

Microwave assisted

Seafood Cow’s milk

Wet ashing Microwave assisted

Legumes

Wet ashing

Nuts

Wet ashing

Coffee Cheese Onion, garlic Honey

Microwave assisted Dry, wet, microwave assisted Wet ashing Microwave assisted

Barley Bread Coffee Fish

Microwave assisted Microwave assisted Microwave assisted Wet ashing

Ca, Cu, Fe, K, Mg, Mn, Na, P, S, and Zn Ba, Cu, Fe, Pb, and Zn Zn, P, Mn, Fe, Mg, Ca, Na, K, Cu, Sr, and Ba Ag, Al, As, Bi, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Hg, K, La, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sb, Sn, Sr, Th, Ti, Tl, U, V, and Zn Ag, Al, As, Bi, Ba, Ca, Cd, Co, Cr, Cs, Cu, Fe, Ge, In, K, Mg, Mn, Na, Ni, Pb, Rb, Sb, Se, Sr, Zn, and Zr Na, Zn, Ca, Fe, Cu, Mn, Rb, Ag, Cd, Hg, Pb, Cs, Sr, Al, and Si As Ba, Ca, Cu, K, Mg, Na, P, and Zn Al, Cd, Cr, Cu, Fe, Mg, Mn, Pb, and Zn Ca, Cr, Cu, Fe, K, Mg, Mn, Na, P, and Zn Co, Cu, and Mn Cd, Co, Cr, Cu, Mn, Ni, Pb, Se, and Zn Cd, Pb, and Zn K, Ca, Mg, Na, Al, Zn, Fe, Mn, Cu, Cr, Ni, Se, Pb, Cd, and As Cr, Cu, Fe, Mn, Ni, an Zn Ca, Fe, K, Mg, and S Al, Ca, Fe, K, Mg, Na, P, and S Cd, Cu, Zn, Al, Fe, Ni, and Pb

Microwave assisted Wet ashing

[16] [17] [18]

[19]

[20]

[21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32]

in the beans. The authors used AES for metal analysis and differentiated the quality of two coffee varieties (arabica and robusta) based on 11 metals (Zn, P, Mn, Fe, Mg, Ca, Na, K, Cu, Sr, and Ba). Similarly, different trace elements in baby foods, vegetables, and milk powders were determined by employing AES [9]. Pehlivan et al. [41] employed AES for the determination of inorganic metals present in edible oils (almond, sunflower, soybean, corn, and virgin olive oil), which possess a potential health risk when exposed to more than the threshold limits. Similarly,

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the presence of heavy metals in common vegetable oils in China was estimated by [42] using AES. The leaching of trace metals into cheese from plastic and tin containers was studied [12]. The authors observed that the microwave digestion procedure of cheese samples is advantageous compared to dry and wet ashing methods because of the simpler, effective, and faster procedure of sample preparation, which produced less contamination. Roncˇevi
c et al. [43] determined the tin content in several canned vegetables and fruits leached out from the tinplate packaging. The authors reported that tin content gives information about the contamination process and provides help to increase canned food quality and safety. Tin levels were evaluated using ICP-AES in different canned products such as vegetables, fruits, meat, dairy products, fish, beverages, and fruit juices [44]. Bakkali et al. [45] evaluated the quality of vegetables available in hypermarkets, supermarkets, and local shops by measuring the target metals. The authors found higher concentrations of analytes in vegetables like spinach, carrot, and pepper obtained from the hypermarket or supermarket compared to local vegetable shops. Using ICP-AES, Pancˇevski et al. [46] studied the amount of heavy metals accumulated in green chilies and green onions grown in the vicinity of lead and zinc smelting plants. The authors observed that the content of zinc and lead in the vegetables was more than the permissible levels noted in their countries’ regulations. Medicinal and aromatic plants commonly used as nutritional supplements, spices, and condiments have the ability to prevent diseases [47]. The authors observed high lead (one of the most dangerous heavy metals) content (4.35 ppm) in a sage variety of herbal tea when compared to other varieties. Today, the use of fertilizers, plants grown in heavy metal-contaminated soils, or contaminated water encourages metals to enter and accumulate in the human body through the food chain, and can cause many human health problems. The effect of location with respect to soil and climatic conditions on macro- and micronutrient concentration of oats varieties can be determined by ICPAES [48]. The contamination of herbal teas by heavy metals (Al, Zn, Cu, Fe, Cd, and Pb) was analyzed in 12 varieties [47]. In another study, estimation of major and minor minerals in 31 aromatic and medicinal plants using ICP-AES was conducted [49]. Emission spectroscopy is used for determining the concentration of elements from various sample matrices. Macro- (Ca, P, Mg, P, and S) and micro- (Cr, Cu, Fe, Mn, Ni, and Zn) elements in raw, rootlet, and malted barley grains were determined [50]. The concentration of Ca, Co, Cu, Fe, Mg, and Zn in medicinal plants and phytochemicals was determined by ICP-AES. It was also observed that the levels of Al, Ba, Cd, Cr, Mo, Pb, Se, and V in the samples were below the limit of detection [51]. Cr, Fe, Co, Ni, Cu, Zn, Cd, and Pb contents were determined in commonly consumed cereals like wheat, barley, rye, and oats [52]. It was observed that the highest and lowest iron contents were in barley and wheat flours, respectively. The levels of Cd, Cu, Fe, Ni, Pb, Zn, and Al were determined by ICP-AES in the muscles and total bodies of Mullus barbatus, Trachurus trachurus, and Engraulis encrasicolus captured from the coast of the Black Sea [53]. A similar range of elements (S, P, Zn, Pb, and Ni) in muscle samples of Indian prawns (Fenneropenaeus indicus) was also determined by ICPAES [54]. Coffee samples from 11 major coffee-producing Ethiopian provinces were profiled for macroelements, including Al, Ca, Fe, K, Mg, Na, P, and S [55]. Juhaimi

Determination of food quality using atomic emission spectroscopy

189

et al. [30] studied the quality of honey-enriched breads by evaluating the mineral content of bread using ICP-AES. After analyzing the micro- and macroelements of bread, the authors concluded that the enrichment of bread increased the nutritional profile as the source of minerals. The estimation of boron in Turkish red and white wines was studied by Ozbek and Akman [56] using MP-AES. The authors reported approximately 100% recovery of trace elements and observed a lower running cost compared to atomic absorption spectroscopy. AES is one of the most accurate tools for the determination of trace elements in food samples. However, sample preparation is the most overlooked aspect during analytical determination using AES. Thus AES plays a significant role in the determination of trace elements in foods.

References [1] M.K. Markiewicz, X.M. Cama, M.P.G. Casado, Y. Dixit, R.M. Cama, P.J. Cullen, C. Sullivan, Laser-induced breakdown spectroscopy (LIBS) for food analysis: a review, Trends Food Sci. Technol. 65 (2017) 80–93. [2] J.Z. Heredia, M. Cina, M. Savio, R.A. Gil, J.M. Camin˜a, Ultrasound-assisted pretreatment for multielement determination in maize seed samples by microwave plasma atomic emission spectrometry (MPAES), Microchem. J. 129 (2016) 78–82. [3] N. Ozbek, S. Akman, Microwave plasma atomic emission spectrometric determination of Ca, K and Mg in various cheese varieties, Food Chem. 192 (2016) 295–298. [4] E.D. Oliveira, Sample preparation for atomic spectroscopy: evolution and future trends, J. Braz. Chem. Soc. 14 (2) (2003) 174–182. [5] K.M.D.G. Andrade, E.S.D.B. Morte, D.C.M.B.D. Santos, J.T. Castro, J.T.P. Barbosa, A. P. Teixeira, M. Korn, Sample preparation for the determination of metals in food samples using spectroanalytical methods: a review, Appl. Spectrosc. Rev. 43 (2) (2008) 67–92. [6] M. Hoenig, A.M. de Kersabiec, Sample preparation steps for analysis by atomic spectroscopy methods: present status, Spectrochim. Acta B At. Spectrosc. 51 (11) (1996) 1297–1307. [7] M. Hoenig, Preparation steps in environmental trace element analysis—facts and traps, Talanta 54 (6) (2001) 1021–1038. [8] J. Sneddon, C. Hardaway, K.K. Bobbadi, A.K. Reddy, Sample preparation of solid samples for metal determination by atomic spectroscopy—an overview and selected recent applications, Appl. Spectrosc. Rev. 41 (1) (2006) 1–14. [9] N.J.I. Miller, Trace element determinations in foods and biological samples using inductively coupled plasma atomic emission spectrometry and flame atomic absorption spectrometry, J. Agril. Food Chem. 44 (9) (1996) 2675–2679. [10] C. Li, H. Dong, D. Luo, Y. Xian, X. Fu, Recent developments in application of stable isotope and multi-element analysis on geographical origin traceability of cereal grains, Food Anal. Methods 9 (6) (2016) 1512–1519. [11] R. G€urkan, N. Altunay, Determination of total Sn in some canned beverages by FAAS after separation and preconcentration, Food Chem. 177 (2015) 102–110. [12] D. Bakircioglu, Y.B. Kurtulus, G. Ucar, Determination of some traces metal levels in cheese samples packaged in plastic and tin containers by ICP-OES after dry, wet and microwave digestion, Food Chem. Toxicol. 49 (1) (2011) 202–207. ´ . Woller, Atomic spectroscopy in food analysis, [13] S.A. Baker, N.J.I. Miller, P. Fodor, A in: Encyclopedia Analytical Chemistry: Applications, Theory and Instrumentation, John wiley & Sons, 2006, pp. 1–31.

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[14] J.J. Otten, J.P. Hellwig, L.D. Meyers, Dietary Reference Intakes: The Essential Guide to Nutrient Requirements, National Academies Press, 2006. [15] S.M. Prats, N.T. Gran
e, V.N. Berenguer, M.L.C. Martı´n, Inductively coupled plasma application for the classification of 19 almond cultivars using inorganic element composition, J. Agril. Food Chem. 45 (6) (1997) 2093–2097. [16] J. Mierzwa, Y.C. Sun, Y.T. Chung, M.H. Yang, Comparative determination of Ba, Cu, Fe, Pb and Zn in tea leaves by slurry sampling electrothermal atomic absorption and liquid sampling inductively coupled plasma atomic emission spectrometry, Talanta 47 (5) (1998) 1263–1270. [17] M.J. Martın, F. Pablos, A.G. Gonza´lez, Characterization of green coffee varieties according to their metal content, Anal. Chim. Acta 358 (2) (1998) 177–183. [18] I. Rodushkin, T. Ruth, A. Huhtasaari, Comparison of two digestion methods for elemental determinations in plant material by ICP techniques, Anal. Chim. Acta 378 (1) (1999) 191–200. [19] M.B. Krishna, J. Arunachalam, Ultrasound-assisted extraction procedure for the fast estimation of major, minor and trace elements in lichen and mussel samples by ICP-MS and ICP-AES, Anal. Chim. Acta 522 (2) (2004) 179–187. [20] J. Falandysz, K. Szymczyk, H. Ichihashi, L. Bielawski, M. Gucia, A. Frankowska, S.I. Yamasaki, ICP/MS and ICP/AES elemental analysis (38 elements) of edible wild mushrooms growing in Poland, Food Addit. Contam. 18 (6) (2001) 503–513. [21] K. Boutakhrit, M. Crisci, F. Bolle, J. Van Loco, Comparison of four analytical techniques based on atomic spectrometry for the determination of total tin in canned foodstuffs, Food Addit. Contam. 28 (2) (2011) 173–179. [22] D.M. Santos, M.M. Pedroso, L.M. Costa, A.R.A. Nogueira, J.A. No´brega, A new procedure for bovine milk digestion in a focused microwave oven: gradual sample addition to pre-heated acid, Talanta 65 (2) (2005) 505–510. [23] A.A. Momen, G.A. Zachariadis, A.N. Anthemidis, J.A. Stratis, Investigation of four digestion procedures for multi-element determination of toxic and nutrient elements in legumes by inductively coupled plasma-optical emission spectrometry, Anal. Chim. Acta 565 (1) (2006) 81–88. [24] C.S. Kira, V.A. Maihara, Determination of major and minor elements in dairy products through inductively coupled plasma optical emission spectrometry after wet partial digestion and neutron activation analysis, Food Chem. 100 (1) (2007) 390–395. [25] N. Oleszczuk, J.T. Castro, M.M. Da Silva, A.K. Maria das Grac¸as, B. Welz, M.G.R. Vale, Method development for the determination of manganese, cobalt and copper in green coffee comparing direct solid sampling electrothermal atomic absorption spectrometry and inductively coupled plasma optical emission spectrometry, Talanta 73 (5) (2007) 862–869. [26] D. Bakircioglu, Y.B. Kurtulus, G. Ucar, Determination of some traces metal levels in cheese samples packaged in plastic and tin containers by ICP-OES after dry, wet and microwave digestion, Food Chem. Toxicol. 49 (1) (2011) 202–207. [27] Z. Pancˇevski, T. Stafilov, K. Bacˇeva, Distribution of heavy metals in some vegetables grown in the vicinity of lead and zinc smelter plant, Contrib. Sec. Nat. Math. Biotech. 35 (1) (2014) 25–36. [28] G. Di Bella, V.L. Turco, A.G. Potortı`, G.D. Bua, M.R. Fede, G. Dugo, Geographical discrimination of Italian honey by multi-element analysis with a chemometric approach, Food Compost. Anal. 44 (2015) 25–35. € [29] N. Svetlana, M.M. Ozcan, Mineral contents of malted barley grains used as the raw material of beer consumed as traditional spirits, Indian J. Tradit. Knowl. 15 (2016) 500–502.

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€ [30] F.A. Juhaimi, K. Ghafoor, E.E. Babiker, M.M. Ozcan, M. Harmankaya, Mineral contents of traditional breads enriched with foral honey, Indian J. Tradit. Knowl. 15 (2016) 223–226. [31] G. Habte, I.M. Hwang, J.S. Kim, J.H. Hong, Y.S. Hong, J.Y. Choi, K.S. Kim, Elemental profiling and geographical differentiation of Ethiopian coffee samples through inductively coupled plasma-optical emission spectroscopy (ICP-OES), ICP-mass spectrometry (ICP-MS) and direct mercury analyzer (DMA), Food Chem. 212 (2016) 512–520. [32] A. G€undog˘du, S.T. C¸ulha, F. Koc¸baş, M. Culha, Heavy metal accummulation in muscles and total bodies of mullus barbatus, trachurus trachurus and engraulis encrasicolus captured from the coast of sinop, black sea, Pak. J. Zool. 48 (2016) 25–34. [33] J.M. Mermet, Is it still possible, necessary and beneficial to perform research in ICP-atomic emission spectrometry, J. Anal. At. Spectrom. 20 (1) (2005) 11–16. [34] A.M. Pustjens, M. Muilwijk, Y. Weesepoel, S.M. van Ruth, Advances in authenticity testing of geographical origin of food products, in: Gerard Downey (Ed.), Advances in Food Authenticity Testing, Woodhead Publishing Series in Food Science, Technology and Nutrition, 2016, pp. 339–367. [35] J. Lachman, D. Kolihova, D. Miholova, J. Kosˇata, D. Titeˇra, K. Kult, Analysis of minority honey components: possible use for the evaluation of honey quality, Food Chem. 101 (3) (2007) 973–979. [36] E. Hondrogiannis, K. Peterson, C.M. Zapf, W. Roy, B. Blackney, K. Dailey, The use of wavelength dispersive X-ray fluorescence and discriminant analysis in the identification of the elemental composition of cumin samples and the determination of the country of origin, Food Chem. 135 (4) (2012) 2825–2831. [37] Y. Zheng, G. Ruan, B. Li, C. Xiong, S. Chen, M. Luo, F. Du, Multicomposition analysis and pattern recognition of Chinese geographical indication product: vinegar, Eur. Food Res. Technol. 238 (2) (2014) 337–344. [38] I. Ortea, J.M. Gallardo, Investigation of production method, geographical origin and species authentication in commercially relevant shrimps using stable isotope ratio and/or multi-element analyses combined with chemometrics: an exploratory analysis, Food Chem. 170 (2015) 145–153. [39] C. Li, H. Dong, D. Luo, Y. Xian, X. Fu, Recent developments in application of stable isotope and multi-element analysis on geographical origin traceability of cereal grains, Food Anal. Methods 9 (6) (2016) 1512–1519. [40] M.J. Martın, F. Pablos, A.G. Gonza´lez, Characterization of green coffee varieties according to their metal content, Anal. Chim. Acta 358 (2) (1998) 177–183. € [41] E. Pehlivan, G. Arslan, F. Gode, T. Altun, M.M. Ozcan, Determination of some inorganic metals in edible vegetable oils by inductively coupled plasma atomic emission spectroscopy (ICP-AES), Grasas Aceites 59 (3) (2008) 239–244. [42] H. Zhao, B. Guo, Y. Wei, B. Zhang, S.S. Zhang, L.J. Yan, Determining the geographic origin of wheat using multielement analysis and multivariate statistics, J. Agri. Food Chem. 59 (9) (2011) 4397–4402. [43] S. Roncˇevi
c, A. Benuti
c, I. Nemet, B. Gabelica, Tin content determination in canned fruits and vegetables by hydride generation inductively coupled plasma optical emission spectrometry, Int. J. Anal. Chem. (2012) 1–7. [44] K. Boutakhrit, M. Crisci, F. Bolle, J.V. Loco, Comparison of four analytical techniques based on atomic spectrometry for the determination of total tin in canned foodstuffs, Food Addit. Contam. 28 (2) (2011) 173–179. [45] K. Bakkali, N.R. Martos, B. Souhail, E. Ballesteros, Determination of heavy metal content in vegetables and oils from Spain and Morocco by inductively coupled plasma mass spectrometry, Anal. Lett. 45 (8) (2012) 907–919.

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[46] Z. Pancˇevski, T. Stafilov, K. Bacˇeva, Distribution of heavy metals in some vegetables grown in the vicinity of lead and zinc smelter plant, Contrib. Sect Nat. Math. Biotech. Sci. 35 (1) (2014) 25–36. [47] H.S. Tercan, F. Ayanoglu, N.P. Bahadirli, Determination of heavy metal contents and some basic aspects of widely used herbal teas in Turkey, Rev. Chim. 67 (5) (2016) 1019–1022. € [48] M.M. Ozcan, A. Bag˘cı, N. Dursun, S. Gezgin, M. Hamurcu, Z. Dumlupınar, N. Uslu, Macro and micro element contents of several oat (Avena sativa L.) genotype and variety grains, Iran. J. Chem. Chem. Eng 36 (3) (2017) 73–79. € [49] M.M. Ozcan, M. Akbulut, Estimation of minerals, nitrate and nitrite contents of medicinal and aromatic plants used as spices, condiments and herbal tea, Food Chem. 106 (2) (2008) 852–858. € [50] N. Svetlana, M.M. Ozcan, Mineral contents of malted barley grains used as the raw material of beer consumed as traditional spirits, Indian J. Tradit. Knowl. 15 (2016) 500–502. [51] S. Ju´nior, R.A. Matos, E.M. Andrade, W.N. dos Santos, H.I. Magalha˜es, F.D.N. Costa, M. D.G.A. Korn, Multielement determination of macro and micro contents in medicinal plants and phytomedicines from Brazil by ICP OES, J. Braz. Soc. 28 (2) (2017) 376–384. [52] R. Vla˜doiu, R.M. Ion, S. Teodorescu, R.M. Ştirbescu, I.D. Dulama˘, Compositional investigations of some Romanian cereals, Bull. Transilvania Univ. Brasov. Ser. I Eng. Sci. 10 (1) (2017) 61–66. [53] A. G€undog˘du, S.T. C¸ulha, F. Koc¸baş, M. Culha, Heavy metal accummulation in muscles and total bodies of mullus barbatus, trachurus and engraulis encrasicolus captured from the coast of sinop, black sea, Pak. J. Zool. 48 (2016) 25–34. [54] A.A. Patil, A.A. Choudhary, J.R. Shedge, B.M. Kansara, Determination of heavy metal concentration in edible fish from Arnala beach, Naigaon Greek, Rangaon beach & Vasai Creek, Imperial J. Interdiscip. Res. 2 (11) (2016) 1404–1406. [55] B. Mehari, M. Redi-Abshiro, S.B. Chandravanshi, S. Combrinck, R. McCrindle, Characterization of the cultivation region of Ethiopian coffee by elemental analysis, Anal. Lett. 49 (15) (2016) 2474–2489. [56] N. Ozbek, S. Akman, Determination of boron in Turkish wines by microwave plasma atomic emission spectrometry, LWT Food Sci. Technol. 61 (2015) 532–535.

Nuclear magnetic resonance spectroscopy for food quality evaluation

11

Yongqi Tian, Qingyan He, Xu Chen, Shaoyun Wang College of Biological Science and Technology, Fuzhou University, Fuzhou, People’s Republic of China

11.1

Introduction

Nuclear magnetic resonance (NMR) is a physical phenomenon that uses the magnetic properties of certain nuclei to provide detailed structural, dynamic, and energy information of molecular compounds. Physicists and chemists have often used NMR as a specialized and precise research tool. The development of hardware and data processing has broadened the application of NMR in various industries. The most successful applications include structural and composition studies of food processing, and food analysis. In recent years, a series of related conferences has been dedicated to the “Application of Magnetic Resonance in Food Science.” There have been quite a few reviews on the work of NMR in food [1]. NMR spectroscopy has been successfully applied in food science [2,3], food analysis [4], authentication [5], and food quality control [6,7]. In addition, applications of low-field, solid-state NMR spectroscopy and magnetic resonance imaging (MRI) in food science have also been reported [8,9]. Meanwhile, the use of NMR in specific topics in food science and analysis, such as milk and dairy products [10], meat [11], fruits, vegetables [12], cereals [13], lipids [14], and edible oil [15], have also been published. The rapidly increasing use of NMR in food science is mainly due to two factors, advances in high-field magnets and probe design, which enhance the analytical capabilities of modern NMR spectrometers. Because of the success of liquid chromatography-NMR, food scientists can now access NMR spectrometers more easily. Although 1H nuclei are sensitive and the most exploited, other nuclei such as 13C and 31P have gained popularity lately because they can be used to solve specific problems in food science. Specifically, 31P NMR has a long history in food science. As early as 1985 there were papers on the application of 31P NMR in meat [16] and milk [17] from a food science perspective. At present, NMR has been considered to be a powerful tool in chemical, biological, and medical research. David [18] was the first to summarize its application in biochemistry, while Quin and Verkade [19] focused on chemical characterization and structural analysis. NMR work in food science has been covered as part of general NMR reviews for milk, meat, and lipids, while a review of NMR work related to olive oil analysis has been published [20]. Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00011-1 © 2019 Elsevier Inc. All rights reserved.

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In this chapter, the theory and basic principles of NMR, experimental procedures, advantages, and limitations will be introduced and then focus will be given to the application of NMR in food science analysis (Fig. 11.1).

11.2

Theory and fundamentals [21]

11.2.1 Spin angular momentum and nuclear magnetism Most atomic nuclei have an inherent angular momentum called spin. Nuclear spin is a vector and is quantized. Its magnitude is pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi I ðI + 1Þh

(11.1)

where I is the spin quantum number of the nuclide in question and h is Planck’s constant divided by 2π. I could be zero, or a positive integer or half-integer (Table 11.1): 1 3 5 7 I ¼ 0, ,1, , 2, , 3, ,… 2 2 2 2

(11.2)

The projection of the angular momentum vector I onto an arbitrary axis (labeled z) is also quantized: Iz ¼ mh

(11.3)

where the magnetic quantum number, m, can have values between + I and  I in integral steps: m ¼ + I, + I  1,⋯,  I + 1,  I

(11.4)

pffiffiffiffiffiffiffiffi 3=2 h and a pffiffiffiffiffi 2 1 z component Iz ¼  2 h; for I ¼ 1 (e.g., H), the spin angular momentum is 2h, and Iz ¼ (x and γ) components of the angular momentum cannot be known once the magnitude and the z component of I have been specified. Closely related to nuclear spin is a magnetic moment μ: The spin of a nucleus with I ¼ 1/2 (e.g., 1H) has magnitude

μ ¼ γI

(11.5)

which is parallel or sometimes antiparallel to I, with a proportionality constant γ called the gyromagnetic ratio. As a result, both the magnitude and orientation of μ are quantized. In the absence of a magnetic field, all 2I + 1 states of a spin-I nucleus are degenerate, and the direction of the quantization axis is arbitrary. In an applied magnetic field B0 with strength B0, the spins are quantized along the field direction (the z-axis) and have an energy E ¼ μB0 ¼ μz B0

(11.6)

Nuclear magnetic resonance spectroscopy for food quality evaluation

Fig. 11.1 Review scheme of nuclear magnetic resonance for food quality evaluation. 195

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Table 11.1 Nuclear spin quantum numbers of some popular nuclear magnetic resonance nuclides I

Nuclide

0  1

12

16

1

13

C H 2 H 11 B 17 O 10 B

2

1  2 5 3

2

3

O C 14 N 23 Na 27 Al

15

N

19

F

29

35

Cl

37

Cl

Si

31

P

Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

where μB0 is the scalar product of the two vectors and μz is the projection of μ onto B0. Since μz ¼ γIz and Iz ¼ mh, it follows that E ¼ mhγB0

(11.7)

That is, the 2I + 1 states are split apart in energy, with a uniform gap Δ E ¼ hγB0 between adjacent levels (Fig. 11.2C and D). The NMR experiment includes the application of electromagnetic radiation of the correct frequency ν to “flip” spins from one energy level to another, under the selection rule Δ m ¼  1, i.e., hv ¼ ΔE ¼ hγB0

(11.8)

which may be rearranged to produce the resonance condition v¼

γB0 2π

Fig. 11.2 Space quantization and energy levels of spin 12 and spin-1 nuclei. (A) and (C) spin 12; (B) and (D) spin-1. The energy level splittings produced by an applied magnetic field depend on the value of the gyromagnetic ratio, γ (here taken as positive). Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

(11.9) + 12 ប

m = – 12

m = + 12 DE = បg B0

– 12 ប

m = – 12

m = + 12

(A)

(C)

+ប

m = +1 m = –1 DE = បg B0

0

m=0

m=0 DE = បg B0

m = +1 –ប

(B)

m = –1

(D)

Nuclear magnetic resonance spectroscopy for food quality evaluation

197

Table 11.2 Gyromagnetic ratios, nuclear magnetic resonance frequencies (in a 9.4 T field), and natural isotopic abundances of selected nuclides

1

H H 13 C 14 N 15 N 17 O 19 F 29 Si 31 P 2

γ (107 T21 s21)

ν (MHz)

Natural abundance (%)

26.75 4.11 6.73 1.93 2.71 3.63 25.18 5.32 10.84

400.0 61.4 100.6 28.9 40.5 54.3 376.5 79.6 162.1

99.985 0.015 1.108 99.63 0.37 0.037 100.0 4.70 100.0

Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

The NMR frequency of a nucleus is proportional to its γ and to the strength of the field; the 2I allowed transitions of a spin-I nucleus have identical frequencies (e.g., Fig. 11.2D). Typical magnetic fields used in modern NMR spectroscopy are in the range 4.7–22.3 T, giving proton (1H) resonance frequencies of 200–950 MHz, falling in the radiofrequency region of the electromagnetic spectrum. Table 11.2 gives the gyromagnetic ratios, resonance frequencies in a 9.4 T field, and natural isotopic abundances of some commonly studied NMR nuclei. The intensity of the observed NMR signal depends on the difference between the numbers of nuclei in the states involved in the transition. At thermal equilibrium the fractional difference in populations, of a spin 1/2 nucleus with positive γ, is given by the Boltzmann distribution: nα  nβ e△  e△ hγB0 hv ¼ ¼ △ △ ffi △ nα + n β e + e 2kT 2kT

(11.10)

where α and β denote the m ¼ + 12 and m ¼  12 levels, k is the Boltzmann constant, and T is the Kelvin temperature. The approximation made in Eq. (11.10) is that the NMR energy gap hγB0 is tiny compared to kT, which is the case in essentially all NMR experiments. For protons (1H) in a 9.4 T field, v ¼ 400 MHz so that △ ¼ 3.2  105, giving a population difference of about one part in 31,000.

11.2.2 Chemical shifts Although the resonant frequency of the nucleus in the magnetic field is primarily determined by γ, it also depends slightly on the immediate surroundings of the nucleus. Chemical shift is critical to the chemical applications of NMR because it allows one to distinguish nuclei in different environments, such that the 1H spectrum of EtOH (Fig. 11.3) shows that there are three types of protons (methyl, methylene, and hydroxyl).

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OH

6

5

CH2

4

3 d (ppm)

CH3

2

1

0

Fig. 11.3 Schematic 1H NMR spectrum of liquid ethanol, C2H5OH. The three multiplets, at chemical shifts of 1.2, 3.6, and 5.1 ppm, arise from the CH3, CH2, and OH protons. The multiplet structure (quartet for the CH2, triplet for the CH3) arises from the spin–spin coupling of the two sets of protons. Splittings are not normally seen from the coupling of the OH and CH2 protons because the hydroxyl proton undergoes rapid intermolecular exchange, catalyzed by traces of acid or base. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. (Copyright Elsevier Publisher 2017).

Because the applied magnetic field B0 causes electrons in atoms and molecules to circulate around the nuclei, this results in the existence of chemical shifts. Somewhat like an electric current in a loop of wire, the swirling electrons generate a small local magnetic field that augments or opposes B0. Like an electric current in a loop of wire, the rotating electrons produce a small local magnetic field that augments or attenuates B0. This induced field Bind is proportional in strength to B0 and, in atoms, is antiparallel to it. The net field B experienced by the nucleus is thus slightly different from B0: B ¼ B0  Bind ¼ B0  σB0 ¼ B0 ð1  σ Þ

(11.11)

The proportionality constant σ is called the shielding or screening constant. The resonance condition, Eq. (11.9), thus becomes ν¼

γB γB0 ð1  σ Þ ¼ 2π 2π

(11.12)

The σ is determined by the electronic structure of the molecule near the nucleus; ν is thus a characteristic of the chemical environment. The relation between the energy levels of a pair of spin 12 nuclei A and X is E mA γB0 ð1  σ A Þ mX γB0 ð1  σ X Þ ¼  h 2π 2π ¼ mA vA  mX vX and the NMR spectrum is shown in Fig. 11.4.

(11.13)

Nuclear magnetic resonance spectroscopy for food quality evaluation mA

mX

–2

1

–2

1

+ 12 (nA+nX)

–2

1

+2

1

+ 12 (nA–nX)

+2

1

–2

1

– 12 (nA–nX)

1

+2

1

– 12 (nA+nX)

+2

A

E/h

199

Fig. 11.4 Energy levels and nuclear magnetic resonance spectrum of a pair of spin 12 nuclei, A and X. mA and mX are the magnetic quantum numbers, νA and νX are the two resonance frequencies, and E is the energy. The spin– spin coupling JAX is zero. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

X

The chemical shift is usually quantified by the parameter δ, which is defined by the resonance frequencies of the nucleus of interest and of a reference compound: 

v  vref δ ¼ 10  vref 6

 (11.14)

where δ is dimensionless and independent of B0; values are usually quoted in parts per million (ppm). (CH3)4Si (tetramethylsilane) is commonly used as a standard compound for 1H, and the 13C NMR spectrum is shown with δ decreasing from left to right; the δ of (CH3)4Si is 0. As a consequence, nuclei with higher resonance frequencies (i.e., those that are less shielded) appear to the left of the spectrum. Although the spectra are now usually recorded at a fixed field strength, the old terms “high field” (more shielded) and “low field” (less shielded) are still commonly used. Chemical shifts can be easily converted to frequency differences using Eq. (11.14). For example, the chemical shifts of the methyl and methylene signals of EtOH (Fig. 11.3) are 1.2 and 3.6 ppm, respectively, giving a difference in resonance frequencies in a 9.4 T field of (3.6 – 1.2)  10–6  400 MHz ¼ 960 Hz. The relative intensities of the signals in the NMR spectrum are proportional to the overall differences (Eq. 11.10), and therefore to the numbers of nuclei responsible for each signal. For example, the CH3, CH2, and OH resonances of EtOH (Fig. 11.3) have integrated areas with a ratio 3:2:1.

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11.2.3 Spin–spin coupling Magnetic nuclei not only interact with the applied and induced magnetic fields, but also interact with each other. Molecules in liquids have fine structures called spin–spin coupling, scalar coupling, or J-coupling, as shown by the 1H spectrum of EtOH in Fig. 11.3. The effect of spin–spin coupling on a pair of nuclear spins A and X is to shift their energy levels by amounts determined by the two magnetic quantum numbers and by the parameter that quantifies the strength of the interaction, the spin–spin coupling constant, JAX. Thus Eq. (11.13) becomes E ¼ mA vA  mX vX + JAX mA mX h

(11.15)

For spin  12 nuclei, the energies are raised or lowered by 14 JAX according to whether the spins are parallel (mA mX ¼ + 14) or antiparallel (mA mX ¼  14). Eq. (11.15) leads to the modified resonance condition for spin A: v ¼ vA  JAX mX

(11.16)

i.e., the resonance frequency of A is shifted from its chemical shift position by an amount that depends on the orientation of the X spin to which it is coupled. Since X has in general 2I + 1 states, the A resonance is divided into 2I + 1 evenly spaced lines, with equal intensities (because the different orientations of X are almost identical). The effect of spin–spin coupling on the energy levels of two spin 12 nuclei is shown in Fig. 11.5. Each nucleus now has two NMR lines (a doublet). The origin of spin–spin coupling is not directly related to the two magnetic moments through space dipolar interaction; being purely anisotropic, this interaction is averaged to zero through the rapid end-over-end tumbling of molecules in liquids. Instead, the nuclei interact with the electrons in the chemical bonds that connect them. When the number of interventional bonds increases by more than three, the interaction usually decreases rapidly, so the presence of scalar coupling between the two cores usually indicates that they are close neighbors in the molecular framework. Eq. (11.16) can easily be extended to describe more than two nuclei: v ¼ vA 

X

JAi mi

(11.17)

i6¼A

where the sum runs over all spins to which A has an appreciable coupling. If A is coupled to N identical spin 12 nuclei (e.g., the three protons in a methyl group), it can be seen from Eq. (11.17) that its resonance is divided into N + 1 equally spaced lines. The relative intensity is given by the binomial coefficient   N N! ¼ ,i ¼ 0, 1,2, …, N i i!ðN  iÞ!

(11.18)

Nuclear magnetic resonance spectroscopy for food quality evaluation mA

E/h

– 12

–2

1

+ 12 (nA+nX) + 14 JAX

–2

1

+2

1

+ 12 (nA–nX) – 14 JAX

+2

1

–2

1

– 12 (nA–nX) – 14 JAX

1

+2

1

– 12 (nA+nX) + 14 JAX

+2

A

mX

201

Fig. 11.5 Energy levels and nuclear magnetic resonance spectrum of a pair of spin 12 nuclei, A and X. mA and mX are the magnetic quantum numbers, νA and νX are the two resonance frequencies, JAX is the spin–spin coupling constant, and E is the energy. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

X

Fig. 11.6 Calculated nuclear magnetic resonance spectra of a pair of spin 12 nuclei for a range of δv ¼ vA  vX values between 16 JAX and zero. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

Thus the CH2 and CH3 resonances in ethanol (Fig. 11.3) are, respectively, a 1:3:3:1 quartet and a 1:2:1 triplet. The discussion of the structure of multiple states (i.e., doublet, triplet, quartet, etc.) arising from spin–spin coupling is effective in the weak coupling limit, i.e., when the difference in resonance frequencies of the coupled nuclei jvA  vX j is much larger than their interaction | JAX |. If this is not the case (strong coupling), the positions and intensities of the lines are modified, as shown in Fig. 11.6. The origin of these effects lies in the NMR transition probabilities. As the coupling becomes stronger, the outside of each double peak in Fig. 11.6 becomes weaker relative to the inside. Within the limit of zero chemical shift difference, the transitions leading to the two exterior lines

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become completely forbidden, and the two interior lines coincide so that only a single line is observed. This is a general result: spin–spin interactions between protons in the same environment do not result in observable splittings.

11.2.4 Free induction decay So far it has been assumed that the unbalanced state generated by the radiofrequency pulse does not relax back to equilibrium. This is a reasonable approximation during the very short pulse. However, to describe the behavior of the spins during free precession after the pulse, relaxation must be included. Traditionally, this is done by allowing Mx and My to decay exponentially back to zero with a time constant T2, while Mz grows back to M0 with a time constant T1: dMx Mx ¼ + γ△BMγ  dt T2 dMy My ¼ + γB1 Mz  γ△BMx  dt T2

(11.19)

dMz ðM z  M 0 Þ ¼ γB1 Mγ  dt T1 where T1 and T2 are the spin–lattice and spin–spin relaxation times. These expressions are known as the Bloch equations.

11.2.5 Spin relaxation Relaxation processes allow nuclear spins to return to equilibrium after interference, for example, a radiofrequency pulse. The relaxation times T1 and T2 characterize the relaxation of the longitudinal and lateral components of the magnetization M, respectively, parallel and perpendicular to B0. Equivalently, T1 is the time constant for the restoration of equilibrium in the spin state population, while T2 is the time constant for the coherence dephasing between spin states. In the absence of any significant spatial heterogeneity of B0, or other spectral line broadening sources such as chemical exchange, the width of the NMR line (in hertz) is 1/πT2. Spin–lattice relaxation is caused by random fluctuations of local magnetic fields. A common source of such fields is the dipolar interaction between pairs of nuclei, which is regulated by tumbling molecules in a liquid. The component of these fields that oscillates at the resonance frequency can cause transitions between the spin states, thus transferring energy between the spin system and the “lattice” (i.e., everything else) and balancing the spin with their surroundings. In the simplest case, T1 depends  on the mean square strength of the local fields B2 loc , and the wave strength at the resonance frequency ω0

Nuclear magnetic resonance spectroscopy for food quality evaluation

 1 ¼ γ 2 B2loc J ðω0 Þ T1

203

(11.20)

where J ð ωÞ ¼

2τc 1 + ω2 τ2c

(11.21)

is the spectral density function and τc is the rotational correlation time (roughly the average time the molecule takes to rotate through 90 degrees). Spin–spin relaxation has two contributions: 1 1 2 2 1  ¼ γ Bloc J ðω0 Þ + γ 2 B2loc J ð0Þ T2 2 2

(11.22)

The first is closely related to spin–lattice relaxation, and is generated from the finite lifetime of the spin states by the uncertainty principle. The second term is due to the loss of coherence caused by very low-frequency local fields (therefore the J(0) factor), which increase or oppose B0 and thus cause an expansion of resonance frequencies, and therefore the phase shift of the transverse magnetization. Fig. 11.7 shows the dependence of T1 and T2 on τc.

Slow

10 T1 1

0.1 T2 0.01

Fast 10–12 Fast

10–10 tc(s)

10–8 Slow

Fig. 11.7 The dependence of T1 and T2 on the rotational correlation time τc, using γ 2 hB2loci ¼ 4.5  109 s2 and ω0/ 2π ¼ 400 MHz. The units for the vertical axis are seconds. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

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Relaxation times contain information on both J(ω) (i.e., on molecular motion) and hB2loci (i.e., on molecular structure via, for example, the r–3 distance dependence of the dipolar interaction). A further relaxation phenomenon that provides important information on internuclear distances is the nuclear Overhauser effect.

11.3

NMR experiment procedures

11.3.1 Experiment 1: Determination of T2 relaxation time Relaxation measurements were carried out on a Niumag Desktop Pulsed NMR Analyzer (Shanghai Niumag Electronics Technology Co. Ltd.). The magnetic field intensity was 0.54 T and the protons of corresponding resonance frequency were 23.01 MHz. The NMR instrument was equipped with a 60 mm probe. Transverse relaxation (T2) was measured using the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence, with a τ value (time between the 90 and 180 degree pulses) of 75 μs. Data from 2000 echoes were obtained as eight-scanned repetitions. The repetition time between two consecutive scans was 2 s. All relaxation measurements were made at 25°C. Using Multi Exp Inv Analysis Software developed by Niumag, the T2 relaxation time was analyzed by the distribution index fitting analysis method. A continuous exponentials distribution of the CPMG experiment was defined by Eq. (11.23): Z∞

AðT Þeτi =T dT

gi ¼

(11.23)

0

where gi is the intensity of the decay at time τi and A(T) is the amplitude of the component with transverse relaxation time T. Eq. (11.23) was solved using Multi Exp Inv Analysis software by minimizing Eq. (11.24) 

Z gi 

m

x¼1

fx eτi =Tx dT

2 +λ

m X

fx 2

(11.24)

x¼1

P 2 In Eq. (11.24), λ is the weighting and λ m x¼1 fx is a linear combination of functions added to the equation to perform a zero-order regularization [22]. Using sampling pruning to reduce the data from 2000 to 200 points, this analysis yielded a plot of the relaxation amplitude versus relaxation time for an individual’s relaxation process. The time constant for each peak was calculated from the peak position, and the corresponding water contents were determined by cumulative integration. All calculations were measured using an internal program written in conjunction with MATLAB (Mathworks Inc., Natick, MA, USA) and Delphi (Borland, USA).

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205

11.3.2 Experiment 2: Magnetic resonance imaging The porosity of the crumb structure of the WWB sections was evaluated by observation with an MRI system (Mini MR-60, Shanghai Niumag Electronics Technology Co., Ltd., Shanghai, China). Image analysis was performed by the spin-echo 2D-FT method using a 0.1 ms echo time and a 0.5 s repetition time according to the testing parameters provided by the instrument manufacturer (Shanghai Niumag Electronics Technology Co. Ltd.). The images were reconstructed on a 192  192 matrix for 2D images, and three layers were scanned, each layer having a thickness of 4.9 mm. The porosity was calculated by using the image twice-threshold segmentation method of MATLAB (version R2010a) to offset the variation error caused by the signal-to-noise ratio of the scanned images. The gray value range of the image was 0–255. The contrast of the images was adjusted and selected from the gray value for detecting the edge of the bread sample; the number of pixels in the bread sample was specified as N1. The threshold value was adjusted and selected for testing the internal chamber of the bread again, and the pixels below the threshold were calculated and designated N2, representing the gas cells of the bread crumb. Therefore the pixels that were higher than the threshold represented the skeleton structure of the bread. The porosity can be calculated from Eq. (11.25) provided by the instrument manufacturer (Shanghai Niumag Electronics Technology Co. Ltd.): Vpore N2 Spixel h N2  100% ¼  100%  100% ¼ N2 Spixel h Vtotal N1

(11.25)

where N is the number of pixels, Spixel is the physical area of a single pixel, h is the thickness of a bread cross-section, Vpore is the total volume of the gas cells, and Vtotal is the total volume of the bread, including the gas cell volume and the volume of bread crumb.

11.4

Advantages and limitations of NMR

NMR spectroscopy is a research technique that provides the possibility of obtaining quantitative and structural information of any molecule characterized by atoms with an intrinsic magnetic moment and angular momentum with minimal sample preparation. The elements are mainly found in food, such as H, O, C, N, and P, having at least one detectable isotope, thus giving the NMR spectrum the title “universal detector.” Also, some NMR experiments do not require separation of multiple food components, requiring little effort for sample pretreatment, and preparation is required compared to conventional methods. The food samples contain lipid, semisolid, and solid. The resulting composite NMR spectra can be further processed with multivariate statistical analysis to obtain additional structural information of food systems. NMR is perhaps the only technique that is suitable for the study of food products at both molecular and microscopic scales. Its stability and inherent ease of quantification

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have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. NMR signals offer the experimentalist a diverse array of measurable parameters such as intensity, frequency (normalized to chemical shift), line shape, line width, and relaxation times. These data have been used to determine structure, diffusion rate, viscosity, and association constants. With increasing computational power, reduced costs, and development of stronger magnetic fields, cryoprobes, solvent suppression techniques, and a large number of versatile 1D and 2D NMR pulse sequences have extended their application in the field of NMR in metabolomics and nutrigenomics because of the distinct advantage of reproducibility. The application of NMR has recently rapidly expanded in the field of food science and technology with the development of NMR instrumentation and improved programs to collect and analyze the data. A wide range of NMR food-related research has covered various fields of food science, including food microbiology, food chemistry, food engineering, and food packaging. However, prior to the potential to express ultrasensitive applications such as dynamic nuclear polarization, NMR spectroscopy is still considered a less sensitive technique than other spectrometric methods. In the exploration of the foodomics space, the second limitation of NMR spectroscopy has been traditionally considered to be a relatively reduced resonance window of proton spectra compared to 13C or 31P, so that many signals appear overlapped, especially when complex mixtures are analyzed.

11.5

Recent technology development of NMR

In conventional NMR instruments, the geometry of the magnet is known as closed geometry and the sample under investigation is placed in a uniform magnetic field. Although this facilitates high signal-to-noise, geometrically correct, spatially resolved MRI, it limits the range of detectable samples. Recently, this limitation has been solved by the development of portable or single-sided NMR [23–25]. The geometry of portable NMR sensors is referred to as an open geometry where the object is exposed to the stray field of the magnet. In this case, the magnet may be placed to one side of the object completely maintaining the integrity and dimension of the sample under study and also allowing the entire packaged product to be measured. Nowadays, single-sided NMR sensors can be divided into two categories. The first group operates in a strong magnetic field gradient [25], while the second one operates in a region under a more or less uniform magnetic field [23,24]. Both methods have their advantages and disadvantages. Unilateral NMR developed by Bl€umich et al. [26] is characterized by a high magnetic field strength operating in the proton frequency range of 13–18 MHz with a strong magnetic field gradient. This sensor requires very short (in the microsecond range) and very powerful radiofrequency pulses to achieve the desired frequency bandwidth. Another class of single-sided NMR instrument has been developed by Marble et al. [23,27]. Among these sensors, the single-sided magnet array is composed of three block magnets all magnetized along the same direction.

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The sensitive point is located approximately 1 cm above the surface of the magnet. The magnet spacings are optimized to create a locally uniform field in this region creating a relatively large magnetic resonance-sensitive volume above the surface radiofrequency coil. The NMR system has a resonant frequency of 4.68 MHz, and all pulse lengths are approximately 8 μs with 6 dB attenuation for the 90 degree pulse, and 180 degree pulses are not attenuated [28]. Manz et al. developed another portable NMR sensor with a novel one-sided entry magnet design called NMR-MOLE (mobile lateral explorer) [24]. This sensor is very effective in terms of sensitivity and penetration depth. The magnet array is based on a barrel magnet operating at 3.3 MHz and the center magnet is positioned to provide a uniform area from 4 to 16 mm away from the probe, with maximum sensitivity at a depth of 10 mm. Due to the lower diffusion attenuation in uniform field sensors, they are more suitable for studying liquid samples, for instance, aqueous solutions and biological tissue requiring unilateral or portable access.

11.6

Recent application progress of NMR

11.6.1 Potential application in food authentication Food certification is one of the main issues in food quality. NMR/MRI techniques applied for detecting authentication in different foods have been widely reported [29–32]. The potential use of NMR in food certification has been applied to several foods and beverages, such as milk and cheese [33], beef [34], truffles [35], vanillin [36], pistachios [37], and saffron extracts [38]. The scope of NMR in food certification will be expanded as the price of the instrument declines and can be more widely accessible allowing more regulatory agencies worldwide to choose the analytical testing method of using NMR samples, while allowing the sample to remain intact if required by laws or for any other reasons. Some representative foods cited in the following section include olive oil, fish, and beverages.

11.6.1.1 Application in virgin olive oil Virgin olive oils (VOOs) have been produced in countries around the Mediterranean Sea for thousands of years, with their quality being related to their geographical origin and processing methods. In terms of specific production areas and production methods, the European Union (EU) has very strict regulations on VOO labels. Because of the high market prices of VOOs, the fraudulent behavior of mislabeling the origin and the act of adulteration occur very often, although several analytical methods have been developed to detect VOO adulteration, based on their geographic origin. NMR fingerprinting has been proven to be a more effective VOO authentication method [39]. For authentication purposes, several variables have been studied, including 1H, 13C, and/or 31P NMR analyses, unsaponifiable fraction of VOOs, and phenolic compounds in the polar fraction of VOOs [40,41].

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11.6.1.2 Application in fish Rnm and Gamr [42] used a pulse 1H NMR technique to determine relaxation time (T2) from CPMG experiments on fillets of Pintado (Pseudoplatystoma corruscans) at –70 to 60°C and on freeze-dried fillets. This work aimed to determine water profiles with different mobilities in Pintado fish exposed to different environmental conditions of temperature, moisture, and water activity. The NMR technique proved that it was an alternative tool to better understand water behavior in complex biological systems. Results of the CPMG pulse sequence experiments are schematically shown in Fig. 11.8 and represent the entire water migration range measured at 35°C (T2 spectrum) in Pintado fish. There are three different sets of water protons with different relaxation times. Water molecules show different mobilities depending on the free energy of hydrogen bonds formed between water molecules and macromolecules of the food. The monolayer hydration formed on the macromolecule surface is referred to as “bound” water with very low mobility. At higher levels of T2, a new group begins to appear at the end of the spectrum, which represents the signal of the sample fat contents. The presence of this group was confirmed through the experimental data of T2 obtained in experiments with isolated fat from the Pintado samples (Fig. 11.9) [42].

Bulk water T22

T22 90,000 80,000

Amplitude

70,000

Free energy hydrogen bonds

60,000 50,000 40,000 30,000

Fat T23 T21

20,000 10,000 0 10–2

10–1

1

101

102

103

T2 (ms)

Fig. 11.8 Schematic representation of a spectrum of T2 of Pintado fish at 35°C. Reprinted with permission from P. Rnm, L. Gamr, Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish, J. Food Eng. 58 (2003) 59–66. Copyright Elsevier Publisher 2003.

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209

T2 of fat (ms)

160

10°C

93.0

25°C

160.2

35°C

222.0

40°C

276.0 60 50 40 35 30 25 20 15 10

120 100 80 60 40 20 748196.6

362289.0

84944.2

175426.2

41131.4

9643.9

19916.5

4669.7

2261.2

530.2

1094.9

256.7

60.2

124.3

29.1

6.8

14.1

3.3

1.6

0.8

0.4

0.2

0.1

0.0

0.0

0.0

0

Temperature (°C)

Amplitude

140

T2 (ms)

Fig. 11.9 Spectrum of a fat T2 from samples of Pintado fish at various temperatures. Reprinted with permission from P. Rnm, L. Gamr, Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish, J. Food Eng. 58 (2003) 59–66. Copyright Elsevier Publisher 2003.

11.6.1.3 Application in beverages The application of NMR for food certification also extends to beverages. 1H NMR spectroscopy showed potential in the discrimination of green tea based on the country of origin or with respect to quality [43]. 1H NMR was capable of simultaneously detecting catechins, amino, organic, phenolic, and fatty acids, as well as sugars from a single green tea extract. It was also used to detect catechins, caffeine, 5-galloyl quinic acid, and 2-O-(α-L-arabinopyranosyl)-myo-inositol, all of which are related to the quality of the tea. Another application field for NMR spectroscopy is to examine the source of the raw material used for making juices. 1H NMR has also been proved to accurately determine the origin or quality of juices [44]. NMR spectroscopy is also widely used in the certification of alcoholic beverages, because these beverages are available at higher prices on the market. Unfortunately, as with VOOs, this increases the risk of fraud by adulteration and intentional mislabeling. Zivania is a traditional Cypriot alcoholic beverage that has been subjected to 1H NMR spectroscopy to determine the authenticity of the country of origin [45]. The results obtained were slightly less accurate than traditional methods, but still considered acceptable. Beer is the third most popular drink in the world after water and tea and is very popular in many cultures [46]. Because some beers are expensive, this popular alcoholic beverage also suffers from adulteration practices, including falsely marking the place of origin. Initially, beer was characterized chemically by high-resolution

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1

H NMR to observe many different chemicals between different beers and the potential of NMR [47]. Further experimentation explored the potential of NMR spectroscopy for the quality control of beer [48]. The same group explored the potential of NMR spectroscopy to observe the composition of beer and relate it to the brewing site and date of production, showing the potential to use principal component analysis/NMR to monitor and control the beer production process [49]. The quality control of beer was explored previously [50] and the results suggested that NMR could be used for quality control and authentication of beer.

11.6.2 Specific NMR application to representative foods 11.6.2.1 Wine and beer Since water, ethanol, and acetic acid constitute the main proton-containing components in degraded wine, the peak intensity measured in the 1H NMR spectrum should able to determine the extent of wine spoilage [51]. Several researchers studied spoilage properties of bottled wines by measuring the acetic acid content down to the level of complex sugars, phenols, and trace elements [51–53]. In some cases, dissolved cocaine is smuggled in bottled wine. Giulio et al. [54] solved this problem by detecting dissolved cocaine resonances in the unopened bottle. This was done with a standard clinical magnetic resonance scanner, which measured at levels of 5 mM (i.e., 1.5 g/L) within 1 min. This technique can check suspicious cargo because it allows nondestructive and fast content characterization. These studies emphasize the utilization of a full bottle NMR approach, being applicable to any type of wine [51,53]. This area of research extends to other alcoholic beverages as well. The synergetic combination of 1H NMR with Fourier transform infrared attenuated total reflectance can separate different beers based on alcoholic content [48]. This provided quick information regarding different types of beer fermentation, which is a key aspect of beer production. Rodrigues et al. [55] identified six useful organic acids: acetic, citric, lactic, malic, pyruvic, and succinic acids. Organic acids play an important role in beer, not only affecting flavor, color, and aroma, but they are also good indicators of fermentation performance. The partial least squares-NMR method for the quantification of organic acids in beer, providing important information on the product’s quality and history, was established.

11.6.2.2 Fruits and vegetables By quantifying certain NMR parameters (i.e., T1, T2, and diffusion coefficient to obtain information about several processes and material properties, such as ice crystallization and water mobility), the use of NMR methods to identify compositions and evaluate quality has also been popular in various fruits and vegetables [56,57]. Studies have applied NMR to leafy vegetables, and lettuce samples with a large number of water-soluble metabolites were distinguished along with key organic solvents [58]. The safety testing of genetically modified organisms is a high priority for

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regulatory authorities, and there is a need for techniques capable of detecting any unintended effect following a genetic modification [59]. With a deeper understanding of genetically modified (GM) foods, Sobolev et al. [58] used NMR to study GM lettuce. This 2010 study compared levels of water-soluble metabolites between GM lettuce and wild-type lettuce resulting in differences in both glucose and fructose contents. Piccioni et al. [59] explored differences between transgenic and conventional maize. Transgenic maize showed higher levels of certain compounds, including primarily ethanol, citric acid, glycine-betaine, and trehalose. Kerr et al. [60] found ice formation and freezing characteristics in various foods such as potatoes, carrots, peas, and chicken legs. Freezing is a very important tool in the food industry and can extend the shelf life of food. The ability to detect freezing times, patterns, and completion times is important to improve food quality. Using MRI, freezing behavior characteristics were monitored noninvasively in different foods, including ice formation associated with the loss of NMR signal intensity, and the time from ice formation to signal loss. NMR data regarding distribution during cooking correlated to texture attributes of potato have also successfully been demonstrated [61,62]. For example, in a study by Mortensen et al. [61], the content of dry matter (DM) was measured with a pulse NMR analyzer, and the water characteristics and water transition between two boxes (low DM and medium DM) of potatoes of the Sava variety during cooking were studied. DM content was of interest in this study because it is related to potato texture and plays a role in water mobility. These studies not only demonstrated the sensitivity of MRI to the changes in water structure and final texture of the potato during processing, but also provided a scientific basis for the development of NMR methodology for predicting the sensory texture properties of other fruits and vegetables. MRI texture analysis (TA) was used to study the effects of maturation and storage of sliced apples. Here TA refers to a series of techniques used for quantifying spatial variation of gray tones in magnetic resonance images. Certain TA parameters were calculated from magnetic resonance images of apple varieties during maturity and long-term storage. Different apple varieties are found to have different TA parameter dynamics during maturation and storage periods. These special TA parameters included skewness and kurtosis (based on histogram parameters) and absolute gradient variance (gradient-based parameters). In addition, different gray-level nonuniformities (parameters based on the run length matrix), and especially those derived from co-occurrence matrices, such as correlation, sum average, sum variance, and sum entropy (within 1-, 3-, and 5-pixel neighborhoods), were also found. These TA parameters were related to chemical and physical properties (firmness of fruits, bruising, soluble solids content, titratable acids) of three apple varieties (i.e., Topaz, Redspur, and Idared) [63]. MRI texture analysis is also suitable for studying other fruits, such as pears [64,65]. Researchers have been able to use 1H NMR to obtain information on the composition of a variety of fermented cocoa beans [66]. The study identified the amino acids, polyalcohols, organic acids, sugars, methylxanthines, catechins, and phenols in cocoa beans from different countries (Ecuador, Ghana, Grenada, and Trinidad) proving this approach to be a rapid method for country identification and quantification of beans.

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Chen et al. [67] used low-field NMR to observe the water distribution and state of soybean. The distribution of water was uniform, and the distribution strength increased significantly with total water content. This proved to be a useful way to understand the role of water (in different states) in the extrusion cooking process, which is a popular manufacturing process for preparing various foods such as cereals and snacks.

11.6.2.3 Meat and fish Recent studies have quantified beef [68,69]. The aim of the study by Graham et al. [68] was to evaluate the ability of 1H NMR to characterize the changes in amino acids, nucleotides, and sugars during postmortem aging. It is worth noting that this method required minimal sample preparation to analyze beef samples and demonstrated that aging does affect the concentration of different metabolites. Their research showed an increase in proteolysis that ultimately affected the concentration of amino acids. To obtain information on the correlations between MRI, texture, and physicochemical parameters, three model systems—fibrinogen-thrombin gel (FTG), meat emulsion (ME), and meat emulsion supplemented with fibrinogen-thrombin (ME-FT)— were used in one study. MRI parameters (T2, T1, and apparent diffusion coefficients) showed that many macropores, large amounts of water, and higher water translational motion are characteristics of fibrinogen and thrombin (FTG and ME-FT) [70]. The main components of smoked salmon have also been identified using NMR spectroscopy [71]. This includes the determination of docosahexaenoic acid (DHA) and other polyunsaturated fatty acids (FAs) as well as carbohydrates, amino acids, dipeptides, and organic acids. Their research created new possibilities for identifying omega-3 FAs for fish and processed fish products. The advantage of these methods is that preparation (chemical pretreatment) and extraction that are essentially required by other methods are avoided. Similarly, Nestor et al. [72] aimed to avoid the extractions and determined the FA composition, eicosapentaenoic acid, and DHA in Arctic char. This study obtained direct information regarding the nutritional value of the fish with a simple analytical technique. Further applications of NMR analysis to fish sample processing methods were demonstrated with regard to salting [73]. In many cultures, salting fish has been a traditional preservation technique for centuries. These methods involved in the production of salting cod can affect the distribution of water in the muscle tissue of cod and protein denaturation. The investigation of water distribution shows that the process of salination and rehydration changes cells irreversibly. These analytical methods have proven to be fast and lossless techniques that can yield valuable information regarding food samples.

11.7

Conclusion and future research

In food science, obstacles to the development of NMR spectroscopy instruments are primarily due to high cost, the expertise involved, and safety issues associated with magnetic field maintenance. Because of lower costs and easier maintenance, food

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researchers can more easily obtain low-field NMR and MRI, but their applications are still limited. The application of NMR technology from research to industrial processes and quality control remains to be realized. To ensure proper data collection and analysis, more NMR-trained staff are needed for food application. Due to the complexity of food, food researchers also face challenges to establish standard operation procedures (SOPs) of NMR/MRI analysis for specific classified food products (i.e., wine, potato). Once SOPs are established, researchers can compare their NMR/MRI results for further improvements. On the other hand, NMR allows a variety of food-based applications, but still has limitations. Integrating with other analyses will provide a complete picture of the results.

References [1] P.S. Belton, I.J. Colquhoun, B.P. Hills, Applications of NMR to food science, Annu. Rep. NMR Spectr. 26 (1993) 1–53. [2] G.A. Webb (Ed.), Part 3: Applications in materials food, and marine sciences, Modern Magnetic Resonance, Springer, 2006. [3] A.M. Gil, Spectroscopy: nuclear magnetic resonance, in: B. Caballero (Ed.), Encyclopedia of Food Science and Nutrition, Elsevier, 2003, pp. 5447–5454. [4] G.L. Gall, I.J. Colquhoun, NMR spectroscopy in food authentication, in: Food Authenticity & Traceability, 53 2003, pp. 131–155. [5] I.J. Colquhoun, M. Lees, Nuclear magnetic resonance spectroscopy, in: P.R. Ashurst, M.D. Dennis (Eds.), Analytical Methods in Food Authentication, Blackie Academic & Professional, London, 1998, pp. 36–75. [6] R. Sacchi, L. Paolillo, NMR for food quality and traceability, in: L.M.L. Nollet, F. Toldra´ (Eds.), Advances in Food Diagnostics, Blackwell Science, 2007, pp. 101–118. [7] A. Spyros, P. Dais, 31P NMR spectroscopy in food analysis, Prog. Nucl. Mag. Res. Spectr. 54 (2009) 195–207. [8] M.J. Gidley, High-resolution solid-state NMR of food materials, Trends Food Sci. Technol. 3 (1992) 231–236. [9] C. Simoneau, M.J. Mccarthy, J.B. German, Magnetic resonance imaging and spectroscopy for food systems, Food Res. Int. 26 (1993) 387–398. [10] J. Belloque, M. Ramos, Application of NMR spectroscopy to milk and dairy products, Trends Food Sci. Technol. 10 (1999) 313–320. [11] W. Laurent, J.M. Bonny, J.P. Renou, Muscle characterisation by NMR imaging and spectroscopic techniques, Food Chem. 69 (2000) 419–426. [12] B.P. Hills, C.J. Clark, Quality assessment of horticultural products by NMR, Annu. Rep. NMR Spectr. (2003) 75–120. [13] B.P. Hills, A. Grant, P.S. Belton, NMR characterization of cereal and cereal based products, in: G. Kaletunc, K.J. Breslauer (Eds.), Characterization of Cereals and Flours: Properties, Analysis and Applications, Marcel Dekker, New York, 2003, pp. 409–436. [14] B.W.K. Diehl, High resolution NMR spectroscopy, Eur. J. Lipid Sci. Technol. 103 (2001) 830–834. [15] F.J. Hidalgo, R. Zamora, Edible oil analysis by high-resolution nuclear magnetic resonance spectroscopy: recent advances and future perspectives, Trends Food Sci. Technol. 14 (2003) 499–506. [16] H.J. Vogel, P. Lundberg, S. Fabiansson, H. Ruderus, E. Tornberg, Post-mortem energy metabolism in bovine muscles studied by non-invasive phosphorus-31 nuclear magnetic resonance, Meat Sci. 13 (1985) 1.

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[17] B. PS, L. RL, R. CP, The 31P nuclear magnetic resonance spectrum of cows’ milk, J. Dairy Res. 52 (1985) 47–54. [18] G. David, Phosphorus-31 NMR: Principles and Applications, Academic Press, London, 1984. [19] L.D. Quin, J.G. Verkade, Phosphorus-31 NMR spectral properties in compound characterization and structural analysis, Z. Phys. Chem. 191 (1995) 282–283. [20] P. Dais, A. Spyros, 31P NMR spectroscopy in the quality control and authentication of extra-virgin olive oil: a review of recent progress, Magn. Reson. Chem. 45 (2007) 367. [21] P.J. Hore, NMR principles, Encycl. Spectrosc. Spectrom. 12 (1999) 1545–1553. [22] S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Modeling of data, in: Modeling of data, in: Numerical Recipes in C: The Art of Scientific Computing, vol. 2, second ed., Cambridge University Press, New York, 1992, pp. 656–706. [23] A.E. Marble, I.V. Mastikhin, B.G. Colpitts, B.J. Balcom, A compact permanent magnet array with a remote homogeneous field, J. Magn. Reson. 186 (2007) 100–104. [24] B. Manz, A. Coy, R. Dykstra, C.D. Eccles, M.W. Hunter, B.J. Parkinson, et al., A mobile one-sided NMR sensor with a homogeneous magnetic field: the NMR-MOLE, J. Magn. Reson. 183 (2006) 25–31. [25] Z. Xu, R.H. Morris, M. Bencsik, M.I. Newton, Detection of virgin olive oil adulteration using low field unilateral NMR, Sensors 14 (2014) 2028. [26] B. Bl€umich, S. Anferova, S. Sharma, A.L. Segre, C. Federici, Degradation of historical paper: nondestructive analysis by the NMR-MOUSE, J. Magn. Reson. 161 (2003) 204–209. [27] A.E. Marble, I.V. Mastikhin, R.P. Macgregor, M. Akl, G. Laplante, B.G. Colpitts, et al., Distortion-free single point imaging of multi-layered composite sandwich panel structures, J. Magn. Reson. 168 (2004) 164. [28] E. Veliyulin, I.V. Mastikhin, A.E. Marble, B.J. Balcom, Rapid determination of the fat content in packaged dairy products by unilateral NMR, J. Sci. Food Agric. 88 (2010) 2563–2567. [29] D. Bertelli, M. Lolli, G. Papotti, L. Bortolotti, G. Serra, M. Plessi, Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance, J. Agric. Food Chem. 58 (2010) 8495. [30] M. Cuny, E. Vigneau, G.G. Le, I. Colquhoun, M. Lees, D.N. Rutledge, Fruit juice authentication by 1H NMR spectroscopy in combination with different chemometrics tools, Anal. Bioanal. Chem. 390 (2008) 419. [31] D.I. Ellis, V.L. Brewster, W.B. Dunn, J.W. Allwood, A.P. Golovanov, R. Goodacre, Fingerprinting food: current technologies for the detection of food adulteration and contamination, Chem. Soc. Rev. 41 (2012) 5706–5727. [32] S. Masoum, C. Malabat, M. Jalali-Heravi, C. Guillou, S. Rezzi, D.N. Rutledge, Application of support vector machines to 1H NMR data of fish oils: methodology for the confirmation of wild and farmed salmon and their origins, J. Radiat. Res. 387 (2007) 1499–1510. [33] M.A. Brescia, M. Monfreda, A. Buccolieri, C. Carrino, Characterisation of the geographical origin of buffalo milk and mozzarella cheese by means of analytical and spectroscopic determinations, Food Chem. 89 (2005) 139–147. [34] L. Shintu, S. Caldarelli, B.M. Franke, Pre-selection of potential molecular markers for the geographic origin of dried beef by HR-MAS NMR spectroscopy, Meat Sci. 76 (2007) 700–707.

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[35] L. Mannin a, A.P.S. Michela Cristinzio, A. Pietro Ragni, A. Segre, High-field nuclear magnetic resonance (NMR) study of truffles (Tuber aestivum vittadini), J. Agric. Food Chem. 52 (2004) 7988–7996. [36] E.J. Tenailleau, P. Lancelin, R.J. Robins, S. Akoka, Authentication of the origin of vanillin using quantitative natural abundance 13C NMR, J. Agric. Food Chem. 52 (2004) 7782. [37] K. Zur, A. Heier, K.W. Blaas, C. Fauhl-Hassek, Authenticity control of pistachios based on 1H- and 13C-NMR spectroscopy and multivariate statistics, Eur. Food Res. Technol. 227 (2008) 969–977. [38] A. Yilmaz, N.T. Nyberg, P. Mølgaard, J. Asili, J.W. Jaroszewski, 1H NMR metabolic fingerprinting of saffron extracts, Metabolomics 6 (2010) 511–517. [39] R.M. Alonsosalces, J.M. Morenorojas, M.V. Holland, F. Reniero, C. Guillou, K. Heberger, Virgin olive oil authentication by multivariate analyses of 1H NMR fingerprints and δ13C and δ2H data, J. Agric. Food Chem. 58 (2010) 5586–5596. [40] R.M. Alonso-Salces, K. Heberger, M.V. Holland, J.M. Moreno-Rojas, C. Mariani, G. Bellan, et al., Multivariate analysis of NMR fingerprint of the unsaponifiable fraction of virgin olive oils for authentication purposes, Food Chem. 118 (2010) 956–965. [41] S. Christophoridou, P. Dais, A. Lihong Tseng, M. Spraul, Separation and identification of phenolic compounds in olive oil by coupling high-performance liquid chromatography with postcolumn solid-phase extraction to nuclear magnetic resonance spectroscopy (LC-SPE-NMR), J. Agric. Food Chem. 53 (2005) 4667. [42] P. Rnm, L. Gamr, Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish, J. Food Eng. 58 (2003) 59–66. [43] G.L. Gall, I.J.C. And, M. Defernez, Metabolite profiling using 1H NMR spectroscopy for quality assessment of green tea, Camellia sinensis (L.), J. Agric. Food Chem. 52 (2004) 692–700. [44] P. Rinke, S. Moitrier, E. Humpfer, S. Keller, M. M€ ortter, M. Godejohann, et al., An 1H-NMR-technique for high throughput screening in quality and authenticity control of fruit juice and fruit juice raw materials—SGF-profiling, Fruit Process 1 (2007) 10–18. [45] P. Petrakis, I. Touris, M. Liouni, M. Zervou, I. Kyrikou, R. Kokkinofta, et al., Authenticity of the traditional cypriot spirit “zivania” on the basis of 1h NMR spectroscopy diagnostic parameters and statistical analysis, J. Agric. Food Chem. 53 (2005) 5293. [46] M. Nelson, The Barbarian’s Beverage: A History of Beer in Ancient Europe, Routledge, London, New York, 2005. [47] I. Duarte, A. Barros, P.S.B. Renton Righelato, M. Spraul, A. Eberhard Humpfer, et al., High-resolution nuclear magnetic resonance spectroscopy and multivariate analysis for the characterization of beer, J. Agric. Food Chem. 50 (2002) 2475–2481. [48] I.F. Duarte, A. Barros, C. Almeida, M. Spraul, A.M. Gil, Multivariate analysis of NMR and FTIR data as a potential tool for the quality control of beer, J. Agric. Food Chem. 52 (2004) 1031–1038. [49] C. Almeida, I.F. Duarte, A. Barros, J. Rodrigues, M. Spraul, A.M. Gil, Composition of beer by 1H NMR spectroscopy: effects of brewing site and date of production, J. Agric. Food Chem. 54 (2006) 700. [50] D.W. Lachenmeier, W. Frank, E. Humpfer, H. Schafer, S. Keller, M. Mortter, et al., Quality control of beer using high-resolution nuclear magnetic resonance spectroscopy and multivariate analysis, Eur. Food Res. Technol. 220 (2005) 215–221. [51] A.J. Weekley, P. Bruins, M. Sisto, M.P. Augustine, Using NMR to study full intact wine bottles, J. Magn. Reson. 161 (2003) 91–98.

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[52] E. Lo´pez-Rituerto, S. Cabredo, M. Lo´pez, A. Avenoza, J.H. Busto, J.M. Peregrina, A thorough study on the use of quantitative 1H NMR in Rioja red wine fermentation processes, J. Agric. Food Chem. 57 (2009) 2112–2118. [53] D.N. Sobieski, G. Mulvihill, J.S. Broz, M.P. Augustine, Towards rapid throughput NMR studies of full wine bottles, Solid State Nucl. Mag. 29 (2006) 191–198. [54] G. Giulio, P. Chiara, L. Antoine, M. Reto, M. Patrice, A. Marc, et al., Non-invasive detection of cocaine dissolved in wine bottles by (1) H magnetic resonance spectroscopy, Drug Test. Anal. 3 (2011) 544. [55] J.E.A. Rodrigues, G.L. Erny, A.S. Barros, V.I. Esteves, T. Branda˜o, A.A. Ferreira, et al., Quantification of organic acids in beer by nuclear magnetic resonance (NMR)-based methods, Anal. Chim. Acta 674 (2010) 166–175. [56] G.H. Brusewitz, M.L. Stone, Wheat moisture by NMR, Am. Soc. Agric. Eng. Microfiche Collect. 30 (1987) 858–862. [57] P. Chen, M.J. Mccarthy, R. Kauten, NMR for internal quality evaluation of fruits and vegetables, Anal. Chim. Acta 32 (1989) 1747–1753. [58] A.P. Sobolev, E. Brosio, R. Gianferri, A.L. Segre, Metabolic profile of lettuce leaves by high-field NMR spectra, Magn. Reson. Chem. 43 (2005) 625. [59] F. Piccioni, D. Capitani, L. Zolla, L. Mannina, NMR metabolic profiling of transgenic maize with the Cry1A(b) gene, J. Agric. Food Chem. 57 (2009) 6041–6049. [60] W.L. Kerr, R.J. Kauten, M.J. Mccarthy, D.S. Reid, Monitoring the formation of ice during food freezing by magnetic resonance imaging, LWT Food Sci. Technol. 31 (1998) 215–220. [61] M. Mortensen, A.K. Thybo, H.C. Bertram, H.J.A. And, S.B. Engelsen, Cooking effects on water distribution in potatoes using nuclear magnetic resonance relaxation, J. Agric. Food Chem. 53 (2005) 5976–5981. [62] A.K. Thybo, P.M. Szczypinski, A.H. Karlsson, S. Dønstrup, H.S. Stødkilde-Jørgensen, H.J. Andersen, Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods, J. Food Eng. 61 (2004) 91–100. [63] J. Letal, D. Jira´k, L. Sˇuderlova´, M. Ha´jek, MRI “texture” analysis of MR images of apples during ripening and storage, LWT Food Sci. Technol. 36 (2003) 719–727. [64] J. Lammertyn, T. Dresselaers, H.P. Van, P. Jancso´k, M. Wevers, B.M. Nicolaı¨, MRI and X-ray CT study of spatial distribution of core breakdown in “conference” pears, Magn. Reson. Imaging 21 (2003) 805–815. [65] R. Zhou, Y. Li, Texture analysis of MR image for predicting the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network, Magn. Reson. Imaging 25 (2007) 727. [66] A. Caligiani, D. Acquotti, M. Cirlini, G. Palla, 1H NMR study of fermented cocoa (Theobroma cacao L.) beans, J. Agric. Food Chem. 58 (2010) 12105–12111. [67] F.L. Chen, Y.M. Wei, B. Zhang, Characterization of water state and distribution in textured soybean protein using DSC and NMR, J. Food Eng. 100 (2010) 522–526. [68] S.F. Graham, T. Kennedy, O. Chevallier, A. Gordon, L. Farmer, C. Elliott, et al., The application of NMR to study changes in polar metabolite concentrations in beef longissimus dorsi stored for different periods post mortem, Metabolomics 6 (2010) 395–404. [69] J. Youngae, L. Jueun, J. Kwon, L. KwangSik, R. DoHyun, H. GeumSook, Discrimination of the geographical origin of beef by 1H NMR-based metabolomics, J. Agric. Food Chem. 58 (2010) 10458–10466. [70] A.M. Herrero, M.I. Cambero, J.A. Ordo´n˜ez, D. Castejo´n, M.D.R.D. Avila, L.D.L. Hoz, Magnetic resonance imaging, rheological properties, and physicochemical characteristics

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of meat systems with fibrinogen and thrombin, J. Agric. Food Chem. 55 (2007) 9357–9364. [71] D. Castejo´n, P. Villa, M.M. Calvo, G. Santa-Marı´a, M. Herraiz, A. Herrera, 1H-HRMAS NMR study of smoked Atlantic salmon (Salmo salar), Magn. Reson. Chem. 48 (2010) 693. [72] G. Nestor, J. Bankefors, C. Schlechtriem, E. Br€ann€as, J. Pickova, C. Sandstr€ om, Highresolution 1H magic angle spinning NMR spectroscopy of intact Arctic char (Salvelinus Alpinus) muscle. Quantitative analysis of n-3 fatty acids, EPA and DHA, J. Agric. Food Chem. 58 (2010) 10799–10803. [73] M. Gudjonsdottir, V.N. Gunnlaugsson, G.A. Finnbogadottir, K. Sveinsdottir, H. Magnusson, S. Arason, et al., Process control of lightly salted wild and farmed Atlantic cod (Gadus morhua) by brine injection, brining, and freezing—a low field NMR study, J. Food Sci. 75 (2010) E527.

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Tao Feng*, Min Sun*, Shiqing Song*, Haining Zhuang†, Lingyun Yao* *School of Perfume and Aroma Technology, Shanghai Institute of Technology, Shanghai, People’s Republic of China, †Key Laboratory of Edible Fungi Resources and Utilization (South), Ministry of Agriculture, Division of Edible Fungi Fermentation and Processing, National Engineering Research Center of Edible Fungi, Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, People’s Republic of China

12.1

Introduction

Food quality is specified in terms of traceable origin, known chemical composition (e.g., fat content, moisture, protein content), adequate physical properties (e.g., texture, color, tenderness), satisfactory sensory evaluation, and safety and health safeguards with respect to microbiological and toxic contamination, and is influenced by the processing and storage of products [1, 2]. Maintaining high food quality is of great importance for food producers, suppliers, and consumers [3]. However, food products show continuous quality changes at every stage of production and food distribution. The increasing demand for food quality requires development and application of highly efficient and reliable detection technologies throughout the food production and distribution process to guarantee high-quality products for consumers. This drives a need for researchers to develop various detection technologies for analyzing, assessing, and certifying product quality. The presence, composition, and content of volatile compounds play important roles in evaluating the quality of many food products. For example, odor sensation, which is triggered by highly complex mixtures of volatile substances, performs a vital role in shaping organoleptic quality and usually occurs in trace-level concentrations [4, 5]. These components may affect health and safety both positively and negatively. Therefore identification and quantitative evaluation of volatile compounds in food will provide important information on the quality of food products. Volatile molecules are present in raw materials and can originate at every production stage from all food components, and they can also be formed during the storage of food products [4]. Until now, more than 7000 volatile molecules have been detected and it has been estimated that up to 10,000 volatiles may be present in food [6]. In addition, new and challenging quality problems have emerged as food supply chains have become increasing global and complex. For example, food fraud and economically motivated adulteration of food are risks gaining increased attention from industry, governments, and standards-setting organizations [7], because adulteration with cheaper ingredients would decrease the quality of food products, mislead consumers, and may imply a Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00012-3 © 2019 Elsevier Inc. All rights reserved.

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health risk. In addition, the occurrence and fate of pharmaceutical compounds in food or in the environment may pose a potential threat to the ecosystem and human health and have also been recognized as emerging and prevailing problems [8, 9]. For instance, antibacterials in food constitute a potential risk to human health because the use of antibiotics in feed additives is common [8]. A classical approach to the evaluation of food quality is based on the exploitation of gas chromatography (GC) analysis, which was one of the first chromatographic separation techniques to be developed and has still today lost none of its eminence [10]. Many food components can be analyzed with great accuracy by GC and it has become one of the main techniques in analytical laboratories concerned with food quality evaluation (Fig. 12.1). The popularity of GC is based on a favorable combination of very high selectivity and resolution, good accuracy and precision, wide dynamic concentration range, and high sensitivity. GC remains a healthy and growing measurement technique with expanding influence in innovative applications, including the analysis of emerging organic pollutants, such as polychlorinated alkanes and polybrominated diphenylethers. The vitality of GC is also reflected by on-field analysis and in the development of new technologies, such as high-speed GC and comprehensive multidimensional GC (GC
GC), which greatly increases the separation capability of a chromatographic system. This chapter gives a general introduction to GC. It deals with basic principles of chromatographic methods and the reader is introduced to the chromatographic separation process. The components of a gas chromatograph are described and the application range of GC for food quality evaluation is presented.

Fig. 12.1 Schematic of gas chromatography techniques for food quality evaluation.

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12.2

221

The basic principles of GC

The separating of individual components in complex mixtures by column was first developed in 1903 by Mikhail Tswett, who introduced the term chromatography in 1906 [11, 12]. However, the chromatographic technique was used only by a few researchers in the following decades [12]. Martin and Synge extended the usefulness of chromatography in separation science and technology based on utilization of partition as the basis of the separation process [13], and the important seminal work was awarded the Nobel Prize in Chemistry in 1952. Chromatography separates components in a sample by introducing a small volume of the sample at the start/head of a column, and has become one of the most widely used techniques in modern analytical chemistry. Chromatography achieves separation of mixtures by partition of components between a mobile phase and a stationary phase. When the mobile phase is a gas, the technique is referred to as GC. The stationary phase could be solid and liquid, and the GC technique is called gas–solid chromatography (GSC) or gas–liquid chromatography (GLC) according to the physical state of the stationary phase. Separation occurs mainly according to adsorption and/or partition chromatography. In GSC, separation is obtained when the components have different adsorptivities to a solid stationary phase. In GLC, the stationary phase is a nonvolatile liquid and separation is obtained if the analytes have different distributions between the mobile and stationary phases. The molecules need to be stable at the temperatures in the injector and/or in the column during the analysis process. Components that are not volatile should be made volatile by derivatization for GC analysis.

12.2.1 GC instrumentation In GC, the mobile phase (the carrier gas) flows continuously to push the components in the injected sample through the column so that they can be separated and eluted from the column outlet. After the column, the carrier gas and sample pass through a detector. This device measures the quantity of the sample, and it generates an electrical signal. The output signal of the detector gives rise to a chromatogram (the written record of GC analysis) for sample qualitative/quantitative data collection and analysis. Schematically, a gas chromatographic instrument includes six basic parts (Fig. 12.2) [14]: carrier gas, flow controller, injector, column, detector, and data system.

12.2.1.1 Carrier gas/mobile phase The main purpose of the mobile phase (carrier gas) is to carry the sample through the column. The mobile phase must be an inert gas, and does not interact chemically with the sample components or the stationary phase. Carrier gas is typically provided by high-pressure tanks connected to the sample introduction chamber (injection port) via metal tubing (Fig. 12.2). The gas that is used must be of high purity (99.995% or higher), and, if necessary, can be purified to remove traces of oxygen, water,

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Fig. 12.2 Typical gas chromatograph: (1) gas flask with carrier gas; (2) reduction valve; (3) injection system; (4) column oven; (5) column; and (6) detector. Reprinted with permission from E. Lundanes, L. Reubsaet, T. Greibrokk, Chromatography: Basic Principles, Sample Preparations and Related Methods, first ed., Wiley-VCH Verlag: Weinheim, Germany, 2014 (Copyright, Wiley-VCH 2014).

and hydrocarbons. It is important that the carrier gas be of high purity because impurities such as oxygen and water can chemically attack the stationary phase in the column and destroy it. Adsorbent tubes containing charcoal and molecular sieves are used to remove low molecular mass hydrocarbons and water, respectively. High-purity He, N2, and H2 are the mainly used carrier gases in GC analysis. For the thermal conductivity detector (TCD), helium is the most popular. In some parts of the world (where helium is very expensive), hydrogen is chosen because of its lower price. To provide high safety in use, hydrogen is not recommended because of the potential for fire and explosions. With the flame ionization detector, either nitrogen or helium may be used. Nitrogen provides slightly more sensitivity, but a slower analysis, than helium/hydrogen. For the electron capture detector (ECD), very dry, oxygen-free nitrogen is recommended.

12.2.1.2 Flow controller system The flow rate of the mobile phase may affect the efficiency (plate height) of the column and the retention of the components. Therefore the measurement and control of carrier gas flow is of great importance for both column efficiency and qualitative analysis. It is important to know and record the flow rates of the gas so that the analysis process can be repeated in the future. Regulators on the gas flask help maintain appropriate working pressures and indicate the amount of gas left in the flask. The inlet to the instrument controls the flow rate of gas supplied to the injection port and to the column. For isothermal operation (i.e., constant temperature throughout the separation), a constant flow rate can be obtained by constant column inlet pressure. The pressure of carrier gas is reduced by the reduction valve (with pressure meters) attached to the gas flask; in addition, pressure control is provided at the gas chromatograph.

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In temperature programming, even when the inlet pressure is constant, the flow rate will decrease as the column temperature increases. For temperature gradient separations, where the temperature is increased throughout the separation time, a flow rate controller is used to assure a constant mass flow rate. In modern GC instruments, flow rate and pressure are equipped with control units and these flows are set electronically. In older instruments, the flow is controlled using pressure regulators built into the instrument. For qualitative analysis, it is essential to have a constant and reproducible flow rate so that retention times can be reproduced. Comparison of retention times is the quickest and easiest technique for compound identification. Note that different components may have the same retention time, but no individual molecule may have two different retention times.

12.2.1.3 Injection system There are many different ways of injecting solutes into a column. Most of them involve injecting the sample into the injection-port liner rather than onto the column directly. The sample inlet should handle a wide variety of samples, including gases, liquids, and solids, and permit them to be rapidly and quantitatively introduced into the carrier gas stream. Problems arising in GC separations can often be traced back to the injection process. Thus understanding the injection process is vital to obtain reproducible results and to optimize the performance of the system. The choice of injection system depends on the column type and the sample composition. In packed columns, the sample is injected directly into the column inlet. The temperature of the injection part is usually kept higher than the column temperature, and high enough to allow rapid evaporation of the sample, both solvent and sample components, when the sample is introduced. About 2–10 mL of sample is transferred from the injection syringe, which is equipped with a thin needle having a sharp beveled tip, to the column inlet through the septum, which is made of a synthetic rubber (silicone) (Fig. 12.3). When the liquid evaporates, it occupies a gas volume that is about 1000 times larger than the liquid volume. The septum is kept in place by the metal septum holder and when the syringe needle is withdrawn, the elasticity of the septum closes the puncture hole made by the needle, keeping the septum gas tight. However, after a number of injections, a permanent hole in the septum is formed and the septum needs to be replaced. The injection temperature defines the choice of septum material. In capillary columns, there are four basic types of injection techniques: isothermal (hot) split and splitless, on-column, and programmed temperature vaporization (PTV). Isothermal split and splitless injections are performed in the same inlet called the split/ splitless inlet (Fig. 12.4). This split/splitless inlet is most common because of its simplicity and robustness. The split injection technique allows only a small part of the sample to be transferred to the column, while the largest part is directed to waste through the splitter outlet valve. Injecting too much sample into capillary columns often leads to poor peak shapes and poor resolution. Therefore injections are often conducted in what is referred to as split injection mode. In split injection mode, flow control valves within the instrument divide the total carrier gas flow between the column and the split vent. The ratio of the flow through the split vent relative to that

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Fig. 12.3 Packed column injector. Reprinted with permission from E. Lundanes, L. Reubsaet, T. Greibrokk, Chromatography: Basic Principles, Sample Preparations and Related Methods, first ed., Wiley-VCH Verlag: Weinheim, Germany, 2014 (Copyright, Wiley-VCH 2014).

Fig. 12.4 Split/splitless injector for capillary columns. Reprinted with permission from E. Lundanes, L. Reubsaet, T. Greibrokk, Chromatography: Basic Principles, Sample Preparations and Related Methods, first ed., Wiley-VCH Verlag: Weinheim, Germany, 2014 (Copyright, Wiley-VCH 2014).

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through the column is called the split ratio. It dictates the amount of sample that enters the column. Typical split ratios vary from 2:1 to 100:1 depending on the analysis being conducted and the nature of the sample. The split ratios are achieved by adjusting both the valve that controls the flow through the split vent and the valve that controls total flow into the instrument. While split injections provide improved peak shapes and resolution, the majority of the sample goes undetected and is wasted. In a sense, by using split injection, the challenge of detecting the molecules in the sample has been greatly increased. For relatively concentrated samples, this is not an issue. For trace solutes, however, split injection may not be practical. The purpose of the splitless injection technique is to introduce the entire injected sample into the column and use it for trace determination. In splitless injection, the sample is introduced into the heated liner as in split injection and brought into the gas phase. When splitless injection is carried out, the column inlet temperature is kept at a temperature that is 20–50°C lower than the solvent Bp. Hence when the sample arrives at the column inlet, the solvent condenses as a thick film on the column wall. This film will act as a plug of the stationary phase into which the sample components will be dissolved. Following sample transfer to the column, the column oven temperature is increased. The solvent evaporates first from the column entrance and thereafter the analytes, which will subsequently be separated in the column. The splitter valve is opened when the whole sample has been transferred to the column to wipe out remains of the sample before the next injection. This injection technique is used for trace determinations and can only be carried out in combination with temperature programming. On-column injection is more difficult to perform and is carried out only when the analytes are temperature labile. The liquid sample is introduced at room temperature by a syringe through a valve directly into the column entrance, or more commonly through a retention gap, when the gas flow through the column is stopped. A fused silica needle with a narrow outer diameter (e.g., 200 mm) must be used with a column of 250 mm inner diameter. For 320 mm inner diameter columns, it is possible to use stainless steel needles. For the narrow columns, a large bore retention gap (e.g., 450 mm internal diameter) connected to the inlet of the column can be used for sample introduction.

12.2.1.4 GC columns and partitioning The chromatographic column contains the stationary phase and is the place where the separation process occurs. Therefore the column is often called the heart of the chromatograph. The choice of column type, dimensions, and stationary phase determines the feasibility, quality, and duration of the analysis. GC separations can be carried out on packed or capillary columns. The column is connected directly to the injector and the detector by nuts and ferrules. Packed columns feature an inner diameter greater than 1 mm and are completely packed with the stationary phase. The packing material causes a resistance against the flow of the mobile phase through the column. Stainless steel is used most often, primarily because of its strength. Glass columns are more inert, and they are often used

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for trace pesticide and biomedical samples that might react with the more active stainless steel tubing. Packed columns are easy to make and easy to use. A large variety of liquid phases is available. Because the columns are tightly packed with small particles, this flow resistivity restricts the maximum length of packed columns to about 10 m, but mostly up to 4 m long columns are in use. Capillary columns have a much smaller inner diameter than packed columns, but the stationary phase is only located as a thin film or layer on the inner wall of the column. This leaves an open longitudinal channel in the middle of the column through which the mobile phase flows. Flow resistivity (backpressure) is only determined by column length and inner diameter. With capillary columns, the length of columns can reach 50 m and longer are possible. In GC analysis, the sample is injected at one end of the column. A carrier gas such as H2 or He serves as the mobile phase and is continuously pumped through the column. The gas is chosen so as not to interact or react with the solutes in the gas phase and is thus chemically inert. It is there simply to push the solutes down the column. When a molecule partitions into the stationary phase, it does not move down the column. However, when the molecule leaves the stationary phase and enters the gas phase, it is swept down the column by the flowing carrier gas. By being swept to a new part of the column, the molecules are in contact with a new portion of the stationary phase and reestablish an equilibrium between the stationary and mobile phases. Thus the solutes reenter the stationary phase some distance down the column. Solutes that are strongly attracted to the stationary phase spend a relatively long time in the stationary phase compared to the time they spend in the mobile phase. In this way, a complete separation of components of a mixture can be achieved.

12.2.1.5 Detectors The detector must be heated to avoid condensation of components eluted from the column, and generally the detector temperature is kept at least 20°C above the highest column temperature. There are a variety of detectors available for gas chromatographs, each with their own advantages and limitations. GC detectors can be classified as mass- or concentration-sensitive detectors. The chromatographic peak area using a mass-sensitive detector is independent of the flow rate used, while the peak area using a concentration-sensitive detector depends on the flow rate. The most common modes of detection for GC are flame ionization, thermal conductivity (TC), electron capture, and mass spectrometry (MS), although other methods also exist. Table 12.1 summarizes the detection limits, selectivity, and linear ranges of these detectors.

Flame ionization detector The flame ionization detector (FID) is one of the most widely used detectors due to its low detection limits, wide dynamic range, affordability, and reliability [15]. In an FID, solutes are swept through a detector component (FID jet) as they elute from the end of the column. At the tip of the jet, the solutes pass through a flame created by the combustion of a hydrogen/air mixture. Thus this detector requires that both H2 and compressed air be supplied via external high-pressure tanks. As organic solutes burn in the

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Table 12.1 Characteristics of common gas chromatography detectors

Flame ionization detector Thermal conductivity detector Electron capture detector Mass spectrometry

Type

Detection limit

Mass sensitive

1 pg C/s

Concentration sensitive

1 ng/mL

Concentration sensitive

10 fg/s

Concentration sensitive

10 pg–10 ng

Selectivity

Linear range

Nonselective: responds to nearly all organic compounds Nonselective: responds if thermal conductivity differs from carrier gas Halogenated compounds

107

Tunable for any species

105

105

104

flame, they create ions. These ions are collected at electrodes called collector plates, creating a current in the detector circuitry. The column effluent is burned in a small oxy-hydrogen flame producing ions in the process. These ions are collected and form a small current that becomes the signal. When a hydrocarbon compound from the column enters the flame, the following happens in the reducing zone: CH radicals are formed from hydrocarbons : (CH) ! CH + O. Formyl cations are formed from CH radicals : CH  ! CHO+ + e–. A potential (300 V) is applied between the jet tip (flame) and the collector. The generated ions in the flame will produce a small current, which is proportional to the amount of compound combusted. The current (signal) is amplified in an electrometer. The FID can detect all organic compounds containing C and H, with the exception of formic acid and methane. It is a mass-sensitive detector. The minimum detectable (MD) mass is about 0.01–0.1 ng. Thus the FID is a specific property-type detector with characteristic high sensitivity.

TC detector One of the first GC detectors developed was the TCD [16]. The TCD consists of a heated metal block with two channels. Each channel is equipped with a filament (metal wire), and the filaments are connected to a Wheatstone bridge. The carrier gas going into the injector/column is led through one of the channels, while the carrier gas from the column is led through the other channel. The filament temperature depends on the heat conductivity of the passing gas. The TCD responds to changes in TC when analytes are eluted from the column. Because the carrier gas is set as a reference, analytes with thermal conductivities similar to the carrier gas will provide small responses, while analytes with thermal conductivities that differ more from the

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carrier gas will provide higher sensitivities. The carrier gas used with the TCD must have a TC that is very different from the samples to be analyzed, so the most commonly used gases are helium and hydrogen, which have the highest TC values [17]. When a compound eluted from the column passes the filament, the conductivity of the gas is decreased and the filament temperature increases. This increase in temperature results in a change in the electrical resistance of the filament, and this change is registered by the Wheatstone bridge system and a change in detector signal is observed. TCDs have remained popular, particularly for packed columns and inorganic compositions like H2O, CO, CO2, and H2. The TCD is nondestructive, and may be used for preparative separations. This detector responds to all compounds regardless of their structure and elemental make-up. The advantages of the TCD are its simplicity and reproducibility; however, this detector is not very sensitive.

Electron capture detector The ECD was developed by Lovelock in 1958 [18] and, similar to the FID, is also an ionization detector. The ECD is a selective detector for organic compounds containing an electron capturing group, for example, a halogen, a nitro group, or a conjugated carbonyl group. These compounds include halogenated materials like pesticides and, consequently, one of its primary uses is in pesticide residue analysis. The detector consists of a heated metal block with a detection channel. The carrier gas from the column enters the detection compartment and is mixed with a reagent gas if the carrier gas itself cannot be ionized. The detection compartment contains a positively charged anode, a cathode, and a β-radiation source. The β-radiation source may be 3H (0.018 MeV) or 63Ni (0.067 MeV). The 63Ni source has some practical advantages and can be used at higher temperatures. The high-energy electrons emitted from radioactive nuclide collide with the molecules or atoms of the carrier gas and the make-up gas and ionize them, thereby liberating thermal electrons of a lower energy. Several hundred thermal electrons can arise as the result of one disintegration event. These electrons are attracted by an anode in the center of the detector cavity, giving rise to a constant baseline current. When a compound with high electron affinity enters the negative zone, it can capture low-energy electrons and form negatively charged ions. Because the negative ions can be more rapidly neutralized than the electrons, the current will be reduced. The decrease in current is registered as the detector signal for the compound. The ECD is a sensitive detector and the MD mass is about 1 pg. For the right compounds, the ECD displays extremely low detection limits in the lower fg/s range. The numbers of halogens in an analyte and the substituent positions have a significant effect on the MD. Unfortunately, it is also very sensitive toward contamination and cannot be used for samples dissolved in chlorinated solvents. One drawback of the ECD is the necessity to use a radioactive source, which may require a license or at least regular radiological testing. A newer type of ECD is operated with a pulsed discharge detector so that it does not require a radioactive source [19].

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Olfactometric detector GC with olfactometric detection is based on sensory evaluation of the eluate from the chromatographic column aimed at discovering the active odor compounds [20]. The role of the detector is played by a specialist or a team of evaluating personnel. Qualitative and quantitative evaluation of the odor is carried out for each analyte leaving the chromatographic column. This establishes whether a given compound is sensory active at a given concentration (i.e., whether it appears in the sample at a level higher than the threshold of sensory detection) and what its smell is, as well as the determination of the time of sensory activity and the intensity of the odor. Determination of the analyte’s odor is possible thanks to the presence of a special attachment, a so-called olfactometric port, connected in parallel to conventional detectors, such as an FID or a mass spectrometer. The flow of the eluate is split in such a way that the analytes reach both detectors simultaneously, and because of this both signals can be compared. A combination of the olfactometric detector with a mass spectrometer is particularly advantageous, because it makes the identification of odor-active analytes possible. However, since the mass spectrometer works under vacuum conditions, while the olfactometric detector works under atmospheric pressure conditions, the retention times of the analytes might differ for the two detectors (typically shorter for the mass spectrometer). This difficulty can be overcome by installing a restrictor (in the form of a narrow bore capillary) before the mass spectrometer to increase the pressure drop between the interface and the flow splitter, as well as through careful selection of the flows of the carrier and auxiliary gases [21].

Mass spectrometry The mass spectrometer has become a very important detector in GC. Combined gas chromatography-mass spectrometry (GC-MS) is probably the most comprehensive instrumental analytical technique available to the scientist in food analysis at present. The technique is well established in food science, and a predominant area of application is in food safety, where reliable information on food contaminants, e.g., pesticides, mycotoxins, and veterinary drug residues, is of vital consequence. Information to be obtained can be both the unequivocal identification or confirmation of the contaminant and the quantification. The mass spectrometer basically consists of an ionization unit (ion source), a mass/ charge (m/z) separation unit (analyzer), and an ion detector. The mass spectrometer is a mass-sensitive detector, where the signal (S) depends on the concentration (C), the mobile-phase flow rate (F), and the split ratio in the chromatographic system (R), if a split is used: S ¼ dm=dt ¼ C  F  R The mass spectrometer can provide structural information, which can be used for identification of the compounds in addition to quantification. In GC, the most common ionization technique is electron ionization (EI). Electrons formed in a filament are sent as an electron beam with energy 70 eV through the

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relatively open ion source. The molecules (analytes) that are in gas phase at low pressures are ionized, and positive monoisotopic molecular ions (M+) are formed. Since the ionization energy of most organic compounds is 7–10 eV, the molecular ion possesses surplus energy causing fragmentation, which is compound specific. The resulting mass spectra have been shown to be reproducible, allowing reference mass spectra in a mass spectrum library to be used for identification of a compound. When molecular mass information is sought, an ionization technique giving little fragmentation, such as chemical ionization (CI), is preferred. CI is a softer ionization technique than EI. In CI, the molecules are ionized by ion/neutral reactions between the molecule and the ions formed in a reagent gas. The reagent gas ions are formed at relatively high pressures (0.1–2 Torr) in a more closed ion source, where the reagent gas is ionized by 200–500 eV electrons and ion/neutral reactions. Common reagent gases are methane, isobutane, and ammonia. Mostly protonated molecules, MH+, are formed, while the fragment ions formed are small in number. In some cases, adduct formation will occur, for example, the formation of (M + NH4)+ when ammonia is used as a reagent gas. Negative ions can be formed at conditions used for CI, that is, when using an ion source that is relatively closed and with a reagent gas (or moderating gas) of high pressure. A moderating gas does not provide negative ions, but due to its presence generates electrons of low energy (thermal electrons) by slowing down 200–500 eV electrons coming from a filament. Thermal electrons can be captured by the analyte and a negative ion is formed. When reagent gases are used, the formation of negative ions is due to ion–molecule reactions between the analyte and the negative ions from the reagent gas. The combination of negative ionization and chromatography has so far not been widely used. Negative MS is, however, especially useful for molecules with high electron affinity as in the case of the ECD. Using MS as a detector in GC and especially in capillary GC is relatively simple. The most common mobile phases do not interfere, and the analytes are volatile and already in gas phase so that EI and CI can be used. The only problem is the pressure difference between the GC and MS units. The outlet of the column is commonly at atmospheric pressure (760 Torr), while a pressure of 106–107 Torr is required in the ion source with EI. A modern mass spectrometer can usually receive 1–2 mL/min of gas and still maintain a low pressure, and this compares with the mobile-phase flow rates used in capillary GC. However, if the end of the column is placed directly into the ion source, this can lead to varying retention times because of varying pressure. Therefore a split interface is often preferred. The analytes are transferred to the mass spectrometer without loss if the carrier gas flow rate at the column outlet is equal to the flow rate to the ion source. If the inlet flow rate is lower, less of the analyte is transferred, and if the carrier gas flow rate is lower, dilution of the analyte occurs. For packed columns, different types of interfaces have been used, but the most common is the jet separator. GC-MS, especially capillary GC-MS, has become a widely used method, and is becoming very important also in routine analyses. Quadrupole mass spectrometers are most common in GC-MS. The quadrupole mass spectrometer has a mass range that covers the molecular masses of compounds, which can be chromatographed by GC.

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In addition to the type of mass spectrometer, the MDs obtainable depend on both the mode of ionization and the mode of operation. Typical values for MD amounts in full scan and selected ion monitoring (SIM) mode may be 10 and 1 pg, respectively, with EI.

12.2.1.6 Data system The major requirement of a good data system is the ability to measure the GC signal with rapid sampling rates. Currently, there is an array of hardware, made possible by advances in computer technology, which can easily perform this function. In general, there are two types of systems in common use: integrators and computers. Microprocessor-based integrators are simply hard-wired, dedicated microprocessors that use an analog-to-digital (A-to-D) converter to produce both the chromatogram (analog signal) and a digital report for quantitative analysis. They basically need to calculate the start, apex, end, and area of each peak. Algorithms to perform these functions have been available for some time. Most integrators perform area percent, height percent, internal standard, external standard, and normalization calculations. For nonlinear detectors, multiple standards can be injected, covering the peak area of interest, and software can perform a multilevel calibration. The operator then chooses an integrator calibration routine suitable for that particular detector output. Many integrators provide BASIC programming, digital control of instrument parameters, and automated analysis, from injection to cleaning of the column and injection of the next sample. Almost all integrators provide an RS-232-C interface so the GC output is compatible with “in-house” digital networks. Personal computer-based systems have now successfully migrated to the chromatography laboratory. They provide easy means to handle single or multiple chromatographic systems and provide output to both local and remote terminals. Computers have greater flexibility in acquiring data, instrument control, data reduction, display, and transfer to other devices. The increased memory, processing speed, and flexible user interfaces make them more popular than dedicated integrators. Current computerbased systems rely primarily on an A-to-D card, which plugs into the PC mainframe. Earlier versions used a separate stand-alone A-to-D box or were interfaced to stand-alone integrators. Because costs for PCs have decreased, their popularity and use have increased.

12.2.2 Theory of GC separations 12.2.2.1 Distribution constant (K) In chromatography, different theories and models have evolved that are applicable and valid under a number of given assumptions. These models are not only useful to explain the chromatographic process from a theoretical point of view, but they also offer valuable input for the practical application of GC. The separation mechanism of GC involves the equilibration of analytes between the stationary phase and the

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mobile phase. The distribution constant for the process may be expressed as a ratio between the concentration of analyte interacting with the stationary phase and the concentration of analyte in the mobile phase. The separation is caused by distinct migration rates of the components due to different strong interactions with the stationary phase. This separation is superimposed with mixing processes (dispersion) during transport through the column, which cause a broadening of the substance bands and counteract the separation since broad bands/peaks impede the resolution of closely eluting peaks. Consequently, we aim to sufficiently maximize the differences in migration rates and minimize dispersion of the components by choosing appropriate column dimensions and operating parameters. The migration rate of a compound is the sum of the transport rate through the column and retention in the stationary phase. The time spent in the mobile phase is the same for all sample components, but the retention is compound specific. It is based on the distribution of an analyte between stationary and mobile phase and is expressed by the distribution constant K. Large distribution constants mean high solubility in the stationary phase and long retention on the column. The distribution constant is defined as K ¼ cs =cm where cs is the concentration of a component in the stationary phase and cm is the concentration of a component in the mobile phase. A separation is only successful if the distribution constants of the sample components are different. The distribution constant can be graphically described with a distribution isotherm with the concentration of the solute in the mobile and stationary phases as x-axis and y-axis, respectively (Fig. 12.5) [22]. The distribution constant is either independent of the concentration of the component (linear isotherm) or changes with the concentration (nonlinear isotherm). In the latter case, the effective migration rate depends on the concentration, which results in unsymmetrical solute bands. A linear isotherm delivers a symmetric solute band (peaks) and the peak maximum is independent of the solute amount. A nonlinear isotherm results in unsymmetrical solute bands and the location of peak maximum depends on the solute amount. A nonlinear isotherm can be formed either convex or concave. In the case of a concave isotherm, K increases with increasing concentrations resulting in a shallow frontal edge and a sharp rear edge of the peak. This is called fronting. As a consequence, the peak maximum moves to higher retention times. In the opposite case, the convex isotherm, K, decreases with increasing concentrations resulting in a sharp frontal edge and a shallow rear edge of the peak. This is called tailing. The peak maximum moves to lower retention times. In practice, linear distribution isotherms are only found if the solute and stationary phase are structurally similar. However, as Fig. 12.5 shows, even for nonlinear distribution isotherms, a quasilinear range exists at low concentration, which delivers symmetric peaks with retention times that are independent of the solute amount. Depending on the shape of the distribution isotherm, GC can be distinguished between linear and nonlinear chromatography for the description of chromatographic processes. The processes further divide into ideal and nonideal chromatography. Ideal chromatography implies a reversible exchange between the two phases with the

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Fig. 12.5 Correlation between the shape of the distribution isotherm and peak form. Reprinted with permission from K. Dettmer-Wilde, W. Engewald, Practical Gas Chromatography, Springer Berlin Heidelberg, 2014 (Copyright, Springer-Verlag Berlin Heidelberg 2014).

equilibrium being established rapidly due to a fast mass transfer. Diffusion processes that result in band broadening are assumed to be small and are ignored. In ideal chromatography the concentration profiles of the separated solute should have a rectangle profile. The Gaussian profile obtained in practice demonstrates that these assumptions are not valid. In case of nonideal chromatography these assumption are not made. With these two types of classification the following four models are obtained to mathematically describe the process of chromatographic separation: l

l

l

l

Linear, ideal chromatography; Linear, nonideal chromatography; Nonlinear, ideal chromatography; Nonlinear, nonideal chromatography.

In GC, the mostly used partition chromatography can be classified as linear nonideal chromatography. In that case, almost symmetric peaks are obtained and band broadening is explained by kinetic theory according to Deemter [23].

12.2.2.2 Retention factor (k) In GC, retention (most commonly measured in units of time) is related to the distribution constant through the phase volume ratio (Vm/Vs), the mobile-phase volumetric flow rate (corrected for gas compressibility), and the unitless retention (or capacity) factor, k: k ¼ ðtR  t0 Þ=t0

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where tR is the retention time for a given analyte and t0 is the time it takes for an unretained compound to transit the entire length of column, also known as the dead time. The retention factor is important because it describes the total amount of interaction between the stationary phase and a given analyte during a separation. Further thermodynamic information about the analytes’ interaction with the stationary phase can be obtained from these relationships [24], but is not of primary interest here. For relative comparison of retention of two analytes on a stationary phase, the selectivity, α, is defined as α ¼ k1 =k2 where k1 is the retention factor of the analyte of interest and k2 is the retention factor of the other analyte. Favorable separation of two analytes is expressed in larger selectivity values and increased chromatographic space between the two peaks. The efficiency of the separation (N) is conventionally given by N ¼ 16ðtR =Wb Þ2 ¼ L=H with the analyte retention time, tR, and peak width at the base, Wb, in units of time. L is the length of the column and H is the theoretical plate height, both in units of length.

12.2.2.3 Separation number and peak capacity A number of additional parameters can be used to characterize column performance. A useful concept for multicomponent analysis is to evaluate the number of peaks that can be separated with a defined resolution in a given range of the chromatogram or the whole chromatogram. The effective peak number (EPN), the separation number (SN), and the peak capacity (nc) can be used. Resolution (Rs) is the absolute physical separation of two adjacent peaks (analytes or interferents) and is expressed as Rs ¼ ðt2  t1 Þ=W b where t2 and t1 are the retention times of the respective analytes and W b is the average width of the analyte peaks. Because Rs is specific to two analytes it is often used as a local metric for determining the suitability of a routine targeted analysis (i.e., the resolution between two standards in a calibration sample or a targeted analyte and a known interferent). Another metric often applied is SN, which is the number of peaks with an Rs of 1.18 that fit between two reference peaks [25]. By definition this only applies to the portion of the chromatogram between the reference peaks and thus represents a metric of more regional scale. While these separation terms and metrics cover the local and regional scale of a chromatogram they are insufficient for evaluating global separation performance. For that purpose, Giddings introduced peak capacity as a metric to give

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“information on the total number of resolvable components” in a separation [26]. More specifically, for a given resolution (Rs ¼ 1, herein) the theoretical peak capacity for a 1D-GC separation, nc, is given by nc ¼ ðtR, f  t1 Þ=W b where tR,f is the separation run time (and could be viewed as the last retained peak at the end of the separation), t0 is the dead time, and W b is the average peak width throughout the chromatogram. Requiring higher resolution (e.g., 1.5–2) will decrease the peak capacity proportionally. From the relationship in the equation, it is clear that with all else being equal, longer separation run times result in higher peak capacities. However, peak capacity as well as SN/EPN are theoretical values. The peak capacity assumes that the peaks are evenly distributed across the chromatogram, which unfortunately never happens in reality. Davis and Giddings demonstrated that peak resolution is already affected if the number of solutes exceeds 37% of the peak capacity [27].

12.3

Procedures for GC

12.3.1 Sample preparation Even though GC is a very powerful separation method, some GC analyses require sample preparation prior to injection. While sample preparation may be as simple as diluting the analyte(s) in an appropriate solvent or loading into a vial, or as complex as multistep extractions, the eventual quality of the method may be more dependent on the sample preparation than on the chromatography. Most sample preparation approaches for GC involve moving the analyte(s) into a solvent phase (usually organic) appropriate for liquid injection using a syringe or into the vapor phase for introduction as headspace, with a sample loop or a gas-tight syringe. In gas chromatographic method development, sample preparation should be considered in concert with the injection technique and the required detection limits of the method. Sample preparation for GC analysis involves techniques that preferentially isolate volatile and semivolatile substances and prevent the presence of ionic or high molecular weight species in the mixture to be injected into the GC. These techniques can be divided roughly into three major groups: distillation, extraction, and headspace methods. The basic goal of sample preparation is to ensure that the foregoing conditions are met, with additional goals that the preparation be reproducible to meet quantitative analysis requirements and straightforward to perform, if the analysis is to be performed routinely, as in quality assurance and in other routine testing laboratories. Nearly all sample preparation methods involve the transition of analyte(s) between phases, commonly either solid or solution to gas, or solid, liquid, or gas to liquid. In any event, gases and liquids are by far the most commonly injected sample phases. Our ability to accomplish this phase transfer is driven first by chemical equilibrium, which determines the amount of analyte that may be transferred from the original

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phase to the final phase, determining recovery, or the amount that is extracted. Second, the kinetics involved in reaching that equilibrium often determine the reproducibility of the method and may affect the recovery if equilibrium in the extraction process is not reached. There are few comprehensive treatments of sample preparation in the literature; however, there are many books and articles describing specific techniques, which are referenced throughout this chapter [28, 29]. There are several implications for all sample preparation methods. 1. Quantitative extraction (100% transfer of the analyte to the extracted phase) cannot happen, although a high partition coefficient and/or multiple extraction steps may nearly achieve it. Extraction phases should generally be chosen to maximize the partitioning into the extract phase. 2. Some amount of analyte (or interference) is always extracted, no matter how low the partition coefficient. 3. Multiple extraction steps will result in a more efficient extraction and will magnify the positive effect of small differences between analyte and interference partition coefficients. 4. Kinetics must be considered to ensure that the extraction reaches equilibrium. If equilibrium is not reached, reproducibility may suffer.

12.3.1.1 Liquid–liquid extraction Liquid–liquid extraction (LLE) usually involves extraction of analytes from a dilute aqueous phase into an organic phase, often with a concentration step to improve sensitivity. LLEs are either macroextractions or microextractions, depending on the volume of extraction solvent used, with the dividing line about 1 mL of extraction solvent. Macroextraction is performed using a separatory funnel, test tubes, or a continuous extraction device. There are a number of techniques and considerations that can affect recovery in LLE and other extractions. These include agitation, salting out, pH, temperature, washing or back extraction, and solvent choice. Extraction requires intimate contact between the two phases, most often with agitation by shaking, stirring, or vortex mixing. Generally, higher agitation speed results in more rapid equilibration, and longer agitation time ensures that equilibrium has been reached. Agitation devices (shaking speed, vortex mixer rpm, stirrer velocity, etc.) should be operated as reproducibly as possible. It is important to adjust extraction timing to reach a plateau. This ensures that small variations in mixing speed, solvent viscosity, or matrix effects should not adversely affect the extraction. Adding a high concentration of a salt such as sodium chloride often enhances extraction recovery of organic compounds extracted from water into organic phases. Increasing the ionic strength often reduces solubility of organic compounds in water, thus increasing the value of Kc and therefore the amount extracted. However, it is difficult to make general statements about whether recovery will be improved for a specific extraction scheme and analytes without testing this experimentally. Many common analytes and interferences are weak organic acids and bases. Since solution pH for these compounds can drastically affect their solubility in an aqueous phase, knowledge of their pKa and control of the solution pH can be used to effect the

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extraction. The aqueous solubility of acidic compounds will be enhanced in basic solution, while the solubility of bases will be enhanced in acid. In both cases, Kc is reduced, thereby reducing extraction recovery. To improve extraction recovery of acids, the aqueous phase can be adjusted to lower the pH, ideally to at least 2 pH units lower than the pKa of the desired analyte. Likewise, for bases, the pH can be raised. If there are multiple ionizable analytes and/or interferences, it may be necessary to adjust the aqueous solution pH by buffering to provide more reproducible control of the original solution pH. The equilibrium position of all chemical processes is affected by the temperature. Generally, to ensure extraction reproducibility, temperature should be controlled as carefully as practicable. This may be as simple as ensuring that all solutions and samples have equilibrated at the laboratory room temperature, or as complex as performing the extraction within an oven or heating block. An increase in temperature will decrease the distribution constant, Kc, thereby reducing the amount extracted. However, at elevated temperature, kinetics is often faster, so extraction speed may be increased. Often, adjusting temperature provides a trade-off between lowered recovery and faster kinetics. Careful temperature control may be required for reproducibility and is especially critical in liquid–vapor (headspace) extraction. The ideal extraction solvent would show very high solubility for analytes of interest and very low solubility for interferences, generating a large difference in the partition coefficients. If the solubilities of analytes and interferences in the original phase and in the extraction phase can be estimated or are known, Kc can be estimated as a ratio of these solubilities. Furthermore, the extraction phase must not be miscible or significantly soluble in the original phase.

12.3.1.2 Single-drop microextraction The concept of single-drop microextraction (SDME), introduced in 1996, is simple: A single drop of organic solvent is suspended from a syringe needle into the aqueous phase, and the system is agitated to drive organic compounds into the drop. The organic drop can then be transferred to the gas chromatograph using the syringe. The equilibrium theory of SDME is similar to that seen in LLE, with the equilibrium concentration of analyte in the organic phase at equilibrium given by ½A2 ¼ Kc ½A1 =ðV1 + Kc V2 Þ where the subscripts 1 and 2 refer to the aqueous and organic phases, respectively. If V2 ≪ V1 and Kc is small, this reduces to ½A2 ¼ Kc ½A1 In other LLE methods, “salting out” increases the amount extracted; however, the opposite has been observed with SDME, due to the higher ionic strength of the aqueous phase decreasing the analyte diffusion rate, thus requiring longer extraction time to reach equilibrium.

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12.3.1.3 Solid–liquid extraction: Soxhlet extraction and accelerated solvent extraction Extractions involving transfer of analytes into an organic solvent are not limited to liquid samples or solutions. In Soxhlet extraction, the solid sample is placed in a porous thimble above a solvent reservoir. As the solvent is heated, distilled solvent drips into the porous thimble, immersing the solid sample. When the thimble is full, solvent is siphoned back into the solvent reservoir and redistilled. Soxhlet extraction is generally used for semi- or nonvolatile analytes because volatiles may be lost through the condenser. Soxhlet extraction is usually slow, often requiring hours. Glassware for Soxhlet extraction is available from many chemical glassware supply houses. In the 1980s and 1990s, supercritical fluid extraction (SFE) was proposed as a useful alternative to Soxhlet extraction and is still used for a few applications; however, difficulties with instrumentation handling of supercritical fluids and reproducibility limited its routine use as an analytical technique. SFE is still commonly used in many industrial applications requiring extraction. Accelerated solvent extraction (ASE) provides an instrumental alternative to both SFE and Soxhlet extraction. As in SFE, in ASE the solid to be extracted is placed in a high-pressure vial and heated. It is then extracted with a traditional solvent that is heated and pressurized, but not to its critical point. High pressure forces solvent into the pores of the solid facilitating extraction, and elevated temperature increases extraction kinetics. The solvent is then vented and the resulting solution is collected for analysis. Traditional solvents are pumped into the extraction cell using a highperformance liquid chromatography (HPLC) pump. The cell is cleaned with a purge of nitrogen. Back pressure is maintained using a valve at the outlet.

12.3.1.4 Liquid–solid extraction: Solid-phase extraction When the sample phase is liquid and the extracting phase is solid, the family of techniques is called solid-phase extraction (SPE). Most commonly, SPE is performed by passing the liquid phase through a column, cartridge, or filter disk, selectively collecting analytes on the surface of the solid phase, while the remaining liquid phase is passed through. Analytes can then be collected by passing a strong eluting solvent over the solid. Thorough reviews of SPE techniques and methods are provided by the vendors of SPE materials. First, the stationary phase must be wetted and equilibrated with an appropriate solvent. Next, the sample is added and passed through. Usually, this is accomplished by slowly decanting the sample into the cartridge and then pulling it through using a vacuum. Because a phase transition from the liquid phase to the solid surface is involved, flow through the cartridge should be slow; to effectively transfer analyte(s) to the surface, often several minutes are needed. Following transfer, the vacuum remains on, thereby allowing the phase to dry. It may then be washed using aliquots of the original sample solvent or a weak additional solvent to remove unwanted interferences. Finally, the analytes are eluted using a strong solvent in which they are highly soluble. SPE is one of the most flexible of all extraction methods.

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There are numerous stationary phases available, allowing extraction of nearly any analyte or class of analytes. A summary of commonly used SPE phases and applications is shown in Table 12.2. When extraction involves gas chromatographic analysis of a vapor phase, usually in equilibrium with a liquid or solid phase, the technique is termed headspace extraction. If the vapor phase is stationary (usually contained within a vial or other container), it is termed static headspace extraction. If the vapor phase is moving (usually bubbled through the liquid phase and collected later), it is termed dynamic headspace extraction, also commonly called “purge and trap.” Static headspace extraction generally requires that analyte partitioning between the liquid and vapor phases reaches equilibrium, so as in LLE, analytes are not exhaustively extracted. The same extraction theory described earlier applies, except that one phase is vapor. Dynamic headspace extraction depends on continuous renewal of the extracting vapor to exhaustively drive analytes from the liquid into the vapor, allowing exhaustive extraction. The myriad applications of solid-phase microextraction (SPME) are described in several texts [30, 31]. Stir-bar sorptive extraction (SBSE) resulted from an SPME application that exhibited low analyte recovery. It was discovered that the analytes had adsorbed on the stir bar that had been added to the sample for agitation. A stir bar is coated with a sorbent material (usually polydimethylsiloxane [PDMS]), placed into the sample and stirred. Following equilibration, the stir bar is removed and placed into a PTV inlet and the analytes are desorbed into the column. SBSE has similar applications to SPME, with the main advantage being higher analyte recovery due to the larger

Table 12.2 Overview of sample preparation techniques by sample type Sample type: Solid Dissolving followed by liquid technique Supercritical fluid extraction Headspace extraction Accelerated solvent extraction Pyrolysis Thermal desorption Microwaveassisted extraction

Sample type: Liquid

Sample type: Gas

Direct “neat” injection

Direct “neat” injection (syringe or sample valve) Membrane extraction

Liquid–liquid extraction Solid-phase extraction (includes solidphase microextraction [SPME], sorbent-based extractions) Headspace extraction (includes SPME, sorbent-based extractions) Membrane extraction Trapping on a solid followed by solid technique

Trapping on a solid followed by solid technique Trapping in a liquid followed by liquid technique

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volume of extraction phase, and the main disadvantage being slower extraction and desorption kinetics also due to the larger extraction phase volume.

12.3.1.5 Solid-phase microextraction SPME was developed in 1989 as a simplified solvent-free extraction method for volatile contaminants from water. An SPME device employs a coated fused silica fiber that is attached to the end of a microsyringe plunger and can be stored within the syringe barrel. Recently, instrumentation for fully automated SPME has become available through the major instrument vendors. Nonpolar PDMS is by far the most commonly employed fiber coating (extraction phase), with about 80% of applications. Other materials include polyacrylate (PA, polar) and several combinations of solid-phase sorbents. Since PDMS and PA are both fundamentally liquids (they are so viscous that they appear to be solids, thus the colloquial description of this as a solid-phase technique) and the fiber coatings are very low volume (1 μL or less). Also, the fiber device is inserted directly into a liquid sample, the advantage of unlimited sample volume, as seen in purge and trap extraction, for analytes with low Kc applies. In SPME analysis, the fiber is first exposed either directly to a liquid sample or to the headspace. All conditions described earlier for LLE apply to these extractions as well. Following exposure, which may range in time from a few minutes to hours, depending on kinetics within the sample phase, the fiber is retracted into the syringe needle and transferred to GC for desorption under splitless inlet conditions. The splitless time, inlet temperature, and initial column conditions must be optimized to ensure complete analyte desorption from the fiber and to assist in chromatographic peak focusing. Depending on sample characteristics and extraction mode, fibers can last for as few as 10 or as many as 100 analyses.

12.3.2 Derivatization Modern capillary GC offers high chromatographic resolution, making it an excellent tool for the analysis of complex mixtures. However, an analyte must have sufficient vapor pressure that allows its transfer into the gas phase without thermal decomposition. Vapor pressure decreases with increasing molecular weight and polarity of a compound until vaporization without decomposition is no longer possible. If the low volatility is caused by strong intermolecular interactions such as hydrogen bonding, a derivatization step can mask the polar groups, which significantly increases volatility. Overall, the range of analytes suitable for GC analysis can be substantially extended by derivatization. Derivatization describes the chemical modification of an analyte into an analog that is amenable to GC analysis. Derivatization does not only aim at increasing the volatility and thermal stability of an analyte, it can also improve the gas chromatographic properties of a compound because interactions with active sites or adsorption is reduced, resulting in a more symmetric peak shape. In addition, the derivatized form of an analyte may provide a better separation from interfering compounds because it elutes in a different part of the chromatogram with potentially fewer coeluting

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compounds. Moreover, derivatization can be performed to transform the analyte into a derivative that allows a more sensitive or selective detection. For example, a halogenated derivatization reagent can be used with subsequent detection of the derivatives carried out by ECD. Derivatization reactions for ECD detection have been recently reviewed [32]. Derivatization can also aid in the identification of unknown analytes. A peak shift in the chromatogram after application of a derivatization reaction typical for a specific functional group aids in the identification of the functional group, and mass spectral detection can reveal the number of functional groups based on the mass shift. Derivatization can also produce more distinct mass spectra, e.g., typical fragment formation, which helps in the identification of unknowns. Finally, derivatization with a chiral reagent can be employed to transform enantiomers into diastereoisomers to facilitate their separation on nonchiral columns. An ideal derivatization reaction should fulfill the following requirements: l

l

l

l

l

l

l

l

l

The reaction should be fast. The reaction must be reproducible. Ideally, a single distinctive derivative is formed (not always the case, e.g., silylation of amino acids, oximation, or hydrazone formation of carbonyl compounds). The derivative must be thermally stable and exhibit good chromatographic performance. The reaction should give a quantitative yield, because an incomplete derivatization with a low derivative yield will negatively affect detection limits and can potentially increase the chromatographic background. However, as long as the derivatization yield is reproducible, the reaction may be used. The analyte composition of the sample should be mirrored in the derivatized sample without discrimination or decomposition of analytes. Formation of derivatization by-products should be minimal and they should not interfere with the analysis. This also applies to reagent excess, which should also not damage the column; otherwise it must be removed before analysis. The reaction should be easy and safe to perform. The derivatization reagent should have adequate chemical stability to allow for a convenient shelf life.

Commonly used derivatization reactions are silylation, alkylation, acylation, oximation/hydrazone formation, and to a lesser extent, cyclization. Despite the huge potential of derivatization, many analysts hesitate to use it because it is an additional step during sample preparation that can be tedious and time consuming and may introduce both qualitative and quantitative errors if not validated rigorously. Furthermore, many derivatization reagents are hazardous because of their usually required high reactivity.

12.3.3 Process optimization GC, as the separation method of choice for separation and quantification of volatile and semivolatile compounds, has been and continues to be evolving to improve the speed and quality of the data and information produced by the separation. Since the valuable information produced in a GC analysis is described by the retention time, width, and shape of the analyte peak, peak capacity is the most often used metric for

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comparing and evaluating the resolving power or information-producing ability of a GC instrument. The random nature of analyte peak distribution within a chromatogram means that the theoretical peak capacity generated during analysis must be much larger (up to an order of magnitude) than the number of peaks to be separated [27, 33]. This reality requires continued improvement to GC separation power available to analysts. One analytical strategy to optimize the information content of a separation could be to hold constant the separation time, while reducing the average peak width, resulting in an overall increase in total peak capacity. Alternatively, another analytical strategy could be to maintain the total peak capacity constant, by concurrently reducing the average peak width and the separation run time. This second strategy provides for higher throughput analyses, while maintaining the information content in a given chromatogram. The inverse relationship between peak capacity production and peak width means that to determine the upper bounds for peak capacity production for a column of given dimensions, it is necessary to further understand the sources contributing to a detected peak’s width. Peak widths can be viewed as due to two different types of contributions: on-column contributions (due to the separation processes) and off-column contributions (due to nonseparation processes such as injection, detection, electronics, dead volumes, etc.). Typically, off-column peak broadening is addressed via instrumental improvements, while on-column broadening is minimized by applying GC theory to determine optimal experimental conditions for a given analysis. The most direct approach to improving peak capacity production is to minimize on-column band broadening by optimizing separation conditions such as column dimensions, carrier gas flow program, and temperature program. In addition, common sources of off-column band broadening can include injection, nonuniform column temperatures, and dead volumes at column connections and/or within the detector, while careful implementation of GC components can minimize many of the sources of broadening (especially dead volumes). There is a large body of work in this area, with Gidding’s text being particularly relevant and useful [34].

12.3.4 Qualitative analysis GC can be used for both qualitative and quantitative analysis. Because it is more useful for quantitative analysis, most of this chapter is devoted to that topic. However, it begins with a brief look at qualitative analysis. Qualitative analysis is often the first step in the examination of a chromatographic separation. We want to know either: “What is in the sample?” or “Are certain compounds present in the sample?” Both approaches intend to identify individual components of a sample. Qualitative analysis can have different aims. It can focus on the recognition of selected analytes in the sample, which is called targeted analysis. Instead of searching for a limited number of analytes, the goal can also be the identification of all components in a sample in a nontargeted approach. One can also compare peak patterns of different samples without knowing the identity of each individual signal. This so-called fingerprinting approach classifies the samples based on the overall signal pattern. This is often used

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in metabolomics, petrochemical analysis, food, flavor, the fragrance industry, or in forensics. Furthermore, qualitative analysis can aim at the identification of biologically active substances by coupling GC to a biosensor, which acts as a detector. An example is the use of the human nose to recognize odorous compounds by means of a sniffing port. Another approach to identify biologically active substances is electroantennographic detection. The starting point for qualitative analysis is the chromatogram as a plot of detector signal over time. The qualitative information gained can be generally divided into two parts. On the one hand, the retention time on a given stationary phase is characteristic for an analyte and, on the other hand, the detection principle can deliver information on the nature of the analyte. Nowadays, MS, in most cases with EI and a quadrupole mass analyzer in combination with a mass spectral library, is often used as an identification tool. However, one should keep in mind that the comparison of the acquired spectrum with library spectra delivers a hit list that does not necessarily contain the correct compound. The match quality, potential isomers, and the overall plausibility of the respective structures must be evaluated carefully. Retention values, structure– retention relationships, and other selective detectors are valuable tools to be employed. Furthermore, selective derivatization or degradation reactions can aid in the identification of unknown signals.

12.3.5 Quantitative analysis GC is an excellent tool to separate complex mixtures to identify the components of the sample. After the question “What is in the mixture?” is answered, we almost always want to know how much of a specific compound or several compounds is in the sample. This second question is answered by quantitative analysis, which aims at the determination of the concentration or mass of an analyte in a given sample. A basic requirement for a reliable quantification is the development of an optimized analytical method, including sampling, sample preparation, and analysis of the target analytes. With regard to the chromatographic method, the injection method, injection parameters, column selection, temperature program, and detection method have to be optimized resulting ideally in baseline-separated, symmetric peaks of the target analytes that can be detected with sufficient sensitivity. It must be ensured that sampling, sample preparation, and the chromatographic process proceed with acceptable accuracy and precision. In chromatographic analysis the detector signal increases, in most cases linearly, with the analyte concentration or mass. This correlation forms the basis for quantitative analysis. To quantify an analyte, the relationship between the extent of the detector signal and the analyte concentration or analyte mass must be established, which in most cases is done by analysis of reference compounds in known concentrations. In elution chromatography, the magnitude of a chromatographic peak is expressed in two ways using either the peak height or the peak area (Fig. 12.6). In frontal chromatography, the step height is a measure for the analyte concentration. However, quantification is mostly performed using elution chromatography. With today’s electronic integrators and computers, peak area is the preferred method,

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Figure 12.6 Peak height and peak area as input data for quantification. Reprinted with permission from K. Dettmer-Wilde, W. Engewald, Practical Gas Chromatography, Springer Berlin Heidelberg, 2014 (Copyright, Springer-Verlag Berlin Heidelberg 2014).

especially if there may be changes in chromatographic conditions during the run, such as column temperature, flow rate, or sample injection reproducibility. However, peak height measurements are less affected by overlapping peaks, noise, and sloping baselines. In the discussions that follow, all data will be presented as peak areas. Gas chromatographic separation should be carried out following the advice given in this and other chromatographic treatises; some objectives are: good resolution of all peaks, symmetrical peaks, low noise levels, short analysis times, sample sizes in the linear range of the detector, and so on. Five methods of quantitative analysis will be discussed briefly, proceeding from the most simple and least accurate to those capable of higher accuracy.

12.3.5.1 Area normalization As the name implies, area normalization is really a calculation of area percent, which is assumed to be equal to weight percent. If X is the unknown analyte, then we obtain Area%X ¼ Ax =

X

! Ai
100%

i

where Ax is the area of X and the denominator is the sum of all the areas. For this method to be accurate, the following criteria must be met: l

l

l

All analytes must be eluted. All analytes must be detected. All analytes must have the same sensitivity (response/mass).

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These three conditions are rarely met, but this method is simple and is often useful if a semiquantitative analysis is sufficient or if some analytes have not been identified or are not available in pure form (for use in preparing standards).

12.3.5.2 Area normalization with response factors If standards are available, the third limitation can be removed by running the standards to obtain relative response factors, f. One substance (it can be an analyte in the sample) is chosen as the standard, and its response factor f is given an arbitrary value like 1.00. Mixtures, by weight, are made of the standard and the other analytes, and they are chromatographed. The areas of the two peaks—As and Ax for the standard and the unknown, respectively—are measured, and the relative response factor of the unknown, fx, is calculated: fx ¼ fs
ðAs =Ax Þ
ðwx =ws Þ where wx/ws is the weight ratio of the unknown to the standard. Relative response factors of some common compounds have been published for the most common GC detectors. For the highest accuracy, one should determine his/her own factors. When the unknown sample is run, each area is measured and multiplied by its factor. Then, the percentage is calculated as before:

Weight%X ¼ Ax fx =

X

! Ai fi
100%

i

12.3.5.3 External standard This method is usually performed graphically and may be included in the software of the data system. Known amounts of the analyte of interest are chromatographed, the areas are measured, and a calibration curve is plotted. If the standard solutions vary in concentration, a constant volume must be introduced to the column for all samples and standards. Manual injection is usually unsatisfactory and limits the value of this method. Better results are obtained from autosamplers that inject at least one microliter. If a calibration curve is not made and a data system is used to make the calculations, a slightly different procedure is followed. A calibration mixture prepared from pure standards is made by weight and chromatographed. An absolute calibration factor, equal to the grams per area produced, is stored in the data system for each analyte. When the unknown mixture is run, these factors are multiplied by the respective areas of each analyte in the unknown resulting in a value for the mass of each analyte. This procedure is a one-point calibration, as compared to the multipoint curve described

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before, and is somewhat less precise. Note also that these calibration factors are not the same as the relative response factors used in the area normalization method.

12.3.5.4 Internal standard This method and the next are particularly useful for techniques that are not too reproducible, and for situations where one does not (or cannot) recalibrate often. The internal standard method does not require exact or consistent sample volumes or response factors since the latter are built into the method; hence it is good for manual injections. The standard chosen for this method can never be a component in a sample and it cannot overlap any sample peaks. A known amount of this standard is added to each sample—hence the name internal standard. The internal standard must meet several criteria: l

l

l

l

It should elute near the peaks of interest. But it must be well resolved from them. It should be chemically similar to the analytes of interest and not react with any sample components. Like any standard, it must be available in high purity.

The standard is added to the sample in about the same concentration as the analyte(s) of interest and prior to any chemical derivatization or other reactions. If many analytes are to be determined, several internal standards may be used to meet the preceding criteria. Three or more calibration mixtures are made from pure samples of the analyte(s). A known amount of internal standard is added to each calibration mixture and to the unknown. Usually, the same amount of standard is added volumetrically (e.g., 1.00 mL). All areas are measured and referenced to the area of the internal standard, either by the data system or by hand. If multiple standards are used, a calibration graph is plotted where both axes are relative to the standard. If the same amount of internal standard is added to each calibration mixture and unknown, the abscissa can simply represent concentration, not relative concentration. The unknown is determined from the calibration curve or from the calibration data in the data station. In either case, any variations in conditions from one run to the next are canceled out by referencing all data to the internal standard. This method normally produces better accuracy, but it does require more steps and takes more time. Some EPA methods refer to spiking with a standard referred to as a surrogate. The requirements of the surrogate and the reasons for using it are very similar to those of an internal standard. However, a surrogate is not used for quantitative analysis so the two terms are not the same and should not be confused with each other. In general, spiking standards are used to evaluate losses and recoveries during sample workup.

12.3.5.5 Standard addition In this method the standard is also added to the sample, but the chemical chosen as the standard is the same as the analyte of interest. It requires a highly reproducible sample volume, a limitation with manual syringe injection.

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The principle of this method is that the additional, incremental signal produced by adding the standard is proportional to the amount of standard added, and this proportionality can be used to determine the concentration of analyte in the original sample. Equations can be used to make the necessary calculations, but the principle is more easily seen graphically. As increasing amounts of standard are added to the sample, the signal increases, producing a straight-line calibration. To find the original “unknown” amount, the straight line is extrapolated until it crosses the abscissa; the absolute value on the abscissa is the original concentration. In actual practice, the preparation of samples and the calculation of results can be performed in several different ways.

12.4

Advantages and limitations of GC

12.4.1 Advantages of GC The basic components of a GC instrument have remained remarkably unchanged since the first commercial instrument was introduced in 1955. Every GC instrument still is composed of a sample introduction/injection system, a device to regulate the flow of the mobile phase, an oven containing the separation column, and a detector. GC is a technique for separating individual components of chemical mixtures via differences in partitioning between a gas mobile phase and a stationary phase. The gaseous state of the mobile phase means the technique is amenable to the analytical separation of mixtures containing semivolatile and volatile analytes. In practice this leads to GC being an important analysis tool in a wide range of applications, including environmental chemistry, the food and flavor industry, the energy and petroleum industries, and the chemical manufacturing industry [35–37]. Its widespread use in both research and industrial settings has made GC a foundational analytical chemistry technique with persistent demand for improved information production via decreased analysis time or increased sensitivity or selectivity. GC has several important advantages: l

l

l

l

l

l

l

Fast analysis, typically minutes; Efficient, providing high resolution; Sensitive, easily detecting ppm and often ppb; Nondestructive, making possible online coupling; e.g., to a mass spectrometer; Highly accurate quantitative analysis, typical relative standard deviations of 1%–5%; Requires small samples, typically μL; Reliable and relatively simple.

Chromatographers have always been interested in fast analyses, and GC has been the fastest of them all, with current commercial instrumentation permitting analyses in seconds. The high efficiency of GC was evident because capillary columns typically have plate numbers of several hundred thousand. Furthermore, there have been many advances in column technology, detectors, injectors, and data-handling techniques, and the suitability of GC for automated analysis has increased its attraction to analysts. Many food components can be analyzed with great accuracy by GC and it has become

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one of the main techniques in analytical laboratories concerned with food analysis. For example, GC has replaced distillation as the preferred method for separating volatile materials. In both techniques, temperature is a major variable, but gas chromatographic separations are also dependent on the chemical nature (polarity) of the stationary phase. This additional variable makes GC more powerful.

12.4.2 Limitations of GC GC is limited to volatile samples. A practical upper temperature limit for GC operation is about 380°C, so samples need to have an appreciable vapor pressure (60 Torr or greater) at that temperature. Solutes usually do not exceed boiling points of 500°C and molecular weights of 1000 Da. This major limitation of GC is listed here along with other disadvantages of GC: l

l

l

l

Limited to volatile samples; Not suitable for thermally labile samples; Fairly difficult for large, preparative samples; Requires spectroscopy, usually MS, for confirmation of peak identity.

In summary: For the separation of volatile materials, GC is usually the method of choice due to its speed, high-resolution capability, and ease of use.

12.5

Recent technology development of GC

12.5.1 Sample preparation development The analysis of food composition present at very low concentrations in complex matrices (e.g., residues and contaminants in food samples) usually requires a complex analytical approach, involving sampling, sample preparation, analyte isolation, and qualitative and quantitative determination. From the sampling procedure up to final data processing, every step might introduce errors compromising the quality of the final analytical result. Sample preparation is usually time consuming, environmentally unfriendly, and is more difficult to automate than other steps. In spite of the tremendous evolution of the analytical instrumentation that has occurred in recent decades, especially in chromatography and MS, complex sample analysis still cannot achieve the desired results if the samples are introduced directly into the analytical instrument without a sample pretreatment step [38]. As a result, more extended methods have been developed to fulfill regulatory and analytical requirements, resulting in methodologies that involve several independent, complex steps. Most of microextraction techniques are based on sorption processes, making the development of novel sorptive materials one of the most active research areas in this field [38]. Nowadays, commercial SPME fibers are available to improve the extraction of nonpolar and polar analytes for sample preparation. However, there are still some drawbacks, such as low thermal stability, poor extraction sensitivity, swelling in organic solvent, extraction variability, and short lifetime due to fiber coating selectivity [39]. Sol–gel technology is a versatile way to overcome these limitations and

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sol–gel-based materials have been widely used as sorbents for sample preparation techniques (SPE and SPME) and chromatography stationary phases. Sol–gel chemistry is based on the hydrolysis and condensation reactions of metal alkoxides (M[OR]x) in the presence of a catalyst (acid or base) and a solvent (water and/or alcohol) prior to forming the polymer network. Much research employing sol–gel coatings in SPME has been published, which uses various precursor and organic additives during hybrid fiber synthesis to modify coating polarity and selectivity [40]. For example, Shu et al. [41] developed a novel SPME coating based on the sol–gel process; the fibers showed good thermal stability at 400°C, chemical resistance to polar organic solvent, and a wide range of pH stabilities. The use of graphene and other graphitized derived materials in sample preparation has also significantly increased in recent years since using graphene as an adsorbent for chlorophenol extraction in SPE in 2011 [42]. Graphene has a large adsorption capability thanks to the morphology of nanosheets that is accessible for molecular adsorption in both surfaces and to the large surface area. This morphology can be an advantage in comparison to carbon nanotubes and fullerenes because steric hindrance may exist when molecules access their inner walls [42]. In SPME, graphene was used for the first time by Chen et al. [43]. SPME performance was evaluated through a mixture containing six pyrethroid pesticides, the results being compared with those obtained utilizing extraction on PDMS and PDMS/divinylbenzene SPME fibers. Ponnusamy and Jen [44] also used graphene SPME fibers in the headspace mode to determine organochlorine pesticides in water samples. There are other ways to prepare SPME fibers containing graphene, such as sol–gel coating [45] and sulfonated graphene sheets [46]. Utilization of greener analytical methods stimulated the development of microextraction techniques [47]. The applications of ionic liquids (ILs) and supported IL phases in extraction and separation techniques have attracted great interest due to their hydrophobic or hydrophilic abilities for improving extraction efficiencies and selectivity [48]. In addition, the QuEChERS (quick, easy, cheap, effective, rugged, and safe) sample preparation approach, which involves liquid–liquid partitioning using acetonitrile and purifying the extract using dispersive solid-phase extraction (d-SPE), is gaining significant popularity in the analysis of food and pharmaceutical products due to its flexibility and cost-effective character [49].

12.5.2 Instrumentation development The essential elements of instruments were developed by the early 1960s, with further developments occurring in short bursts of innovation and advances in technology followed by longer periods of evolutionary changes and consolidation. Many advances were catalyzed by advances in column technology or electronics. With the introduction of robotic autosamplers at about the same time, the gas chromatograph could now operate without human intervention, 24 h operation became standard practice for routine analysis in high sample throughput environments, and gas chromatographs were deployed to remote locations and monitored electronically with only occasional visits for service and routine maintenance [50].

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The ability to identify the analytes separated by GC and deconvolute the analytical data depends on the characteristics of the detector. GC detectors can be concentration sensitive or mass sensitive. TC and ECDs are examples of concentration-sensitive detectors. In mass-sensitive detectors, the signal is related to the rate at which solute molecules enter the detector. Moreover, GC detectors can be classified as destructive, where the eluent is transformed by, for example, combustion in the FID, or nondestructive, where the analytes are detected without being significantly chemically altered. Some detectors such as the FID are not able to provide qualitative information directly, but rather rely on the reproducibility of an analyte’s retention time under consistent method parameters. Other detectors (e.g., ECD) provide unique selectivity in the detection of specific compound classes. Yet, this characteristic may be a compromise between specificity and the application range of the detector. MS is currently the most broadly applicable detector that can provide both qualitative and quantitative information. The vacuum ultraviolet (VUV) spectrophotometer was developed recently as an alternative chromatography detector. Using this detector, qualitative and quantitative information can be obtained. Additionally, the deconvolution of coeluting analytes and pseudo-absolute quantitation can be performed [51]. Additionally, the VUV detector can be used in combination with GC-MS (GC-VUV-MS) as a complementary technique to give dual qualitative information, which could be useful in food analysis. Another increasingly used type of GC detector is the atomic spectrometric detector. Atomic spectrometry is one of the oldest, most prominent, and widely used methods for elemental analysis. Different types of atomic spectrometry, including atomic absorption spectrometry, atomic fluorescence spectrometry, and plasma atomic emission spectrometry/mass spectrometry, have been coupled to GC as detectors. These detectors are able to determine the speciation of an element and chemical forms of analytes, which are usually more meaningful than merely total element contents [52]. Analytical samples are often at risk for sample contamination, decomposition, degradation, and loss during storage and transport from the collection site to the laboratory for analysis. This has resulted in a growing trend toward efforts to bring the lab to the sample when possible. Significant efforts have been invested to develop and test portable instrumentation. A new commercially available portable GC with detection provided by a toroidal ion trap mass spectrometer has been developed and described by researchers at Brigham Young University and Torion Technologies (www.torion. com) [53]. In addition, there have been several advances in the area of microfabricated and miniaturized GC detectors, including mass analyzers, ion mobility spectrometers, optical sensors, and microcantilever arrays [54].

12.5.3 Multidimensional GC GC is one of the highest resolution separation methods available to food quality evaluation. Many gas chromatographic systems are capable of achieving hundreds of thousands of theoretical plates and hundreds of peaks in a single chromatogram. Even with this great separation power, there are still many analytical samples that are even more complex. Complete separation of such samples by traditional GC is not practical. Multidimensional separations involving GC can employ two gas chromatographic

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columns or may employ HPLC followed by GC. Multidimensional gas chromatography (MDGC) is generally employed for the separation and isolation of target analytes of complex samples where linear GC has proven to be unsuccessful [55]. The aim of a typical MDGC system is to either increase the peak capacity of a separation system or increase the speed of analysis. The increase in speed of analysis is very important in industrial applications where the routine analysis of complex samples is common but it is the increase in peak capacity that is imperative. To increase the peak capacity using linear GC the analyst can choose to lengthen and/or decrease the internal diameter of a column; however, these gains in peak capacity are very limited due to practical/technical problems. MDGC offers excellent separation efficiency that serves advanced characterization of volatiles and semivolatiles in food samples [37]. An early application of MDGC in the area of food flavor was reported in 1978 [56]. Dimandja et al. [57] reported applying GC
GC to the analysis of essential oils in 2000, which was followed by further foodrelated analysis by GC
GC. In general, MDGC applies high-resolution approaches for the analysis of complex samples by providing improved separation of volatile analytes [58]. Most often, MDGC will be hyphenated with FID or MS, although other detectors (e.g., ECD) may offer specific compound analysis as required [59, 60]. For example, MDGC can be hyphenated with olfactometry for odorant analysis [20]. Due to the complexity of food samples, where aroma-active compounds are to be distinguished from other components by using olfactometry, high-resolution techniques such as MDGC are required. This is to minimize background interference prior to odor detection and to narrow down a range of possible odorants in olfactometric analysis, especially for parallel MS detection. Poorly resolved compounds will make attribution of the sensed odor to a specific compound recorded by MS difficult. Different MDGC-olfactometry-based methodologies have been developed and reviewed for analysis of complex food samples to distinguish individual odor compounds, and then their contribution to global aroma and odor characteristics of a sample was assessed [61]. Characterization of chemical constituents in food (qualitative or quantitative) by MDGC approaches is essential for the improved assessment of food safety and quality. This enables enhanced analysis and differentiation of food products with more complete descriptive and informative parameters for evaluation of food compared to conventional analytical approaches, according to bulk factors such as smell, texture, flavor, or color. High-resolution GC techniques play an important role in food analysis. Approaches that provide greater separation power than 1D-GC, such as a range of MDGC methodologies, should be increasingly attractive to provide various desirable goals for analysis of volatile and semivolatile compounds, particularly for identification with high confidence.

12.6

Recent application progress in different types of foods

From the multitude of methods applied in food analysis, GC has a key function. Presently, besides the appearance of the product, the products’ internal nutritional and health properties are becoming more and more important for consumers, especially

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in the case of beneficial compounds related to well-being as well as residues and neoformed compounds related to safety issues. There are defined prescriptive limits and maximum residual levels for residues and contaminants. However, even these aspects are the result of a scientific and political negotiation process in which economic interests with food safety aspects are weighed. This is shown by the variation of maximum residual levels (e.g., for pesticides) in different countries. For beneficial compounds, no regulations exist up to now. Food analysis is the major tool not only for ensuring food quality but also for supporting the development of new food products or technologies. From the multitude of methods applied in food analysis, GC has a key function. Although it provides the best chromatographic resolution, it was used for many decades only for the determination of more or less complex volatile compound mixtures with simple detectors such as the FID. However, at that time, use of MS in GC was limited. Parallel to the modern developments in MS (electrospray ionization, atmospheric pressure CI), which are coupled primarily to liquid chromatography, MS detection became affordable and ubiquitous for GC too. In this subsection several examples for the application of GC, including specific detection systems or sample preparation techniques, are reviewed. These examples cover major topics (sensory properties, food safety, authenticity, and health benefits) necessary for evaluating food quality.

12.6.1 Determination of volatile flavor compounds Aroma, a complex mixture of volatile compounds, plays a critical role in the perception and acceptability of fruits and vegetables. For example, durian, a kind of tropic fruit, has a unique sweet taste and strong stinky aroma. The special aroma of durian makes this fruit favored only by limited consumers. The volatiles profiles of durians have been investigated. In the study of aroma, GC analysis is the most frequently used. Li et al. [62] detected 46 odor-active compounds from Thai durian “Monthong” using aroma extract dilution analysis and GC-olfactometry. Among them, 24 were never reported to be found in durian before. The authors conducted further research, which disclosed that durian pulp overall odor can be mimicked by only two compounds: ethyl (2S)-2methylbutanoate and 1-(ethylsulfanyl) ethanethiol [63]. To get new opportunities for a wider durian marketplace, plant breeders are currently attempting to develop new cultivars of durian that have milder aromas and flesh with no seeds but a sweet taste and attractive color. Studies on characterization and identification of volatiles are important for new cultivar development. Belgis et al. [64] analyzed volatiles of six lai and four durian cultivars grown in Indonesia using SPME/GC-MS. According to their results, lai cultivars have less diverse sulfurs and esters as compared to durian, which were most probably the key reason for the different aroma characteristics of lai and durian. Lai was characterized by a less intense sulfury, fruity, and sweet aroma since it contained fewer sulfur and ester compounds than durian. Lower sulfur in lai cultivar increases its potency to be induced into new durian cultivar expansions. GC-MS with headspace solid-phase microextraction (HS-SPME) was used for the quantification of the different volatile components of bananas. Typical banana-related aroma components such as hexanal, 2-pentanone, 2-pentanol, 3-methyl-1-butanol,

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3-methylbutyl acetate (isoamyl acetate), and eugenol were detected, and differences in flavor profile were observed between ethylene and nonethylene-treated bananas at the same color stage and between bananas from different origins [65]. Nearly 250 volatile constituents have been identified for several fresh and processed banana products; however, only some of them have been recognized as banana flavor contributors. It is important to identify the trace compounds contributing significantly to banana aroma. For this purpose, it is necessary to achieve proper isolation (using adequate solvent and solventless methods) and identification of odor-contributing constituents in combination with sensory evaluation of the fruit and its individual components. SPME, simultaneous distillation–extraction, and LLE, combined with GC-FID, GC-MS, aroma extract dilution analysis, and odor activity value were used to analyze volatile compounds from banana fruit cv. Giant Cavendish and to estimate the most odor-active compounds. The analyses led to the identification of 146 compounds, and 124 of them were positively identified. Thirty-one odorants were considered as odor-active compounds and contributed to the typical banana aroma; 11 of them were reported for the first time as odor-active compounds [66]. GC-MS has been used to characterize tomato pericarp composition in transgenic plants, to assess metabolic diversity of tomato species, to measure metabolic changes associated with tomato fruit development, and to characterize biochemical changes during the development, ripening, and postharvest shelf life of tomato fruit. Mannose, citramalic, gluconic, and keto-l-gulonic acids were shown to be strongly correlated to final postharvest stages. During on-vine ripening, an increase was observed for the major hexoses, glucose and fructose, cell wall components such as galacturonic acid, and for amino acids such as aspartic acid, glutamic acid, and methionine. Major changes were also observed at the level of the tricarboxylic acid cycle, showing a decrease in malic and fumaric acids, and accumulation of citric acid [67]. Wang et al. [68] investigated the differences in volatile profile between pericarp tissue and locular gel in tomato fruit. Based on headspace solid-phase microextraction and gas chromatography-mass spectrometry (HS-SPME-GC-MS) analysis, a total of 42 volatile compounds were detected in FL 47 and Tasti-Lee tomato fruits. Regardless of cultivars, a substantially higher concentration of total volatile compounds was observed in pericarp than in locular gel, associated with higher levels of aldehydes, hydrocarbons, and nitrogen compounds. Pericarp tissue possessed higher levels of cis-3-hexenal, hexanal, heptanal, octanal, nonanal, cymene, terpinolene, undecane, dodecane, 2phenylethanol, 6-methyl-5-hepten-2-one, 2-methylbutyl acetate, 1-nitro-pentane, and 1-nitro-2-phenylethane, while the abundances of 2-methylpropanal, butanal, 2-methylbutanal, 2-methyl-2-butenal, 2-methylpropanol, 3-methylbutanol, 2-methylbutanol, and 2-butanone were higher in locular gel.

12.6.2 Determination of pesticides, toxins, and pathogenic fungal disease Pesticides are a numerous and diverse group of chemical compounds, which are used to eliminate pests in agriculture and households. They enable the quantities and the quality of crops and food to be controlled, and help to limit many human diseases

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transmitted by insect or rodent vectors. However, despite their many merits, pesticides are some of the most toxic, environmentally stable, and mobile substances in the environment [69]. They are particularly dangerous in fruit and vegetables, by which people are exposed to them. Especially, the presence of pesticide residues in baby food ingredients implies a potential risk to this vulnerable consumer group, since their metabolic pathways are still immature and food consumption rates per body weight are higher when compared to adults. Evidence suggests that early exposure to pesticides and other environmental toxicants increases the risk of developing chronic diseases, including certain cancers and neurodegenerative diseases, as well as dysfunctions in the endocrine and reproductive systems. It is therefore crucial to monitor pesticide residues in fruits and vegetables using all available analytical methods. GC can be used to determine the residues of all classes of pesticides. The choice of chromatographic column is extremely important for separating analyses and for their qualitative and quantitative determination. The chromatographic column should be highly efficient and resistant to changes in the parameters of the separation process. The solid (stationary) phase should be thermally stable and highly selective with respect to the constituents of the mixture being analyzed. The multiresidue determination of pesticides in fruits and vegetables is generally carried out by GC-MS, due to its excellent characteristics of efficient chromatographic separation, sensitivity, and confirmation power based on electron-impact ionization mass spectra. Prior to GC-MS detection, the samples of fruits and vegetables were prepared according to the material under investigation. Usually, the fruits and vegetables need to be homogenized and then different methods used to extract pesticide residues effectively. The usual techniques for fruit and vegetable extracts are: (1) (2) (3) (4)

SPE; LLE; SPME; liquid-phase microextraction (LPME).

Although SPE and LLE methods yield accurate results, they are expensive, time consuming, tedious, and hazardous for the environment and health due to the use of the relatively high volumes of organic solvents. Therefore SPME and LPME methods have been developed as replacements for LLE and SPE. SPME is a solvent-free method in which the pesticide residues are simultaneously extracted from aqueous samples or the headspace of the samples on a fiber. However, SPME is a relatively expensive technique, its fiber is fragile, and sample carryover can be a problem. In recent years, LPME has attracted increasing attention as a new technique for sample preparation. In LPME a few microliters of a water-immiscible solvent are used as an acceptor phase for the analytes and generally an aqueous solution is used as a donor phase. The LPME method, after introduction in 1996, was performed in different modes, including SDME, hollow fiber–liquid-phase microextraction, dispersive liquid–liquid microextraction (DLLME), air-assisted liquid–liquid microextraction, solidification of floating organic droplets liquid-phase microextraction, and homogeneous liquid–liquid microextraction (HLLME). The HLLME method is an extraction method in which the selected analytes are extracted from a homogeneous solution into

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a water-immiscible extraction solvent by performing a phase separation phenomenon such as using a change in temperature, ionic strength, or pH. In HLLME, the initial state is a homogeneous solution and there is no interface between the aqueous phase and the extraction solvent. Therefore it has the advantage of extremely fast extraction speed due to the absence of obstacles from the surface contact between the aqueous phase and the organic phase during the extraction procedure. Torbati et al. [70] developed a new microextraction method called salt and pH-induced HLLME in a home-made extraction device for the extraction and preconcentration of pyrethroid insecticides from different fruit juice samples prior to GC-MS. Namely, an extraction device made from two parallel glass tubes with different lengths and diameters was used in the microextraction procedure. In their method, a homogeneous solution of a sample solution and an extraction solvent (pivalic acid) was broken by performing an acid–base reaction and the extraction solvent was produced in whole in the solution. The produced droplets of the extraction solvent went up through the solution and solidified using an ice bath. They were collected without a centrifugation step. With the aim of developing a new GC-MS method to analyze 24 pesticide residues in baby foods at the level imposed by established regulation, two simple, rapid, and environmentally friendly sample preparation techniques were compared based on QuEChERS with DLLME and QuEChERS with d-SPE by Petrarca et al. [71]. Both sample preparation techniques achieved suitable performance criteria, including selectivity, linearity, acceptable recovery (70%–20%), and precision (20%). A higher enrichment factor was observed for DLLME and consequently better limits of detection and quantification were obtained. Nevertheless, d-SPE provided a more effective removal of matrix coextractives from extracts than DLLME, which contributed to lower matrix effects. Bakirci et al. investigated pesticide residues in fruits and vegetables from the Aegean region of Turkey. A total of 1423 samples of fresh fruits and vegetables collected from 2010 to 2012 were analyzed to determine the concentrations of 186 pesticide residues, among which 43 pesticide residues were detected by GC-MS. As for GC detection, the instruments and apparatus were as follows: GC analysis was conducted using a GC-ECD, and the detected pesticides were confirmed by GC-MS. The GC-ECD analyses were performed on an Agilent 6890N equipped with a split/splitless injector and a 7683B autoinjector (Agilent, Santa Clara, CA, USA). GC-MS analysis was performed on an Agilent 7890A Turbo MSD 5975C equipped with a PTV inlet and a 7683B autoinjector (Agilent, Santa Clara, CA, USA). Helium was used as the carrier gas at a flow rate of 1.0 mL/min. Argon was used as the collision gas. Separations were conducted using an HP 5-MS 30 m
0.25 mm
0.25 μL column for GC-ECD and an HP 5-MS Ultra Inert 30 m
0.25 mm
0.25 μL column (Agilent, Santa Clara, CA, USA) for GC-MS. The injection volume was 25 μL and the injector temperature was held at 280°C. Samples were analyzed as follows: the temperature program was set for an initial temperature of 70°C (held for 2 min), increased to 150°C at 25°C/min (held for 1 min), raised to 200°C at 3°C/min (held for 1 min), and finally increased to 280°C at 8°C/min (held for 15 min) for GC-ECD and GC-MS analyses [72].

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Estrogenic chemicals, including bisphenol A, alkylphenols, and natural estrogens, have attracted public attention due to their negative effects on human and environmental health, and wide occurrence in various environments and foodstuffs like vegetables and fruits. Lu et al. [73] developed a simple, reliable, and sensitive analytical method for the analysis of estrogenic contaminants in vegetables and fruits by using an isotope dilution technique coupled with GC. The isotopically labeled standards of related environmental estrogens were used as the isotope dilution standards to form the following analyte/surrogate pairings: octylphenol/13C6-4-n-nonylphenol, 4-n-nonylphenol/13C6-4-n-nonylphenol, 4-nonylphenol/13C6-4-n-nonylphenol, bisphenol A/13C12-bisphenol A, estrone/13C6-estrone, 17-α-estradiol/13C6-β-estradiol, 17-αestradiol/13C6-β-estradiol, 17-α-ethynylestradiol/13C2-17-α-ethynylestradiol, and estriol/D4-estriol. Plant samples were homogenized and extracted ultrasonically with acetone. Acid pretreatment greatly increased peak intensities for the analytes. Acid hydrolysis pretreatment was important for liberating conjugates of estrogenic contaminants in plant materials. Recoveries of the spiked analytes were greater than 90%. Method limits of detection ranged from 0.01 to 0.20 g/kg, while limits of quantification ranged from 0.04 to 0.60 g/kg. Bisphenol, nonylphenol, and natural estrogens were detected in vegetable and fruit samples obtained from local markets, illustrating the feasibility of this method for determining trace estrogenic contaminants in vegetables and fruits. The method has significant environmental implications in terms of the simultaneous analysis of estrogenic contaminants in vegetables and fruits. Aspergillus, Penicillium, Mucor, and Fusarium are responsible for the rotting of fruits like apples, pears, and cherries, and they can produce a kind of mycotoxin patulin (4-hydroxy-4H-furo[3,2-c] pyran-2(6H)-one). Although no general consensus has been reached about the degree of toxicity of patulin, government agencies in the European Union have regulated the following maximum patulin concentrations in food products intended for infants and young children: 50 μg/kg in juices; 25 μg/kg in solid apple products; and 10 μg/kg in apple products. 5-Hydroxymethylfurfural (HMF) is one of the main products of the Maillard reaction, which may occur during food processing and storage, particularly at high temperatures in carbohydrate-rich products. Moreover, HMF can also be produced during the acid-catalyzed dehydration of hexoses via 1,2 enolization or by glucosamine hydrolysis. It is present naturally in products in which water coexists with monosaccharides in acid medium, such as balsamic vinegar and fruit juice. Patulin and 5-HMF can be considered as markers of the quality of a fruit-derived product. Simultaneous GC analyses of patulin and HMF in apple and pear juice were reported by Marsol-Vall et al. [74]. The GC-MS and GC-MS/MS analyses were performed with an Agilent 7890 GC (Agilent Technologies, Palo Alto, CA, USA) with a multimode injector and a splitless liner containing a piece of glass wool. A fused silica high-temperature capillary column J&W DB–5MS (30 m
0.25 mm internal diameter; 0.25 μm film thickness) from Agilent was used at constant flow. The detector was an Agilent 7000B triple quadrupole mass spectrometer with inert EI ion source. The mass spectrometer worked in SIM or multiple reaction monitoring mode with EI ionization source at 70 eV. Helium with a purity of 99.9999% was used as carrier gas and quenching gas, and nitrogen with a

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purity of 99.999% as collision gas, both supplied by Air Liquide (Madrid, Spain). A quantity of 5 g of the homogenized sample was placed into a 50-mL centrifugation tube. Subsequently, 10 mL of EtOAc was added. The mixture was vigorously shaken for 1 min by hand. Next, the tube was centrifuged for 5 min at 5000 rpm (Multi Reax; Heidolph, Schwabach, Germany). A volume of 1.5 mL of the upper layer was transferred into a 2-mL Eppendorf vial containing 100 mg of anhydrous sodium sulfate. The vial was manually shaken for 1 min and centrifuged for 3 min at 12,000 rpm (Hettich Eppendorf Centrifuge MIKRO 22 R; Germany). Finally, the organic phase was transferred to a crimp-cap vial for injection into the gas chromatograph. Optimal conditions for injection-port derivatization were 270°C, 0.5 min purge-off, and a 1:2 sample:derivatization reagent ratio (v/v). Strawberry is one of the most currently consumed berries and the fifth most preferred fresh fruit in the United States after bananas, apples, oranges, and grapes. New information on the health benefits of strawberries, because of their high nutritional values (which include high contents of folate, potassium, vitamin C, and fiber), has stimulated domestic consumption rates. However, strawberry fruits are highly perishable and vulnerable to tissue damage during harvest and postharvest handling and storage. The ripe fruits usually have a short postharvest life, estimated to be less than 5 days due to rapid dehydration, physiological disorders, bruising, mechanical injuries, and infections caused by a wide range of phytopathogenic fungi, bacteria, and viruses. Strawberry fruit decay caused by fungal infection usually results in considerable losses during postharvest storage; thus discerning the decay and infection type at an early stage is necessary and helpful for reducing losses. Fruits infected by pathogenic microorganisms produce a different array of volatile compounds, and the compounds characteristic to a specific infection may be assessed by GC-MS. In the study of Pan et al. [75], three common pathogenic fungi belonging to Botrytis sp., Penicillium sp., and Rhizopus sp. were individually inoculated into ripe strawberry fruits; noninoculated fruits were used as controls. The strawberry fruits were stored at 5  1°C for 10 days. During storage, inoculated fruits began rotting on day 2, while control fruits began rotting on day 4. The volatile compounds emitted by the fruits were analyzed by GC-MS. The volatile compounds of strawberry fruits were collected and analyzed by HS-SPME-GC-MS. The strawberry fruits in each of the three replicates were cut up and blended for volatile gas identification and quantification. The volatiles in the sample headspace were extracted and concentrated using an SPME fiber (PDMS, 100 μm, Supelco, USA), separated and identified by GC-MS (7890A/ 5975C, Agilent, USA). SPME fiber was aged in the GC inlet port at 250°C for 30 min at 1 mL/min to remove the residual gas. Approximately 10 g of strawberry fruits from one sample were weighed and placed in a 20-mL vial. The volatile was equilibrated at 40°C for 40 min in the vial sealed with a polytetrafluoroethylene (PTFE)/butyl septum and absorbed by the extraction head of SPME from the vial. Following equilibration, the extraction head was injected into the GC inlet port in a splitless mode. Subsequently, the volatile compounds absorbed in the SPME fiber were thermally desorbed at 250°C for 3 min and transferred to the GC system. The volatile compounds were then separated by a capillary column (30 m
0.25 mm
0.25 μm of film thickness) (HP-5MS, Agilent, USA). The GC

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parameters were as follows: initial oven temperature (50°C) was held for 5 min, the oven was subsequently programmed from 50 to 200°C at a rate of 2°C min/L and the temperature was maintained for 10 min after temperature programming the helium flow to 1 mL/min. The compounds were analyzed by MS using the following parameters: electron-impact mass spectra were recorded at 70 eV ionization energy by scanning MS from m/z 30 to 450; the temperatures of the ion source and the quadrupole were 230 and 150°C, respectively. The peaks were identified by comparing their mass spectra with the spectra of the NIST library (NIST, 2008), and compounds with an N80% match were used. The relative content was represented by the peak area, which accounts for the total peak area of all the acquired compounds. There were 20 major volatile compounds acquired by HS-SPME for the four strawberry fruit groups and analyzed by GC-MS for the three replicates on day 2. On the basis of their chemical and biological properties, all volatile compounds were divided into even broader categories as esters (11), aldehydes (1), alcohols (1), acids (2), phenols (1), and olefins (4). A multiple comparison test of the relative contents of the major aromas was used to find the difference in volatiles emitted from strawberry fruits of the four groups. The key compounds can be selected and confirmed. This resulted in six volatiles (ethyl hexanoate, hexanoic acid hexyl ester, hexyl isovalerate, 2-propen-1-ol, 3-phenyl acetate, styrene, limonene) being included. The six compounds showed a significant difference in relative contents between uninfected samples (CK) and infected samples (BO, PE, and RH). GC-MS results of the four strawberry fruit groups on day 2 identified several key characteristic volatile compounds for each infection treatment compared with the control. This could be used to detect pathogenic fungal disease at an early stage.

12.6.3 Determination of nitrosamines in vegetable and meat products Inorganic nitrates are ubiquitous in the environment and can occur in foodstuff as additives (E252) or contaminants. The major contribution of nitrate to human diet is due to vegetables, where NO 3 an easily reach the part-per-thousand level. Despite nitrate being relatively nontoxic for humans, attention should be given to nitrite, which is its main metabolic by-product. In this regard, NO3  reduced to NO2  by enzymatic reactions occurring in saliva, and the resulting nitrite may react with secondary and tertiary amino compounds to form highly carcinogenic N-nitroso compounds. Furthermore, NO2  inactivates hemoglobin by converting the oxyHb (Fe2 + ) into metHg (Fe3 + ). This last effect can have severe consequences for infants and may lead to a deadly condition known as methemoglobinemia. Campanella et al. [76] present a novel isotope dilution GC method for the determination of nitrate in vegetables. The analyte was extracted in water at 70°C and mixed with isotopically enriched 15 NO3  internal standard. The sample was centrifuged and the supernatant reacted with sulfamic acid for removal of nitrite, and with triethyloxonium tetrafluoroborate for converting NO3  into volatile EtONO2. This simple aqueous chemistry allowed for separation of analyte from the sample matrix

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in the form of a gaseous derivative, which could be sampled in the headspace before GC-MS analysis. This key feature of the method made possible the collection of clean chromatograms within an elution time of only 1.8 min. Detection of EtONO2 could be performed using electron-impact ionization with a standard GC-MS setup. The method was optimized and validated for the analysis of nitrate in fresh vegetables in the 10–10,000 μg/g range with a detection limit of 2 μg/g. Due to the use of primary isotope dilution quantitation, high-precision traceable results were attained. Meat products are substrates for diverse microorganisms, some of which have the potential to lead to harmful infectious diseases. To prevent this risk, several decades ago the application of nitrate and nitrite to meat products was invented. These additives especially act on Clostridium botulinum, known as the organism forming one of the most potent toxins in nature. Further advantages of adding nitrate are the stabilization of the product color, texture, and flavor formation. Unfortunately, the major disadvantage is the ability of these compounds to react with amino groups and amides to N-nitrosamines, some of which are highly carcinogenic. For the analysis of nitrogen compounds, the use of an element-specific detector seems to be highly recommended because conventional universal detectors such as the FID or even MS in full scan mode often are not sensitive enough to detect organic nitrogen compounds at low concentrations. More specificity and sensitivity are achieved by applying chemiluminescent detection. To analyze several N-nitrosoamines, Ozel et al. [77] developed a comprehensive 2D-GC coupled to a fast-responding nitrogen chemiluminescent detector, because the nature of GC
GC approaches requires detectors that have high-speed responses. With this method they were able to determine six N-nitrosamines in various Turkish meat products [77]. This methodology might also be used for other products such as fish products or water.

12.6.4 Determination of lipophilic compounds GC is generally the technique of choice when analyzing lipophilic compounds, such as fatty acids, fatty alcohols, phytosterols, and triterpenes. Free steroidal compounds in vegetable oils were determined by Maesol-Vall et al. The GC-MS analyses were performed on an Agilent 7890 GC (Agilent Technologies, Palo Alto, CA, USA) with a multimode injector and a splitless liner containing a piece of glass wool. A fused silica high-temperature capillary column J–5MS (30 m
0.25 mm internal diameter; 0.25 μm film thickness) from Agilent was used at constant flow. The detector was an Agilent 7000B triple quadrupole mass spectrometer with inert EI ion source. The mass spectrometer worked in SIM mode with EI ionization source at 70 eV. Helium with a purity of 99.9999% was used as carrier and quenching gas, and nitrogen with a purity of 99.999% as collision gas, both supplied by Air Liquide (Madrid, Spain). The gas chromatograph temperature was programmed as follows: 150°C (held for 1 min) to 220°C at 20°C/min and to 320°C at 5°C/min (held for 1 min) at a constant flow regime of 2 mL/min. The cap of the vial containing the derivatization reagent was PTFE/silicone/PTFE, which allowed repeated injections [78]. Xu et al. [79] developed a new method that was based on comprehensive 2D-GC coupled to time-of-flight MS to analyze steroidal compounds in vegetable oils, which

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could provide better separation and higher sensitivity than conventional 1D-GC, and allowed determination of 31 sterols and triterpene alcohols in one injection. With this method, more elaborate and complete information regarding the distributions and concentrations of free phytosterols and triterpene alcohols in safflower seed oil, soybean oil, rapeseed oil, sunflower seed oil, and peanut oil were obtained. The proposed method could potentially open a new opportunity for more in-depth knowledge of the steroidal compounds of vegetable oils.

12.6.5 Determination of the authenticity of foods using GC Prerequisite analytes for the determination of authenticity are compound classes such as aroma compounds, secondary plant metabolites, or fatty acids. Due to their high numbers and diverse chemical structures, as well as varying concentrations, a unique profile of substances is present. Furthermore, the possibility is given that singlecompound (sub)classes or even single compounds are specific for a raw material (e.g., plant species) or geographical origin. The class of substances that is most unique in food products comprises the volatile compounds. These several thousand substances are responsible for the flavor of foods and their raw products. Due to their high number and different formation pathways, they provide high specificity and their volatility makes them perfect analytes for gas chromatographic determination. A prominent example for isotope analysis is the determination of the kind of sugar (beet, corn, or cane) added to products that originally contain only their endogenous sugars (e.g., honey, wine, fruits). Due to biosynthesis, sugar beets and sugarcane develop different 13C ratios in their isotope pattern. As a result an adulteration can be detected because of an unusual isotope ratio [80]. To analyze mixtures of vegetable oils and the determination of their geographical as well as their botanical source, GC directly coupled to isotope ratio MS via a combustion interface can be used. In the combustion interface the fatty acids are oxidized to carbon dioxide of which amounts of 12CO2 and 13CO2 will then be analyzed [81, 82].

12.7

Summary and outlook

Food analysis is the major tool not only for ensuring food quality but also for supporting the development of new food products or technologies. The association of GC separation and various detection techniques is a key that opens up a rich and multidimensional analytical space for the investigation of complex mixtures with high sensitivity, selectivity, and specificity. GC techniques play an important role in food quality evaluation. While GC technologies will not be available in all laboratories, deciding to use a GC system and approach clearly depends on a compromise between user aims (e.g., analysis quality and level of compound detail required) and expense of time, system complexity, and analysis cost. Clearly, adoption of more advanced methods in a routine laboratory will require a significant investment in time, cost, education, and commitment.

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Approaches that provide greater separation power than 1D-GC, such as a range of MDGC methodologies, should be increasingly attractive to provide various desirable goals for the analysis of volatile and semivolatile compounds, particularly for identification with high confidence. However, the full capabilities of these techniques are expected to be more widely applied in the future following the growing need to characterize food samples more completely, the introduction of new types of food, to monitor foods for toxicity or adulteration, as well as the increasing demand for chemical information to meet more stringent new regulations, or discovery of benefits of chemical species. The recent commercial interest in MDGC and the introduction of related devices for MDGC augurs well for increasingly sophisticated gas-phase separations. This is now deliverable through GC
GC and MDGC developments, such as those described in this chapter. It is likely that the distinction between conventional MDGC and GC
GC will become increasingly blurred into a continuum of multicolumn methods suited to volatile chemical analysis, with hybrid systems incorporating facets of both technologies.

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[13] A.J.P. Martin, R.L.M. Synge, A new form of chromatogram employing two liquid phases: a theory of chromatography. 2. Application to the micro-determination of the higher monoamino-acids in proteins, Biochem. J. 35 (12) (1941) 1358–1368. [14] E. Lundanes, L. Reubsaet, T. Greibrokk, Chromatography: Basic Principles, Sample Preparations and Related Methods, first ed., Wiley-VCH Verlag, Weinheim, Germany, 2014. [15] J.V. Hinshaw, The flame ionization detector, LCGC Europe 19 (4) (2005) 206–216. [16] J.V. Hinshaw, The thermal conductivity detector, LCGC North Am. 19 (6) (2006) 344–351. [17] A.E. Lawson, J.M. Miller, Thermal conductivity detectors in gas chromatography, J. Chromatogr. Sci. 4 (8) (1966) 273–284. [18] J.E. Lovelock, Ionization methods for the analysis of gases and vapors, Anal. Chem. 33 (2) (1961) 162–178. [19] H. Cai, W.E. Wentworth, S.D. Stearns, Characterization of the pulsed discharge electron capture detector, Anal. Chem. 68 (7) (1996) 1233–1244. [20] B. Plutowska, W. Wardencki, Application of gas chromatography-olfactometry (GC-O) in analysis and quality assessment of alcoholic beverages—a review, Food Chem. 107 (1) (2008) 449–463. [21] C. Hochereau, A. Bruchet, Design and application of a GCSNIFF/MS system for solving taste and odour episodes in drinking water, Water Sci. Technol. 49 (2004) 81–87. [22] K. Dettmer-Wilde, W. Engewald, Practical Gas Chromatography, Springer, Berlin Heidelberg, 2014. [23] J.J.V. Deemter, F.J. Zuiderweg, A. Klinkenberg, Longitudinal diffusion and resistance to mass transfer as causes of nonideality in chromatography, Chem. Eng. Sci. 5 (6) (1956) 271–289. [24] V.R. Reid, R.E. Synovec, High-speed gas chromatography: the importance of instrumentation optimization and the elimination of extra-column band broadening, Talanta 76 (4) (2008) 703–717. [25] C. Leonard, A. Grall, R. Sacks, Temperature programming for high-speed GC, Anal. Chem. 71 (11) (1999) 2123–2129. [26] J.C. Giddings, Maximum number of components resolvable by gel filtration and other elution chromatographic methods, Anal. Chem. 39 (8) (1967) 1027–1028. [27] J.M. Davis, J.C. Giddings, A. Chem, Statistical theory of component overlap in multicomponent chromatograms, Anal. Chem. 55 (3) (1983) 418–424. [28] S. Mitra, Sample Preparation Techniques in Analytical Chemistry, John Wiley & Sons, New York, 2003. [29] N. Snow, G. Slack, Sample preparation techniques, in: R. Grob, E. Barry (Eds.), Modern Practice of Gas Chromatography, fourth ed., John Wiley & Sons, Hoboken, NJ, 2004. [30] J. Pawliszyn (Ed.), Applications of Solid Phase Micro-Extraction, Royal Society of Chemistry, London, 1999. [31] S. Wercinski (Ed.), SPME: A Practical Guide, CRC Press, Boca Raton, FL, 1999. [32] C.F. Poole, Derivatization reactions for use with the electron-capture detector, J. Chromatogr. A 1296 (12) (2013) 15–24. [33] J.C. Giddings, Sample dimensionality: a predictor of order-disorder in component peak distribution in multidimensional separation, J. Chromatogr. A 703 (1–2) (1995) 3–15. [34] J.C. Giddings, Unified Separation Science, Wiley, New York, 1991. [35] M. Fischer, B.M. Scholz-B€ottcher, Simultaneous trace identification and quantification of common types of microplastics in environmental samples by pyrolysis-gas chromatography-mass spectrometry, Environ. Sci. Technol. 51 (9) (2017) 5052–5060.

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13

Qixing Nie, Shaoping Nie State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, People’s Republic of China

13.1

Introduction to high-performance liquid chromatography

Liquid chromatography (LC) is a technique of great importance in the field of separation science and originated in the early experiments carried out by Tsweet. In 1903, Tsweet separated plant pigment absorbed on filter paper using alcohol–petroleum ether mixtures; later on he used finely divided CaCO3-packed glass columns for plant pigment separation [1]. The term chromatography was introduced in 1906; it designates several similar techniques that allow the separation of different components from a mixture. The components subjected to separation exist in samples that consist of analytes and a matrix. The analytes are the molecular species of interest, and the matrix is the rest of the components in the sample [2]. Application of chromatography from laboratory to industrial practices is very prevalent in many fields. Modern agriculture and the food industry often involve the use of chemicals (fertilizers, pesticides, antibiotics, hormones, colorants, preservatives, antioxidants, etc.) to improve productivity and thus increase competitiveness and profit margins. However, overuse of these chemicals has been considered to be a serious threat to human health. The official tolerance levels of these chemical additives, residues, and contaminants present in different food products have been established in many countries, and analytical methods should be reliable and highly sensitive to ensure compliance with these regulatory requirements. Because of their high separation capacity, chromatographic procedures have found increasing employment in food science for the qualitative and quantitative analysis of a large number of molecules [3]. Chromatographic techniques (especially high-performance liquid chromatography, HPLC) and it based method are officially designed for the analysis of a large number of components present in food products. Generally, the application of HPLC in food analysis mainly involves the analysis of normal constituents of foods (sugars, fats, proteins, amino acids, and organic acids), food additives (preservatives, colors, flavors, and sweeteners), toxins (mycotoxins, fish biotoxin), chemical residues (fertilizers, pesticides, antibiotics), and food adulteration. This method breaks down complex mixtures into individual compounds, which in turn are identified and quantified by suitable detectors and data handling systems (Fig. 13.1). Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00013-5 © 2019 Elsevier Inc. All rights reserved.

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Fig. 13.1 Role of high-performance liquid chromatography for food quality evaluation.

13.1.1 Basic principles and procedures The principle of separation in LC is based on different adsorption/desorption abilities of components that are distributed between two phases, namely the stationary phase and the mobile phase. For chromatographic separation, the sample is introduced in a flowing mobile phase that passes a stationary phase. Components were separated under the impetus of the mobile phase, but the retention time of components in the stationary phase was different due to the difference in physicochemical properties and structures. When the mobile phase is a gas, the chromatography is indicated as gas chromatography (GC), and when it is a liquid it is indicated as LC [4]. Thin-layer chromatography (TLC), GC, and HPLC continuously occurred after the emergence of LC. Among these, LC has become an important method to separate/depurate organic or inorganic substances, and this effective separation method is also used in complex mixtures and isomers. HPLC has become a very reliable instrument in many fields based on the rapid development of technologically advanced material and knowledge. The HPLC instrument physically separates the components of a sample, typically in solution, and provides information about the concentration of each separated component. HPLC is generally composed of a solvent supply system (solvent container and degasser), pumping system (high-pressure pump and gradient device), sampling system (autosampler or manual syringes), separation system (chromatographic column), detection system (different types of detectors), and data processing system. A dual-piston design working in parallel is schematically shown in Fig. 13.2. The solvent supply system provides the solvent(s) necessary as a mobile phase for HPLC. The mobile phase is usually delivered from a solvent container to an instrument by using pumping systems controlled manually or by computers, then the mobile phase carries samples that are injected by the sampling system into the separation system. Generally, the mobile phase is also degassed before and during the chromatographic run; this procedure is necessary to avoid the presence of air in the mobile phase, which can disturb the

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Fig. 13.2 Schematic description of a simple high-performance liquid chromatography system. (1) A solvent supply system (solvent container and degasser), (2) a high-pressure pumping system (a dual-piston mechanical pump is pictured), (3) an injector (a syringe with the sample and a switching valve in two positions: A—load loop, B—inject), (4) a chromatographic column (possibly with a precolumn or guard column), (5) one or more detectors (a spectrophotometric detector is schematized), and (6) a controller/data processing unit. Cited from S.C. Moldoveanu, V. David, Basic Information About HPLC. 2013; V. David, Essentials in Modern HPLC Separations. 2013, Elsevier. p. 21.

analysis by reducing sensitivity or flow stability. To get a constant flow of solvent through the injector, chromatographic column, and detector, the pumps must be able to generate a high pressure, which is needed mainly to overcome the resistance to flow of the chromatographic column [4]. Columns designed for separation in LC systems have been realized with different materials, including glass, fused silica, and PEEKs, and components were separated after a series of interactions such as adsorption, desorption, distribution, and ion exchange between samples, mobile phase, and stationary phase. The nature of the stationary phase is selected based on the type of chromatography utilized for the separation (normal phase, reverse phase, ion exchange, size exclusion, etc.). Finally, the separated components are detected online by different types of detectors based on the characters of different samples. Ultraviolet (UV), diode array detector (DAD), refractive index detector (RID), evaporative light scattering detector (ELSD), fluorescence detector (FLD), and mass spectrometric detector are frequently used detectors in LC analysis [5].

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The first HPLC detectors were modified UV photometers with flow-through cuvettes [6]. The UV detector is probably the most popular spectrophotometric detector for compounds that have absorptive capacity at certain working wavelengths. DAD is similar to the UV detector but can detect variable wavelengths at the same time. ELSD belongs to the class of optical detectors that can be utilized to reveal compounds that exhibit weak absorption of UV wavelengths. This tool is suitable for polymers, carbohydrates, and lipids analysis. RID is another common detector in HPLC based on the deviation of the direction of a light beam when passing under an angle from one medium to a medium with a different refractive index (RI). This deviation depends on the difference in the RI between the two media, and this tool can be applied without the need for chromophore groups, including fluorescence-bearing groups, or other specific properties in the molecules of the analytes [4]. Fluorescence is the process of emission of light by a molecule after absorbing an excitation light. FLD is probably the most sensitive among the existing modern HPLC detectors. It is possible to detect compounds that have specific functional groups that are excited by shorter wavelength energy and emit higher wavelength radiation. Typically, sensitivity of FLD is 10–1000 times higher than that of the UV detector for strong UV absorbing materials, which is normally used as an advantage in the measurement of specific fluorescent species in samples. Detectors are also coupled in series in modern HPLC systems, although they are not necessarily used simultaneously.

13.1.2 Advantages and limitations HPLC is used increasingly in food analysis, including product research, quality control, nutritional labeling, and additives and contaminants detection. The advantages of HPLC in food analysis lie in its versatility. Amounts of material to be detected can vary from picograms and nanograms (analytical scale) to micrograms and milligrams (semipreparative scale) to multigrams (preparative scale) [7]. Another prominent advantage of HPLC or its based technology is its applicability to diverse analyte types, from small organic molecules and ions to large biomolecules and polymers [8]. Others advantages such as no requirement for volatile compounds or derivatives; nondestruction detection enabling the collection of fractions for further analysis; aqueous samples that can be run directly after a simple filtration; ability to inject different sample amounts; and compounds with a wide polarity range that can be analyzed in a single run make HPLC techniques very useful in food analysis [7]. Because separation and detection of HPLC occur at or slightly above ambient temperature, this method is suitable for compounds of limited thermal stability. Collectively, HPLC is an effective technique ideally suited for the detection of diverse components in complex food matrices, which offers the wide range of separating modes, and the combination of qualitative and quantitative separation. The majority of HPLC limitations have been mitigated by the recent development of chromatography techniques. However, a HPLC system is rather expensive compared with other analytical tools; analytical columns are expensive with a relatively short operating life, solvents are expensive, and disposal of used solvent is becoming a real problem [7]. The bewildering number of HPLC modules, columns, mobile phases, and operating parameters renders HPLC difficult for the novice.

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13.1.3 Recent technology development Over the last decade, studies were carried out to improve the quality of the particles used as the stationary phase by looking at more homogeneous and more chemical- and pressure-resistant materials. To achieve higher separation efficiency, the particle size of the column was reduced to sub-2 μm to improve resolution and sensitivity of analysis. Ultrahigh-pressure liquid chromatography (UHPLC) is a derivative of HPLC whose underlying principle is that as column packing particle size decreases, efficiency and thus resolution increases. The introduction of UHPLC improved chromatographic performance compared with conventional HPLC, which is capable of operations at high back pressures (up to 1200–1500 bar) to cope with the reduced size of columns, and can capture detector signals at high data rates for fast eluting peaks [9, 10]. Thus UHPLC technology allows a drastic reduction in time analysis while increasing resolution and sensitivity. At present, attention is being paid to the avoidance of laborious sample pretreatments that can be an important source of errors mainly for complex matrices (such as food or food-related matrices). Application of monodimensional (single column) does not always provide the enough resolution and separation power in food analysis. Multidimensional chromatographic systems have been introduced for the enhancement of separation power and peak capacity in complex matrices analysis. Multidimensional LC (MDLC) can be divided into three groups of techniques: offline MDLC, online MDLC, and comprehensive LC (LC
LC). In offline MDLC, the fractions of interest eluting from the first LC separation are collected manually, evaporated, and injected into a second LC separation; in online MDLC, special interfaces are used to allow coupling between the two separation dimensions; in comprehensive LC, the eluate from the first dimension is transferred in the second dimension by employing specific interfaces able to collect the entire first fraction and reinject it [11]. It is well known that LC analysis is based on chromatographic data such as retention time and standard compounds. However, these data may be insufficient to give complete information about the analytes. Liquid chromatography-mass spectrometry (LC-MS) is an analytical chemistry technique that combines the physical separation capabilities of LC with the mass analysis capabilities of MS. The basis of MS is the production of ions that are subsequently separated or filtered according to their mass-to-charge (m/z) ratio and detected. The resulting mass spectrum is a plot of the (relative) abundance of the produced ions as a function of the m/z ratio [12]. This tandem technique gave it an invincible edge as “the perfect analytical tool” because of the combination of excellent separation capability (HPLC) and unsurpassed sensitivity and specificity (MS). HPLC-MS is rapidly becoming the standard platform technology for analysis of biochemical, organic, and inorganic compounds commonly found in complex samples of food, environmental and biological origin with high molecular specificity and detection sensitivity. Other innovations such as chiral separations, ion mobility, and novel stationary phases promote HPLC to higher performance in diverse applications, yielding faster speed, higher resolution, greater sensitivity, and increased precision [8]. HPLC is widely used to separate a large number of compounds for both analytical and preparative purposes, which has been demonstrated to be a powerful tool in the

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food industry, including those related to speeding up the resolution of food safety issues, to improve food quality and food traceability, and to understand the bioactivity of food and food ingredients. For food quality evaluation, HPLC analysis with chemometric data analysis is being used in the food industry as a quality assurance tool, and this technology has also been expanded to monitor food adulteration. In this chapter, we summarize HPLC technology used in different types of food, such as fish, cereals, milk, fruits and vegetables, etc., and as a reference for those in the fields of food quality evaluation.

13.2

Recent application progress in different types of foods

13.2.1 Role of HPLC in fish and fishery products analysis Fish and fishery products are some of the most important components of human diets for their high nutritional value and delicate taste. The supply of fish by wild fisheries and aquaculture was about 168 million tons in 2015 worldwide, and the global seafood supply in the 1960s increased from 9.9 kg live weight equivalent per capita to 18.4 kg in 2009, which has increased at an annual average rate of 3.1% since 1961 [13]. Therefore it is vital to guarantee the safety of edible fish for human health. The diverse hazard factors with regard to fish safety and quality can be divided into three groups: chemical factors, biological factors, and physical factors. Many of these contaminants accumulated in fish tissues are transferred to humans through the food chain with possible damage to human health. HPLC is an essentially analytical technique to monitor chemical contaminants in fish prior to consumption to protect consumers from the potential risk of food-borne diseases.

13.2.1.1 Biogenic amines Biogenic amines (BAs) are low molecular weight substances formed mainly by decarboxylation of amino acids or by amination and transamination of aldehydes and ketones; their biological participation is primarily associated with psychoactive, neuroactive, or vasoactive processes. Therefore the foods (especially fish, meat, egg, dairy products, etc.) most likely associated with the formation of BAs are those containing favorable conditions (rich in proteins or free amino acids) for microbial growth or biochemical activity [14]. In fish and fishery products, the most studied BAs consist of histamine, cadaverine, putrescine, tyramine, spermidine, and spermine [14]. Identification and quantitation of BAs in food can be used as indicators of freshness, and the contents of BAs present in foods (such as fish) can also be used for quality evaluation [15]. Different methodologies such as HPLC, enzyme-linked immunosorbent assay (ELISA), and biosensor and capillary electrophoresis were introduced for the detection of BAs, among which HPLC is a reliable and highly sensitive method for BA analysis in food matrices.

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Generally, sample pretreatment, including extraction and derivatization, should be carried out before HPLC analysis. For the extraction of BAs, this procedure is usually performed in an acid medium such as trichloroacetic acid (TCA), hydrochloric acid (HCl), perchloric acid (HClO4), and methanesulfonic acid (CH4O3S). Some organic solvents such as methanol, acetone, acetonitrile, or dichloromethane have also been used alone or combined with the acids mentioned previously. All these reagents are used for the precipitation of protein in food matrices [16]. However, selection of acid for the extraction of BAs appears to be determined by the characteristic of the foodstuff to be analyzed, and TCA and HClO4 are generally used for the extraction of BAs in fish and their products. Due to low volatility and lack of chromophores of BAs, the majority of studies used HPLC-UV/vis or HPLC-FLD detection, following derivatization (pre-, on-, or postcolumn) with various derivative agents (i.e., dansyl chloride, orthophthalaldehyde, benzoyl chloride, and succinimidylferrocenyl propionate). However, the procedure of cleaning up and concentration was generally performed by solid-phase extraction before the derivatization of BAs [17, 18]. Fish and fish products are classically known for the present high levels and wide variety of BAs, but only histamine, cadaverine, and putrescine have been found to be significant in fish safety and quality determination, among which histamine is responsible for the illness “scombroid fish poisoning” (whose effect is enhanced by the copresence of cadaverine). European regulation limits the contents of histamine in fish and their products, from species associated with a high amount of histidine, to 100 and 200 mg/kg, and examinations of histamine in fish and their products must be carried out in accordance with specified HPLC analytical reference methods [19]. Altieri et al. [20] established an HPLC-UV-DAD method for the determination of histamine in fish products. A column of Zorbax (Eclipse XDB C18 4.6
150 mm 5 μm) and mobile phase (A: ammonium acetate 0.1 M at pH 7.9 and B: acetonitrile) with gradient eluted were applied for chromatographic separation. The results showed the recoveries of the method for histamine were in the range from 97% to 103%, and limit of detection (LOD) and limit of quantification (LOQ) were 3 and 10 mg/kg, respectively. Those values are, respectively, 3% and 10% of the lowest limit set by the European regulation for histamine in fishery products (100 mg/kg). Detection of various BAs simultaneously is another advantage of the HPLC-based method. Zhai et al. [21] performed the reverse-phase HPLC-FLD method to determine eight BAs (histamine, tryptamine, putrescine, 2-phenylethylamine, cadaverine, tyramine, spermidine, and spermine) in 13 species of fish and 49 fish products that are commonly consumed in southern China. The total level of BAs in fish samples ranged from 5.03 to 156.17 mg/kg (with a mean value of 44.17 mg/kg, and levels of histamine were less than 21.85 mg/kg), which were not high enough to indicate fish decomposition. However, several fermented and packaged fish products showed higher levels of BAs (484.42 mg/kg in lightly cured horse mackerel and 166.45 mg/kg in packaged eel). Especially, the lightly cured horse mackerel (a traditional fish product in southern China) was found rich in 2-phenylethylamine (57.61 mg/kg), cadaverine (244.41 mg/kg), and tyramine (62.85 mg/kg). The identification of BAs by molecular weight by LC was a fundamental step for the most accurate and reliable identification of BAs in foods. An MS detector had been

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widely used for BA determination in biological samples with high specificity, since it not only records the retention time of the compounds but also depicts their structure, thus solving the problem of distinguishing different amines with similar physicochemical properties [22]. Fu et al. [23] used an ultrahigh-performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-TQMS) with beadbeating disruption extraction (5-sulfosalicylic acid as extraction reagent) and benzoyl chloride derivatization to analyze BAs in different fish. The method showed good linearity with a linear range of three to four orders of magnitude and regression coefficients ranging from 0.9966 to 0.9999. LOD and LOQ could even reach lower pg/mL levels. Satisfactory recovery was obtained from 74.9% to 119.3%, and the derivatives were stable within 48 h at 4°C. The results indicated that this method was suitable for analysis of BAs. Despite the advantages offered by LC-MS, application of the instrument is not regularly performed, presumably because UV and FLD are cheaper and enough to provide accurate and repeatable results for BA detection [16].

13.2.1.2 Fish biotoxin Marine biotoxins have a negative effect on public health and marine resources. Ciguatoxins (CTXs) and tetrodotoxins (TTXs) are the two main biotoxins present in some species of fish.

Ciguatoxins Ciguatoxic fish may have normal appearance, taste, and smell, but these toxic fish are a serious threat to human health. The lipid-soluble toxin is produced by the coral reef species the dinoflagellate Gambierdiscus spp. CTX is stored by the fish after ingestion of the toxin-containing microalgae, and it is subsequently moved upward through the food chain, which concentrates the toxin as smaller fish are eaten by larger fish, which are consumed by humans [24]. The CTX toxin group can be divided into Caribbean CTX, Pacific CTX, and Indian CTX based on the different structural backbone of the molecules, and which have posed a great potential risk to human health in the past decades. Global estimates suggest that ciguatera fish poisoning affects between 50,000 and 500,000 people annually [13]. Analytical methods, including HPLC, mouse bioassay, and immunoassays have been introduced to determine these toxins to support fish products monitoring, and the HPLC method was improved during the past decade. LOD of the primary HPLC-FLD method used for determination of CTX may not be sufficient for the submicrogram per kilogram levels that are required for its detection in fish [13]. Yogi et al. [25] established a sensitive LC-MS/MS method for the detection of a wide variety of CTX1B and CTX3C toxins that occur in Pacific regions, which investigated toxin profiles by using 14 isolated reference toxins on 8 representative species of fish collected in 4 different areas of the Pacific. The method is desirable to meet the very stringent hazard advisory levels of 0.01 μg/kg CTX1B equivalents by the US Food and Drug Administration (FDA), and the calculated CTX1B contents were 0.181 μg/kg flesh in Lutjanus monostigma and 0.079 μg/kg flesh in Variola louti. The most widespread protocol for CTX analysis is the Ciguatoxin Rapid Extraction Method established by Lewis, who formed the

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systemically working methodologies that allow for small quantities to be efficiently tested for CTXs at clinically relevant levels above 0.1 ppb [26]. Actually, there is no rapid and widely accepted analytical technique to detect CTX in fish and fishery products because of the complex sample procedure.

Tetrodotoxin Puffer fish poisoning, also known as blowfish, toadfish, globefish, balloonfish, patkafish, and fugu, is widespread in coastal countries of East and Southeast Asia [24]. Puffer fish poison is called TTX, which is one of the most potent nonprotein neurotoxins, binding to sodium channels in nerve and skeletal muscle, thereby blocking the propagation of action potentials resulting in asphyxiation and death. It has been estimated that merely 2 mg of pure toxin could kill a grown man [13]. Determination of TTX in marine and other sources is similar to CTX detection, HPLC separation followed by alkaline treatment, and fluorescence assay. A drawback of the HPLCFLD methodology is its limited sensitivity toward some of the TTX analogs, specifically 5-deoxyTTX and 11-deoxyTTX, which are more than 20- and 100-fold less sensitive than TTX, respectively [27]. HPLC coupled with a mass analyzer significantly improved sensitivity and efficiency, and is the official reference method for marine lipophilic toxins, replacing the mouse bioassay in many countries. TTX and its deoxy analogs, 5-deoxyTTX, 11-deoxyTTX, 6,11-dideoxyTTX, and 5,6,11trideoxyTTX, were quantified in the tissues of three female and three male specimens of the marine puffer fish by the LC-MS/MS method. TTX and 5,6,11-trideoxyTTX were detected in all three puffer fish species as the major TTX analogs, which indicated that the deoxy analogs of TTX are common analogs in a range of puffer fish [28].

13.2.1.3 Fishery drugs Up to now, various fishery drugs have made a great contribution to aquaculture. Fishery drugs used in aquaculture, which suffer from certain diseases, need to be treated with veterinary drugs. However, some unapproved substances are used around the world as fungicide, ectoparasiticide, and antiseptic in the aquaculture. The misuse or overuse of fishery drugs leads to severe harm to consumers. An increasing number of analytical methods have been developed in recent years for the determination of fishery drugs such as malachite green (MG), fluoroquinolones (FQs), chloramphenicol (CAP), etc.

Malachite green MG is an illegal agent in aquaculture production due to its potential carcinogenicity, mutagenicity, and teratogenicity in many countries. The United States and the European Union have set maximum residue limits for MG and leucomalachite green (LMG) in foods using a zero tolerance policy [29]. MG is thermostable and thus may not be degraded during routine fish processing. MG has a strong chromophore at 618 nm and is positively charged, so methods such as LC-UV/DAD or LC-MS are suitable for the determination of MG in fish and fishery products [30]. A cleanup procedure should be used before the HPLC-UV/vis method, and immunoaffinity column (IAC) cleanup based on the highly specific interaction

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between antigen and antibody may be more effective than solid-phase extraction. Xie et al. [31] established a novel IAC-HPLC-Vis method with high sensitivity and low cost to detect MG, crystal violet (CV), LMG, and leucocrystal violet residues in fish samples after precolumn oxidation with 2,3-dichloro-5,6-dicyano-1,4-benzoquinone and an IAC cleanup procedure. LOD for MG was lowered to 0.15 ng/g and recovery of the method ranged from 71.6% to 96.8% with relative standard deviations (RSDs) of 5.1%–12.3%. Another HPLC-MS/MS method was established by Nebot et al. [32] for the simultaneous determination of MG and LMG in hake muscle. The decision limit and limit of quantification of MG and LMG were below 2 and 1 mg/kg, respectively. Generally, as compared to visible spectroscopic techniques, MS methods provide greater sensitivity and concurrent residue confirmation for the detection of MG.

Chloramphenicol CAP is a broad-spectrum antibiotic, which is usually used in aquaculture production with the objectives of inhibiting the growth of microorganisms as well as the treatment and prevention of diseases. Intensive use of CAP in food animals has led to concerns regarding residues that have serious toxic effects in humans. However, these antibiotics are still used illegally in aquaculture for their relatively low cost and high effectiveness against certain fish diseases. Different approaches for the determination of CAP in food matrices are available in the literature, including HPLC, GC-MS, ELISA, immunosensing, and electrochemical methods [13]. HPLC is the preferred method for CAP and other phenicol drugs determination because the minimum required performance limit and maximum residue limit (MRL) can be achieved simultaneously. Chromatographic separation was carried out with a C18 column with a gradient elution using water and acetonitrile; such mobile phases were acidified in some studies with formic acid, acetic acid, or buffer solutions to improve separation from interference. For the detector in CAP analysis, the most widely used was the MS detector; other detectors, including UV, DAD, and FLD, were also presented in a previous study [33]. To meet the regulation of CAP, LC-MS with electrospray ionization (ESI) is the most recommended approach for the analysis of CAP in food matrices [33].

Fluoroquinolones The approved administrative MRL for FQ traces in animal tissues ranged from 10 to 100 μg/kg in Europe. The US FDA prohibits the application of FQs for fish consumed by humans. Analysis of FQs for the determination in biological matrices has been carried out mainly by HPLC-DAD or HPLC-FLD. For HPLC-DAD, the mobile phase consisted of acetonitrile and phosphoric acid–triethylamine and detection wavelengths were 278 or 289 nm. Qualitative and quantification analysis was performed by an external reference method through comparison of the retention times and peak area of chromatogram peaks [34]. For HPLC-FLD, separation of the FQs was achieved on a C18 column with a gradient mobile phase consisting of MeOH:H2O, and the fluorescence detector was operated at an excitation wavelength of 280 nm and an emission wavelength of 450 nm [35].

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13.2.2 Role of HPLC in cereals analysis Cereals are traditionally used as basic foods in our daily life. Cereals such as rice, maize, wheat, barley, rye, and oat are grown in many different geographical regions of the world, and are the common food staples that collectively provide over 50% of dietary calories for the world’s population. Cereals provide significant amounts of most nutrients such as carbohydrates and proteins, essential fatty acids, all the B complex vitamins, vitamin E, iron, and other important trace minerals, phytochemicals, and fiber, playing an important role in both human and animal nutrition. While over the past 2 decades, product assortment and nutritional content in the ready-to-eat cereal industry have changed dramatically, average nutrition quality was decreased between 1988 and 2001 and, despite recent improvements, current nutrition quality is still lower than that observed in 1988 [36]. Therefore an analytical method is essential for quality control of cereal crops, which increase the use of fertilizers and pesticides. There are two approaches for the quality control of plant-derived products: marker compound analysis and fingerprint analysis. Marker compound analysis measures one or more chemical compounds, while fingerprint analysis evaluates all detectable compounds in samples.

13.2.2.1 Marker compound analysis in cereals Cereal protein Protein from the milled rice grain has very diverse properties, suggesting that protein composition and not just protein content may contribute to rice grain eating quality. Their estimated relative percentages in rice of the four classes of endosperm proteins are glutelins (80%), prolamins (10%), globulins (5%), and albumins (5%) [37]. A high-resolution HPLC-DAD method was a typical method for rice protein analysis in different types of rice grains because proteins have strong UV absorbance at 280 nm. Balindong et al. [38] standardized an extraction method of rice grain protein and found that the most efficient extraction solvents for prolamins and glutelins were 60% n-propanol and 5 M acetic acid, respectively, before HPLC analysis. To increase HPLC peak resolution, columns of C18, C8, C4, and C3 were tested and C8 proved to be best for separation of protein from cereals. The improved peak resolution in this study enhanced the capacity to measure quantitative differences in both total and individual rice grain protein content and supported a useful reference for rice grain eating quality. Another research reported that rice grain protein composition influences instrumental measures of rice cooking and eating quality by HPLC coupled with viscosity analysis and texture analysis, in which globulin content displayed little variation between medium and long grain and the mean glutelin content was higher in long grain rice lines than in medium grains. Individual medium grain prolamin HPLC peaks, total prolamin content, and the prolamin: (glutelin + prolamin) ratio were positively correlated with several rapid viscoanalyzer parameters. These results suggested that the composition of rice grain protein could contribute to breeding high-quality rice [39].

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Cereal vitamins Vitamins are a wide group of organic compounds that are required for normal body function. Thiamine and riboflavin are two vitamins abundant in cereals that are involved in glucose metabolism, nervous transmission, replication of genes, and biosynthesis of corticoids in the human body. Both thiamine and riboflavin can be rapidly and easily determined in pharmaceutical products using HPLC-UV/DAD or HPLCFLD methods. However, for foods, more specific pretreatment techniques should be applied before HPLC analysis due to the presence of a large number of interfering compounds in different complex food matrices. Reverse-phase HPLC, with a C18 column and methanol/water as the eluent, is a common method for vitamin analysis, although some ion-exchange columns or amide-based columns have been used in previous research [40, 41]. Rodriguez et al. [42] reported an effective and sensitive isocratic HPLC-UV method for vitamin B1 and B2 analysis on different complex cereal samples. Ion-pair reagents (sodium hexanesulfonate or heptanesulfonate) were used for the separation of both compounds from other interfering substances in the cereal extracts. This method has the advantage of rapid and simultaneous determination of both vitamins in a single chromatographic run with a wavelength at 268 nm, and which was comparatively validated and compared to reference AOAC spectrofluorimetric methods, providing comparable linearity and accuracy, with better specificity and precision parameters, as well as practical applicability. Ndolo et al. [43] applied LC-MS/ MS to identify niacin in aleurone layers of yellow corn, barley, and wheat kernels. The procedure of alkaline hydrolysis was used to extract niacin from aleurone layers and aleurone cell content, and an ultrasonic processor is a quick method of releasing cell contents from aleurone layers. The separation of niacin was achieved by 0.1% formic acid and methanol eluent in a Phenomenex C18 column. Concentrations of niacin were highest in wheat aleurone and lowest in corn aleurone, and the fragment pattern from MS/MS revealed that niacin in cereal grain is composed of a pyridine ring attached to a carboxyl group. Therefore an LC-based method can be of great interest for the analysis of complex enriched cereal products in special labeling regulations.

Cereal mycotoxins Infection of crops and stored cereals with fungi can result in the production of secondary toxic metabolites usually referred to as mycotoxins, which constitute a main concern for all involved in food safety issues due to their implication on human and animal health. Throughout history, mycotoxins have caused several epidemics, such as St. Anthony’s Fire in the Middle Ages (caused by ergot alkaloids) or the more recent Turkey “X” disease in the 1960s (caused by aflatoxins, AFs) [44]. Mycotoxins are generally produced by filamentous fungi such as Aspergillus, Penicillium, and Fusarium. Currently, more than 400 mycotoxins are identified but the most significant and important classes of mycotoxins based on their toxicity and economic losses are AFs, ochratoxin A (OTA), deoxynivalenol (DON), and zearalenone (ZEN) [45]. Traditionally, determination of mycotoxin was performed by immunoanalysis, TLC, and HPLC, including standard methods of AOAC or the European Committee for Standardization [46].

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AFs are toxic secondary metabolites produced by Aspergillus fungi and the four main AFs are called B1, B2, G1, and G2, among which AFB1 is the most potent genotoxic and carcinogenic; AF has been classified in group 1 by the International Agency for Research on Cancer [47]. Because AFs exhibit strong fluorescence in their native state, HPLC is the most popular method for the determination of AFs in foodstuff. However, in reverse-phase chromatography, the fluorescence of AFB1 and AFG1 is strongly quenched in the used aqueous mobile phase and a derivatization step by trifluoroacetic acid is required [48, 49]. However, the drawbacks of derivatization such as the long reaction time, the limited possibility of automation, and the relative instability of derivatives influence the detection of AFs. Those drawbacks can be overcome by online photochemical derivatization, which does not need any chemical reagents [50, 51]. In a current study, a total of 229 samples of cereal products, available in retail markets in the main cities of Punjab, Pakistan, were collected for the analysis of mycotoxins by using the reverse-phase HPLC-FLD method. The results revealed that 121 (53%) out of 229 samples of cereal products were found positive for AFB1 and total AFs. Samples of 22% and 12% were found higher than the maximum level for AFB1 and total AFs. The highest level of AFB1 and total AFs was found in porridge samples, i.e., 3.90 and 5.60 μg/kg, respectively [45]. ZEN is a nonsteroidal estrogenic mycotoxin that is mainly produced by numerous species and subspecies of Fusarium, including Fusarium graminearum, Gibberella zeae, and Fusarium tricinctum, and the plant pathogenic fungi can infect a wide variety of cereals. Methods including magnetic bead-based ELISA, HPLC-DAD, and HPLC-FLD were used for the detection of ZEN previously. A relatively low separation capacity offered by conventional LC has been overcome by UHPLC. Ok et al. [52] evaluated the effectiveness of HPLC and UHPLC for the detection of ZEN in noodles, cereal snacks, and infant formulas. An analytical column of C18 was used for separation and an excitation wavelength of 275 nm and emission wavelength of 450 nm were set for FLD. LODs in HPLC and UHPLC were found to be 4.0 and 2.5 μg/kg, respectively, and UHPLC methods showed that the levels of ZEN in naturally contaminated samples ranged from 3.1 to 17.6 μg/kg. Although both methods were suitable, UHPLC gave faster results with better sensitivity. In Korea, ZEN was detected in 38 out of 432 samples (8.8% incidence) by the HPLC method and the level was in the range of 6.0–17.8 μg/kg for snacks, biscuits, and other cereal products [53]. The HPLC-FLD method is significantly cheaper than the LC-MS/MS method with equivalent sensitivity, but it should be mentioned that the methods are usually optimized for a single analyte or a chemical group of analytes.

Multimycotoxins detection HPLC tandem MS is gaining tremendous popularity as a method of choice for accurate identification and quantification of multimycotoxins. MS detection is differentiated from conventional UV/FLD detection in that the ionization mechanisms involving either ESI or atmospheric pressure chemical ionization (APCI) mode are heavily modulated by the chemistry of individual analytes in individual food matrices. Using the example of regulated multimycotoxins by HPLC-MS detection, up to 1000 multiple

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reaction monitoring transitions can be monitored simultaneously on some instruments equipped with triple quadrupole or ion trap, and the selectivity of MS/MS detection allowed the elimination of time-consuming cleanup steps [54]. Bolechova´ et al. [55] simultaneously determined 17 mycotoxins, including AFs, fumonisins, OTA, DON, nivalenol, and ZEN, in different species of barley by using UHPLC-MS. The detection limit of the method ranged from 0.5 to 15 μg/kg and all samples were contaminated with at least one mycotoxin. A multimycotoxin analysis by LC-MS/MS revealed the contamination of sorghum and finger millet by 84 and 62 metabolites [56]. The prevalence of major mycotoxins was lower than 15% in sorghum except ZEN, which occurred in one-third of the samples at an average level of 44 mg/kg. In finger millet, major mycotoxins occurred at a prevalence of 6%–52% with ZEN being dominant and occurring at an average level of 76 mg/kg. Table 13.1 is an overview of mycotoxin detection in different cereals and cereal products by an LC-based method. Although a large number of LC-MS/MS methods have been used for multimycotoxin analysis without derivatization, the main disadvantage of LC-MS/MS is that coeluting compounds might suppress or increase the ionization of desired analytes, possibly decreasing LOD, LOQ, linearity, precision, and reliability of the method [61]. Table 13.1 Liquid chromatography-based techniques developed in recent years for different mycotoxins Title

Technique

Determination of 17 mycotoxins in barley and malt in the Czech Republic

UPLC-MS/MS

Simultaneous determination of AFs and OTA in single and mixed spices Evaluation of a modified QuEChERS method for analysis of mycotoxins in rice Multimycotoxin analysis of sorghum (Sorghum bicolor L. Moench) and finger millet (Eleusine coracana L. Garten) from Ethiopia

HPLC-FLD

QuEChERS with UHPLCMS/MS

LC-MS/MS

Specificity toward toxins

Food material

AFB1, B2, G1, G2; FB1, B2; enniatins A, A1, B, B1; OTA, ZEN, T-2, HT-2, beauvericin LOD (0.3–24 μg/kg), LOQ (1–80 μg/kg) AFB1, G1 (0.1 μg/ kg), AFB2, G2 (0.05 μg/kg), OTA (0.1 μg/kg) 14 mycotoxins (0.5 μg/kg)

Barley, malt

[55]

Mixed spices

[57]

Rice

[58]

Sorghum, finger millet

[56]

ZEN, AFB1, B2, G1, G2 and M1, OTA, etc. 84 toxins in sorghum 62 toxins in finger millet

References

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Table 13.1 Continued Simple and highthroughput method for multimycotoxin analysis in cereals and related foods by UHPLC-MS/MS

UHPLC-MS/ MS

Natural occurrence of OTA in some marketed Nigerian foods Determination of mycotoxins in cereals by LC-MS/ MS

HPLC-UV

HPLC and UPLC methods for the determination of ZEN in noodles, cereal snacks, and infant formula

UPLC-FLD

LC-MS/MS

AFB1, B2, G1, G2 and M1; FB1, B2; OTA, HT-2, T-2, ZEN, deoxynivalenol, LOD (0.01–2.1 μg/kg), LOQ (0.03–6.30 μg/ kg) OTA (0–139.2 μg/kg, presented in 98.2% of the samples) AFB1, B2, G1, G2; OTA, ZEN, DON, FB1, FB2, T-2, HT-2. LOD (0.01–20 μg/ kg), LOQ (0.02–40 μg/kg) ZEN (3.1–17.6 μg/kg) LOD (2.5 μg/kg) LOQ (8.3 μg/kg)

Maize, walnuts, biscuits, and breakfast cereals

[54]

Maize, millet, sorghum, and acha Rice, wheat, barley, oat, and maize meal Noodles, cereal snacks, and infant formula

[59]

[60]

[52]

HPLC-FLD, high-performance liquid chromatography-fluorescence detector; HPLC-UV, high-performance liquid chromatography-ultraviolet; LC-MS/MS, liquid chromatography-tandem mass spectrometry; LOD, limit of detection; LOQ, limit of quantification; OTA, ochratoxin A; QuEChERS, quick, easy, cheap, effective, rugged, and safe; UHPLCMS/MS, ultrahigh-performance liquid chromatography-tandem mass spectrometry; UPLC-MS/MS, ultraperformance liquid chromatography-tandem mass spectrometry; ZEN, zearalenone.

13.2.2.2 Fingerprint analysis in cereals Analytical method for fingerprinting Fingerprinting is described as a variety of analytical techniques or methods that can inform the composition of some foods in a nonselective way with the main aim of characterizing or authenticating the food [62]. Fingerprint analysis will give a comprehensive view because the goal of this approach is to detect as many chemical compounds present in a sample as possible. HPLC-based fingerprint analysis has long been accepted as a powerful tool for quality control and authentication of herbs in both raw materials and their finished products (Fig. 13.3). Fingerprint from HPLC has many advantages, including the ability to separate and detect a large number of chemical compounds present in a sample simultaneously, the ability to reveal chemical compounds and chemical characteristics of samples, good sensitivity, and good selectivity. In addition, multivariate analyses such as principal component analysis (PCA), discriminant analysis (DA), and hierarchical clustering analysis (HCA) were

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Fig. 13.3 Role of high-performance liquid chromatography fingerprinting for food analysis.

commonly coupled with HPLC fingerprint for identification, authentication, and discrimination of plant-derived products because of its rapid and accurate and it can deal with complex variables well [63].

Fingerprint in cereal analysis Buckwheat belongs to Fagopyrum (Polygonaceae) and is widely planted worldwide, but authentication and quality control of buckwheat is difficult. Fingerprint obtained from HPLC coupled to a UV/vis detector may be a simple and rapid method for quality control or habitat/variety identification. Samples were extracted by 60% methanol after being dried and ground, and then the wavelength was set at 280 nm for UV/ vis analysis. HPLC fingerprint varied among buckwheat samples from different sources after PCA and HCA analysis, and the method can effectively distinguish Fagopyrum tataricum (L.) Gaertn and Fagopyrum esculentum Moench based on their chemical features [64]. In addition, buckwheat always includes antioxidants such as rutin, quercetin, and other flavonoids, and HPLC fingerprint combined with antioxidant ability analysis may be a more powerful method than single HPLC fingerprint or single antioxidant analysis [65]. Wang et al. [66] established a chromatographic fingerprint method for quality authentication of corn peptides (CPs) from Enshi, China, and 28 common peaks were found in all the CPs of corn samples. Subsequently, the major chemical constituents of these common peaks were identified respectively using the LC-MS system. The systematical analysis of the primary structure of CPs facilitated subsequent studies of the relationship between structures and functions, and could accelerate holistic research on CPs. Geng et al. [67] developed a fuzzy chromatography mass spectrometric fingerprinting method to differentiate breads made from whole wheat flour and refined wheat flour. The UPLC-APCI method demonstrated that alk(en)ylresorcinols were the most important markers for differentiating between wheat and refined wheat flour/breads. In addition, diglycerides and phosphatidylethanolamine also contributed significantly to the classification. Rice bran is a by-product from the rice-milling process and contains several bioactive compounds. Results from HPLC-DAD fingerprint of a variety of rice brans showed a satisfactory classification for the test samples. Then discrimination of pigmented and unpigmented rice bran was performed by using HPLC fingerprint combined with chemometrics

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analysis. The results indicated that the developed method was suitable as a quality control method for rice bran in terms of identification and discrimination of the different rice brans [68].

13.2.3 Role of HPLC in fruits and vegetables analysis (pesticides) Fresh fruits and vegetables are an important part of a healthy diet because they are a significant source of vitamins and minerals. However, fresh fruits and vegetables can also be a source of noxious toxic substances—pesticides. Pesticides help to increase the production of high-quality fruits and vegetables by controlling the spread of pests during growth. The use of pesticides for plant food has to be controlled due to their high toxicity, and the maximum residue limits of a variety of pesticides have been set by different regulatory and monitory agencies [69]. A great portion of the pesticide residues are fungicides and herbicides, which are used primarily to control spoilage of fruits and vegetables through fungal attack and to control weeds. The HPLC technique has become the most powerful analytical tool for pesticide determination at mg/kg or even ng/kg level providing the sensitivity, selectivity, and specificity needed to meet legislation [70]. Procedures of pesticide residue analysis in food matrices are presented in Fig. 13.4.

13.2.3.1 Steps of pesticide residue analysis in food Prior to the measurement step, the concerned food item should pass through three to four major processes: namely sampling, extraction, cleanup, and confirmation steps (if needed). These processes vary from one food commodity to another, the types of analyte contaminant, the available analyzer, and so on [71]. Sample preparation is one of the key steps in any analytical methodology; advances in sample preparation aim to minimize laboratory solvent use and hazardous waste production, save employee labor and time, and improve the efficiency of analysis. The quick, easy, cheap, effective, rugged, and safe (QuEChERS) method was originally developed for extraction of a wide range of pesticides in fruits and vegetables and has become very popular since it was introduced by Anastassiades et al. [72]. Nowadays, the QuEChERS method has become an official approach to analyze food samples due to its ability to extract pesticide presence in a wide variety of complex matrices [73]. Usually, food matrices contain low concentration levels of pesticide residues, so their quantification often requires extensive sample extraction and purification prior to analysis. The official AOAC QuEChERS method combines procedures of extraction and cleanup and was found to be very effective compared with the conventional solid-phase extraction method, up to the point that it is currently the standard sample-preparation procedure for pesticide analysis in fruits and vegetables with further extensions to other applications [74]. For pesticide extraction, samples were completely ground in a food processor until homogenized and then acetone, acetonitrile, and ethyl acetate were selected as extractor solvent. Indeed, these organic solvents extract a wide range of compounds with different physicochemical properties from food matrices with high recoveries, but

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Evaluation Technologies for Food Quality Sample preparation (Sampling, homogenization)

Extraction with solvents (Remove the analyte from the matrix)

Cleanup (Remove unwanted coextractives)

Modification (Convert the target analyte to a derivative, if required)

Resolution (Separate the analyte from remaining interferences)

Concentration under vacuum and redissolving in solvent

Detection (Obtain a response related to the amount of analyte present)

Measurement (Relate the response of the analyte to known standard(s)

Confirmation (Provide assurance that the primary method gives correct and accurate results)

Fig. 13.4 Steps for pesticide residue analysis in food. Cited from Y. Wong, R.J. Lewis, Analysis of food toxins and toxicants, in: Y.C. Wong, R.J. Lewis, S.A. Mansour (Eds.), Residual Pesticides and Heavy Metals Analysis in Food. John Wiley & Sons. 2017, pp. 537–570, esp. p. 543.

these nonselective solvents may result in many coextractives that could be present in the sample extract to interfere with the analyte. Selection of the ideal sorbent in the cleanup procedure was essential based on the determination of coextracted materials removed by the sorbents. The procedure increases the loss of analytes and reduces recovery of some analytes in samples, and avoiding this step may result in better recovery and preservation of sample integrity [75]. Other extraction techniques such as liquid–liquid extraction, solid-phase extraction, matrix solid-phase dispersion, and stir-bar sorptive extraction have also been introduced for sample preparation [71]. In addition, magnesium sulfate was always used to induce phase separation of the organic from the aqueous phase and sodium acetate was used to adjust the extract’s pH after the extraction procedure had finished.

13.2.3.2 Pesticide detection Pyrethroids are insecticides used worldwide with a broad spectrum of application. A supercritical fluid extraction method was applied for extraction of three pyrethroid residues, including fenpropathrin, cyhalothrin, and fenvalerate from apple, peach,

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cucumber, and tomato. The three pesticides were detected at 225 nm by the HPLC-UV method, and the results showed that the recoveries of the method for pyrethroid detection were in the range from 89.2% to 106.8% with RSDs between 5.2% and 9.3%, respectively. LOD calculated as a concentration at a signal-to-noise ratio of 3 was 0.1 mg/kg, and pyrethroids in all samples were not detected [76]. Sousa et al. [75] simultaneously detected carbendazim, thiabendazole, fuberidazole, carbofuran, carbaryl, 1-naphthol, and flutriafol in tomato, carrot, beet, and lettuce by the HPLCDAD method. To overcome interference, a chemometrics cleanup using a multivariate curve resolution-alternating least squares model was proposed as a substitute for QuEChERS. In spite of five pesticides being found in the vegetable samples, the predicted concentration was not over the limit established by European Commission, except for carbofuran in lettuce, carrot, and tomato. Rial et al. [77] developed a multipesticide residue determination method for 14 fungicides in white grapes. The proposed method was based on liquid–liquid extraction and solid-phase extraction followed by HPLCDAD detection. Dichloromethane-acetone (75:25, v/v) was found to be the most appropriate solvent mix for extracting fungicides in white grapes, and concentrations of these fungicides in five different white grape samples used for vinification were lower than those established by European legislation. Alternatively, hydrophilic interaction liquid chromatography (HILIC) can be applied to separate hydrophilic compounds that are not retained under conventional reverse-phase conditions. This approach is appropriate for a smaller number of residues than reverse-phase HPLC, and provides better separation efficiency of highly polar pesticides and veterinary drug residues [70].

13.2.3.3 Multipesticide detection by LC-MS LC-MS or LC-MS/MS is the most common and well-established method for targeting multiresidue detection in a complex food matrix, and it was mainly used for the detection of thermolabile, polar, and nonvolatile pesticides. Some pesticides such as phenoxy acids herbicides, triazines, chloroacetanilides, and pyrethroids can be analyzed by both GC-MS and LC-MS/MS. However, LC-MS/MS is considered a more favorable instrument for the detection of phenoxy acid herbicides and carbamates because it does not require a derivatization step prior to analysis [71]. Carbamates and phenylurea pesticide residues were simultaneously detected in fruit juices by using LC-MS and LC-quadrupole ion trap MS. The MS analyses were carried out by using an ESI source operating in the positive mode for both single quadrupole and quadrupole ion trap mass analyzers operating in selected ion monitoring and in multiple reaction monitoring modes, respectively [78]. Another LC-MS/MS method determined pesticide residues of benzimidazole, carbamates, and organophosphorus pesticides in a number of vegetables and fruits (lemon, orange, apple, mango, carrot, onion, sweet potato, cucumber cabbage, Brussel sprout, etc.). The method was valid for 57 different pesticides (linuron, methiocarb, methomyl, oxamyl, oxamyl-oxime, disulfoton, etc.) and their metabolites in one single determination step at 0.01 mg/ kg [79]. Although the same analytical technique is used, parameters and criteria for identification of residues vary depending on where in the world the analysis is

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performed and for what purpose. Mol et al. [80] assessed systematically the experimental variability of the key identification parameters (retention time and ion ratio) obtained in five different LC-MS/MS instruments for 120 pesticide residues in matrices with different degrees of complexity. The two parameters in today’s LC-MS systems are sufficiently stable to meet or even overcomply with the different guidelines. All in all, the LC-based method can be used to estimate the pesticide residues in fruits and vegetables and is an effective tool for quality evaluation.

13.2.4 Role of HPLC in milk and dairy product adulteration Milk is among the most important components in the human diet and is rich in proteins, carbohydrates, minerals, and vitamins, all of which are essential to human health. Global production and consumption of milk have increased in the past decades because of its high nutritional value. However, it is worth mentioning that the decreased quality of milk and its products has become an important social problem that threatens public health. The HPLC method is used for dairy products, including: (1) identifying and quantifying milk compounds such as vitamins, carbohydrate, proteins and so on; (2) determining the authenticity and traceability of milk; and (3) detecting adulterated dairy products [81]. Among these, milk adulteration is the most important for quality evaluation.

13.2.4.1 Milk adulteration with foreign nitrogenous compounds (melamine) Milk adulteration typically involves illegitimate dilution and/or addition of inexpensive, low-quality, and foreign chemical compounds to milk for various aims, such as increasing the volume, masking inferior quality, or increasing milk shelf life for economic gain. Toxic substances such as formaldehyde, hydrogen peroxide, hypochlorite, dichromate, salicylic acid, melamine, and urea were the commonly used adulterants in milk adulteration [82]. Melamine is commonly used to increase the apparent protein content of liquid and powdered milk due to its high nitrogen content and low cost. This type of adulteration is usual because the nonprotein nitrogen cannot be distinguished by the Kjeldahl and Dumas methods, which are commonly used for determining total protein content in dairy products [82]. HPLC coupled with UV, FLD, and MS are widely applied tools and accepted by regulatory agencies for determining nitrogenous compound adulterants in milk and its products. For the HPLC-UV or DAD method, a C18 column is generally used, along with methanol/water as mobile phases. However, they can be less selective because the wavelength for melamine detection is below 250 nm and many organic compounds can be absorbed in the same spectral region. Therefore the analytical procedure always involves laborious sample treatment using acetonitrile or TCA, followed by filtration, cleanup, and preconcentration by solvent evaporation, solid-phase extraction, or matrix solid-phase dispersion [82, 83]. Filazi et al. [84] detected the presence of melamine in milk (pasteurized and ultrahigh temperature [UHT] milk) and dairy products (powdered infant formula, fruit yogurt, soft cheese, and milk powder) by the reverse-phase HPLC-UV

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method. There was an absence of melamine in infant formulas and pasteurized UHT milk, but 2% of cheese, 8% of milk powder, and 44% of yogurt samples contained melamine at the levels of 121, 694, and 294 μg/kg, respectively. Another HPLC-FLD method detected melamine content in bovine UHT whole milk. The detection and quantification limits of melamine were 0.0081 and 0.027 μg/mL, respectively [83]. For HPLC-MS analysis, melamine can be detected by using a triple quadrupole mass detector with ESI in positive mode, and the LC-MS/MS method has been established by the FDA for the determination of melamine in infant formula due to molecular specificity and high sensitivity compared with UV or FLD detectors. A zwitterionic HILIC column is generally used along with 0.1% formic acid in acetonitrile (5:95) and 20 mmol/L ammonium formate in acetonitrile (50:50) as mobile phases. Melamine and cyanuric acid have been detected in powdered infant formula and other dairy-containing food products or ingredients by using a triple quadrupole mass detector with an ESI in positive mode. LOQ of the procedure was 0.25 mg/kg (FDA). In spite of the good analytical performance, the extensive sample pretreatment (extraction in an aqueous formic acid solution followed by filtration, centrifugation, and dilution) limited its application in routine analysis. Dong [85] developed a simple and selective method for the determination of melamine in milk using a magnetic molecularly imprinted polymer (MMIP) as sorbent. The MMIP has been prepared by using melamine as template molecule, methacrylic acid as functional monomer, ethylene glycol dimethacrylate as crosslinking agent, and Fe3O4 magnetite as magnetic component. The extraction procedure was performed in a single step by blending and stirring the MMIPs and the milk sample, which was diluted with water, and MMIP had high affinity and selectivity toward melamine. Table 13.2 represents an overview of melamine detection in milk and dairy products by the LC-based method. Table 13.2 Overview of liquid chromatography-based analytical procedures for determination of melamine in milk and its products

Technique HPLCMS/MS

HPLCMS/MS

Sample preparation Protein precipitation, filtration, centrifugation, and dilution Melamine extraction using magnetic molecularly imprinted polymer as sorbent

Linear range (mg/kg)a

LOD (mg/kg)a

0.25–5.00

0.01–1.0

Remarks

References

–

HILIC column and electrospray ionization

FDA

0.026

Zorbax 300SCX column and electrospray ionization

[85]

Continued

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Table 13.2 Continued HPLC-UV

HPLCDAD

HPLCFLD

HPLC-UV

HPLC-UV

HPLCDAD

Matrix solidphase dispersion with molecularly imprinted polymers Protein precipitation, sonication, centrifugation, solid-phase extraction, and filtration Protein precipitation

Centrifugation, dilution in methanol 50%, and filtration 1 mL sample diluted in 5 mL acetonitrile: water (1:1) Protein precipitation, sonication, centrifugation, solid-phase extraction, and filtration

0.24–60.0

0.05

UV detection at 210 nm

[86]

0.1–49

0.018

C18 column and detection at 235 nm

[87]

0.05–9.7

0.023

[83]

1.0–78

0.1

Zorbax SB-C18 column; fluorescence at 365 nm C18 column and detection at 240 nm

0.05–5

0.035–0.11

Nucleosil C8 column

[84]

1–485

0.2

Diamonsil C18

[89]

[88]

FDA, Food and Drug Administration; HILIC, hydrophilic interaction liquid chromatography; HPLC-DAD, high-performance liquid chromatography-diode array detector; HPLC-FLD, high-performance liquid chromatographyfluorescence detector; HPLC-MS/MS, high-performance liquid chromatography-tandem mass spectrometry; HPLC-UV, high-performance liquid chromatography-ultraviolet; LOD, limit of detection; UV, ultraviolet. a Milk density ¼ 1.03 g/mL was considered to convert mg/L to mg/kg.

13.2.4.2 Milk adulteration with foreign proteins Milk proteins are generally classified as caseins, whey proteins, and milk fat globule membrane proteins. These proteins in milk associate with different bioactive properties considered as a functional milk fraction having positive effects on health. Alternation of protein composition in milk potentially threatens consumer health and food safety. HPLC-based analysis has been developed for the determination of milk protein composition because different chromatographic profiles are obtained for milk from

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different species (such as κ-, α-, and β-caseins in raw and processed milk) [3]. Milk adulteration with soybean or pea powder is a typical type of fraud to raise the protein content of milk. The European Commission Regulation specifies an HPLC-UV method for the measurement of glycomacropeptides as markers for whey adulteration of skim milk powder (SMP). HPLC-UV has been used to detect adulteration of soy milk with 5% SMP after hydrolysis of proteins and chemometrics analysis of the peptide fingerprint [90]. Jablonski et al. [91] successfully detected SMP adulterated with soy, pea, brown rice, and hydrolyzed wheat protein at higher adulteration levels (0.5%–10%) by using the UHPLC-UV method, in which feature-rich protein adulterants such as soy and pea protein present in SMP at approximately or above 3% (w/w) produced observable additional peaks in the nominal SMP UHPLC profile. β-Lactoglobulins forming whey proteins is another useful marker for the detection of milk adulteration. For example, different chromatographic patterns were obtained by qualification and quantification of β-lactoglobulins in bovine, ovine, and caprine milk mixtures and fresh and ripened cheeses. This allowed quantification of milk species within the concentration range of 5%–95%, and was successfully applied for authenticity evaluation and quantitative determination of ovine and caprine milk percentages of commercially protected denomination of origin cheeses [92]. HPLC-MS also detected adulteration of goat’s milk with cow’s milk by determining β-lactoglobulins (milk addition as low as 5%) [93]. A proteomics model obtained from UPLC-quadrupole time-of-flight MS was found to be a useful approach to differentiate authentic milks from their counterparts adulterated with nonmilk proteins. This approach is able to detect soybean and pea powder-adulterated milks at as low as 1% (w/w). In addition, the method achieved characteristic peptide sequences from milk authentication by PCA. The novel proteomics approach coupled with chemometrics analysis could be successfully applied for milk quality control in the future [94].

13.2.4.3 Other adulterants in milk Salicylic acid and formaldehyde can be used to decrease microbial growth and thus increase the product shelf life of milk and its products. The use of salicylic acid in animals producing milk for human consumption is forbidden and this species is often determined in multiresidue analysis of nonsteroidal antiinflammatory drugs by HPLC-MS/MS [95]. Rezende et al. [96] determined formaldehyde in bovine milk using a high-sensitivity HPLC-UV method. The formaldehyde was detected at 360 nm after formaldehyde derivatization reaction with 2,4-dinitrophenylhydrazine at a pH of 4.0, and formaldehyde in the milk samples ranged from 0 to 55 μg/L. Fat is one of the major components of milk and generally constitutes 3%–5% (m/m) of cow’s milk. Milk has always been subjected to adulteration by addition of cheaper vegetable oils or animal fats, which has been one of the main targets of adulteration to compensate for the effect of fraudulent dilution. Among milk fat products, butter is the most important because of its widespread use. Generally, a GC-based method is widely used to study lipids (such as FAs, triacylglycerols, and minor lipid compounds of the unsaponifiable fraction) from milk and its products. Yoshinaga et al. [97] developed a quantification method for the detection of milk fat adulteration, in which

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Evaluation Technologies for Food Quality

1,2-dipalmitoyl-3-butyroyl-glycerol (PPBu) was used as an indicator of milk fat content by HPLC-APCI-MS/MS. An octacocyl silylation (C28) column was used for separation of the triacylglycerol positional isomer, 1,3-dipalmitoyl-2-butyroyl-glycerol, and PPBu, and the milk fat contents in butter, butter-blended margarine, and butter cookies were successfully quantified through a model of multiple reaction monitoring. A linear response was achieved in the concentration range of 1–250 μg/mL with recoveries within 99.9% and 105.0%. Determination of vegetable oils in milk fat by HPLC has also been reported in an online GC-HPLC method by using β-sitosterol as a marker compound [98].

13.2.5 Role of HPLC in fruit wine and fruit juice analysis Fruit juice is the unfermented but fermentable liquid obtained from the edible part of sound, appropriately mature, fresh fruit or fruit maintained in a sound condition by suitable means, including postharvest surface treatments applied in accordance with the applicable provisions of the Codex General Standard [99]. Fruit wines are fermented alcoholic beverages made from a variety of base ingredients and can be made from virtually any plant matter that can be fermented, and has been highlighted as the new alcoholic fermented beverage [100]. Fruit juices and fruit wine, in particular orange juice, apple juice, and grape wine, are three fruit-based products among the top eight foods reported between 1980 and 2010 as the most common targets of adulteration [101]. In the context of the great complexity in the composition of these fruitbased products, the use of robust analytical techniques represents an important tool for characterizing these products [102]. In recent years, an LC-based method had been widely used for the analysis of complex compounds, including sugars, alcohols, organic acids, and phenolic compounds, in these fruit-based products. The method is relatively efficient and robust, and therefore ideally suited for routine analyses in the fruit industry.

13.2.5.1 Analysis of sugar Sugars present in the majority of fruit juice, which are primary substrates during alcoholic fermentation, are responsible for the formation of ethanol as well as a number of secondary products, and their concentrations are used to indicate the endpoint of fermentation. Concentrations of sugar present in juice and wine can also be considered as an indicator for its quality assurance. Sugar analysis by HPLC is performed using different columns and detectors. Separation for sugar is usually performed by an NH2 column and cation-exchange column, and detection is usually performed using detectors based on RI and ELSD because UV absorbance or fluorescent response are extremely weak in sugar [103]. Water, mixtures of acetonitrile and water, or acidic solutions are used as the mobile phase under different conditions of temperature and flow. Niu et al. [104] determined sucrose, glucose, and fructose in five cherry wines, which exhibited marked differences in taste and mouth feel by HPLC-RI. The three sugars were well separated after a Waters Sugar-Pak I cation-exchange column at 85°C with water as the mobile phase at a flow rate of 0.4 mL/min. An acidified

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mobile phase has been most commonly used for the analysis of sugars in fruit wines compared with water as the eluent. Duarte et al. [105] used a 100 mM perchloric acid solution as the mobile phase for the determination of glucose, fructose, and sucrose in gabiroba wine at a flow rate of 0.8 mL/min with an ion exclusion column. Compared to RI, ELSD offers increased sensitivity and gradient compatibility, although this type of detector commonly produces nonlinear calibration curves and a gradient-dependent response [106].

13.2.5.2 Analysis of organic acid Organic acids affect the taste and mouth feel of juice and wine, enhance color stability, limit oxidation, and together with ethanol are largely responsible for the microbial and physicochemical stability of fruit juice and wines [106]. Analysis of organic acids in these products has been performed by HPLC. Mena et al. [107] analyzed malic, acetic, tartaric, and citric acids in pomegranate wine by using HPLC-UV. Separation of organic acid was carried out by a Supelcogel C610H column with a mobile phase of water:phosphoric acid (99.9:0.1) and a UV detector operated at 210 nm. Similarly, Navarrete-Bolan˜os et al. [108] also analyzed organic acid in prickly pear wine by the HPLC-UV method using a Prevail organic acid column. The solvent elution was operated at 1.5 mL/min using monobasic potassium phosphate solutions with a pH of 2.5 and a UV detector operated at 210 nm. Importantly, organic acids analysis also plays a fundamental role in the authenticity testing of fruit juice or fruit wine. For example, tartaric acid is usually considered an indicator of the addition of grape juice to a more expensive juice. Similarly, excess malic and/or quinic acid can be used as an indicator of apple juice addition to a more expensive juice. In addition, organic acids ratios such as souinic/citric, quinic/malic, and citric/malic ratios can be used as indicators in determining the authenticity of fruit juice [109]. In some cases, multiple compounds in fruit juice and wine can be detected by a chromatographic system, which consists of more than one detector connected in series. Such chromatographic separation of sugars, ethanol, glycerol, and various organic acids in different fruit wines was carried out by a cation-exchange column using an acidified mobile phase (100 mM perchloric acid or sulfuric acid). Sugars and alcohols were detected by RI, and acids were detected by UV at 210 nm [105, 110]. In the determination of sugars and acids in fruit wines, some specific methodologies should be employed for sample treatment. Samples should be diluted if some compounds present in the matrix reach relatively high concentrations. The injection of samples into HPLC is preceded in most cases by centrifuging (for example, twice at 10,000 rpm, 10 min, 4°C) and filtration using 0.22- or 0.45-μm filters [102].

13.2.5.3 Analysis of phenolic compounds Phenolic compounds are very influential constituents in fruit products, and are very promising markers for the determination of food authenticity due to their taxonomic specificity. Phenolics affect organoleptic properties through their contribution to astringency, bitterness, and color. In addition, the phenolic content is partially

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responsible for the health benefits of fruit products. Reverse-phase LC on C18 or equivalent stationary phases are virtually exclusively used for phenolic analysis. For quantification of the principal wine phenolics, UV detection is commonly employed, while the recent trend has been the increasing application of MS for structural elucidation and quantification purposes (the latter commonly using tandem MS) [106, 111]. Liazid et al. [112] developed a rapid, reliable, and reproducible LC method for the determination and quantification of 13 polyphenols (gallic acid, protocatechuic aldehyde, gentisic acid, catechin, vanillinic acid, caffeic acid, vanillin, epicatechin, syringaldehyde, p-coumaric acid, ferulic acid, sinapic acid, and resveratrol) in grapes and their derived products. The fast method (14 min) utilized a 100 mm Chromolith Performance RP-C18 column operated at 2.5 mL/min in combination with UV and FLD. Similarly, Toit et al. [113] also utilized an LC-UV method on a monolithic column (Chromolith Performance RP-18) to quantify 21 noncolored phenolics and anthocyanins, as well as polymeric pigments in South African red wines. A combination of selected phenolic compounds that were detected with LC-MS/ MS has also been used for the classification of wines in terms of variety, region of origin, and vintage. Leonhard et al. [114] developed a rapid LC-MS/MS method for quantification of phenols and polyphenols in authentic wine samples with high sensitivity. The phenolic pattern was assessed in 97 authentic wine samples comprising 11 geographical Austrian regions, 6 grape varieties, and 5 vintages, and the phenolic pattern from the method can support the origin and grape variety-based classification of authentic wines. LC data combined with statistical and chemometrics methods has been introduced as a useful tool for authenticity assessment. Abad-Garcı´a et al. [115] developed HPLC-DAD and HPLC-MS/MS methods for the quantification and identification, respectively, of phenolic compounds in Citrus juices. These polyphenolic profiles, being representative of Spanish Citrus fruit juice production, were studied with the aim of differentiating Citrus juices according to the species used for their elaboration: sweet orange, tangerine, lemon, or grapefruit. This promising method systemically identified potential markers and developed classification and regression models for detecting sweet orange juice adulterations with tangerine juice, although it required an external validation. Another study classified wines from three different Spanish appellations of origin by using phenolic composition information extracted from HPLC-UV-FLD chromatograms. DA allowed the correct classification of most of the samples being studied [116].

13.3

Summary and outlook

HPLC is widely used to separate a large number of compounds for both analytical and preparative purposes, and has been demonstrated to be a powerful tool in the food industry. The HPLC-based method focuses on food safety issues, improvement of food quality and food traceability, and understanding the bioactivity of compounds in different food matrices, which provide simple, precise, sensitive, and reproducible quantitative methods for routine analysis in the food industry. The concern of consumers and authorities regarding food safety is forcing the development of more

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accurate analytical methods for the analysis of normal constituents of foods, food additives, toxins, chemical residues, and food adulteration, and the LC-based method could facilitate the enforcement agent to take timely and appropriate legal action to combat illegal food producers and to protect public health.

References [1] Nollet, Food Analysis by HPLC, third ed, CRC Press, 2012. [2] S.C. Moldoveanu, V. David, Basic Information About HPLC, Elsevier, 2013. [3] C. Fanali, L. Dugo, L. Mondello, Chapter 10—Advances in chromatographic techniques for food authenticity testing, in: G. Downey (Ed.), Advances in Food Authenticity Testing, Woodhead Publishing, 2016, pp. 253–284. [4] V. David, Essentials in Modern HPLC Separations, Elsevier, 2013. [5] L.R. Snyder, J.W. Dolan, Milestones in the Development of Liquid Chromatography, Elsevier, 2013, pp. 1–17. [6] J.J. Kirkland, High-performance ultraviolet photometric detector for use with efficient liquid chromatographic columns, Anal. Chem. 40 (2) (1968) 391–396. [7] M.C. McMaster, Advantages and Disadvantages of HPLC, John Wiley & Sons, 2006. [8] M.W. Dong, The essence of modern HPLC: advantages, limitations, fundamentals, and opportunities, LCGC North Am. 31 (6) (2013) 472–479. [9] A. Kumar, et al., UPLC: a preeminent technique in pharmaceutical analysis, Acta Pol. Pharm. 69 (3) (2012) 371–380. [10] M. Gumustas, et al., Erratum to: UPLC versus HPLC on drug analysis: advantageous, applications and their validation parameters, Chromatographia 76 (21–22) (2013) 1565–1567. [11] M. Herrero, et al., Multidimensional chromatography in food analysis, J. Chromatogr. A 1216 (43) (2009) 7110–7129. [12] N. Mu, M. Khan, Z. Alothman, History and Introduction of UPLC/MS, CRC Press, 2014. [13] F. Han, X. Huang, G.K. Mahunu, Exploratory review on safety of edible raw fish per the hazard factors and their detection methods, Trends Food Sci. Technol. 59 (2017) 37–48. [14] M.P. Costa, et al., Chapter 2—Biogenic amines as food quality index and chemical risk for human consumption, in: A.M. Holban, A.M. Grumezescu (Eds.), Food Quality: Balancing Health and Disease, Academic Press, 2018, pp. 75–108. [15] D. Zare, H.M. Ghazali, Assessing the quality of sardine based on biogenic amines using a fuzzy logic model, Food Chem. 221 (2017) 936–943. [16] C.A.L.D.L. Torre, C.A. Conte-Junior, Chapter 6—Detection of biogenic amines: quality and toxicity indicators in food of animal origin, in: A.M. Holban, A.M. Grumezescu (Eds.), Food Control & Biosecurity, Academic Press, 2018, pp. 225–257. [17] L. Pinto, et al., Handling time misalignment and rank deficiency in liquid chromatography by multivariate curve resolution: quantitation of five biogenic amines in fish, Anal. Chim. Acta 902 (2016) 59–69. [18] M. Papageorgiou, et al., Literature update of analytical methods for biogenic amines determination in food and beverages, Trends Anal. Chem. 98 (2018) 128–142. [19] C. Europea, S.Y. Consumidores, Commission Regulation of on Microbiological Criteria for Foodstuffs, The Commission of the European Communities, 2005. [20] I. Altieri, et al., European official control of food: determination of histamine in fish products by a HPLC-UV-DAD method, Food Chem. 211 (2016) 694–699.

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High-performance capillary electrophoresis for food quality evaluation

14

Adele Papetti, Raffaella Colombo University of Pavia, Department of Drug Sciences, Pavia, Italy

14.1

Introduction

The electrophoresis principle is based on the migration of ions in a buffer solution under an applied voltage and separation is determined by their mass to charge ratio. In 1937 the Swedish biochemist A. Tiselius was the first to introduce the electrophoretic technique applied to serum proteins by using a U-shaped cell [1]. From 1960 to 1980 horizontal polyacrylamide or agarose gel slabs became very diffused to analyze low or high molecular weight samples, respectively, thus developing so-called gel electrophoresis, a technique widely used to separate analytes (mainly peptides, proteins, and nucleic acids) based on their size. In the early 1980s, J.W. Jorgenson and K.D. Lukacs, and then S. Hjerte`n reported the first electrophoretic separations within open, fused-silica capillaries [2–4]; therefore electrophoresis principles have transferred from conventional slab gel electrophoresis to automated and fast

channel (10-200 mm I.D.)

polymer coating (12-20 mm thickness)

substrate (150-360 mm O.D.)

Amines Amino acids Carbohydrates Catecholamines Fatty acids Inorganic ions Nucleosides and nucleotides

Migration

Organic acids Peptide and proteins Polyphenols Vitamins detection point

high voltage power supply

sample buffer inlet

buffer outlet

Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00014-7 © 2019 Elsevier Inc. All rights reserved.

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capillary column systems, giving a basis to the modern high-performance capillary electrophoresis (HPCE), also known as capillary electrophoresis (CE). In addition, novelties in the semiconductor industry allowed a progression of electrophoresis from capillary to microchip, which has the advantages of integrating multiple analytical steps (such as sample pretreatment) and ensuring portability [5].

14.2

Basic principles

14.2.1 Electrophoresis, migration time, and mobilities Mobility represents the velocity of an ion/analyte through the capillary. Therefore separation by electrophoresis is based on differences in solute velocity in an applied electric field (E); the velocity of an ion can be given by the product of its electrophoretic mobility (μe) and E (v ¼ μeE). Mobility for a given ion and medium is a constant that is characteristic of that ion and is determined by the electric force (FE) that the molecule experiences, balanced by the frictional ones (FF) through the medium (steady-state electrophoresis) [6, 7]: FE ¼ FF

(14.1)

FE ¼ qE FF ¼ 6πηrv

(14.2)

where q ¼ ion charge, η ¼ viscosity of the background electrolyte (BGE) solution, r ¼ ion radius, and v ¼ ion velocity. Solving for velocity: v ¼ qE/6πηr and considering that v ¼ μeE: μ ¼ q=6πηr

(14.3)

From this equation it is evident that small, highly charged species have high mobilities, whereas large, minimally charged species have low mobilities. Analogously, the magnitude of electroosmotic flow (EOF) is expressed as a mobility term (μEOF or μ0) and is represented by the velocity of the bulk flow (vEOF) in an applied electric field (E): vEOF ¼ μEOF E

(14.4)

The apparent mobility (μapp) refers to the observed mobility of an analyte and to the parameters of electrophoretic separation, i.e., diffusion constant of the particle, applied E, time spent in the capillary, and resolution of components. When EOF is present, it is the combination of two mobility terms (apparent, μapp and effective, μp) that produces the observed migration of analytes: μapp ¼ μEOF + μp

(14.5)

In normal polarity and with injection at the anode pole, cations and neutral species migrate with EOF and anions migrate against it. In CE it is also possible to work in reverse polarity, where EOF is driven in the opposite direction, by introducing a positive charge on the capillary wall to mobilize an anionic double layer toward the anode [6, 7].

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From the measured migration time and other electrophoretic parameters, it is possible to calculate the mobility: μapp ¼ l=tE with E ¼ V=L

(14.6)

μapp ¼ lL=Vt

(14.7)

where l (effective length of the capillary, cm) and L (total length of the capillary, cm), V ¼ applied voltage (V), and t ¼ migration time (s).

14.2.2 Dispersion and efficiency The resolution of two migration zones is dependent on their length. Zone length is strongly correlated to the dispersive process, which should be controlled because it increases zone length and the mobility difference necessary to obtain the separation. For a Gaussian peak, dispersion corresponds to the baseline peak width wb ¼ 4σ (σ ¼ standard deviation of the peak). Under CE ideal conditions (for example, with small injection plug length and without analyte-wall interactions) the only contribution to solute-zone broadening is longitudinal diffusion along the capillary. Radial diffusion across the capillary is irrelevant, because of the plug flow profile, and also convective broadening cannot be considered thanks to capillary anticonvective properties [6, 7]. Thus the parameter of efficiency N (number of theoretical plates) can be related to the molecular diffusion coefficient of the solute (D): σ 2 ¼ 2Dt ¼ 2DlL=μV

(14.8)

N ¼ μVl=2DL ¼ μEl=2D

(14.9)

14.3

Procedures

14.3.1 Instrumentation In CE, separations are carried out in a bare fused silica capillary (30–100 cm long), which are circular in cross-section with an inner diameter (i.d.) of 10–200 μm (mainly 50–100 μm) and an outer diameter (o.d.) of 150–360 μm. Both capillary ends (inlet and outlet) must be immersed in buffer containers, into which the platinum electrodes are located. A high-voltage power supply is connected to electrodes and can apply constant voltage values between 10 and 30 kV. The electric field is simply the ratio between the applied voltage and the capillary length (V/cm). The detection system is online/real time near a capillary zone, called the revelation or detection window [6, 8]. CE instruments provide mechanisms to control the temperature of the capillary (15–60°C) with high-speed forced-air coolers or recirculating liquid coolant systems, reducing joule heating (Q) and zone broadening, generated by electrophoresis and high electric fields. A constant temperature is a crucial parameter; it is important to maintain buffer viscosity and by consequence to achieve reproducible migration times. It is also possible to set and control the temperature of samples (10–40/60°C) with an external water bath or a sample storage unit [7, 8].

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14.3.2 Capillaries The capillary is characterized by two lengths: the capillary length, also known as total length (L), on which the voltage is applied, and the effective capillary length to the detector (l), which is the distance between the injection site (inlet) and the detection window on-capillary. Capillaries are made up of a hard, high-temperature pure glass (e.g., fused silica and quartz), used for ultraviolet-visible (UV-vis) components, and they are covered with a layer of polyimide (10–20 μm thick), which is a copolymer with high heat resistance and flexibility, not transparent to UV light. So, at a certain distance from the outlet, which is independent of the CE instrument, it is necessary to remove polyimide to create a detection window transparent to UV [8]. Capillaries can be uncoated or coated. In an uncoated capillary the walls are made of silanol groups (Si-OH) on the glass surface and they become negatively charged (SO–x ) in basic solutions and buffers. When the voltage is applied to the circuit, one electrode becomes net positive and the other net negative. The wall silanol anions pair with mobile buffer cations, producing an electrical double layer along the wall. The remaining buffer cations are attracted to the negative electrode, giving foundation to the so-called EOF, which, for an uncoated capillary, is toward the negative electrode [9]. This phenomenon is also known as electroosmosis and exists in any electrophoretic system when the liquid near a charged surface is placed in an electrical field, resulting in the bulk movement of fluid near that surface. The electric potential near the wall is called ζ potential (V) and represents the potential at the interface of the compact and diffuse layers, where the EOF shear takes place. Because the surface volume ratio is very high inside a capillary, EOF becomes a significant factor in CE [8, 9]. The velocity of the EOF through a capillary is given by the Smoluchowski equation [8]: VEOF ¼ ðεζ=4πηÞE

(14.10)

where ε ¼ dielectric constant of the BGE solution, ζ ¼ potential, π ¼ constant, η ¼ viscosity of the BGE solution (P), and E ¼ applied potential (V/cm). Not all separations can be optimized using bare silica; in fact, in some applications, wall-analyte interactions, which produce an EOF mobility variation, cause great difficulty in obtaining reproducible analysis time. To avoid these problems, the modification of inner wall and the reduction/elimination of EOF represent important solutions, preventing adsorption of the analyte and also stabilizing the pH. A capillary inner wall can be chemically modified with covalent attachments of silanes (covalent coating) with neutral or hydrophilic substituents [9–12] or with the addition of polymeric modifiers (N,N-dimethylacrylamide, N,N-diethylacrylamide, poly(vinylpyrrolidone) [PVP], polybrene, poly(ethylene oxide) [PEO], and hydroxypropylmethylcellulose [HPMC]) in BGE (dynamic or adsorptive coating) [8, 9, 12]. An uncoated fused-silica capillary is prepared for its first use by rinsing it with 10–15 column volumes of NaOH or KOH (0.1–1 M), followed by 10–15 column volumes of the run buffer. For a coated capillary, NaOH is replaced with other solvents, such as ethanol or toluene. By using commercially coated capillaries, it is very important to follow the manufacturer’s instructions for cleaning and

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regeneration procedures, because coated capillaries are easily converted into bare silica capillaries [7–9].

14.3.3 Buffers Capillary ends must be immersed in and filled with a fresh buffer solution, called run buffer or BGE, responsible for conductivity when the voltage is applied. Buffers are compounds used to control the pH of a solution, which is responsible for reproducibility in CE. They are generally weak acids or bases that can accept or donate protons, reducing the change in pH that is caused by the introduction of additional acid or base. CE separation takes place in the run buffer, in which the differences in migration time and mobility can exist. Buffers can either be made or purchased [6, 7]. A suitable CE buffer should have high purity, high buffer capacity at the pH of interest (within one pH unit of the buffer pKa), low absorbance at the wavelength of interest, low mobility to minimize current generation, and low temperature coefficient (change in pH per °C). Preference should be given to BGEs with a high buffering capacity and low specific conductivity [13]. The choice of a buffer depends on the nature of the separation, and it is connected to the desired pH value, ionic strength, type of salts (inorganic or organic), and operating temperature. For instance, a change in pH can affect the current, a change in current can affect temperature, and a change in temperature can affect pH. The pH directly modulates the rate of dissociation of surface groups. Silanol groups on the surface behave as weak acids (pK 7) and remain protonated in acidic pH, but gradually dissociate to generate negative siloxy groups (SiO–) when the pH increases toward alkaline conditions. A capillary can be used in a pH range 2–9, because the fraction of negatively charged silanol groups becomes significant at a pH of about 2 and increases with pH to reach saturation around 9 [9]. The buffer capacity and the ionic strength are correlated: by increasing ionic strength, the buffer capacity increases. In addition, a high ionic strength reduces ζ potential and EOF [9, 13]. Concentration of the BGE is very important also in relation to peak shape and method sensitivity. In fact, by reducing the concentration/conductivity of the sample buffer relative to the BGE or by increasing the concentration/conductivity of the BGE relative to the sample buffer, it is possible to obtain the so-called “sample stacking.” This effect increases peak efficiency and method sensitivity, and it is recommended to keep samples at about one-tenth the concentration of the BGE to optimize it [6, 8]. Organic buffers have high buffer capacity compared to inorganic ones and have the advantage of having low mobility/conductivity and giving less joule heating, with the possibility of using higher voltages and increasing peak efficiency [13]. On the contrary, organic buffers have the disadvantage of absorbing UV-vis light, and for high-sensitivity experiments the background absorbance may become a very important issue. Buffer molecules exhibit a temperature coefficient and the pH of a buffered system changes with temperature. Although the capillary temperature can be fixed and monitored by efficient systems (liquid coolant), during electrophoresis the temperature

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inside the capillary cannot be completely controlled and measured, and it can be estimated only by calculating the joule heating (Q) [8, 13], as reported here: Q ¼ E2 Λc

(14.11)

where Q ¼ joule heating generated (Ω/cm3), E ¼ voltage gradient (V/cm), Λ ¼ molar conductivity of the BGE (cm2 mol–1 W–1), and c ¼ concentration of the BGE solution (mol/L). Capillary performance is optimal when it is dedicated only to a specific type of buffer species [8].

14.3.4 Rinse, injection, and separation A rinsing procedure (conditioning or flushing) is necessary to charge the surface of a new capillary and also for so-called “capillary regeneration.” This procedure consists of filling the capillary to create the same surface on its inner wall prior to every analytical run (conditioning inter-run). Positive pressure and vacuum are the most common methods of rinsing capillaries with typical rinse values of 20 and 10 psi, respectively, but positive pressures up to 100 psi can also be applied in all the latest generation of CE instruments [8]. Capillaries used in CE have a total volume of a few nanomicroliters and to avoid potential band broadening, only a small fraction of the capillary can contain sample. So, only a minute sample amount of 1–50 nL (injection plug, mm; or injection volume, nL/s) can be injected into the capillary. A long injection plug may cause wide bands and poor resolution. For injection by pressure and vacuum (hydrodynamic injection) it is possible to choose the desired value of pressure and time. Typical injection pressures and times are 0.5–1 psi and 3–10 s, respectively. Rinsing and injection pressures are supplied from an on-board air pump that applies pressures to the headspace of a buffer reservoir/vial. To calculate the volume of liquid injected by pressure, it is necessary to use the Poiseuille equation [8]: V ¼ ðΔPd4πtÞ=ð128ηLÞ

(14.12)

where P ¼ pressure drop down the length of the capillary (Pa), d ¼ capillary’s internal diameter (m), t ¼ time during which the pressure is applied (s), η ¼ viscosity of the BGE solution (Pa s), and L ¼ total length of the capillary (m). As an alternative to hydrodynamic injection, low-voltage values (electrokinetic or electrophoretic injection) can be applied for short times. This injection type is subjected to errors and low reproducibility because components that migrate more rapidly in the electrical field will be overrepresented in the sample compared to slowermoving components. After sample injection, the voltage is applied and the migration of components in the electrical field occurs. An electrophoretic separation can be affected by various factors: pH and viscosity of the BGE, temperature of the system, and hydrodynamic radius of the molecules. For example, temperature influences the electrical resistance and current, the viscosity, and finally the velocity of the molecules [6, 8].

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14.3.5 Detection systems Most CE detection systems are done on-capillary and the short path length in CE detection and its low concentration sensitivity represent the main problem that hinders the widespread application of this technique. Absorbance detectors are the most commonly encountered types of detector in CE instrument systems. The simplest absorbance detector (UV detector) uses only a portion of the available energy, while another type of absorbance detector, called the photodiode array detector (PDA), delivers the entire spectrum of light available from the source lamp. A PDA detector is very useful for confirming the identity of an analyte and for estimating its peak purity, and nowadays every commercial CE instrumentation is equipped with a PDA [6]. Absorbance detection is sufficient for many biological analytes, but in some cases the analyte has a weak chromophore and does not appreciably absorb the UV wavelengths. For example, inorganic ions do not absorb in UV and carbohydrates and some acids do not exhibit strong UV absorbance. To overcome this problem, indirect UV detection can be used. Indirect detection uses a wavelength-absorbing substance in a BGE, which also has a mobility close to that of the analytes. When analytes migrate in the BGE, they displace the absorbing substance, giving origin to a decrease in the absorbance and a negative peak [6, 8, 14]. Laser-induced fluorescence (LIF) detection systems for CE are also available. They are fluorescent detectors in which lasers are used as the source of the excitation energy and analytes must be fluorescent. Its application is not very diffused, as only a limited number of molecules contain a natural fluorophore and so a derivatization either on- or off-capillary is necessary [6, 8]. The CE-LIF method is applied to many sample matrices, from plasma to food samples (analysis of amino acids, carbohydrates, DNA, biogenic amines, vitamins, etc.) [14]. Chemiluminescence (CL) is a method based on the production of an electronically excited species derived from chemical reactions without the presence of an external source. CL systems in food analysis include the luminol reaction, the peroxyoxalate reaction, and the tris(2,20 -bipyridine)ruthenium(II) system, and are applied, for example, for derivatized amino acids, carbohydrates, and pesticides [14]. Electrochemical detection (ED) represents a powerful approach to the analysis of food samples. This consists of voltammetric or amperometric mode detection (VD or AD), which is able to measure voltage or current, respectively. This detection is based on oxidation/reduction reactions, which occur when an analyte interacts with an electrode at the outlet of the CE capillary and it can be used for inorganic ions, amino acids, carbohydrates, and biogenic amines. This type of detection is independent from path length and by consequence it has better sensitivity than a UV detector, but it is not very diffused because of the difficulty in aligning the capillary with the electrodes and the necessity to separate the high electric field applied for separation (kV potentials) from that used for detection (mV potentials) [8, 14]. It is also possible to couple CE to detectors that are outside of the capillary, although this requires a specialized interface, as in the case of CE-mass spectrometry (MS) with an electrospray ionization (ESI) interface, in which the outlet end of the CE capillary is inserted. MS adds the additional data of molecular weight, and MS/MS systems (MS2) also provide structural information. The design of these systems also

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Table 14.1 Capillary electrophoresis detectors (type and principle) and relative detection limits Detection type

Detection principle

Detection limit (M)

Spectrophotometric UV Spectrophotometric indirect-UV Spectrophotometric LIF Chemiluminescence Electrochemical Mass spectrometry

Absorption Absorption Fluorescence Electromagnetic Amperometric Mass to charge ratio

10–5–10–7 – 10–14–10–16 10–9–10–12 10–10–10–11 10–8–10–9

allows the use of UV or other detectors prior to the MS interface and the coupling of CE-MS has important advantages thanks to the speed and the resolving capacity of CE and the selectivity and sensitivity of MS [8]. CE-MS has many important applications in food quality and safety and in foodomics. For example, among food safety analysis, CE-MS has been mostly applied to the analysis of traces of contaminants and residues in different samples (water, milk, etc.). In addition, CE-MS is very useful for food metabolomics (metabolic profiling or metabolic fingerprinting) to study smallmolecule metabolites, mechanisms in food production processes and transgenic foods, and searching for new biomarkers of quality and authenticity [14–17]. See Table 14.1 for a comparison of detection systems and their relative detection limit.

14.3.6 CE separation modes Until now, we have discussed CE, which refers to capillary zone electrophoresis (CZE) also called free-solution capillary electrophoresis. CZE is the most widely used, but CE versatility is due not only to its wide number of applications, but also to its numerous different separation modes. In fact, it is sufficient to modify the medium into the capillary or the capillary internal surface to obtain different techniques and applications. The different CE modes achieve different selectivities because of their different separation mechanisms [6]. In Table 14.2 CE modes with principles and type of analyte and buffer modifiers are summarized. The CE analytical methodologies used in food analysis and mentioned here are: CZE, micellar electrokinetic chromatography (MEKC), capillary electrochromatography (CEC), capillary gel electrophoresis (CGE), capillary isotachophoresis (cITP), chiral capillary electrophoresis (CCE), and nonaqueous CE (NACE) [14, 18]. In food analysis, CZE represents the most diffused technique to analyze, for example, amino acids, biogenic amines, and contaminants [14, 18, 19]. The name CZE is misleading because MEKC and CGE are also zonal techniques (see later). CZE is applied only to charged analytes, while MEKC is a mode of CE that allows neutral molecule separation, adding surfactants to the BGE in a concentration sufficiently high to allow the formation of micelles (critical micelle concentration). These detergents, which can be anionic (sodium dodecyl sulfate, SDS), cationic (dodecyltrimethylammonium bromide; cetyltrimethylammonium bromide), nonionic (Triton X-100), or zwitterionic (3-(3-cholamidopropyl)dimethylammonio)1-propanesulfonate), arrange in dynamic micelles with a hydrophobic core and a

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Table 14.2 Capillary electrophoresis (CE) separation modes CE mode

Basic principles

Analyte

Medium

CZE

Mobility

BGE

MEKC

Hydrophobic/ionic interaction Mobility, hydrophobic/ionic interaction Molecular size

Charged molecules (ions, proteins) Neutral and charged molecules Charged/neutral molecules

CEC

CGE cITP cIEF CCE NACE

Moving boundary Isoelectric point Formation of diastereomeric entities Homo- and heteroconjugation ion pairing

Proteins, DNA Proteins, peptides Closely related proteins Chiral analytes Low-soluble analytes

BGE + surfactant BGE (organic, volatile) Gel (polymer sieving matrix) Two buffers Ampholytes BGE + chiral selector BGE + organic solvents

hydrophilic outer surface. The analytes can interact with micelles with hydrophobic and/or ionic interactions and move at their velocity. When they do not interact with micelles, they migrate with the EOF, if it is present. This technique is called “chromatography” because micelles constitute a pseudophase and for neutral species only the partition coefficient drives the distribution/separation of the analytes. Hydrophobic compounds interact more strongly with micelles and have longer migration time, so to decrease these interactions and to accelerate the chromatographic kinetics, the addition of organic modifiers (methanol, acetonitrile [ACN], and 2-propanol) up to 50% v/v is recommended. In addition, buffers with a basic pH are recommended to maintain EOF and to decrease undesired interactions between the capillary inner walls and the surfactant/solute [20, 21]. MEKC is used often in the analysis of flavonoids, vitamins, amino acids, and also racemic amino acids and different types of contaminants in food samples and beverages [18, 19, 22]. In CEC, capillaries are packed with a stationary phase as high performance liquid chromatography (HPLC) columns, giving foundation to a hybrid technique between LC and CE. Therefore separation is carried out thanks to two mechanisms: partition and mobility (chromatographic retention and electromigration separation mode, respectively) [8, 23]. This technique is widely used to analyze aromatic compounds such as polyaromatic hydrocarbons and aromatic carboxylic acids [23]. In food analysis, open tubular (OT)-CEC systems find applications for nitrites and nitrates [15], vitamins, nucleosides and nucleotides, contaminants [14], and also in enantiomeric determinations of organic acids [24]. CGE derives directly from slab gel electrophoresis with the great advantage of applying higher voltages (10–100 times) without the joule heating effect and with rapid analysis time. The term “gel” in CGE is ambiguous; in fact, solid matrices are not necessary, but polymers, which can be covalently cross-linked (bis-polyacrylamide) and

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hydrogen bonded (agarose), or linear polymer solutions (polyacrylamide or methylcellulose), are used. The polymer concentration necessary is inversely proportional to the size of the analyte. Linear polymers lead to more stable gels in respect of those generated from cross-linked ones. Resolution and efficiency in CZE and CGE are identical, and also for these modes it is possible to modulate the selectivity using different types of reagents (ion-pairing solvents, chiral selectors, complexing agents), which can be added to the BGE or covalently bound to the gel. CGE was created to study proteins, but nowadays detection of genetically modified organisms (GMOs), food-borne pathogens, and authenticity testing are the prevalent CGE applications for DNA analysis in foods [14, 15, 18]. For example, CGE-LIF is very diffused mainly to analyze DNA markers in varieties and GMO-derived DNA sequences, and to identify food-borne pathogens with a sensitivity and a rapidity comparable to real-time polymerase chain reaction (PCR) [15]. In cITP, a combination of two buffer systems (leading and terminating electrolytes) is used to create a state in which the separated zones move at the same velocity; in fact, cITP is considered a “moving boundary” technique. For example, for anionic analytes the buffer must be selected so that the leading electrolyte contains an anion with an effective mobility that is higher than that of the solutes. Similarly, the terminating electrolyte must contain an anion with a lower mobility than that of the solutes. Since the leading anion has the highest mobility, it moves fastest, followed by the anion with the next highest mobility, and so on. The individual anions migrate in discrete zones, but all move at the same velocity, as defined by the velocity of the leading anion. Since cITP is usually performed in constant current mode, a constant ratio must exist between the concentration and the mobility of the ions in each zone. Zones that are less (or more) concentrated than the leading electrolyte are sharpened (or broadened) to adapt to the proper concentration. This solute-concentrating principle is a sort of preconcentration step (stacking effect), which enhances efficiency and selectivity [25, 26]. This technique is particularly used in online combinations with CZE-UV to enhance CZE sensitivity, and some interesting applications in quality food control are present in the literature [18, 27, 28]. In a few old studies, cITP is applied alone, for example, to the analysis of ions or ionizable sweeteners of different food matrices [15, 29]. In capillary isoelectric focusing (cIEF), the capillary is filled with ampholytes, which are mixtures of buffers with a range of pKa able to create a pH gradient within the capillary. Analytes will migrate to the point where the pH of the gradient equals their isoelectric point (the pH at which a molecule, although charged, has a neutral behavior but behaves as if it was neutral). At this pH value, the analyte, which has no net charge, stops its migration. When voltage is applied, the samples and the ampholytes migrate to their appropriate positions in the gradient. When focusing is complete, the mixture is distributed throughout the length of the capillary, and to detect each component it is necessary to mobilize them (pressure or chemical mobilization). This CE mode is almost exclusively used for the separation of closely related protein species in the characterization of isoforms, resolving the broad bands that characterize these samples in CZE or CGE [8]. cIEF experiments are most frequently performed in coated capillaries or with dynamic coatings with very few applications in the food field [30].

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A CE mode, called CCE, is specific for chiral analysis [31]. CCE, with the simple addition of chiral selectors to the BGE and without any need of a chiral stationary phase, represents a powerful technique for the separation of racemic amino acids also in food analysis [15]. A great number of chiral selectors are available, but cyclodextrins and different types of antibiotics are often used and offer versatile applications [32]. NACE is a CE mode with purely nonaqueous BGE, which can be used as an alternative to MEKC. NACE analyzes substances with very low solubility in aqueous media and improves selectivity by using organic solvents (mainly methanol and ACN) [8, 18, 33]. MEKC [22, 34, 35], CEC [24], and NACE [34] applications, in few cases, allow enantioselective procedures also for food characterization without additives.

14.4

Advantages and limitations

The small i.d. of the capillary and the high surface area-to-volume ratio allow an anticonvective system with a controlled joule heating effect. In addition, the geometry of the capillary, in which the velocity of liquid is nearly uniform, results in a “plug flow.” This condition reduces the band broadening in contrast to the laminar/parabolic flow typical of the HPLC technique. CE analyses are usually very fast (10–20 min) and efficient, with a very low consumption of sample and reagents (solvents and buffers) and require minimum sample preparation, even in complex matrices [6–8]. On-capillary detection eliminates the problems of coupling the capillary to flow cells or other devices, removing the problem of dead volumes, but the effective length of the light path through the capillary is very small (for example, a capillary with a 50 μm i.d. has an effective path length of 32 μm) and by consequence the absorbance signal obtained from a CE system is low (low sensitivity). To overcome this limitation and increase sensitivity, capillaries with particular cells (low-volume flow cell or bubble cell) with a modified path length are also available [8], and many online and/or offline preconcentration methods in different CE modes and formats have been proposed [36]. The great number of available parameters (capillary type and length, buffer pH, type and concentration, injection type and parameters, voltage, temperature) represents a potential analytical resource in a method set up to resolve an analytical problem [8]. Quantitative analysis by CE can be a critical point; it depends on many parameters (temperature variations, sample adsorption, precise injection of small sample plugs) and some factors can be directly affected by the operator, while a few are completely instrument dependent. In addition, peak area results from different migration velocities of the solutes, so solutes of low mobility remain in the detection window for a longer time (overestimation of peak area) than those of higher mobility (underestimation of peak area). However, this phenomenon can be corrected simply by dividing integrated peak area by migration time (area normalization) [6, 7]. In addition, CE has demonstrated great potential for a wide range of applications, from small to large molecules (ions, drugs, proteins, natural products) [8, 18, 37]. In particular, regarding food analysis, CE was often used for a large variety of

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Table 14.3 Capillary electrophoresis (CE) separation modes with specific advantages and limitations CE mode

Advantages

Limitations

CZE MEKC

Simplicity Neutral molecules Surfactants enhance analyte solubility Velocity High efficiency Ideal for MS detectors Velocity

Only for charged molecules Interaction surfactant/hydrophobic solute and inner walls Not for proteins Accurate optimization of medium composition Need of high voltages No multilane capacity of SDS-PAGE Cross-linked polymers cause rapid gel polymerization, bubble formation, and gel rigidity Hydrodynamic injection cannot be applied Zone sharpening with samples rich in salts

CEC

CGE

cITP

cIEF CCE

NACE

High sensitivity Online coupling with CZE Ideal for conductance and MS detectors Velocity High resolution Simplicity Efficiency Small amounts of selectors Solvents enhance analyte solubility Selectivity High efficiency Velocity

Protein wall adsorption Dynamic coating instability More accurate selection of separative conditions Solvents induce changes of pKa values and mobility

food-related complex molecules, including amino acids, peptides, proteins, phenols, polyphenols, lipids, carbohydrates, DNAs, vitamins, additives, and contaminants, as well as small organic and inorganic compounds. In fact, CE versatility makes this technique an important analytical tool in food quality and safety, in food processing and stability, and also in foodomics [14, 15, 18]. In Table 14.3 supplementary advantages and limitations of each CE separation mode are reported.

14.5

Recent technology development

Miniaturized CE systems (microchip-CE devices) offer simple, rapid, and sensitive methods; for example, they are used both for monitoring analytes (such as proteins) and for detecting frauds or contaminations. This approach consists of a unique chip platform, which includes sample pretreatment, solution distribution/mixing, separation, and detection [36, 38–40].

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Novel immunoassays are based on electrophoretic principles (immunoaffinity CE); it combines CE advantages (high speed, efficiency, and small sample consumption) with selectivity of antibodies as binding agents. These systems are very sensitive and useful for enrichment and quantification of low abundant analytes or contaminants from complex matrices with important applications in allergen detections [41–43]. New coating polymers for CEC and development of pressurized (p)-CEC coupling: The development of molecular imprinted polymers (MIPs), which are novel materials with high specificity and versatility, or OT-CEC coating materials as stationary phases significantly increased CEC application fields [44, 45]. In addition, p-CEC systems with new columns/materials/mechanisms have been set up. In p-CEC, a micro-HPLC pump is connected to the inlet end of the capillary column to minimize the typical CEC problem of bubble formation [46–48]. Advances in coupling CE with electrochemical detectors: CE contactless coupled detection (CE-CCD) and CE capacitively coupled contactless conductivity detection (CE-C4D). In particular, CE-C4D is a sensitive technique that found interesting applications in pharmaceutical, biological, and food fields for the analysis of cations, inorganic/organic anions, biogenic amines, and free fatty acids [49]. Recent advances in combining CE with MS with the development and improvement in CE-MS interfaces, such as ESI, matrix-assisted desorption/ionization, and inductively coupled plasma (ICP), allow versatile applications in food analysis and foodomics, with interesting results in comparison with the conventional LC-MS methods [16, 17, 50–52]. Optimization of NACE-MS coupling allowed important applications for trace analysis in food samples [53]. Concentration procedures. Developments in preconcentration techniques coupled with CE: solid-phase extraction (SPE)-CE. In inline and online systems the SPE columns are integrated with the CE instrumentation, allowing simple, rapid, and economic analysis with minimal sample loss. The development of new SPE materials and on-chip SPE-CE allow the analysis of low-abundance analytes in different matrices with high rapidity and sensitivity [54].

14.6

Recent application progress in different types of foods

This section reports and discusses the most important CE applications present in the literature of the last decade. It is organized into six main sections: Solid food, Beverages and liquid food, Other foods (honey, food supplements, and baby foods), Additives, Contaminants, and Foodomics. For solid and liquid foods, a further classification based on vegetal or animal origin has been applied. Regarding Sections 14.10 and 14.11, CE techniques and applications are reported focusing on the type of detected molecules/compounds in all the mentioned foods. Analysis of nucleosides and nucleotides, as components of nucleic acids and as bioactive metabolites, is reported for every different food sample, while a specific section is dedicated to foodomics.

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CE modes of analysis are reported with general comments, and for liquid foods, food supplements, additives, and contaminants a table of applications is presented to simplify the widely available literature. Sample pretreatments are discussed when particular procedures are necessary due to the use of CE techniques. For every detail regarding sample preparation and CE methods, please see the cited references.

14.7

Solid food

14.7.1 Fruits and vegetables 14.7.1.1 Adrenergic amines and catecholamines In the last few years the use of Citrus aurantium (bitter orange) fruit extracts in food supplements for hypocaloric diets dramatically increased due to the significant content of adrenergic amines such as synephrine, octopamine, and tyramine, which could increase thermogenesis and induce lipolysis in the human body. A maximum dose of 30 mg/day was suggested to avoid undesirable effects, including muscular fasciculation, arrhythmia, and tachycardia. Therefore it is important to quantify these compounds for quality control purposes and for the detection of food adulteration in commercial formulations. A CZE method was developed for the simultaneous analysis of synephrine, octopamine, and tyramine; it was validated, obtaining satisfactory values of precision and extraction yield, and used for the analyses of water extracts of C. aurantium dried whole fruits or fruit parts (endocarp, mesocarp, and exocarp) or of commercial formulations [55]. Other substances that could regulate many physiological processes in humans are catecholamines, i.e., dopamine, epinephrine, and norepinephrine. Banana fruit is a clear example of food containing all these compounds, and microchip electrophoresis was demonstrated as a suitable technique to quantify them in food. Because these substances exhibited native fluorescence, a microchip-CE method with LIF detection was set up for the analysis of Cavendish dessert banana extracts. Catecholamine and its precursors tyrosine and tryptophan were quantified and their chemical structures were confirmed by microchip-CE-MS (single quadrupole with a nanoelectrospray). The only disadvantage registered was analyte ion suppression due to the high signal of mono- and disaccharides present in the food matrix [56].

14.7.1.2 Amino acids, peptides, and proteins In foods, amino acids are responsible for nutritional and organoleptic properties. Their natural composition could be modified by a technological process (for example, fermentation, aging, and distillation could affect amino acids modifying their concentration or also generating new amino acids). Thus amino acids and their relative concentration in a food matrix can also be considered as an important marker of authenticity, quality, and origin. Many researches described methods based on protein patterns to detect food adulteration. Therefore the importance of having high-throughput, sensitive, and not so expensive analytical techniques to detect amino acids, but also their aggregates, i.e., peptides and proteins, is extremely important.

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CE may be used as an alternative method to sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and chromatographic methods because of its many general and sometimes specific advantages. CZE-UV methods are the most applied for the detection of amino acids and proteins in fruits and vegetables. Some examples are listed later. As regards amino acids, Cebolla-Cornejo [57] reported the application of this technique for the analysis of glutamic acid in muskmelon, winter squash, and orange (using the same method, carboxylic acids and sugars were also detected). CZE-UV was also used for the analysis of methanol-soluble proteins of sweet cherry, performed after a simple direct evaporation with nitrogen of the solvent. The results provided information on the most important physicochemical parameters related to the sensorial quality of the fruit and therefore this method could be used routinely for quality control [58]. This technique is also the choice for discriminate and/or fingerprint proteins for food authentication purposes as in the case of the investigation of adulteration of Spanish smoked paprika “Pimento´n de La Vera” with foreign paprika of an inferior quality [59]. In this work, the most important aim was to develop a simple procedure for protein extraction based on temperature-induced phase partition with Triton X-114 that allowed high sensitivity in the determination of smoked paprika adulteration. The same procedure was adopted for discriminating autochthonous varieties of peppers by the protein profile produced by different applied drying processes [60] and as a good alternative to DNA-based analysis methods or to the morphological analysis of plants in the differentiation of lentil cultivars from false lentil species (i.e., single-flour vetch and common vetch) [61]. Over the last decade, the market for basic and dairy-like soybean products has increased day by day, therefore there is a need for good alternatives to milk products for people with allergies or intolerance to animal milk proteins. Most of the CZE methods reported in the literature are dated in the 1990s and generally require only a sample dissolution in an appropriate separation medium [62]. The only improvement could be to consider the method proposed by Kanning [63] in 1993 for the characterization of β-conglycinin (7S) and glycinin (11S) soybean protein fractions. This has been performed by using a hydrophilically coated capillary and a polymeric hydrophilic additive to the BGE. CZE methods applied to the quantitative analysis of soybean proteins in basic and dairy-like soybean commercial products showed similar results for precision, accuracy, and robustness to those obtained by reverse-phase (RP)-HPLC methods. A CE-method was developed for the separation and quantification of soybean proteins in powdered and liquid soybean milks, and in soybean infant formulas derived from soybean protein isolates (SPI), but the distinction between different types of products within each SPI was not possible, differently by HPLC. The same occurred for soybean flour, textured soybean, and liquid soybean milks derived from whole soybean seeds [64]. Conversely, the results obtained by the CZE method were similar to those obtained by chromatographic methods in the detection of undeclared additions of bovine whey proteins in powdered soybean milk samples [65].

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In 2006, a satisfactory method was set up for the detection and quantification of soybean proteins in gluten-free bakery products in the presence of possible interferences such as egg and milk proteins; limit of detection (LOD) and limit of quantification (LOQ) values were one order of magnitude higher than those registered for an HPLC method [66]. More recently, a 2D microchip-CE device has been developed to assess the adulteration of soybean proteins in dairy products. This system integrated different CE modes: an IEF on a microchip and an ITP/CZE in the embedded capillary. The advantage of this technique was the isolation of specific fragments of proteins by on-chip IEF that can remove most milk proteins in a short time rather than conventional sample pretreatment procedures. Further analysis of this protein fragment by ITP/CZE in an embedded capillary allowed the detection of a low percentage of soybean proteins (0.1%) in total dairy proteins. This microchip-CE device provided a more sensitive technique, for example, to discover the adulteration of soybean proteins in dairy products (see Fig. 14.1A) [67], representing a very promising alternative to CE in the rapid detection of food frauds.

Petit Manseng

0.012 Sauvignon

UV absorbance

0.010

3 Chardonnay

0.008

2

0.006

Ugni Blanc

8 6

0.004

4

9

1

0.002

Semillon

7

5 Chenin

0.000 2

(A)

4

6

8

10

Time (min) P3 P4 P5 P6 P1

(B)

20

P2

P10 P11

P7 P8 P9

40 Time (mn)

Fig. 14.1 Capillary electrophoresis protein analysis in food frauds: (A) discrimination of milk (peak 1) and soybean (peaks 2–9) proteins in adulterated bovine milk products by a 2D microchip-capillary electrophoresis-ultraviolet device [67]; (B) protein fingerprint (P1–P11 peaks) of different white wine variety compositions by capillary zone electrophoresisultraviolet. ACS Reprinted with permission from D. Chabreyrie, S. Chauvet, F. Guyon, M.H. Salagoity, J.-F. Antinelli, B. Medina. Characterization and quantification of grape variety by means of shikimic acid concentration and protein fingerprint in still white wines, J. Agric. Food Chem. 56 (2008) 6785–6790. Copyright (2008) American Chemical Society.

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A CGE-UV method has been developed for the first time by Montealegre [68] to differentiate olive varieties depending on the protein profile. The sample preparation consisted of a homogenization of stone and pulp (separately), followed by treatment with a chloroform/methanol mixture for removing lipid components, and of protein precipitation with cold acetone. This technique over commonly used SDS-PAGE has the advantages of full automation, online detection, small sample amount, and high resolution. Therefore it was possible to identify a number of protein fractions not detected by using SDS-PAGE. In addition, by applying discriminant analysis a classification of olive varieties according to their geographical origin was possible. An interesting application concerned the use of carbosilane dendrimers (symmetrical macromolecules with 3D structures) as a nanoadditive to improve the separation of soybean and olive seed proteins by MEKC-UV. These dendrimers have interior carbon-silicon bonds and were negatively charged in the surface with carboxylic acid as functional groups. In particular, the dendrimer with 32 surface carboxylate groups allowed improvement in protein profiles (six peaks instead of two and five peaks instead of two were registered by adding dendrimers in the BGE for olive seed and soybean proteins, respectively). This represents a useful tool to obtain the specific fingerprinting of protein for the differentiation and classification of varieties. Sample preparation required a simple extraction with a mixture of water:ACN and then Tris-HCl added with SDS and dithiothreitol for soybean and olive seed proteins, respectively. An advantage deriving from the use of dendrimers in the separation buffer in CE originated from their uniform and versatile structure whose skeletons and surfaces may be modified changing their cationic or anionic nature, concentration, and structure to adjust themselves to a specific application and to improve the separation selectivity [69]. Finally, CZE-UV was also used to analyze nonprotein amino acids, such as N-oxalyl-L-α-diaminopropionic acid (β-ODAP), and homoarginine. β-ODAP is present in Lathyrus species legumes, an alternative protein source for human and animal nutrition. When consumed at high doses it could lead to lathyrism. A CZE-UV method was used to quantify β-ODAP and homoarginine (another nonprotein amino acid present in Lathyrus spp. with interesting implications for human and animal nutrition) in two different Lathyrus species seeds, comparing the performance of two extraction methods: a simple extraction of ground seeds with an ethanol:water mixture for 24 h and a homogenization of ground seeds with the same solvent mixture. In both cases, the supernatants obtained were evaporated before the resuspension in buffer prior to analysis. The second sample preparation method was more sensitive [70].

14.7.1.3 Organic acids CZE-UV is the preferred CE technique used in the analysis of organic acids present in foods. In fact, citric and malic acids were detected in muskmelon [71], and together with oxalic acid in other fruit and vegetable crops using a method that improved the resolution of malate/citrate by the optimization of BGE parameters and rinsing procedures [57]. The effect of the commercial supply chain on citric and malic acids concentrations in different accessions of rocket salad has been investigated by Bell [72].

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14.7.1.4 Carbohydrates (mainly oligosaccharides) Glucose, fructose, and sucrose are key components in the taste intensity of fruits and vegetables or of fruit-derived beverages such as juices or wines. Their detection by CZE-UV is common and in the literature many applications are reported: muskmelon [71], grape [18, 73], salad [72], and other fruits and vegetables [57].

14.7.1.5 Secondary metabolites (polyphenols, glucosinolates) Consumption of foods containing high amounts of bioactive natural products is increasing day by day and therefore these compounds are more and more extensively studied for their beneficial effects. Among the substances synthetized and accumulated in plant organs, secondary metabolites such as polyphenols are the target of the highest number of studies. RP-HPLC coupled with UV or MS detectors is a widely used technique; however, in the last decade the use of CE has become more frequent for developing rapid, accurate, and simple methods of analysis. Grape berry polyphenolic composition was extensively studied and one interesting application dealt with the evaluation of the effects of UV-B radiation, which upregulates some genes of the phenylpropanoid and flavonoid biosynthetic pathways, on grape berry skin extracts without a sample cleanup procedure [74]. A more complex sample preparation was used for the CZE analysis of phenolic acids in exotic fruit; in fact, a preconcentration step of the phenolic fraction after different liquid-liquid extractions and an alkaline hydrolysis step for the release of esterified phenolic acids were set up. A 32 factorial design was successfully applied to the electrolyte optimization and the resulting method proved to be suitable for the analysis of free and bound phenolic acids [75]. Another method was specifically set up for about 20 phenolic acids carrying out a univariate optimization of time of analysis, selectivity, and peak shape (only one parameter changes keeping the others constant); after validation, the method was applied to the analyses of the simple methanolic extract obtained from different avocado varieties harvesting at two ripening degrees (see Fig. 14.2A) [76]. Another group of fruits extensively studied are red fruits. CZE-UV was applied to define the polyphenolic profile of cranberries, blueberries, grapes, and raisins. The collected data treated with principal component analysis showed that samples were mainly clustered according to the fruit origin, confirming the results obtained by the HPLC method [78]. Two different CE-MS approaches, i.e., nontargeted and targeted (full scan or a multiple reaction monitoring method, respectively), were developed for the study of the profile of polar metabolites in complex samples such as avocado fruit. Thus it was possible to follow the quantitative evolution of different classes of metabolites detected in methanolic extracts, including not only phenolic acids and flavonoids, but also a carbohydrate, an organic acid, a vitamin, and a phytohormone, which change their levels during ripening [79].

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Fig. 14.2 Identification and quantification of (A) 21 phenolic acids in avocado samples [76] and of (B) 17 phenolic compounds in extra virgin olive oil samples [77] by validated capillary zone electrophoresis-ultraviolet methods.

Brassica vegetables are rich in phenolic acids and glucosinolates. A simple and rapid CZE method to quantify the phenolic acid contents was described by Lee [80] who used an SPE to replace the solvent extraction step in the isolation of the analytes, thus simplifying part of the extraction procedure. The same research group

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set up a CZE method involving an online large volume sample stacking procedure to preconcentrate kaempferol and quercetin, generally present in broccoli at low concentrations, below the limits of quantification of CZE-UV. This stacking procedure enabled sensitive detection and reproducible quantification of low concentrations of flavonoids, avoiding the need for other expensive detectors [81]. A method for the simultaneous separation and quantification of flavonoids and phenolic acids in tomato was set up using MEKC-UV. An accurate optimization of BGE composition (borax, ACN, methanol, and SDS concentrations) was performed. The repeatability, LODs, and recoveries in tomato samples are similar to the results of other authors working with similar or more sensitive techniques [82]. Glucosinolates are biologically inactive precursors of bioactive substances, i.e., isothiocyanates (ITCs), that could be activated by the action of enzymes (myrosinase) accumulated in a separate compartment after tissue damage. Gonda and coworkers [83] set up a fast, robust, and simple MEKC method for the simultaneous detection of glucosinolates, myrosinase enzyme activity, and ITC conversion rates in Brussels sprouts, radish, watercress, and horseradish. MEKC parameters have been optimized, followed by optimization of a myrosinase-compatible derivatization procedure for ITCs. The method was suitable not only for the screening of glucosinolates and allyl ITC, but also as a higher specificity myrosinase assay that also allows quantification of online generated ITCs.

14.7.1.6 Glycerophospholipids Glycerophospholipids are polar lipids, which have a glycerol backbone esterified with fatty acids in positions sn-1 and sn-2, and with a phosphate group in sn-3. Analysis of this class of compounds generally involves a number of problems due to the high variety of fatty acids that can be present in each phospholipid class, low abundance with respect to the nonpolar triglycerides, and lack of chromophores and the consequent low UV absorption. A NACE method with ESI-MS detection (NACE-ESI-MS) was developed. The results obtained for the qualitative/quantitative composition of olive fruits (and also olive oils) indicated the presence of phosphatidylcholine, phosphatidylethanolamine, lysophosphatidylethanolamine, phosphatidylinositol, phosphatidic acid, lysophosphatidic acid, and phosphatidylglycerol, and a correlation between their relative abundance and the botanical and geographical origin was pointed out. Moreover, interesting differences in the glycerophospholipid compositions for olive stone and pulp were shown [84].

14.7.1.7 Phytohormones (auxins) Interesting applications of CE deal with the detection and quantification of phytohormones produced by plants, a group of substances displaying vital roles in a plant’s lifecycle. Today, these substances are applied exogenously to regulate plant growth, and sometimes their concentration in vegetables and fruits is so high that it could be

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considered to have potential side effects for humans and animals (carcinogenicity, neurotoxicity, impaired reproduction). The simultaneous determination of gibberellic acid, indole-3-acetic acid, abscisic acid, jasmonic acid, indole butyric acid, 1-naphthalene acetic acid, and 2,4-dichlorophenoxy acetic acid (phytohormones containing a carboxyl group) was possible in banana using an efficient and sensitive CZE method with LIF detection based on the derivatization with 6-oxy-(acetypiperazine) fluorescein [85]. A CZE method proposed for the detection of auxins was based on the addition of chitosan-modified silica nanoparticles into the running buffer solution as a pseudostationary phase in capillary electrophoresis. The optimization of nanoparticle concentration, pH, and running buffer solution concentration led to the separation of indole-3-acetic acid, indole butyric acid, 2,4-dichlorophenoxyacetic acid, and 1-naphthaleneacetic acid extracted by octyl alcohol from bean sprout, radish, garlic bolt, cherry tomato, and kiwifruit. High separation efficiency, very short time of analysis, and simplicity in operation, in addition to high recovery, demonstrated applicability to real sample analysis [86].

14.7.1.8 Acrylamide Proteins or amines can react with reducing compounds to give foundation to Maillard reaction products, which play an important role in the formation of flavors and colors in foods during processing and storage. Among them, acrylamide has been classified as “probably carcinogenic to humans” by the International Agency for Research on Cancer. It is easily formed in cooked carbohydrate-rich foodstuffs at elevated temperatures and thus its determination using sensitive and selective analytical techniques is necessary. A CZE-MS2 method applying an inline preconcentration injection mode (field amplified sample injection [FASI]) in reversed polarity was developed, validated, and used for quantifying acrylamide in potato crisps, biscuits, crisp bread, breakfast cereals, and coffee. A particular sample preparation step was set up; it consisted of a defatting process followed by purification by Strata-X-C SPE cartridges prior to the derivatization step of amino acid with 2-mercaptobenzoic acid to obtain an ionizable compound suitable for CE analysis [87]. More recently, a microchip-CE method based on a five-step online multiplepreconcentration process was developed for the analysis of acrylamide in potato chips and French fries. This technique combined prolonged field-amplified sample stacking (FASS) and reversed-field stacking to extend the FASS time and remove the vacant sample matrix. After optimization, a LOD value about 700-fold lower than the ones previously reported for CE methods without the concentration process was obtained [88].

14.7.1.9 Nucleosides and nucleotides Dietary nucleosides and nucleotides play an important role in the maintenance of functions of bone marrow hematopoietic cells, intestinal mucosa, and brain. Therefore the quantification of these compounds in food is very important too.

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As an example, details of a CZE-UV method can be found in the paper of Lignou [71].

14.7.1.10 Inorganic cations A method for the direct injection from fruit and vegetable tissues (zucchini, apple, and mushroom) without any sample pretreatment has been developed by Kalsoom, allowing the determination of different cations, as shown in Fig. 14.3A [89]. A small piece of the tissue was simply placed into a capillary electrophoresis vial followed by application of an electrokinetic injection. The addition of HPMC to BGE allowed for an accurate electrokinetic injection of sodium, potassium, calcium, and magnesium ions from the plant material. The main disadvantage of this approach was the limitation of the applicability to analytes that can be charged on application of voltage; however, the main advantage was the simplicity of the approach.

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14.7.2 Cereals 14.7.2.1 Amino acids, peptides, and proteins Another interesting application concerned the detection of contamination of durum wheat flour with less expensive wheat by the detection of gliadins and albumins [91]. The separation of albumins by CZE-UV may encounter difficulties; in fact, this protein fraction has a high tendency to bind to the inner walls of fused-silica capillaries for the presence of high amounts of basic amino acids. A good resolution was the use of acidic buffers, such as phosphate/L-alanine buffer, that provided good resolution and repeatability [92]. The simultaneous separation and identification of triticale high molecular weight glutenin and secalin subunits by CZE is an efficient alternative to SDS-PAGE and should facilitate the breeding of valuable cultivars. A method using hydrophilic polymers, such as PVP and HPMC mixtures, for dynamic coating of the capillary inner wall and a low-concentration solution of PEO for the isoelectric separation buffer, was set up. In this case, the sample preparation procedure was quite complex; in fact, after having removed albumins, globulins, and gliadins using a saline solution and organic solvents in sequence, glutenins and secalins were extracted with Tris-HCl buffer. A final step of selective preparation of high molecular weight glutenin and secalin subunits was required. In comparison with previous CZE methods, the presented method offered significant improvements in terms of both run-to-run reproducibility and separation efficiency of faster-migrating subunits; moreover, the method also allowed the quantification of individual subunits [93, 94]. High molecular weight glutenin subunits in common wheat are a class of heterogeneous proteins and are generally difficult to separate and characterize; a cIEF method was developed and applied to 16 different cultivars of wheat with good results [30].

14.7.2.2 Carbohydrates A CZE-UV method was set up for the analysis of carbohydrates in breakfast cereals. This method did not require derivatization and presented many advantages compared to traditional reducing sugar and glucose-specific methods because it was possible to quantify more than one sugar in a single run even when present in low concentrations. In comparison with chromatographic methods, CZE did not require complex sample preparation, but only a grinding of the sample (500–1000 μm particle size) and its dilution in water [95].

14.7.3 Cocoa and coffee beans CZE-UV was used to analyze a nonprotein amino acid N-phenylpropenoyl-L-amino acid (NPA). It was quantified in raw and roasted cocoa beans and the results obtained by the CE method lacked sensitivity when compared with those obtained by ultrahighperformance liquid chromatography (UHPLC); however, CE needed a very simple sample preparation with respect to UHPLC requiring an SPE cleanup procedure.

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Therefore the CZE-UV method is suitable for cocoa samples with high NPA contents compared to the UHPLC method recommended for low concentrations [96].

14.7.3.1 Carbohydrates (mainly oligosaccharides) Today, the accidental or more frequently fraudulent adulteration of coffee is one of the problems affecting the economy of coffee producers. A method based on home-made CE-C4D equipment was developed, validated, and applied to the analysis of instant coffee powder adulterated with corn and coffee husks. This method was based on the controlled acid hydrolysis of xylan and starch present in adulterants, followed by the analysis of the corresponding resulting monosaccharides, i.e., xylose and glucose, respectively [97].

14.7.4 Dairy products and eggs 14.7.4.1 Fatty acids Nowadays, determination of the level of cis-trans fatty acids in food is an important quality parameter since their intake is a key dietary factor, especially for people affected by hypertension, heart disease, and obesity. CZE and indirect UV could be considered a good alternative method to monitor total trans-fatty acids in raw material such as hydrogenated vegetable fat, which is widely used as a dairy cream substitute in the manufacture of spreadable processed cheese and in final products, as demonstrated by de Castro [98, 99]. No derivatization or extraction procedures for sample preparation were needed, but only a simple saponification step and appropriate sample dilution. Therefore the method had a high analytical throughput. An alternative method with direct UV detection was proposed by Porto [100] and applied for the analysis of butter toffee, a mix for Brazilian cheese bread, and also cake mix, stuffed wafers, and chocolate. The main advantages of this method were represented by the use of an aqueous tetraborate buffer without cyclodextrin that made analysis simpler and cheaper, and by the use of a polyimide capillary that is stable when exposed to BGE solution and can be used for a larger number of analyses. Considering that fatty acids are a complex class of molecules being constituted by a high number of isomers and homologs, in addition to their low solubility in aqueous media and low molar absorptivity, a multivariate approach (23 central composite design) could be useful to optimize CE variables [101]. A suitable CZE-UV method for the quantification of cis-trans long chain fatty acids (stearic [C18:0], elaidic [C18:1t], oleic [C18:1c], palmitic [C16:0], linoleic [C18:2cc], and linolenic [C18:3ccc]) in butter, margarine, and filled cookies (and also in olive oil, soy oil, and hydrogenated vegetable fat) was developed. The obtained results were not statistically different from those obtained applying the AOAC official gas chromatography (GC) method. Much research has been focused on the particular healthy properties of omega-3 fatty acids; for example, a qualitative differentiation between natural and enriched

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chicken eggs was successfully proposed by the evaluation of omega-3 fatty acid profiles using a simple and rapid CZE-UV method [102]. The detection of saturated fatty acids is generally performed by GC-MS or LC-MS methods, which have two main disadvantages: the need to derivatize the sample and/ or the need to detect fatty acid types having specific chain lengths. Conversely, a sensitive and selective CZE-MS method was developed by using dicationic ion-pairing reagents (IPRs) forming singly charged complexes with anionic fatty acids. The best results were obtained using 1,5-pentanediyl-bis(1-butylpyrrolidinium) difluoride as an ion pair reagent, and the use of a postcolumn IPR infusion method gave the most efficient sensitivity. The developed CZE-paired ion ESI-MS method was applied for the quantification of fatty acids in coffee powder, cheese, and also in coffee extracts (see Section 14.8 [103]).

14.7.4.2 Peptides and proteins The nutritional value attributed to goat milk increased the consumption of goat milk cheeses, and thus the investigations on changes in microbiological and chemical properties of fresh goat milk cheese during storage have attracted researchers. A CZE-UV method was used to monitor proteolysis in goat milk cheese stored in different conditions. By CZE, the degradation of the main casein fractions and the formation of new peptides was followed [104]. The same analytical methodology was applied to compare the effect of different proteases on the protein composition of Prato cheese [105] and also to characterize the peptide profile of the insoluble protein fraction in acidic medium (pH 4.6) of 10 different brands of Prato cheese. This last work concluded that commercial Prato cheeses, even if produced with different raw materials and under different processing conditions, showed very similar peptide profiles when assessed at the molecular level [106]. Another very similar application consisted of the analysis of the water-insoluble protein fraction of Estonian hard cheese Old Saare; the different products generated by primary proteolysis (especially casein degradation) during ripening were detected [107].

14.7.5 Fish and meat 14.7.5.1 Peptides and proteins Recently, CZE-UV was used in parallel with 1H nuclear magnetic resonance (NMR) to study the effect of an in vitro-simulated digestion process on the bioactive dipeptide carnosine in two commercial samples of the Italian cured beef meat bresaola. Sample preparation involving raw material was simple and consisted of a homogenization step, acidification at pH 2.5, and analysis of the supernatant. The results obtained using the two applied analytical techniques were in agreement. Considering the digested samples, CZE analysis was still performed for gastric digested samples because the pH value used in this step was the optimum for CE analysis (pH 2.5), whereas NMR spectroscopy was performed for intestinal digested samples at pH 7.

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Applying this procedure, it was possible to evidence the difference between the total carnosine content, measured by CZE, and its free diffusible fraction detected by NMR; this difference represented the not accessible carnosine for intestinal absorption (it was adsorbed to the food matrix dispersed in the digestion fluid) [108].

14.7.5.2 Biogenic amines Biogenic amines are molecules widespread in nature deriving from the enzymatic decarboxylation of natural amino acids; they are especially present in protein-rich food, such as fish and meat. Their excessive consumption could generate human disorders and tumor development; therefore it is important to set up detection and quantification methods. The analysis of histamine, spermidine, cadaverine, putrescine, phenylethylamine, and spermine in oriental crucian carps was performed by a new MEKC method based on multiphoton excitation fluorescence (MPEF) detection after derivatization with fluorescein isothiocyanate (FITC), using a home-built CE-MPEF system. Results indicated a higher resolution in comparison to MEKC with single photon excitation fluorescence detection using an attoliter detection volume [109]. A nonionic MEKC method coupled to LIF detection was developed for the quantitative determination of FITC-derivatized biogenic amines in two Turkish traditionally processed fish products: brined Atlantic bonito and dry-salted sardine. The salting process can enhance the production of biogenic amines, especially if the salt contains nitrates or nitrites, and therefore their quantification in processed foods is an important public health issue. Use of the CZE-LIF technique had the great advantage of being able to accurately analyze food matrices extremely rich in salt that generally creates an important matrix effect. In fact, very low analyte concentrations can be usefully employed in CZE-LIF, thus making it possible to create an appropriate dilution of the sample and avoid the matrix effect generated by high salt concentration. Other main advantage of this technique consisted of the use of an additive in a BGE separation electrolyte that enhanced the fluorescent intensity of FITC-labeled biogenic amines considerably, thus shortening the separation time; furthermore, the additive could adjust the selectivity of amines without any change in current, avoiding the joule heating problem. This method is highly sensitive and could be easily used for the rapid analysis of biogenic amines even present in low concentrations [110]. A selective and rapid CZE-UV method was proposed for the quantification of histamine in tuna fish samples. Experimental design proved to be an important tool in choosing the appropriate BGE components and separation conditions without the need for experiments; thus it was possible to set up and validate a method free of interferences in a complex food matrix [111]. In 2015, the concentrations of putrescine, histamine, tyramine, phenylethylamine, and spermidine were evaluated in oyster samples under different storage conditions by the CZE-electrochemiluminescence (ECL) method [112]. A cITP method was set up and validated for the quantification of the content of spermine, spermidine, putrescine, cadaverine, histamine, tyramine, tryptamine, and 2-phenylethylamine in fresh white and red meat samples and also in samples fortified

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with additives and stored for 4 days at 4°C. The aim of this study was to develop a suitable method for the estimation of the impact of different additives on the formation of biogenic amines in meat stored at refrigerated temperatures [113]. Gizzerosine, or 2-amino-9-(4-imidazolyl)-7-azanonanoic acid, is a biogenic amine formed following the reaction between the amino group of lysine and the imidazole ring of histidine during an overheated fish meal manufacturing process. A sensitive detection method based on microchip-CE with LIF detection was developed. Gizzerosine was derivatized with FITC prior to CE analysis. The proposed method was advantageous in terms of reagent and sample consumption, analysis time, assay sensitivity, and applicability to complex samples such as fish meals, and trace levels could be quantified without any preenrichment [114].

14.7.5.3 Fatty acids For the analysis and separation of 15 n-3 and n-6 fatty acids in complex food matrices, such as grass-fed and grain-fed beef samples, a direct and sensitive CE-UV method using aqueous borate buffer containing beta cyclodextrin, SDS, urea, and ACN was used [115].

14.8

Beverages and liquid food

14.8.1 Water The analysis of water samples represents an important issue in food quality and safety, to monitor different types of analytes and contaminants in environmental, tap, or mineral waters. Concentration of analytes in the aquatic environment is usually low and this, in addition to CE low sensitivity, makes a sample preparation step necessary before instrumental analysis. To overcome this problem, which represents the most critical step in the analysis of water samples, pretreatments consisting of simple filtration or neutralization and many offline and/or online preconcentration methods were set-up and are continuously suggested in the literature [36, 116, 117]. Because of its sample preparation issue, this section deals with an overview of the particular preconcentration steps that must precede CE analysis, while for a detailed list of applications in the analysis of specific water contaminants (pesticides, toxins, and others), see Section 14.11. Water samples can be treated by using the common SPE procedure [54, 118–120] or the most innovative solid-phase microextraction (SPME) [121] and molecularly imprinted SPE (MI-SPE) techniques [117]. SPME is solventless and requires the use of fibers coated with a liquid (polymer) and/or a solid (sorbent). It is more rapid than SPE, ensuring good linearity and sensitivity, but fibers have higher costs and a shorter lifetime [121]. The MI-SPE technique is based on the MIPs mechanism and many examples of the use of MIPs to preconcentrate phenolic compounds in water samples are present in the literature [117]. Among offline pretreatments, offline liquid phase microextraction (LPME), by using, for example, supported liquid membranes, has great potential to clean up

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and preconcentrate analytes with low costs (low consumption of solvents) and a great facility to couple with analytical systems. In LPME, analytes are extracted from aqueous samples across or into volumes of organic solvents, which are water immiscible. New interesting developments in LPME concern so-called electromembrane extraction and microelectromembrane extraction (μ-EME) across free liquid membranes (FLMs), which exhibit the highest preconcentration capability among sample pretreatment techniques. In this method, an electric field moves analytes across the FLMs [122–125]. Many authors have focused their attention on online sample preconcentration techniques in CE, in which the enrichment process has been mainly performed in the separation capillary. Principal online sample preconcentration techniques applied in food analysis include field-amplified stacking and transient isotachophoresis (tITP) [126]. Among them, electrokinetic supercharging (EKS) and FASI are valid procedures with important applications in water sample analysis. They are different methods, in which analytes are introduced electrokinetically, and FASI is particularly suitable for large amounts of samples [116, 126, 127]. Also, the combinations of preconcentration methods represent an important solution in CE applications. For example, FLMs online combined with EKS show a very highly efficient preconcentration technique, in comparison with EKS or FASI alone, and it is very useful for hydrophilic contaminants [116]. Other examples are represented by a particular LPME technique called dispersive liquid-liquid microextraction (DLLME), which can be directly coupled with CZE for the determination of mercury and its compounds [128], and by using the stacking effect coupled with microemulsion electrokinetic chromatography, in which BGE is a microemulsion, useful for the analysis of hydrophobic analytes (phenols and chlorophenols) [129]. For the detection of aldehydes, which can originate during water disinfection, a precapillary derivatization step is necessary and only a few methods are available [130, 131]. For more details, see Section 14.11. A second option to detect analytes in traces in water samples and soft drinks is the use of sensitive detectors, such as electrochemical detectors (AD or C4D) [122, 124, 125, 132, 133] or LIF [120], but also in these cases a preconcentration step is recommended. In addition, for the detection of cations and anions in water samples, indirect UV detection [90] can be used as an alternative to complex interfaces (direct connection between sampling and separation capillary) coupled with C4D [134]. Ultrasensitive methods, such as CE-ICP-MS, have been applied in the analysis of mercury compounds to directly determine ultratraces without any preconcentration step in water and fish samples [135]. In detail, CE applications for water samples (environmental, tap, mineral) or soft drinks concern the detection of water ions (cations and anions) [90, 125, 134] and contaminants (pesticides, toxins, drugs, and other pollutants, for which see Section 14.11). As regards the analysis of mono- and divalent cations (Na+, K+, Li+, Ca2+, Mg2+ ions) in tap and mineral water, dynamic coating-CZE-indirect UV (see Fig. 14.3B) [90] and flow-gating interface CZE-C4D [134] are the two most recent applications. For the analysis of anions (ClO–4, Cl–, NO–3, and SO–2 4 ) in tap water, μ-EME across FLMs and CZE-C4D [125] were proposed.

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14.8.2 Milk 14.8.2.1 Bovine milk In the analysis of milk, when the target is protein content/degradation, milk samples are only defatted by centrifugation (for 10–30 min at 4°C) and filtrated to remove the fat layer [136, 137]. Conversely, to analyze nonprotein analytes or contaminants, due to the high concentrations of lipids, carbohydrates, and proteins present in milk products, previous protein precipitation and preconcentration steps are required. For milk preconcentration, many methods were proposed: from simple SPE [138] or DLLME procedures [139] to MIPs [140] or combined techniques [141–143]. Milk proteins are classified into caseins (αS1, αS2, β-casein, and κ-casein) and whey proteins (mainly α-lactalbumin and β-lactoglobulin, considered as small whey proteins), and are prone to degradation by native and bacterial enzymes. Milk protein composition affects both industrial milk processing and its nutritional value, and the monitoring of protein profiles becomes very important to ensure milk quality and also to investigate the correlation between protein profile and casein genetic variants [136, 137]. The concentration of caseins and the concentration and conformation of β-lactoglobulin affect many different milk products, mainly fermented products or milk powders. CZE monitors the change in protein profile/degradation, which can also depend on different bacterial strains, obtaining the percentage of degradation in relation to a sterile control milk [136], and also identifying protein genetic variants, as in the case of β-casein [137]. Adding a sample preconcentration step (for example, t-ITP), the CZE-matrix-assisted laser desorption/ionization (MALDI)-MS technique obtains a very sensitive method (LOD  2.1 nM) for both whey proteins [42]. Monitoring whey protein content has an important application in the control of adulteration of expensive caprine or ovine milk with bovine milk; CZE-ESI-MS offers a rapid and accurate approach to quantify whey proteins of bovine milk in “nonbovine” ones [144]. A recent work proposed a CZE-UV method as an alternative to conventional techniques (GC) to detect another type of milk adulteration, i.e., the addition of whey, which can be detected by monitoring the fatty acids profile [145]. Proteins or amines can react with reducing compounds to give origin to Maillard reaction products, which play an important role in the formation of flavors and colors in foods during processing and storage. In milk products, furosine concentration increases with increasing processing time/temperature conditions; it can be detected by CZE-UV or CZE-MS2 [146, 147]. Studies on other nonprotein amino acids are present in the literature, concerning bovine and human breast milk [147, 148]. A very important aspect in food safety regards the abuse or illegal use of drugs (nonsteroidal antiinflammatory drugs [NSAIDs], antibiotics, synthetic estrogens), in particular in animal food. Their wide use in veterinary medicine and the presence of drug residues in food products represent a potential risk for human health. For the analysis of these substances, preconcentration steps [141–143] or ECL detectors [149] are necessary. For the detection of NSAIDs, Alshana set up a rapid competitive DLLME-FASS-CZE-method, compared with conventional techniques (mainly

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SPE-LC-UV and SPE-LC-MS) [141]. Many methods have been proposed for antibiotics: β-lactams [142], tetracyclines [149], fluoroquinolones [138, 140], and 5-nitroimidazoles [150]. Moreover, CZE-UV methods able to simultaneously separate and quantify different antibiotics (β-lactams, tetracyclines, quinolones, amphenicols, and sulfonamides) are present in the literature [143]. For estrogens, different CE modes, such as MEKC or CEC coupled with MS or AD detectors, have been suggested to obtain simple and sensitive analyses [46, 139]. In the literature, many CE applications have been reported for the rapid detection of melamine in human foods, as an alternative to the most diffused LC methods. This is a very important issue for health safety because this molecule is a trimer of cyanamide, used as a fertilizer, derived from plastic materials and resins manufacturers and insecticide metabolism. It is a very important food and food-contact materials contaminant, but it is also used to adulterate milk products and animal feed, because it is able to increase the apparent protein content. Simple CZE-UV methods [151–153] are available for this application. Some CZE-UV methods that rapidly and effectively detect polycyclic aromatic hydrocarbons (important environmental pollutants contaminating animal feed) in milk are also present in the literature [154]. For a list of specific applications in the detection of drugs, melamine, and other pollutants in milk and derivatives, see Section 14.11.

14.8.2.2 Goat milk Goat milk has a particular composition and it is a high-quality product, mainly for infants and elderly people. It is more digestible in comparison to bovine milk. A CZE-UV method has been proposed to simply recognize milk from goats receiving organic products or commercial feed by monitoring the presence of organic acids (end-products of carbohydrate metabolism in lactic acid bacteria or additives) and quantifying hippuric acid content [155].

14.8.2.3 Human breast milk As regards human milk samples, many CZE applications dealing with the analysis of carbohydrate content are present in the literature. MEKC-UV, CZE-LIF, and CZE-UV have been demonstrated to be very useful for analyzing and quantifying neutral or acidic human milk oligosaccharides, which are very important dietary factors [156]. The main CE mode is CZE with UV [157] and C4D detection systems (for oligosaccharides determination, see other sections: energy drinks, juices, wine, honey) [158]. In addition, hyphenated CZE-LIF-ESI-MS methods rapidly obtain an oligosaccharide profiling with a characterization of about 50 molecules [159]. Another important application for human breast milk concerns the presence of drugs, but for these analytes, see Section 14.11. Detailed CE applications are reported in Table 14.4.

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Table 14.4 Determination of different analytes in milk by capillary electrophoresis (CE) Analyte

Food matrix

CE technique

References

αS1, αS2, β-casein, κ-casein, α-lactalbumin, and β-lactoglobulin Protein genetic variants Small-molecular weight whey proteins Bovine milk proteins Organic acids Oligosaccharides

UHT and degraded bovine milk

CZE-UV

[136]

Bovine milk

CZE-UV

[137]

UHT milk and skimmed milk powder Ovine or caprine milk Goat milk Human breast milk Human breast milk Human breast milk Human breast milk

t-ITP, followed by immunoaffinity CZEUV and CZE-MALDI-MS CZE-ESI-MS

[42]

Nonprotein amino acids

Fatty acids

Furosine Nonsteroidal antiinflammatory drugs β-Lactam antibiotics Tetracyclines Fluoroquinolones 5-Nitroimidazoles β-Lactams, tetracyclines, quinolones, amphenicols, and sulfonamides Estrogens Melamine

Bovine and human breast milk

Adulterated bovine milk (with whey) Bovine milk Bovine milk

Fortified bovine milk Bovine milk Bovine milk Bovine milk Bovine milk Bovine milk

Bovine milk Bovine milk Milk powder, bovine milk Milk powder Bovine milk

[144]

CZE-UV CZE-LIF-ESI-MS CZE-UV CZE-C4D MEKC-UV, CZE-LIF and CZE-UV CZE-UV

[155] [159] [157] [158] [156]

CZE-LIF, CZE-C4D, and MEKC-LIF CZE-UV

[147] [145]

CZE-UV and SPE-CZE-UV CZE-MS2 (FASS)-CZE-UV

[146] [147] [141]

SPE-LVSS-CZE-UV

[142]

CZE-ECL SPE-CZE-UV MSPE-HPCE-UV LLE-SPE-CEC-UV CZE-UV

[149] [138] [140] [150] [143]

SPE-p-CEC-AD DLLME-MEKC-ESI-MS2 SPE-CZE-UV

[46] [139] [151]

CZE-UV SPE-CZE-UV

[152] [153]

[148]

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14.8.2.4 Soy milk and drinks A very few CE publications are present in the literature for soy milk and drinks, e.g., the detection of aspartic acid enantiomers in soy milk (and beer) [160], of furosine, a Maillard reaction product in soy beverages [161], and of isoflavones, the main representative flavonoid compounds in soy beverages [162]. For pesticides analysis, see Section 14.11.

14.8.3 Olive oil Studies of olive oil mainly concern the detection of adulterations. In fact, mixing olive oil with seed oils of lower economic value is a common fraudulent practice. This represents an important issue for human health, because seed oils (hazelnut, peanut, sesame, soybean) may be allergenic or toxic. Many CZE methods are present in the literature, in particular those that detect phenolic compounds (simple phenols, secoiridoids, lignans, flavonoids, and phenolic acids), fatty acids profile, chlorophylls, betaines, and tocopherols, allowing assurance of quality and botanical origin of olive oil. The content of phenolic compounds depends on olive variety, environmental conditions (fly attack included), and extraction procedure, and it represents a marker of olive oil quality. Their extraction is very simple and is usually carried out with liquid liquid extraction (LLE) or SPE; on the contrary, microextraction techniques for olive samples are seldom explored [163]. Phenols can be monitored by CZE-UV [164] and CZE-MS, often preceded by sample derivatization [165, 166] or by advanced CZE, which is able to provide complete phenolic profiles (see Fig. 14.2B) [77] and different CE modes (NACE or CEC) [167, 168], which obtain a more complete phenolic profile. Fast and sensitive microchip-CE-AD techniques are also available [169]. An interesting CZE-UV method is able to quantify secoiridoids (oleocanthal and oleacein), a particular class of phenolic compounds with antiinflammatory properties [170]. The analysis of fatty acids (mainly oleic and palmitic acids and in less concentration linoleic acid) and their derivatives is both a measure of olive oil acidity and stability, and the concentration of these compounds is also correlated to botanical origin. It is usually carried out by GC approaches, but CE can overcome GC’s necessity to have volatile compounds [163]. Among CE modes, CZE with indirect UV detection is the suggested method [171]; in fact, only older publications on other CE modes are available [172]. Monitoring of fatty acids cannot detect when olive oil is adulterated with hazelnut oil. In addition, other oil species, such as corn and sunflower, have been measured only in small quantities. As an alternative, a very sensitive approach of a combined PCR-CE system has been proposed to analyze olive oil adulteration (with a sensitivity to detect fraud as low as 5%) with different oil types (soybean, palm, rapeseed, sunflower, sesame, cottonseed, and peanut). The obtained barcode profile gives the direct percentage of each plant oil [173]. This approach has been used in the past for the first time to discriminate olive oil botanical origin [174]. See also Section 14.12.

High-performance capillary electrophoresis for food quality evaluation

333

In 2010–11, the first CZE-UV and CZE-MS methods to detect the pyridine betaine trigonelline were set up. Trigonelline is an alkaloid with many positive bioactivities (it shows hypoglycemic, hypocholesterolemic, osmoregulatory, and antitumoral effects). These analytical methods became very useful to discriminate the adulteration of olive oil with sunflower and soy oils. In fact, trigonelline is present in many vegetables and derived products, including sunflower and soy oils, but it is not detected in olive oil; so, it represents a marker of olive oil quality [166, 175]. Chlorophylls are important markers of authenticity, quality, and product stability and therefore their monitoring is often used as a quality control parameter. Thanks to chlorophyll fluorescence, CZE-LIF is the ideal technique allowing to discriminate natural pigments from synthetic ones that are often added, as in the case of refined olive oil [176]. Tocopherols (vitamin E) content represents an important issue to reveal sophisticated adulteration of olive oil (for example, with hazelnut oil) too. These compounds have important antioxidant properties, increasing the product shelf life. Because of their apolarity, CEC and NACE modes have been suggested [177, 178]. The analysis of proteins in olive oil by CE methods is relatively recent because proteins are minor components, and by consequence minor markers of olive oil quality; however, monitoring protein content could be considered as a control of the refining process, which is the main cause of protein loss [179]. On the contrary, nonprotein amino acids, such as ornithine, β-alanine, γ-aminobutyric acid, alloisoleucine, citrulline, and pyroglutamic acid, which can be monitored by CZE-UV and CZE-MS2, are considered new markers of adulteration [180]. For the analysis of aldehydes, representing toxic pollutants with remarkably unpleasant pungent odors in vegetable oils (and in wine), see Section 14.11. Detailed CE applications are reported in Table 14.5.

14.8.4 Coffee, tea infusion, and energy drinks 14.8.4.1 Coffee beverage/extract Concerning the quantification of alkaloids in coffee, tea, and soft drinks, an interesting publication of Li showed the set-up of a CZE method with the introduction of a nanoinjector and a particular electrical system suitable to decrease potential current instability, thus increasing precision [181]. Simple CZE methods can also be applied for detecting inorganic and organic anions, but even these methods are only reported in relatively older publications [182]. Fatty acids, important molecules for human health because they contribute to increased cholesterol level, are responsible for coffee acidity. Different CE methods reported in the literature in the last decade showed the separation of different types of fatty acids [103, 183]. A CZE-MS method proposed by Lee allowed the separation of a wide range of fatty acids without derivatization compared to GC-MS and LC-MS techniques. The same validated method offers potentialities also for trace anions detection in food samples [103].

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Table 14.5 Determination of different analytes in oils by capillary electrophoresis (CE) Analyte

Food matrix

CE technique

References

Phenolic compounds

Olive oil

CZE-UV and LIF, CZEESI-MS, SPE-NACE-UV, and LIF LLE-CZE-UV

[163]

Secoiridoids (oleocanthal and oleacein) Fatty acids

Protein

Nonprotein amino acids Trigonelline

Betaines Chlorophylls Tocopherols

Extra virgin olive oil Olive oil Olive oil, seeds oil Extra virgin olive oil Olive oil Olive oil Olive oil Olive oil

CZE-ESI-MS CZE-ESI-MS2 CZE-UV

[165] [166] [77]

SPE-NACE-ESI-MS LLE-CEC-UV Microchip-CE-AD LLE-CZE-UV

[167] [168] [169] [170]

Olive Olive Olive Olive Olive Olive Olive

CZE-UV, CZE-LIF CZE-indirect UV MEKC-UV PCR-CZE-fluorescence PCR-CZE-fluorescence FASI-CZE-UV CZE-UV and CZE-ESI-MS2

[163] [171] [172] [173] [174] [179] [181]

CZE-ESI-MS2 or CZE-UV CZE-UV

[147] [175]

CZE-ESI-MS2

[166]

SPE-CZE-LIF CEC-UV, NACE-LIF LLE-CEC-UV SPE-NACE-LIF

[176] [163] [177] [178]

oil oil oil oil oil oil oil

Olive oil Sunflower, soy, and extra virgin olive oils Extra virgin olive and seed oils Olive oil Olive oil Vegetable oils Vegetable oils

[164]

In coffee quality control, the problem relative to the presence of adulterants is very important and suitable analytical methods are needed. In particular, methods able to monitor the different carbohydrates composition could recognize a specific adulteration, as, for example, for xylose; its quantification is a useful indicator of husk and twigs addition to coffee. Another example is represented by the high level of glucose, which is a marker of adulteration with maltodextrin, caramel, or cereals, while high fructose levels indicate the addition of chicory. A very recent CZE-MS method was set up to obtain a complete profile of monosaccharides (fucose, galactose, arabinose, glucose, sucrose, rhamnose, xylose, mannose, fructose, and ribose) composition, as an index of coffee adulteration with soybean and corn [184].

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Concerning nonprotein amino acids [148] and Maillard reaction products (melanoidins and acrylamide) [87, 146], CZE is the common CE mode used, often with an online preconcentration step.

14.8.4.2 Tea infusion In the literature, only a few CE works are presented on the determination of phenolic content in tea, but the selectivity of this technique represents an important advantage in the separation of closely related phenolic compounds and gives results comparable to those obtained by HPLC [185]. In particular, the detection and quantification of catechins and/or methylxanthines are very important for their biological positive effects and for tea quality and stability. In fact, catechins are responsible for the bitter taste of tea and caffeine for tea flavor. For both classes of compounds, MEKC is the most commonly used technique [35, 186, 187]. A limited number of methods have been developed for the analysis of amino acids [188] and nonprotein amino acids [147, 148] in tea samples. A publication concerning nucleosides and nucleotides analysis in tea samples has also been reported [189].

14.8.4.3 Energy drinks A very few publications are present on energy drinks. In addition to the monitoring of oligosaccharides [190] and vitamins [191], a very important issue concerned the determination of taurine. Taurine is a nonprotein amino acid added to energy drinks, mainly to stimulate the brain. This substance has a positive effect also on the liver and cardiovascular system, but it also has potential negative effects if consumed at high doses, especially in relation to caffeine content; therefore its monitoring is very important [147, 192]. The main applications for coffee, tea, and energy drinks are reported in Table 14.6.

14.8.5 Fruits and vegetables juices Fruit juices are among the most popular beverages consumed around the world and their economic value makes this product easily disposed to adulteration. The most common fruit juice adulteration practices are dilution with water, addition of sugars or pulp wash, and blending with cheaper fruit juices. To overcome this problem in 2010 the Association of the Industry of Juices and Nectars provided guidelines for general fruit authenticity and quality criteria [193]. Regarding this, the main CE application concerns the detection of organic acids, which are responsible for juice acidity and are frequently substituted with cheaper material in frauds. The issue since the 2000s has been to find methods able to analyze these compounds in different juice matrices because they are markers of quality that distinguish a pure juice from juice mixtures. The main CE techniques reported in the literature concern CEC-indirect UV [194, 195] and CZE-MS [196]. The enantiomeric separation of DL-malic, DL-tartaric, and DL-isocitric acids has been performed by CCE-UV [197] or OT-CEC-UV [24]. Analysis of carbohydrates profile by CE provides a simple way to classify juices from different fruits and to evaluate adulterated juice mixtures. The analysis of

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Table 14.6 Determination of different analytes in coffee, tea, and energy drinks by capillary electrophoresis (CE) Analyte

Food matrix

CE technique

References

Methylxanthines

Tea, coffee, Coca-Cola Tea Energy drink Tea

CZE-UV

[181]

Chiral-MEKC-UV Short capillary MEKC-C4D/UV CZE-UV

[35] [192] [185]

Polyphenolic compounds

Fatty acids Anions

Oligosaccharides Amino acids Nonprotein amino acids

Melanoidins Acrylamide Vitamins (water soluble) Nucleosides and nucleotides

Tea Tea Coffee Coffee Coffee beverage Coffee Coffee Energy drinks Tea Coffee, tea

MEKC-UV chiral-MEKC-UV CZE-PIESI-MS2 MEKC-UV CZE-indirect UV

[186] [187] [103] [183] [182]

CZE-PIESI-MS2 CZE-ESI-MS2 Short capillary CE format-C4D OT-CEC-UV CZE-UV, MEKC-UV

[103] [184] [190] [188] [148]

Tea, energy drink Energy drink Energy drink Coffee Coffee Coffee Energy drinks

CZE-LIF and micro-CE-LIF, or MEKC-C4D Flow-gating interface CE-C4D Short capillary MEKC-C4D/UV CZE-UV FASI-CZE FASI-CZE-MS2 MEKC-UV

[147] [134] [192] [146] [146] [87] [191]

Tea

CZE-UV

[189]

oligosaccharides has been generally made by CZE-indirect UV [198, 199] and CZE-C4D [158]. Different CE modes (CZE, MEKC, CCE) can be applied for amino acids, nonprotein amino acids, and Maillard reaction products. In more detail, MEKCUV has been adopted for amino acids [200], MEKC-LIF, CCE-MS [148], CZE-UV, and CZE-C4D [146, 147] for nonprotein amino acids, and finally MEKC-UV for hydroxymethylfurfural [146]. With a preconcentration step, based on magnetic nanoparticles, it is possible also to control the presence of metals, such as Co, Zn, Cu, Ni, and Cd. The simple addition of a chelating agent and the consequent formation of complexes with metals overcome the conventional problem in the determination of transition metal ions by CE. In fact, they have similar mobilities because they have similar size and identical charge [201]. For more details, see Section 14.11.

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14.8.6 Sauces In sauces samples, a specific CE application concerns the monitoring of taste enhancers, such as glutamic acid, a compound correlated to a number of toxic processes (allergy and obesity). CZE-C4D and in-capillary derivatization-CZE-UV methods have been developed for its detection in soy, fish, and chili sauces [202, 203]. These methods provide a different strategy to obtaining a stacking effect offline or in-capillary. A few other applications in soy sauce have been reported: a CEC-indirect UV method for the analysis of organic acids in soy sauce [194] and a MEKC-LIF method for the quantification of biogenic amines in soy sauce [204]. CE techniques have also been applied to detect benzoic and sorbic acids, which are added to different sauces (soy, fish, and chili sauces) as preservatives; for more details, see Section 14.10.

14.8.7 Alcoholic beverages 14.8.7.1 Wine Regarding wine samples, the first CE methods were set up to analyze mainly polyphenols and protein contents as reported in the review by Coelho [205]. Wines present a large amount and variety of phenolic compounds that could be easily oxidized; therefore the detection of these compounds is a key factor not only for their biological effects but also because polyphenols (especially phenolic acids, catechins, proanthocyanidins, and some flavonoids) play an important role in wine quality (especially in red wines), contributing to flavor and color properties. In addition, in white wine the profile and quantification of some polyphenols is correlated to grape varieties [206]. Simple CZE methods with UV [207, 208] or AD [209, 210] can obtain rapid separation without the need of other sample treatments less than a simple filtration. In addition, phenol oxidation and the subsequent formation of phenol-protein complexes, which represent a key parameter for food manufacturing control and wine stability, could be monitored by CE, as proposed by Trombley on a model protein [211]. Amino acids, which are due to enzymatic degradation of the grape proteins and to autolysis of dead yeast cells, are markers of wine nutritional properties and are known to influence the aroma of wine and the foam characteristics of sparkling wines. Protein fingerprint and amino acid content can be monitored by CZE-UV [212], as shown in Fig. 14.1B for different white wines, or by CZE-LIF [213], and the determination of chiral amino acids is possible by CCE-UV with a simple precapillary derivatization procedure [214]. The determination of biogenic amines in wine samples represents a toxicological issue. They derive from the degradation of amino acids and their content is correlated to grape variety and environmental factors. In addition, they represent an indicator of alteration and potential health risk, so the search for pretreatment procedures and advanced analytical methods able to detect these compounds is very important.

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The main techniques used are MEKC-LIF and MEKC-UV [215], but also automatized online combination of cITP-CZE coupled with UV detector [216] and CZE-MS2 are available [217]. Organic acids, i.e., tartaric acid, malic acid, citric acid, succinic acid, acetic acid, and lactic acid are the main acids responsible for wine taste, and CE is considered a conventional method for their detection and quantification in wine, as demonstrated by the most recent CZE-indirect UV [218] and MEKC-indirect UV [219] applications. These methods have been developed for the analysis of white and red wine samples and rice wine, respectively. Among the analysis of organic acids, the enantioseparation of tartaric acid is important, because D-tartaric acid, which is not a natural product, can be added to beverages, but it has to be declared. This detection can be carried out with a particular chiral separation mode by applying a CE-CCD method [220]. Other markers of authenticity and quality of food products are carbohydrates. Recently, CE methods were also developed for their quantification in wine; a particular coated CZE-UV method has been set up for the quantification of oligosaccharides in red wines, similar to apple juice [221]. The search for melatonin and its isomers in grape-related foodstuffs, mainly wine, is relatively recent. This molecule has not only positive physiological functions, but it is also an index of alcohol fermentation and its content depends on grape products. Melatonin content is commonly determined by LC, and the first CZE and CEC methods to detect melatonin in wine were set up in 2010 [222, 223]. Also, the presence of carbonates, such as monoalkyl carbonate, which is correlated with the type of vinification, can be monitored by CE. Some examples perform the analysis of monoethyl carbonate in sparkling wine and other alcoholic carbonated beverages, such as beer and soft drinks, by CZE-C4D [224]. In conclusion, CZE remains the main mode for wine analysis [206]. Particular mention should be given to the development of microchip-CE, able to detect different wine compounds (polyphenols, amines, organic acids, sugars, and contaminants) with important applications in wine quality and authentication, and to obtain wine fingerprinting useful for wine characterization and classification [225]. Conversely, capillary and chip-based systems are mainly applied for monitoring wine-making processes (i.e., fermentation and cell culture processes) [226]. The detection of pesticides, mainly fungicides, in wine represents an important issue in relation to the lack of uniformity in the maximum residue limits (MRLs). In spite of its poor sensitivity, offline or online preconcentration steps can help CE to become competitive with conventional LC or GC techniques [227]. A more detailed description of the most used CE applications for the detection of pesticides and other substances in wine samples and derivatives is reported in Section 14.11. Other applications are listed in Table 14.7.

14.8.7.2 Beer Food quality control of beer samples is generally performed by GC techniques for sensory and chemical evaluation of beer aroma and analysis of pesticide residues, or by LC methods applied for the detection of amino acids, gluten peptides, phenolic acids,

High-performance capillary electrophoresis for food quality evaluation

339

Table 14.7 Determination of different analytes in alcoholic beverages by capillary electrophoresis (CE) Analyte

Food matrix

CE technique

References

Polyphenols

White wine White, ros
e, and red wines Red wine White wine White wine Wine Wines Red wine

CZE-UV CZE-UV

[206] [207]

CZE-UV CZE-AD CZE-AD Microchip-CE-UV CZE-UV CZE-UV

[208] [209] [210] [225] [208] [211]

Whiskey

CZE-UV

[228]

White wine White and red wines Wine Beer Beer Vinegar

CZE-UV CZE-LIF CCE-UV CZE-UV CZE-UV MEKC-LIF

[212] [213] [214] [229] [230] [148]

Wine

MEKC-UV/LIF, microchip CE-fluorescence MEKC-LIF cITP-CZE-UV CZE-ESI-MS2 CZE-CD CCE-direct UV/indirect UV CZE-indirect UV MEKC-indirect UV CE-CCD

[215]

[218] [219] [220]

Microchip-CE CZE-UV CZE-C4D Microchip-CE-UV CZE-UV, CEC-UV CZE-UV, CEC-UV CZE-ESI-MS CZE-UV or CZE-ESI-MS

[225] [221] [158] [225] [222] [223] [233] [189]

CZE-C4D

[224]

Microchip-CE-UV/C4D

[40]

Polyphenol– protein complexes Phenolic aldehydes Amino acids

Nonprotein amino acid Biogenic amines

Wine Red wines Wine and beer Beer Beer Organic acids

Carbohydrates

Melatonin Iso-α-acids Nucleoside and nucleotide Carbonate Anions

White and red wines Rice wine and beer White and red wines, wine grapes Wine Red wines Wine Wine White and red wines White and red wines Beer Beer Wine, beer, and drinks Whiskey

[204] [216] [217] [231] [232]

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Evaluation Technologies for Food Quality

aldehydes, and contaminants [230]. The application of CE still remains limited. It has been proposed for the detection and quantification of amino acids playing a significant role in beer fermentation, flavor, and quality, but because their content is low, derivatization was necessary to overcome amino acid sensitivity limits. CE methods with in-capillary derivatization have been suggested [229]. A known method of coordinating interaction between amino acids and copper ions has been proposed to obtain direct monitoring of amino acids content without derivatization with an online preconcentration step followed by CZE-UV [230]. As previously reported, in wine, beer, and in all fermented products, biogenic amines represent important food toxics. Direct rapid and sensitive methods without derivatizations and using CZE-UV [231] or CCE-UV [232] are present in the literature. In addition, CE can also be applied for the detection of α-acids and β-acids in hops and iso-α-acids in beer. Hops are prone to oxidation and deterioration, and storage conditions are the main factors affecting these processes, thus creating these acids, which cause changes in beer flavor [233]. The development of CE methods suitable for the rapid detection of organic acids (oxalic, tartaric, formic, citric, malic, lactic, succinic, acetic), important compounds for taste, flavor, and aroma of beverages (see also wine), as well as for monitoring the fermentation process needs the addition of a chromophore to BGE [219]. Other applications are listed in Table 14.7.

14.8.7.3 Whiskey The definition of chemical composition and markers is useful to certify whiskey quality and authenticity to protect consumers from adulterated and/or falsified products. For this purpose, GC and LC techniques are available to monitor different substances (phenolics, furans, sugars, alcohols). CE represents an interesting alternative to the analysis of the content in phenolic aldehydes (vanillin, syringaldehyde, coniferaldehyde, and sinapaldehyde), which are known to be markers of authenticity. With simple stacking procedures before CE analysis, the results were comparable to those obtained by LC-MS2 methodologies [228]. Chips with C4D detection have also been proposed for a rapid and high-throughput analysis of anions (Cl– and F–) to discriminate authentic whiskey from diluted samples (see Fig. 14.3C) [40].

14.9

Other foods

14.9.1 Honey The mineral profile of honey gives an important indication of the geographic origin. Inorganic anions are related to the conductivity of honeys, and this parameter is regulated in the quality control process of honey samples. A CZE-indirect UV method has been set up for the detection of cations, mainly Na+, K+, Ca2+, Mg2+, and Mn2 + [234, 235], and anions, i.e., chloride, nitrate, sulfate, and phosphate [236].

High-performance capillary electrophoresis for food quality evaluation

341

Among the functional ingredients present in this food, the most widely studied group is the family of antioxidants, i.e., flavonoids/phenolic compounds or phenolic derivatives, which could be considered markers of healthy properties or of toxicity, respectively. MEKC-UV was one of the first CE modes set up to detect flavonoids and phenolic acids in honey samples [237]; by using trace amounts of poly-βcyclodextrin wrapping carbon nanotubes for microextraction (microSPE) before the application of CZE-LIF, better results have been obtained [238]. A programmed nebulizing-gas pressure mode for quantitative CZE-ESI-MS was used to quantify phenolic derivatives [239]. According to Codex Alimentarius a minimum of 60% (w/w) for monosaccharides and a maximum of 5% (w/w) for sucrose has been established for honey. Therefore suitable methods, including CE methods, for the determination of monooligosaccharides (fructose, glucose, and sucrose) has been set up to describe the quality and authenticity of honey. As an example of CE applications, noteworthy methods could include CZE-C4D [158], MEKC-UV [240], and microchip CE-AD/ED [241]. In addition, the proline content can be considered not only a quality marker, but also a marker of honey maturity, and it is very useful to detect sugar adulteration, as demonstrated by Dominguez [242] who developed and validated an MEKC-indirect UV method for the simultaneous quantification of oligosaccharides and proline. As previously reported for other food matrices, the quantification of organic acids could be performed by CZE-UV methods [243]. Honey sample preparation before CE analysis is generally simple; it consists of dissolution in an appropriate solvent/buffer before a dilution step or a simple filtration procedure [234, 236]. For phenolic compounds and flavonoids analysis, LLE or SPE steps are generally added [237–239].

14.9.2 Food supplements The consumption of food supplements increases day by day, as well as the number of commercially available products. Directive 2002/46/CE [244] strictly regulates vitamins and minerals levels that could be added to food supplements, including in Annex II the list of permitted vitamins and minerals sources. Furthermore, the European Commission prepared a report on the use of substances other than vitamins and minerals to be used in food supplement formulations. Therefore to protect consumers against potential health risks following the consumption of adulterated food supplements, suitable analytical methods had to be developed. CE analysis of vitamins, sugars, or amino acids in different types of supplements has been reported in the literature [245–247]. A lot of brand of supplements containing carnitine are on the market; carnitine is a nonprotein amino acid and an essential nutrient, in particular for infants and for patients with certain diseases (renal, cardiovascular, and Alzheimer’s diseases). The formulations generally contain only L-carnitine isomer or the racemic mixture. Whereas L-carnitine is highly effective, D-carnitine displays serious side effects, therefore it is essential to apply suitable detection methods for carnitine [248].

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A similar issue involves lipoic acid, whose natural form is R-lipoic acid, but synthetic lipoic acid is racemic and the potency of S-lipoic acid is poorly clarified. Electrophoretic methods (CZE and CCE) are useful alternatives to the conventional LC and GC approaches in enantiomeric separation. They could determine the content of the enantiomers, which is very important to monitor the chemical process and purity of L-carnitine [249, 250] or R-lipoic acid preparations [251]. The main problem with food supplements is adulteration with an indiscriminate and illegal addition of active principles, which should be absent. Food supplements for weight control represent one of the classes too often adulterated with caffeine or drugs. To detect these substances in the formulations, CZE-UV and CZE-MS2 are the most used CE methods [55, 252–254]. A more recent issue regards the addition of metallic nanoparticles (such as gold, platinum, and palladium) during the manufacturing process to improve the performance of dietary supplements. Electrophoretic methods (mainly CZE-ICP-MS) allow rapid and high-resolution monitoring and characterization [255]. Other details of the methods are listed in Table 14.8.

14.9.3 Baby foods α-Lactalbumin, immunogloublin G, and lactoferrin are commonly used as additional components in infant formula with the aim of promoting defense from infections. Since their beneficial effects are strictly dependent on manufacturing and storage Table 14.8 Determination of different analytes in food supplements by capillary electrophoresis (CE) Analyte

CE technique

References

Vitamins (ascorbic acid, thiamine, riboflavin, nicotinic acid, and nicotinamide) Glucosamine Amino acids (leucine, isoleucine, valine)

MEKC-UV

[245]

CZE-CCD CZE-UV or indirect UV CZE-C4D CCE-MS CCE-UV CZE-UV CZE-UV CZE-UV CZE-ESI-MS2 CZE-ESI-MS2

[246] [247] [249] [250] [251] [252] [252] [55] [253] [254]

CZE-ICP-MS

[255]

Carnitine Lipoic acid Caffeine Furosemide, norephedrine, and ephedrine Adrenergic amines Amphetamines Furosemide, trichlormethiazide, hydrochlorothiazide, triamterene, spironolactone, acetazolamide, dioctyl sulfosuccinate, bisacodyl, sennoside A, sennoside B, picosulfate, phenolphthalein, phentermine, sibutramine, N-didemethylsibutramine, fenfluramine, N-nitrosofenfluramine, mazindol, fluoxetine, diazepam Metallic nanoparticles

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343

conditions, their detection and quantification, frequently together, are important key points. In addition, the developed CE methods could often be used for the simultaneous detection of other milk proteins, such as β-lactoglobulin and bovine serum albumin, with CE results in good agreement with those obtained by LC methods. One of the first CE methods applied for the detection of α-lactalbumin, β-lactoglobulin, and bovine serum albumin in infant formula was a CZE-LIF method reported in 2005 by Veledo [256]. More recently, a microchip-CE-UV was used for quantifying α-lactalbumin, β-lactoglobulin, immunoglobulin G, lactoferrin [257], and a simple CZE-UV for the detection of lactoferrin [258]. In addition, a validated CGE method with UV detection was proposed to give a complete protein profile, including high-molecular weight (> 50 kDa) whey proteins (immunoglobulins, bovine serum albumin, lactoferrin) in different infant formulas. Applying this method, it is possible to quantify the ratio of whey to casein in infant products, produced with different whey ingredients [259]. CZE-UV can be useful also in the monitoring of nitrate and nitrite (mainly coming from vegetables) in baby foods. Nitrate is not toxic, but under low pH condition or by bacteria action it can be reduced to nitrite, which can lead to methemoglobinemia, also called blue baby syndrome [260]. In hypoallergenic infant formulas, the detection of peptides deriving from complex bovine milk protein hydrolysates to prevent allergy is very important. Peptides, which exhibit different bioactivities (immunostimulating, antimicrobial, opioid, angiotensin converting enzyme inhibition, mineral binding, antithrombotic, allergenic), can be derived from enzymatic reactions or can be a consequence of particular food processing systems. For this purpose, CE-MS (mainly CZE-MS) applied after an SPE purification step provides further rapid methods in comparison to LC-MS [261]. Other analytes detected by CE methods in baby food, are: l

l

l

Anions (nitrite, nitrate) by CZE-UV [260]; Nonprotein amino acids (carnitine) in baby food supplements by CCE-MS2 [262]; Nucleosides and nucleotides in infant formula and baby foods by CZE-MS [263, 264].

14.10

Additives

14.10.1 Dyes Among additives, dyes represent one of the class of substances often illegally added to foodstuff, as in the case of Sudan dyes and tartrazine (E102), most used to enhance the red/orange color of food. Sudan dyes have been classified by the International Agency for Research on Cancer as category 3 (they induce liver and bladder cancer in animals [265]); nowadays, it is still frequently used because of its low cost and wide availability. Tartrazine can cause hyperactivity in children, allergy, and asthma, and the acceptable daily intake has been fixed at 7.5 mg kg–1 body weight. Sudan dyes are used often in sauces, and tartrazine in different types of foods (milk, soft drinks, juices, candies, cakes, cereals, and soups). Different CE modes are available to monitor dyes [266–268]. Fig. 14.4A shows the determination of four Sudan dyes in a chilli tomato sauce by MEKC-UV [267].

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Evaluation Technologies for Food Quality

0.0008

0.0006

(a) AU

0.0004 I.S. S II 0.0002 SI S III S IV 0.0000

(b) –0.0002 3.0

4.0

5.0

(A)

6.0

7.0 Minutes

8.0

9.0

16 SA

BA

(a) ∗

Absorbance (mV)

(b) (c) 8

(d) (e) ∗

(f) (g)

0 3.5

(B)

4.5 Time (min)

5.5

Fig. 14.4 Determination of different additives by micellar electrokinetic chromatographyultraviolet (MEKC-UV): (A) chilli tomato sauce (a) and chilli tomato sauce spiked with Sudan dyes (I–IV) (b) [267], (B) preservatives (benzoic acid, BA and sorbic acid, SA) in different samples (juice—(b); soft drinks—(c) and (d); soy sauces—(e) and (f ); wine—(g)) [269]. ACS Reprinted with permission from T.S. Fukuji, M. Castro-Puyana, M.F. Tavares, A. Cifuentes, Fast determination of sudan dyes in chilli tomato sauces using partial filling micellar electrokinetic chromatography, J. Agric. Food Chem. 59 (2011) 11903–11909. Copyright (2011) American Chemical Society.

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14.10.2 Preservatives Benzoic acid and sorbic acid are used as preservatives to inhibit bacterial and antifungal development, respectively, but their use can be set under control. In fact, benzoic acid is nontoxic, but it can give foundation to carcinogenic benzene, and sorbic acid can produce mutagenic products. Sodium benzoate and potassium sorbate remain the most used preservatives because of their good solubility in water. CE food applications in the monitoring of preservatives are mainly focused on sauce samples (soy, tomato, fish, and chili sauces) [269–271] and beverages, such as wine and soft drinks (see Fig. 14.4B) [269], and also allow simultaneous and rapid determination of many different types of preservatives [272]. Of interest is an electrokinetic flow analysis system equipped with an electroosmotic pump, five solenoid valves, and one online home-made SPE for cleaning up and concentrating samples. These systems combined with CZE were set up for the analysis of benzoic and sorbic acids and their relative salts in fruit jams (and also in milk beverages and soy sauce). An ionpairing reagent (tetrabutylammonium bromide) was added to sample solutions to enhance the breakthrough content of the preservatives on the SPE column [273].

14.10.3 Sweeteners Sweeteners are used often in sugar-free soft drinks, juices, jellies, and chocolate, and their potential toxicity often remains controversial. C4D is mainly proposed as the CE application and the main advantage, in comparison with standard LC techniques, is the short analysis time. Also, cITP can be applied for this application allowing the simultaneous determination of different sweeteners [29]. Several papers have demonstrated the utility of CE for the analysis of different artificial sweeteners, from the most diffused aspartame and saccharin [274] to new substances, such as stevia [275] and neotame [276]. Stevia, a natural sweetener extracted from plants belonging to the Stevia rebaudiana family, is a natural substitute for saccharose, and its use has recently been increased; its sweetness is mainly due to its content in glycoside derivatives (rebaudiosides, stevioside, steviolbioside, and dulcosides). Moreover, it has no calories and does not interfere with insulin. CE methods to detect sugar alcohols or polyols (erythritol, maltitol, xylitol, and sorbitol), which are low-calorie sweeteners particularly adapted for diabetics, are also available [277].

14.10.4 Synthetic antioxidants Only a few CE publications are present in the literature for the determination of synthetic antioxidants, such as alkyl gallates, butylated hydroxyanisole, butylated hydroxytoluene, and tert-butylhydroquinone [278].

14.10.5 Other additives The detection and quantification of benzoyl peroxide, a specific additive used for wheat flour, commonly known as “flourbrightener,” is extremely important for the toxic effect of its metabolite benzoic acid in humans when present in high concentrations. A simple

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Evaluation Technologies for Food Quality

Table 14.9 Determination of different additives in food by capillary electrophoresis (CE) Analyte

Food matrix

CE technique

References

Sudan dyes

Sauces Sauces Milk, soft drinks, juices, candies, cakes, cereals, and soups Milk, soft drinks, juices, candies, cakes, cereals, and soups Soy sauce and beverages Sauces Wine and soft drinks Wine and soft drinks Milk beverages, soy sauces, and fruit jams Sauces

CEC-AD MEKC-UV CZE-UV

[266] [267] [268]

MEKC-UV

[268]

FASI-CZEC4D MEKC-UV MEKC-UV MEKC-UV EFA-SPECZE-UV CZE-UV

[270] [269] [269] [272] [273]

Soft drinks

CZE-C4D

[274]

Fruit beverages Nonalcoholic beverages Chocolate

CZE-C4D CZE-UV CZE-C4D

[275] [276] [277]

Food supplement

MEEKC-UV

[278]

Wheat flour

CZE-UV

[279]

Tartrazine

Sorbic and benzoic acids

Sorbic and benzoic acids, monosodium glutamate Aspartame, cyclamate, saccharine, and acesulfame-K Stevia Neotame Erythritol, maltitol, xylitol, and sorbitol Alkyl gallates, butylated hydroxyanisole, butylated hydroxytoluene, tert-butylhydroquinone Benzoyl peroxide

[271]

method using CE was developed and benzoyl peroxide was determined as benzoic acid after a reduction step by potassium iodide added to the suspension of flour in methanol. The precision, accuracy, and LOD and LOQ values were in agreement with those using HPLC methods and thus the set-up CE methods could be considered a good alternative for routine monitoring in wheat flour [279]. Other details of CE in additives analysis are reported in Table 14.9.

14.11

Contaminants

14.11.1 Pesticides, herbicides, and fungicides The Food and Agriculture Organization/World Health Organization (WHO) and Codex Alimentarius Commission fixed the MRL for pesticides and contaminants in foods, and subsequently the European Union set different directives for MRL levels

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347

for various fruits and vegetables [280]. Therefore it is fundamental to develop analytical methods suitable for the detection of pesticide residues aimed at food quality control. The main disadvantage of CE in analyzing pesticide residues and other contaminants is inadequate sensitivity of UV detection. Among analytical techniques, CE-MS is certainly the ideal technique for trace contaminant analysis in food [281], but recent developments in sample pretreatment represent a valid alternative. In the last few years, CE low sensitivity was mainly overcome with sample enrichments by SPE and SPME, mainly used for liquid samples [282]. Conversely, LLE, LPME, and DLLME techniques [283–285] and particular SPE modes (i.e., matrix solid-phase dispersion or stir-bar sorptive extraction) [286–288] have been demonstrated to be very efficient for solid samples, such as vegetables and fruits. Also, MIPs technologies were applied for the selective recognition of pesticide residues [288–290]. A list of the most recent CE applications in the detection and quantification of pesticides, herbicides, and fungicides in different food matrices can be found in Table 14.10 [47, 227, 236, 291–295]. Table 14.10 Determination of contaminants in food by capillary electrophoresis (CE) Analyte

Food matrix

CE technique

References

Triazines Bipyridylium compounds

Fruits and vegetables Water

MI-MSPD-MEKC-UV SPE-CZE-UV

[288] [118]

Water

[116]

Water Water and grape Cereals Water

FASI-CZE-UV EKS-CZE-UV FLMs-EKS-CZE-UV SPE-LVSS-CZE-UV LVSS-MEKC-UV EME-CZE-UV

Soy milk and drinks

CZE-ESI-MS

[282]

Fruits and vegetables

SPE-MEKC-UV SBSE-MEKC-UV

[286]

Fruits and vegetables

pCEC-indirect AD

[47]

MEKC-LIF Microchip-CE-CCD

[291] [292]

MSPD-CZE-ECL DLLME-NACE-UV

[287] [284]

Sulfonylureas Benzimidazole derivatives Triazolopyrimidine sulfoanilide compounds Acrinathrin, bitertanol, cyproconazole, fludioxonil, flutriafol, myclobutanil, pyriproxyfen Organophosphate derivatives Sodium monofluoroacetate Phenylureas Imazalil, prochloraz, and thiabendazole

Fruits and vegetable juices Vegetables and rice Fruit juices and vegetables

[116] [119] [285] [123]

Continued

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Evaluation Technologies for Food Quality

Table 14.10 Continued Analyte

Food matrix

CE technique

References

Multiclass pesticides Dithiocarbamate

Wine Vegetables Vegetables Fruits Fruits and vegetables Honey

SPME-MEKC-UV CZE-UV CZE-ICP-MS MIPs-CEC-UV CZE-indirect LIF CZE-indirect UV PCR-CGE-UV PCR-CGE-UV Microchip-CEfluorescence PCR-CZE-LIF CZE-UV MEKC-UV CZE-MS PCR-MEKC-UV

[227] [293] [294] [290] [295] [236] [296] [297] [298]

PCR-MEKC-UV

[304]

PCR-MEKC-UV

[305]

[306] [120] [307]

Water Seafood Water Beverages Water, soft drinks Milk, dairy products

CPE-CZE-UV SPE-CZE-LIF tITP-CZE-UV tITP-CZE-C4D Online FASI-CZE-AD CZE-ICP-MS SPME-CZE-UV cITP-CZE-UV CZE-UV, CZE-MS DLLME-FASS-CZE-UV

Milk, yogurt Milk, yogurt Meat Bovine milk Bovine milk Feedstuff Bovine milk Honey

DLLME-MEKC-MS/MS p-CEC-AD SPE-CZE-MS2 SPE-CZE-UV MIPs-CZE-UV CZE-UV CZE-ECL CZE-UV

[139] [46] [309] [138] [140] [310] [149] [311]

Thiabendazole Strobilurin Formic acid Food-borne pathogens

Mycotoxins

Bacterial endotoxins Shellfish toxins

Acidic drugs Antimalarials Nonsteroidal antiinflammatory drugs Estrogens Quinolones Fluoroquinolones Tetracycline

Apple juice Apple juice Fruit juices, cereals Nuts, fruits, cooked meat, dry fermented sausage, cured cheese Cooked ham, dry fermented sausage, peach Fruits, nuts, cereals, spices, dry-ripened foods Cereal Water Mussel

[299] [300] [301] [302] [303]

[133] [308] [121] [27] [34] [141]

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349

Table 14.10 Continued Analyte

Food matrix

CE technique

References

Macrolide antibiotics, tetracycline β-Lactams 5-Nitroimidazole Sulfonamide

Bovine milk

LLE-SPE-CZE-UV

[143]

Bovine milk Bovine milk Meat Meat Meat Dairy products, chicken eggs, honey Meat

SPE-LVSS-CZE-UV CCE-UV CEC-MS Microchip-CE-LIF MEKC-UV FASS-CZE-UV

[142] [150] [312] [313] [314] [315]

SPE-CZE-CL

[316]

Meat Pork meat Human breast milk

NACE-MS CSEI-sweep-MEKC-UV LLE-CZE-UV

[317] [318] [319]

Rice Fish Fish Water Water Water Water Treated water Water Water Water Soft drinks Soft drinks

CZE-ICP-MS CZE-ICP-MS CZE-ICP-MS DLLME-CZE CZE-ICP-MS EME-CZE-C4D FASI-CZE-C4D μSPE-MEKC-UV LPME-CZE-AD MISPE-CZE-UV DLLME-MEEKC-UV FASI-MEKC-UV EME-CZE-C4D

[320] [321] [322] [128] [135] [122] [132] [130] [131] [117] [129] [127] [124]

Sulfamethoxazole and trimethoprim Sulfadimidine, sulfadiazine, and sulfathiazole β-Agonists Tricyclic antidepressants Heavy metals

Metals Aldehydes Phenolic compounds Chlorophenols Bisphenols

14.11.2 Intracellular food-borne pathogens To detect microorganisms and their toxic products is a challenging issue because of many different formats and combinations of ingredients. In addition, microorganisms and toxins are not usually equally distributed in foods and an aliquot tested may not necessarily be representative of the overall sample. Many CE modes (CZE, MEKC, CGE) are applied to detect different microbial contaminants in different types of food (milk, juice, wine, corn, fruits, meat, fish, baby foods) [323]. Some food-borne pathogens represent important food contaminants able to provoke serious injury and death, mainly in subjects at risk such as pregnant women, infants, and elderly people [298]. During the last few years an increased development of PCR-CE systems, coupled with amplification reactions or preenrichment steps, allowed the detection of many

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Evaluation Technologies for Food Quality

different food-borne pathogens in a single run [296, 297, 299, 324]. Also, microchipCE applications are present in the literature, ensuring more rapid and reproducible methods in comparison to PCR-CE [298].

14.11.3 Toxins Mycotoxins, including more than 100 compounds such as aflatoxins, ochratoxins, patulin, ergot alkaloids, and verrucosidin, are natural, toxic, secondary metabolites of filamentous fungi; they are considered the major contaminants of agricultural products and foods (cereal products, fruits, vegetables, juices, and meats) and represent important toxicological agents for human health. In fact, they have teratogenic, carcinogenic, and/or mutagenic effects, and can cause autoimmune diseases or allergies. Because a single filamentous fungus can produce many different mycotoxins, analytical methods able to identify and quantify in a single sample more than one mycotoxin are needed [325–327]. The maximum levels of mycotoxins in foodstuffs have been specified in Commission Regulation (European Commission, EC) No. 1881/2006 as amended by Commission Regulation (European Union, EU) No. 165/2010. Provisions for sample preparation and analytical methods for the official control of mycotoxins are laid down in Commission Regulation (EC) No. 401/2006 as amended by Commission Regulation (EU) No. 178/2010. Therefore a modern issue requires methods able to analyze multiple mycotoxins in a single run and for this purpose CE-MS hyphenation and PCR-MEKC techniques represent ideal strategies [302–305]. Potential contaminations of an aqueous environment and/or seafood, which require accurate and ultrasensitive determinations, regard endotoxins [120] and paralytic shellfish toxins, which are strong inflammatory agents and neurotoxins, respectively [133, 307, 308]. For these and other applications [300, 301, 306], see Table 14.10.

14.11.4 Drugs Today, the control of antibiotic residues in edible animal tissues is mandatory; in fact, EU Directive 96/23/CE [328] has been established to control special substances and their residues, potentially toxic to the consumer, in food of animal origin, setting also the MRLs in Directive 2377/90/EEC [329]. This is a consequence of the misuse of antibiotics not only in humans but also in food-producing animals that leads to the transfer of antibiotic-resistant bacteria to humans. Another class of drugs commonly used in veterinary medicine, whose maximum residue levels have been established by the EU Council Regulation, is quinolones [328, 329]. A particular inline SPE concentration system was used in the development of the CZE-MS2 method for the quantification of danofloxacin, sarafloxacin, ciprofloxacin, marbofloxacin, enrofloxacin, difloxacin, oxolinic acid, and flumequine in chicken muscle samples. A detailed study of kind of sorbent, sample pH, volume, elution plug composition, and design of the system was performed. Moreover, a pressurized liquid extraction (PLE) method was also developed and the resulting combination of inline SPE-CE-MS2 with PLE contributed to the validation of a suitable method for

High-performance capillary electrophoresis for food quality evaluation

351

mAU 8 6 4 2 0

(A)

2.5

5

7.5

10

12.5 min*

mAU IS

8 6 4 2

TIL

TC OTC DOC TYL

0

(B)

2.5

5

7.5

10

12.5 min*

Fig. 14.5 Determination of different drugs (macrolides and tetracycline antibiotics) in feedstuffs by capillary zone electrophoresis: (B) simultaneous separation of tilmicosin—TIL, tetracycline—TC, oxytetracycline—OTC, doxycycline—DOC, and tylosin—TYL, in comparison with blank sample (A) [310].

the simultaneous detection of a high number of quinolones in complex matrices [309]. For fluoroquinolones, see Table 14.10 [138, 140]. Also, macrolide and tetracycline antibiotics could be detected and analyzed by CE [143, 149, 311]. A real application was that reported by Tong (Fig. 14.5) [310] who simultaneously quantified tetracycline, oxytetracycline, doxycycline, tilmicosin, and tylosin in feedstuff by a CE method taking advantage of simplicity, speed, and economic cost. Most of the CE applications reported in the literature in the last decade concern sulfonamides. Nine of these antiobiotics were separated and quantified by CEC-MS; in particular, a series of poly(divinylbenzene-alkyl methacrylate) monolithic stationary phases prepared in situ by polymerization of divinylbenzene and various alkyl methacrylates were developed as separation columns. Better resolution was obtained using the poly(divinylbenzene-octyl methacrylate) monolith, and the crosssectional roughness of the monolithic column end, which was used to couple to the ESI interface, strongly influenced the electrospray stability in CEC-MS. Furthermore, a simple polishing on the end of the monolithic column increased mass signal reproducibility. A sample cleanup procedure for meat samples was performed using SPE cartridges [312].

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Evaluation Technologies for Food Quality

A method leading to the separation of sulfamethazine, sulfamethoxazole, sulfaquinoxaline, and sulfanilamide in chicken samples (muscles) within 1 min was set up by Wang [313]. Microchip electrophoresis with LIF detection was used and the plastic microfluidic chips used were cheap and disposable. The analytes were extracted from samples with ACN after a homogenization step and then derivatized with fluorescamine solution in acetone at 80°C for 20 min before an appropriate dilution for analysis. An MEKC-UV method was set up for the simultaneous determination of seven sulfonamides (sulfamethazine, sulfamerazine, sulfathiazole, sulfachloropyridazine, sulfamethoxazole, sulfacarbamide, and sulfaguanidine) and three amphenicol-type antibiotics (chloramphenicol, thiamphenicol, and florfenicol) in 20 commercial muscle, liver, and skin with fat poultry samples. A simple SPE cleanup step was required for the extraction of analytes from tissues. The application of this method resulted in a selective determination of each analyte without interference; therefore it could be successfully adopted for routine screening of foodstuffs instead of LC-MS methods because it achieved a sufficient sensitivity to detect and quantify residues at levels lower than the established EU MLR values [314]. A new feasible online preconcentration step in combination with CZE-UV was developed for the detection of sulfamethoxazole and trimethoprim (an antibacterial agent used to treat bacterial infections commonly used in veterinary medicine in combination with sulfamethoxazole) in dairy products, chicken egg, and honey. Combining micelle to solvent stacking (MSS) with FASS an highly improved sensitivity and reduced LODs by more than 100-fold occur. In fact, by the optimization of MSS, different migration velocities of the analytes either being complexed by the pseudostationary phase or eliminated from the pseudostationary phase could be applied, and by using FASS the velocity of the analytes between the sample matrix and BGS could be optimized [315]. Another interesting application for the quantification of sulfadimidine, sulfadiazine, and sulfathiazole was based on CZE with online CL detection. This method took advantage of the inhibitory effect of the analytes on Ag(III) complex anions used for luminol oxidation, thus reducing the generation of CL signals. Cleanup and enrichment of analytes from pork and chicken meat samples were performed by strong cation exchange SPE columns. The system was reliable, selective, and sensitive and therefore useful for the determination of veterinary drug residuals in animal-derived food [316]. NACE-MS was used for trace analyses of clenbuterol, salbutamol, and terbutaline, β-agonists misused to increase meat production in pork meat. A preconcentration step of the analytes was necessary and an SPE procedure using mixed mode reversed phase/cation exchange cartridges was set for improving LOD values up to 0.3 ppb. A combination of hydrodynamic and electrokinetic injection enhanced the sensitivity of the method. This methodology could be used as a good alternative to HPLC-MS2 [317]. Cation-selective exhaustive injection sweeping micellar electrokinetic chromatography (CSEI-sweep-MEKC), an online stacking capillary electrophoresis method, was developed by Wang [318]. Fractional factorial design and response surface

High-performance capillary electrophoresis for food quality evaluation

353

methodology were used as tools of the chemometrics experimental design to optimize all parameters. The optimized method was applied for the quantification of ractopamine and dehydroxyractopamine in porcine meat and the results obtained agreed with those obtained by MS techniques or by the use of commercial testing kits. The main advantage of the CSEI-sweep-MEKC method with respect to a CZE method was the higher sensitivity (about 900-fold), enabling nanogram/gram levels in the analysis. For the detection of tricyclic depressants and aminoglycosides [319] and for all detailed applications in the detection of drugs, see Table 14.10.

14.11.5 Heavy metals 14.11.5.1 Cereals Exposure to inorganic arsenic has long been a concern of both public health agencies and scientists. Cereals, and in particular rice and its products, contain different forms of inorganic and organic arsenic compounds, thus accumulating higher concentrations than other crops. In 2014, WHO proposed a draft maximum level of 0.2 mg/kg for inorganic arsenic in polished rice. The toxicity and bioavailability of arsenic highly depends on its chemical form; in fact, inorganic arsenic compounds As(III) and As(V) are considered to be class I human carcinogens, while organic forms, such as dimethylarsinic acid (DMA) and monomethylarsonic acid (MMA), can be considered much less toxic. In 2015, an interesting capillary electrophoresis coupled with inductively coupled plasma mass spectrometry (CZE  ICP-MS) method was developed to quantify the common arsenic species in rice and rice cereal. This method was successfully applied to different commercially available rice samples for the quantification of arsenic species. A sample preparation consisting of an enzyme (i.e., α-amylase)-assisted water-phase microwave extraction was necessary to reduce the sample viscosity, which subsequently increased the injection volume and enhanced the signal response. The method can be considered an excellent alternative to disadvantages of HPLC methods consisting both of column deterioration because of carbohydrates in the sample (leading to poor resolution and precision as the number of injections increases) and of the presence of unknown arsenic species that could interfere with the determination of As(III) [320].

14.11.5.2 Fish An interesting application for the detection and quantification of four arsenic species (As(III), As(V), MMA, and DMA) in Mya arenaria Linnaeus and shrimp samples was reported by Yang [321]. The novelty of this application consisted of the use of an improved sheath-flow interface for coupling CZE with ICP-MS that contributed to transport analyte solution to ICP-MS easily and more efficiently, to avoid laminar flow in the CE capillary caused by suction from ICP-MS, making electric supply more stable in CE. Furthermore, two different quantitative analysis modes were possible:

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continuous sample-introduction mode, working as a normal sheath-flow interface in which CE eluent was continuously transported into ICP-MS suitable for samples containing high concentrations of analytes, and collective sample-introduction mode, in which a pump ensured that one analyte at a time after its complete separation and elution out of the CE capillary was transported to ICP-MS for determination, and stopped working until the second analyte was completely separated and eluted out of the CE capillary. This technique had the advantages of reducing dead volume, avoiding sample dilution, and giving much lower LOD and better electrophoretic resolution. Using the sample environmentally friendly microwave-assisted extraction procedure, it was possible to completely extract organic and inorganic lead from marine animal samples, and using the same instrumentation it was possible to quantify traces of inorganic lead, trimethyl lead chloride, and triethyl lead chloride [322]. Details of CE methods used to detect each contaminant considered in this section in the different food matrices are reported in Table 14.10.

14.12

Foodomics

Foodomics is a relatively new approach to the study of food and nutrients with the application of genomics, proteomics, peptidomics, and metabolomics to investigate food safety, quality, traceability, storage, nutritional value, and bioactivity [15]. Genomic studies are the basic approach for the authentication of species, the identification of botanical origins, and the detection of allergen species [173]. For these studies, DNA-based methods (analysis of DNA length polymorphism) are less complicated compared to proteomics and metabolomics, and several publications are present in the literature by using very fast PCR-CGE-LIF assays, as, for example, for olive oil [173, 174] and tea authenticity [330] or for the analysis of GMOs (yeasts) in wine [331]. PCR is often combined with laboratory-on-a-chip CGE technology; as an example, see a study on spelt flour adulteration with soft wheat [332]. CE-MS represents a very powerful tool, not only for proteomics, peptidomics, and metabolomics [17, 51, 333], but also for genomics. The contribution of these hyphenated techniques in the study of GMO characterization and traceability has become essential [334]. In addition, also for the identification and quantification of intracellular food-borne pathogens and toxins the development of specific, rapid, and sensitive high-throughput foodomics methodologies, among which is the CE-MS approach, represents a new alternative to the most conventional PCRCGE-LIF procedure (see also Section 14.11) [335]. The use of CE-MS in metabolomics is still relatively new but is rapidly increasing, thanks also to technological developments (i.e., new interfaces) and online preconcentration strategies (i.e., SPE coupled to CE-MS) able to improve the sensitivity of CE-MS-based systems [52, 333]. Also, microchip-CE systems have been proposed for metabolite profiling [336]. Global profiling of metabolites is important to determine key compounds and metabolic pathways associated with food quality and stability. In addition, knowledge of metabolite compositions of plants ensures the possibility of controlling environmental and manufacturing conditions and

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modifying plant growth, and by consequence the nutritional value and/or the qualitative aspects (aroma, taste) of products [337]. Concerning GMOs, which can represent a very important health risk, results of the detection of transgenic DNA provided by PCR-CGE and of protein profiling obtained by CZE-UV [338] can be implemented by CE-MS techniques [334]. In fact, profiling technologies provide rapid information with important applications in the labeling and traceability of approved GMOs and in the control of nonauthorized GMOs [334, 336, 339]. Some examples of CE-MS applications in different types of foods are: l

l

l

l

l

l

Metabolite profiling of lettuce leaves [337]; Analysis of protein fraction of transgenic cultivars (soybeans) [339]; Analysis of metabolite profiling of transgenic cultivars (maize, soybean) [336]; Metabolite profiling of meat [340]; Metabolite profiling of wine and juice samples [341]; Metabolite profiling of Japanese sake [342].

14.13

Summary and outlook

This chapter offered a comprehensive overview of both principles and applications of HPCE techniques. CE represents a powerful analytical tool that is widely applied in food quality and safety, and is also a novel interesting approach in foodomics. Starting with a brief introduction on its evolution from gel electrophoresis to microchip-CE devices, basic principles, detailed general procedures, and detection systems were described. Different CE separation modes were also summarized to show the wide range of applications of this technique in food analysis. Advantages and limitations of each CE mode and new technical improvements were also reported. After the general section, recent application progress in different types of foods were considered. In the first part, applications were divided into solid and liquid foods (vegetable and animal origin) and other foods (honey, food supplements, and baby foods). In the second part, the analysis of certain food additives and contaminants was discussed. The last section of this chapter introduced foodomics applications. The chapter provided the most up-to-date information of the last decade in food quality and safety evaluation by CE.

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[293] J.S. Aulaki, A. Fekete, A.K. Malik, R.K. Mahajan, P. Schmitt-Kopplin, Capillary electrophoretic-ultraviolet method for the separation and estimation of zineb, maneb, and ferbam in food samples, J. AOAC Int. 90 (3) (2007) 834–837. [294] J. Chen, F. Fu, S. Wu, J. Wang, Z. Wang, Simultaneous detection of zinc dimethyldithiocarbamate and zinc ethylenebisdithiocarbamate in cabbage leaves by capillary electrophoresis with inductively coupled plasma mass spectrometry, J. Sep. Sci. 40 (19) (2017) 3898–3904. [295] X. Guo, K. Wang, G.H. Chen, J. Shi, X. Wu, L.L. Di, Y. Wang, Determination of strobilurin fungicide residues in fruits and vegetables by nonaqueous micellar electrokinetic capillary chromatography with indirect laser-induced fluorescence, Electrophoresis 38 (16) (2017) 2004–2010. [296] E. Delibato, A. Gattuso, A. Minucci, B. Auricchio, D. De Medici, L. Toti, M. Castagnola, E. Capoluongo, M.V. Gianfranceschi, PCR experion automated electrophoresis system to detect Listeria monocytogenes in foods, J. Sep. Sci. 32 (21) (2009) 3817–3821. [297] S.Y. Kim, B. Chung, J.H. Chang, G.Y. Jung, H.W. Kim, B.Y. Park, S.S. Oh, M.H. Oh, Simultaneous identification of 13 foodborne pathogens by using capillary electrophoresis-single strand conformation polymorphism coupled with multiplex ligation-dependent probe amplification and its application in foods, Foodborne Pathog. Dis. 13 (10) (2016) 566–574. [298] V.M. Bennett, D. McNevin, P. Roffey, M.E. Gahan, Characterization of Yersinia species by protein profiling using automated microfluidic capillary electrophoresis, Forensic Sci. Med. Pathol. 13 (1) (2017) 10–19. [299] J. Ruan, M. Li, Y.P. Liu, Y.Q. Li, Y.X. Li, Rapid and sensitive detection of Cronobacter spp. (previously Enterobacter sakazakii) in food by duplex PCR combined with capillary electrophoresis-laser-induced fluorescence detector, J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 921–922 (2013) 15–20. [300] T. Guray, M. Tuncel, U.D. Uysal, A rapid determination of Patulin using capillary zone electrophoresis and its application to analysis of apple juices, J. Chromatogr. Sci. 51 (4) (2013) 310–317. [301] M. Murillo-Arbizu, E. Gonza´lez-Pen˜as, S. Am
ezqueta, Comparison between capillary electrophoresis and high performance liquid chromatography for the study of the occurrence of patulin in apple juice intended for infants, Food Chem. Toxicol. 48 (8–9) (2010) 2429–2434. [302] P. Li, Z. Zhang, X. Hu, Q. Zhang, Advanced hyphenated chromatographic-mass spectrometry in micotoxin determination: current status and prospects, Mass Spectrom. Rev. 32 (6) (2013) 420–452. [303] M.I. Luque, A. Rodrı´guez, M.J. Andrade, A. Martı´n, J.J. Co´rdoba, Development of a PCR protocol to detect aflatoxigenic molds in food products, J. Food Prot. 75 (1) (2012) 85–94. [304] A. Rodrı´guez, M.I. Luque, M.J. Andrade, M. Rodrı´guez, A. Asensio, J.J. Co´rdoba, Development of real-time PCR methods to quantify patulin-producing molds in food products, Food Microbiol. 28 (6) (2011) 1190–1199. [305] A. Rodrı´guez, M. Rodrı´guez, M.J. Andrade, J.J. Co´rdoba, Development of a multiplex real-time PCR to quantify aflatoxin, ochratoxin A and patulin producing molds in foods, Int. J. Food Microbiol. 155 (1-2) (2012) 10–18. [306] E. Felici, C.C. Wang, L.P. Ferna´ndez, M.R. Gomez, Simultaneous separation of ergot alkaloids by capillary electrophoresis after cloud point extraction from cereal samples, Electrophoresis 36 (2) (2015) 341–347. [307] A.S. Abdul Keyon, R.M. Guijt, C.J. Bolch, M.C. Breadmore, Transient isotachophoresiscapillary zone electrophoresis with contactless conductivity and ultraviolet detection for

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es-Barrero, J.M. Bosque-Sendra, In-line solid-phase extraction preconcentration in capillary electrophoresis-tandem mass spectrometry for the multiresidue detection of quinolones in meat by pressurized liquid extraction, Electrophoresis 29 (10) (2010) 2117–2125. J. Tong, Q. Rao, K. Zhu, Z. Jang, S. Ding, Simultaneous determination of five tetracycline and macrolide antiobiotics in feeds using HPCE, J. Sep. Sci. 32 (23–24) (2009) 4254–4260. S. Casado-Terrones, A. Segura-Carretero, S. Busi, G. Dinelli, A. Ferna´ndez-Guti
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Supercritical fluid chromatography for food quality evaluation

15

Karamatollah Rezaei, Ali Aghakhani Department of Food Science, Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran

15.1

Introduction

Supercritical fluids (SFs) have been paid exclusive attention over the past 30–40 years mainly due to the safer operational conditions of the substrate being processed and its components. Considering the general properties of SFs, they have certain special features, including low viscosities, high diffusivities, and relatively high solvent powers for hydrophobic compounds that make them suitable mobile phases for the separation of sample components in the analysis of hydrophobic compounds such as lipids [1]. The main SF applied in numerous food applications is supercritical CO2, which is available in large quantities and low prices [2–8]. Since CO2 is chemically inert, exposure of food components to CO2 during the operation does not impose any damage to the compounds being processed with CO2. Also, the fact that CO2 is not flammable is a major advantage for operations under SF conditions. Moreover, being in its gaseous state at ambient conditions, CO2 is not maintained in food products after the operation is over, and more importantly when consumed with a food product, it is not toxic in human gastric conditions. Schematic representation of the main components of this chapter is depicted in Fig. 15.1.

15.2

The basic principles

One operational parameter for SFs is pressure, which is typically applied at 100–400 bar (or even higher as needed); however, there is always a risk of explosion due to the extra high pressure inside the vessels and containers. Considering this issue, appropriate equipment has to be obtained from certified vendors so that the safety of the operator and that of the laboratory is ensured. Regardless of this issue and how it is addressed with different SF equipment, despite the general perception about the use of high pressure, it has not been destructive to the food components being processed under such conditions. Đurđevi
c et al. [9] extracted pomegranate seed oil applying an SF extraction system at 379 bar and 47°C with a prior treatment of the seeds by microwaves. Other than triacylglycerols (TAGs), carotenoids and tocopherols were Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00015-9 © 2019 Elsevier Inc. All rights reserved.

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Evaluation Technologies for Food Quality

UHPSFC

SFC-ELSD

Chiral-SFC

SFE-SFC

Lipid analysis

SFC-UHPLC

SFE-MS/MS

Pesticides analysis

Recent technology development

Chiral triacylglycerols

Essential oils

Flavonoids and isoflavonoids

Food applications

Supercritical fluid chromatography (SFC)

Amines, amides and amino acids

Monosaccharides Basic principles Vitamins and provitamin

Advantages and limitations

Miscellaneous compounds

Fig. 15.1 Schematic representation of the main components of the current chapter. SFE, supercritical fluid extraction; SFC, supercritical fluid chromatography; UHPLC, ultra-high-performance liquid chromatography; ELSD, evaporative light scattering detector; UHPSFC, ultra-high-performance supercritical fluid chromatography.

also found in the extracted oil without any decomposition or breakdown under SF conditions applied in that study. Even the polyunsaturated fatty acids (FAs) that are among the most sensitive lipid compounds and comprise about 83.5% of the lipids in pomegranate seed oil were safely extracted under the high pressure (379 bar) applied to the pulverized seeds. It is noteworthy that high pressure can have a preventive effect against the damage that other parameters (such as high temperature) may impose on the compounds. One general impression for the effect of pressure is that it can squeeze and damage the matrix due to a strong force. In fact, there is no such force on a small portion of the food matrix or its components if the process of pressurization and depressurization of the vessel containing the food is carried out in a smooth and gradual manner. Generally, since there are numerous small or large pores within food matrices, they allow the SF to penetrate into the matrix and such rupture does not occur unless a sudden pressurization or depressurization is applied to the food matrix. Nevertheless, when

Supercritical fluid chromatography

381

dealing with high pressure at elevated temperatures, one might want to be cautious about the use of water that may react with compounds such as lipids under high pressure and temperature conditions and result in unpredicted products. Contrary to what was mentioned regarding the effects of high pressure, elevated temperatures can result in numerous undesirable reactions for many compounds, especially when exposed to oxygen or atmospheric conditions. Although the precautions mentioned earlier may/may not be the case with SFs, the main question is whether we need such high temperatures when using SFs in a supercritical fluid chromatography (SFC) system. As a new chromatographic technique, SFC is applied to separate the components of a sample under certain operational conditions that are appropriate for the analysis. In the early stages of studies with SFC, pressure and temperature were the main parameters being considered for the manipulation of solvent power of the mobile phase for a better separation of the sample components. These parameters at the same time had significant impact on the properties of the solutes and especially on their mobility levels within the SF as the medium for the separation [2–8]. Considering those parameters, only nonpolar analytes (mainly lipids from food components) were targeted for analysis and other components were not attended seriously. A lower temperature increases the density of SF and therefore the interactions between the stationary phase and the analytes are increased. In a study published by Huang et al. [10] on the analysis of flavonoids, an increase in the temperature from 32°C to 40°C resulted in better separation of neighboring peaks with longer retention times of the components [10]. Further studies in this area allowed the use of liquid modifiers (such as methanol, acetone, ethanol, and even water) and additives at small quantities to help improve the solvent power of SF for the uptake of somewhat polar compounds such as carotenoids. Huang et al. [10] reported that the addition of methanol (as a modifier) to the CO2 stream resulted in tailing of the peaks. However, when acidic additives (acetic or formic acid) were used along with methanol, the extent of tailing was reduced due to improvements in the polarity of the mobile phase [11].

15.3

Procedures

Growing interest in the area of SFC led to enormous developments both in the conditions of the mobile phases and in the types and configurations of columns being used for such analysis. Recent studies have opened up new avenues in the application of SFC for the analysis of more polar and even ionic compounds [12, 13]. Furthermore, as the new results led to major improvements in the possibility of using SFC in wider applications, some studies have even broken the boundaries set for the SFC conditions and have slowly shifted to lower temperature conditions, which are considered subcritical, and have explored new possibilities with a fluid that is no longer a supercritical [14, 15]. Numerous studies reported interesting data intended to be obtained under SF conditions, but, the applied experimental conditions were below the critical points of CO2 [14–19]. To elucidate on that, the impact of temperature and pressure on the diffusion coefficients of the compounds also needs to be considered when working

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Evaluation Technologies for Food Quality

with an SFC system, where high diffusion coefficients may result in excessive broadening of the peaks and as a consequence poor resolution on the chromatogram. Rezaei and Temelli [2] applied SFC to determine the diffusion coefficients of several lipid classes in supercritical CO2. They reported a number of parameters (such as pressure, temperature, use of a modifier, molecular weight, and number of double bonds) influencing the diffusion coefficients of the studied lipid compounds. Therefore, diffusion coefficient needs to be considered when designing a gradient programming with SFC applying different pressure, temperature, and modifier levels. Rezaei and Temelli [2] also reported that the diffusion coefficients of these compounds are reduced as the density of SF is increased. Although there may be some exceptions, the general trend is that an increase in the temperature can increase the diffusion coefficient and an increase in the pressure can result in a decrease in this parameter [2] due to the effects of temperature and pressure on the density of the SF as the medium. Tables 15.1 and 15.2 list some of the applied temperatures when performing SFC analysis in several studies on food components (lipids, amino acids, carbohydrates, etc.). The highest temperature applied in the SFC analysis of the lipids (Table 15.1) belongs to the analysis of milk fat (130°C) in 1995, when SFC was at its early stages of development. However, all the latest studies reported here (Tables 15.1 and 15.2) have used a temperature as high as 60°C. Some studies have not paid attention to the critical temperature of CO2 (31°C), which is the minimum temperature required for SF conditions, and carried out the analysis under subcritical conditions. To improve the elution power of an SF in an SFC operation, one may want to reduce the temperature, which is exactly the opposite to what is considered with a gas chromatography (GC) operation such as what is reported for the analysis of FA methyl esters of corn oil by Rezaei et al. [43]. Also, while pressure is not a major working parameter to be manipulated during GC and high-performance liquid chromatography (HPLC) operations, an increase in the pressure (up to the maximum recommended level recommended by the unit manufacturer) can improve the elution power of the mobile phase when carrying out an SFC analysis. Furthermore, similar to what is practiced with isocratic GC analysis, in some cases, changes in the temperature may not be considered to manipulate the elution power of the SF during the SFC analysis. Qu et al. [11] applied two different temperature levels of 40°C and 60°C for the analysis of free FAs by SFC-MS and reported that at a higher temperature both the peak width and total analysis time were increased, which is contrary to what occurs in a GC operation (Fig. 15.2). Subra and Vega [20] used SFC to study the detoxification of essential oils obtained from bergamot peel. They optimized SFC conditions (pressure from 75 to 160 bar and temperature from 37°C to 57°C) and measured psoralens, which are phototoxic compounds from the family of coumarins found in bergamot peel oil. The highest selectivity levels against psoralens were obtained at 80–105 bar and 47°C, under which the terpenes and the psoralen derivatives were fully separated. In the HPLC systems, however, the column temperature is usually maintained at a recommended fixed level, which is typically within 25–70°C, throughout the entire analysis as has been the case for the analysis of polycyclic aromatic compounds in olive oil [44]. Other than pressure and temperature, type of modifier can also play a major role in the peak resolution and run time of an SFC operation. Speybrouck et al. [26] studied the effect of propylamine addition (at

Table 15.1 Comparison of instrumental conditions applied for the analysis of lipids under sub- and supercritical fluid chromatography Operational conditions

Sample analyzed

Pressure

Mode

Mobile phase

Analyte: 3-monochloropropane-1,2diol fatty acid esters LOD: 0.063 mg/kg Samples: corn oil, rapeseed oil, soybean oil, crude palm oil, refined palm oil (liquid), refined palm oil (solid) Analytes:free fatty acids LOD: 0.1 μg/mL Edible oils: peanut, corn, soybean, sunflower, olive, sesame, fish, beef, mutton, pork fat Analytes: TAG, DAG, and FAs Samples: Kniphofia uvaria seeds Bergamot peel oil

100 bar

Gradient

CO2

Phospholipids, glycolipids, neutral lipids, and sphingolipids Kniphofia uvaria seed

Modifier

–

–

Modifier 1: MeOH mixed with 0.1% (w/ v) ammonium formate Modifier 2: IPA

Temperature (°C)

Columns

Number of columns

Analysis time (min)

System/ detector

Source

35

1. Inertsil CN-3 2. Inertsil Diol 3. Inertsil ODS-4 4. Inertsil ODS-SP 5. Inertsil ODS-P All: 250
4.6 mm i.d., 5m

5

9

SFC-QqQ MS

[1]

100 bar

Gradient

CO2

MeOH/ACN (50:50, v/v) with 0.1% formic acid

30–60

HSS C18 SB

1

3

SFC-ESIQqQ-MS

[11]

100 bar

Isocratic

CO2

MeOH (at 10%) (v/v)

9

6 Kinetex C18 + 1 Accucore C18 All: 150
4.6 mm, 2.6 mm Stainless-steel tube (15 cm
4.6 mm i.d.) filled with silanized silica particles (63–200 μm) Inertsil ODS-3

7

100

SFC-APCIHRMS and SFC-UV

[15]

SFC-UV

[20]

SFC-MS

[21]

6 Kinetex C18 and 1 Accucore C18, each 150
4.6 mm, 2.7 m

7

UHPSFCAPCI-QTOFHRMS

[14]

85–105 bar

100 bar

100 bar

CO2

Gradient

CO2

CO2/MeOH (90/10, v/v)

47

MeOH with 0.1% (w/w) ammonium formate (pH 6.4) 10%–30%, 20 min MeOH

35

9

1

1

15

Continued

Table 15.1 Continued Analytes: TAG isomer: 70 TAGs 20 isomers 6 regioisomeric Samples: palm and canola oils Analytes: TAGs Samples: argan and rapeseed oils

Outlet pressure: 100 bar Initial inlet pressure: 150 bar Back pressure: 100 bar Inlet pressure: below 400 bar

CO2

MeOH with 0.1% (w/w) ammonium formate

35

YMC carotenoid All: 250
4.6 mm i.d.; 4m

1

50

SFC-QqQ-MS

[22]

Isocratic

CO2

12% of ACN/MeOH (90/10; v/v)

17

5

60

UHE-LPSFC-UVELSD

[16]

1

25

SFC-FID

[23]

Gradient

CO2 (d ¼ 0.2 g/ mL) (ramp: 0.012–0.45 g/ mL) CO2

Superficially porous particles: 4 Kinetex C18 + 1 Accucore C18 theoretical plate number: above 100,000 20 m
50/μm i.d. SB phenyl 5 with 0.25/μm film thickness (Dionex)

Gradient

CO2

Cholesterol of milk fat

TAG profiles: soybean

Fatty acid source: fish oil Sample preparation: hydrolysis + phenacyl ester derivatives

Outlet pressure: 100 bar Initial inlet pressure: 180 bar Outlet pressure gradient was from 150 bar (2 min) to 300 bar at 1.5 bar/min

130

MeOH with 0.1% (w/w) ammonium formate

35

Chromolith performance RP-18e column (100
4.6 mm i.d.)

3

8

SFC-MS

[24]

ACN/IPA (6/4)

40

Strongly acidic cationexchange columns (Nucleosil 100-5 SA, 25 cm L
4.6 mm i. d.
5 m dp) loaded with silver ions

2

60

Ag-SFC
RPLC and RP-LC
2RPLC UV and ELSD

[25]

ACN, acetonitrile; Ag-SFC, silver-ion chromatography; APCI, atmospheric pressure chemical ionization; DAG, diacylglycerols; ELSD, evaporative light scattering detector; ESI, electrospray ionization; EtOH, Ethanol; FA, fatty acid; FID, flame ionization detector; HRMS, high-resolution mass spectrometry; IPA, isopropyl alcohol; MeOH, methanol; QqQ-MS, triple-quadrupole mass spectrometer; QTOF, quadrupole timeof-flight; RP-LC, reversed phase-liquid chromatography; SFC, Supercritical fluid chromatography; TAG, triacylglycerols; UHE-LP-SFC, ultrahigh efficiency/low-pressure supercritical fluid chromatography; UHPSFC, ultrahigh-performance supercritical fluid chromatography; UHR-Q-TOF, ultrahigh resolution quadrupole time-of-flight; UV, ultraviolet.

Table 15.2 Comparison of instrumental conditions applied for the analysis of hydrophobic compounds under sub- and supercritical fluid chromatography Operational conditions

Sample analyzed

Pressure

Mode

Mobile phase

Flavonoids in traditional Chinese medicines

200 bar Flow: 3 mL/ min

Gradient

CO2

PA: primary amine SA: secondary amines TA: tertiary amine

105 bar Flow: 3.5 mL/min

Isocratic

CO2/ EtOH 85/15

Furostanol saponins from traditional Chinese medicines (Dioscorea zingiberensis C.H. Wright) within Triterpenoids from apple pomace extracts

110 bar Flow rate: 1.0 mL/min

Gradient

CO2

120 bar

Isocratic

CO2MeOH 97:3 (v/ v)

Pyroglutamide

150 bar Flow rate: 3–4 mL/min

Polar isomeric uncapped peptide

120 bar Flow rate: 2 mL/min

Gradient

CO2

Columns

Number of columns

Analysis time (min)

System/ detector

Source

40

ZORBAX RX-SIL

1

18

SFC-UV

[10]

35

Chiralpak AD-3 100
4.6 mm

1

20

SFCDAD

[26]

40

Acquity UPC2 Torus Diol (1.7 μm, 150
3.0 mm)

1

22

SFC-Q/ TOF-MS

[27]

MeOH

20

1. Viridis ethyl-pyridine 2. Synergi Polar-RP

1

20

SFCELSD

[17]

CO2/EtOH 85:15 CO2/MeOH 85:15 0.1% TFA/ 0.1% IPA in 90:10 MeOH: water

40

Chiralpak AD-H, Chiralpak AS-H, Chiralcel OD-H, and Chiralcel OJ-H (250
4.6 mm i.d.; 5 μm) Silica Princeton Diol Princeton (4.6
250
5 μm)

1

9

SFCDAD

[28]

1

17

SFCELSD SFC-MS

[29]

Temperature (°C)

Modifier: MeOH Additive: 0.1% phosphoric acid Modifier: EtOH Additive: 2-propylamine MeOH (containing 0.2% NH3  H2O and 3% H2O)

Modifier

60

Continued

Table 15.2 Continued Antioxidant from rosemary

150–370 atm at 10 atm/min

CO2

–

100

Polymethoxyflavones from orange (Citrus sinensis) peel

100 bar Flow rate: 70 mL/min

50% liquid CO2

30

Thymus vulgaris L. extract

150 bar Flow rate: 3 g/min 150 bar Flow rate: 2 mL/min

CO2

50% MeOH (containing 0.25% diethylamine) EtOH 3%

Modifier: MeOH Additive: 0.05% phosphoric acid MeOH with NH3

50

Modifier: MeOH, EtOH, or a 1:1 mixture of MeOH:ACN Additive: ammonium formate or ammonium acetate MeOH/water/ formic acid (91:5:4, v/v/v)

40

Isoflavones in dietary supplements containing (medicinal plants)

Gradient

CO2

Enantiomers of methamphetamine in drug abuse in forensic applications

172 bar Flow rate: 1 mL/min

Isocratic

CO2: modifier 92.5/7.5

Amino acids in human plasma

150 bar

Gradient

CO2

Monosaccharides in plant gum binders in a painting medium LOD: 0.01–0.12 ng/mL

138 bar Flow rate: 2 mL/min

Gradient

CO2

50

10

35

Silica particles coated with SE-54 (5% phenyl, 95% methyl silicone) and Carbowax 20 M (poly [ethylene glycol]) DAICEL AD chiral column, 30
250 mm, 10 μm

1

30

SFC-FID

[30]

1

6.5

SFC-UV

[31]

25 cm
4.6 mm i.d. Kromasil 60-5SIL packed column Acquity UPC2 BEH 1.7 μm (3.0
100 mm)

1

10

SFC-UV

[32]

1

8

SFC-PDA

[33]

Chiralpak IC-3/SFC and Chiralcel OJ-3/SFC 3.0 mm i.d. and 2.5 mm particle size column length of 50 and 150 mm Phenomenex Luna HILIC column (150
3 mm, 3 mm)

1

15

SFC-PDA SFC-MS

[18]

1

7.5

SFCAPCI-MS and SFCESI-MS

[34]

1

4.5

UHPSFC/MS

[35]

Acquity HSS C18SB (100
3 mm i.d., 1.8 μm)

Table 15.2 Continued Carotenoids in dietary supplements LOD: 0.33–1.08 μg/mL Carotenoids in the extracts of microalgae (Scenedesmus sp.) LODs: between 0.02 and 0.05 mg/L

152 bar

Gradient

CO2

120 bar Flow rate: 5 mL/min

Gradient

CO2

Epoxy carotenoids in human serum and lowdensity lipoprotein

100 bar Flow rate: 3 mL/min

CO2

Trans-stilbene oxide enantiomers

100–300 bar Flow rate: 1.35 mL/min

Asymmetric sulfoxides

150 bar Flow rate: 1–3 mL/min

Racemic spirocyclic norisoprenoids

138 bar Flow rate: 2.5 mL/min 120 bar Flow rate: 1.5 mL/min

Nonylphenol ethoxylates and octylphenol ethoxylates in leafy vegetables Phospholipids in plasma

100 bar Flow rate: 3 mL/min

Gradient

1:2 (v:v) MeOH/EtOH mixture MeOH

35

35

CO2

MeOH with 0.1% (w/v) ammonium formate MeOH

CO2

MeOH

25

CO2

Gradient

32

30–40

30

CO2

MeOH/ACN (3:2, v/v)

CO2

MeOH with 0.1% (w/v) ammonium formate

35

Acquity UPC2 HSS C18 SB column (150
3.0 mm, 1.8 μm) 1. SunFire C18 (4.6
250 mm, 5 μm ˚ pore particle size, 100 A size) 2. Viridis SFC silica 2-ethylpyridine (4.6
250 mm, 5 μm) ˚ pore particle size, 100 A size Puroshere RP-18e

1

10

UHPSFC-PDA

[36]

2

10

SFESFC-PDA

[37]

1

20

SFCQqQ-MS

[38]

Chiral AD-H column, Daicel, Co. Japan, 4.6 mm i.d. and 20 cm packed with 5 μm particles 1. Lux Cellulose-4 2. Lux Cellulose-2 3. Lux i-Cellulose-5 (250
4.6 mm, 5 μm) Chiralpak IA and IF, 250
4.6 mm (i.d.) and a particle size of 5 μm Viridis BEH 100
3 mm, with sub-1.7 μm particles

1

14

SFC-UV

[39]

1

20–60

SFC-UV

[19]

1

20

SFC-PAD

[40]

1

5

UHPSFCMS/MS

[41]

PC HILIC column (250
4.6 mm i.d.)

1

15

SFESFC/MS/ MS

[42]

ABPR, automated backpressure regulator; DAD, diode array detector; IPAm, isopropylamine; PDA, photodiode array; UHP-SFC, ultra-high performance supercritical fluid chromatography.

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Evaluation Technologies for Food Quality

Fig. 15.2 Effect of temperature on the retention time and peak width in the analysis of fatty acids with supercritical fluid chromatography on an HSS C18 SB column. (A) A mixture of four unsaturated free fatty acid standards (C18:1, oleic acid; C18:2, linoleic acid; α-C18:3, alpha linolenic acid; γ-C18:3, gamma linolenic acid). (B) A mixture of four saturated free fatty acid standards (C14:0, myristic acid; C16:0, palmitic acid; C18:0, stearic acid; C20:0, arachidic acid). From S. Qu, Z. Du, Y. Zhang, Direct detection of free fatty acids in edible oils using supercritical fluid chromatography coupled with mass spectrometry, Food Chem., 170 (2015) 463–469, with permission from Elsevier.

0.3%–10%, v/v) as an additive on the selectivity levels and resolutions of a series of amines using a Chiralpak-AD stationary phase. When propylamine was used at 10%, resolution of the two consecutive peaks was improved significantly (Fig. 15.3). They also found that by changing the concentration of the additive, the order of enantiomers on the chromatogram was reversed indicating a major effect of the ratio of additive/modifier in the enantioselectivity of the column. The applied pressure and temperature for this study were 105 bar and 35°C, respectively. For all chromatographic systems, extra columns can provide better opportunities for the separation of the components from each other. This has been demonstrated by Duval et al. [15], who observed 11 extra peaks when the number of columns was increased from 5 to 7. In another study, Duval et al. [14] separated 53 lipid components using 7 connected columns. Also, Lesellier et al. [16] used four Kinetex columns and one Accucore C18 column (total length 75 cm) in series to enhance the theoretical plate numbers (above 100,000) and selectivity in the separation of triglycerides. However, the analysis time in these conditions increased to above 60 min [14–16].

Supercritical fluid chromatography

% 2-propylamine

389

CO2/EtOH 70/30

CO2/EtOH 85/15

CO2/EtOH 90/10

CO2/EtOH 92.5/7.5

α=1.75

α=2.01

α=2.16

α=1.18

α=1.26

α=1.32

α=1

α=1.03

α=1.10

α=2.20

10%

5%

α=1.46

α=1.12

3%

α=1

α=1

α=1.02

α=1.03

α=1.1

α=1.11

α=1.13

α=1.15

0.6%

α=1.13

α=1.16

α=1.2

0.3%

α=1.18

α=1.21

α=1.27

2%

1%

0

1

2

3

0

5

10

0

10

α=1.24

20

α=1.32

0

10

20

Fig. 15.3 Effect of modifier (ethanol, EtOH) and additive (2-propylamine) concentrations on the separation efficiency of an enantiomeric paired amine using supercritical fluid chromatography (at 105 bar and 35°C) on a Chiralpak AD-3 column (100 mm
4.6 mm) with CO2 flow rate: 3.5 mL/min. From D. Speybrouck, et al., The effect of high concentration additive on chiral separations in supercritical fluid chromatography, J. Chromatogr. A. 1510 (2017) 89–99, with permission from Elsevier.

15.4

Advantages and limitations

Use of SFC for the analysis of numerous components has resulted in good separation/ resolution applicable for the analysis of weakly polar compounds [27]. Also, studies with the latest developments in SFC show that many separations with SFC can be achieved at significantly shorter times when compared to HPLC [21] or GC analysis. Furthermore, with the new developments in the coupling of various detectors such as mass spectrometry (MS), evaporative light scattering detector (ELSD), and other combinations, the analytical sensitivity can also be improved accordingly [17]. Also, due to the need for fewer organic solvents, it is safer for the environment and, as a consequence, SFC can be considered a green technology [28]. Some of the organic solvents applied for HPLC analysis are flammable and their storage and use require extra precautions to prevent accidental damage due to fire, exposure, and other issues in the laboratory. One other issue with the use of these liquids is associated with the high costs of their purchase and disposal. A new development in the use of certain

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Evaluation Technologies for Food Quality

additives along with specific columns has also provided the possibility of separating ionic sample components (such as amine salts) by SFC [29].

15.5

Recent technology development

Guillarme et al. [45] reviewed different possibilities of coupling SFC systems with an MS detector in a way that identification of the components is facilitated for samples analyzed by SFC. Specifically, with the SFC-MS combination, the sensitivity of the detector is also impacted if a gradient elution is applied in the SFC compartment [45]. When using an MS detector, separated components need to be introduced to an interface especially designed for this detector [45] and, as a consequence, the original operational conditions of SF (including pressure and temperature levels and modifier concentration) are totally replaced by the conditions assigned for the MS detector. Hori et al. [1] reported an advanced subcritical fluid chromatography system coupled to triple quadrupole MS to analyze 3-monochloropropane-1,2-diol (3-MCPD) FA esters, the toxic compounds produced during the refining process of edible oils with adverse effects on the kidneys (maximum tolerable daily intake: 2 μg/kg body weight per day) [1]. According to Hori et al. [1], the analytical method suggested by the German Society of Fat Science was based on a hydrolysis process followed by phenylboronic acid derivatization and GC-MS detection, which were able to measure total 3-MCPD FA esters without identifying their original forms in the oil. The method reported by Hori et al. [1] was able to identify the molecular species of major 3-MCPD FA esters in each edible oil studied and resulted in 10 times better sensitivity levels (0.013–0.063 mg/kg) and a linear dynamic range within 0.1–100 mg/kg concentrations. One major finding of Hori et al. [1] was that 3-MCPD FA esters were produced during the refining process of palm oil and there was no indication of these compounds in the crude palm oil. The liquid refined palm oil was rich in low-melting-point FAs (oleic and linoleic acids), while solid refined palm oil was rich in a high-melting-point FA (palmitic acid). Duval et al. [15] reported a subcritical fluid chromatography system coupled to atmospheric pressure chemical ionization (APCI), high-resolution MS, and UV detection systems for the characterization of the lipid components in a complex unknown vegetable oil (an extract obtained from Kniphofia uvaria seeds). They could apply several chromatographic columns for the separation of components in the aforementioned complex matrix. In a different study, Duval et al. [14] applied foregoing system to analyze other components of the seed extract (including anthraquinones, free FAs, DAGs, hydroxylated TAGs, and TAGs). To improve the detection of minor components, they introduced all the compounds with no splitting and no make-up (a solution of silver nitrate in methanol to produce silver-TAG ions) prior to APCI. However, the addition of make-up was further optimized for quantification purposes. They showed that the addition of make-up increases the robustness of the mass responses quantitatively [14] with a peak area repeatability of 3%. Jiang et al. [46] developed a rapid chiral SFC system to measure prothioconazole (a pesticide) in the soil and also in tomatoes. The separation was performed on a cellulose tris(3,5-dimethylphenylcarbamate)-coated chiral column resulting in a retention

Supercritical fluid chromatography

391

time ( O > S > P (L: linoleic acid, O: oleic acid, S: stearic acid, and P: palmitic acid) (Fig. 15.5). The product ion with the highest intensity is considered in the optimization of the MRM transition conditions. Some TAGs can produce product ions with the same m/z regardless of how they are fragmented. For example, triolein (OOO) produces only [OO]+ DAG fragments, while SOL (S: stearic acid and L: linoleic acid) can produce three different fragments, including [SO]+, [OL]+, and [SL]+where [SL]+ has the same m/z ratio as [OO]+ (603.5) indicating that [SL]+ is not a good identifier fragment to be used with the MRM approach explained above [22]. In addition, the regioisomeric TAG pairs (SSO/SOS, LLO/LOL, PPO/POP, with P being palmitic acid) can produce similar fragments with the same m/z ratios. As a consequence, for such cases an on-column separation is necessary prior to MS detection [22].

15.6.3 Use of SFC for the analysis of essential oils Ramı´rez et al. [30] developed an SFE-SFC method for the extraction and separation of antioxidant compounds in rosemary using silica particles coated with SE-54 (5% phenyl, 95% methyl silicone) and Carbowax 20 M (poly[ethylene glycol]) as stationary phases, which separated polar compounds without any modifiers in the mobile phase. Li et al. [31] developed an SFC method for the isolation of four polymethoxyflavones from orange (Citrus sinensis) peel, which could be used for in vivo studies. SFC showed the best and the fastest separation under chiral mode in comparison with nonchiral SFC and RP-HPLC. Furthermore, SFC showed the capability of stack injection, which provided the possibility of large quantity purification. Garcı´a-Risco et al. [32] developed a pilot-plant-scale SFE-semipreparative-SFC method to extract and separate the components of Thymus vulgaris L. such as thymol, carvacrol, borneol, etc. The main parameters applied in the SFE and SFC systems (pressure, temperature, and amount of cosolvent) were optimized. SFE extract fed into the semipreparative

Supercritical fluid chromatography

395

Fig. 15.5 Analysis of regioisomeric pairs of six triacylglycerols (A–F) in palm and canola oils applying the multiple reaction monitoring method. (A) SOS (a)/SSO (b), (B) SOP (c)/ SPO (d), (C) SLnP (e)/SPLn (f ), (D) POP (g)/PPO (h), (E) PLP (i)/PPL (j), (F) PLnP (k)/ PPLn (l). L, linoleic; Ln, linolenic; O, oleic; P, palmitic; S, stearic. From J.W. Lee, et al., Profiling of regioisomeric triacylglycerols in edible oils by supercritical fluid chromatography/tandem mass spectrometry. J. Chromatogr. B. 966 (2014) 193–199, with permission from Elsevier.

SFC produced three different fractions. All fractions were analyzed using a GC-MS system (Fig. 15.6).

15.6.4 Use of SFC for the analysis of flavonoids and isoflavonoids Huang et al. [10] analyzed 12 flavonoids in traditional Chinese medicines with SFC and found that analysis was three times faster and had higher overall resolution than HPLC. The separation was performed on a ZORBAX RX-SIL column using both modifier and additive in the mobile phase (Fig. 15.7). The optimized method showed good repeatability and sensitivity in the analysis of five flavonoids in Chrysanthemum morifolium Ramat. Ganzera [33] developed a fast, sensitive, precise, and accurate SFC method for the analysis of nine isoflavones in dietary supplements containing medicinal plants, soy, or other functional ingredients.

396

Evaluation Technologies for Food Quality

Fig. 15.6 A typical gas chromatography-mass spectrometry chromatogram for thyme essential oils extracted by supercritical CO2. From M.R. Garcı´a-Risco, et al., Fractionation of thyme (Thymus vulgaris L.) by supercritical fluid extraction and chromatography. J. Supercrit. Fluids 55(3) (2011) 949–954, with permission from Elsevier.

80

Absorbance (mAU)

70 60 0.1%H3PO4 50 0.1%HAc

40 30

0.1%FA

20 0

5

10 15 Time (min)

20

25

Fig. 15.7 Effect of different additives (formic acid [FA], acetic acid [HAc], and phosphoric acidic [H3PO4]) at 0.1% (v/v) in methanol as modifier in supercritical fluid chromatography (with CO2 as the mobile phase) for the separation of flavonoids on a ZORBAX RX-SIL column (150 mm
4.6 mm). From Y. Huang, et al., Development and validation of a fast SFC method for the analysis of flavonoids in plant extracts. J. Pharm. Biomed. Anal. 140 (2017) 384–391, with permission from Elsevier.

Supercritical fluid chromatography

397

15.6.5 Use of SFC for amines, amides, and amino acids Segawa et al. [18] developed a method for the separation of enantiomers of methamphetamine using SFC based on an enantioselective cellulose-based packed column for the investigation of drug abuse in forensic applications. Among the parameters studied, the concentration of ammonia (the basic additive used in the study) had a major impact on peak resolution. Baudelet et al. [28] studied the enantiomeric separation of three pyroglutamide derivatives by both SFC and HPLC using a polysaccharide-based chiral column and showed that in comparison with HPLC, SFC has better sensitivity and shorter analysis time (Fig. 15.8). Considering the environmental issues, SFC required 10 times less organic solvent. Wolrab et al. [34] investigated the suitability of atmospheric pressure chemical ionization (APCI) and ESI sources in the SFC-MS analysis of polar amino acids in biological samples. Their results suggest that APCI is a preferred method for the analysis of amino acids with polar side chains. However, ESI was more suited for the analysis of amino acids with hydrophobic residues. In addition, they modified the SFC system with cryostat cooling, which resulted in higher temperature stability in the booster pump and led to better reproducibility of retention times, improved peak shape, and enhanced sensitivity.

15.6.6 Use of SFC for monosaccharide analysis Pauk et al. [35] added two polar additives (water and formic acid) in methanol as the modifier using ultra-high performance supercritical fluid chromatography-tandem mass spectrometry (UHPSFC-MS) for the analysis of several monosaccharides for plant gum binders in a painting medium. The separation of isomers was performed in a few minutes (4.5 min). The peak area for the SFC chromatogram was used for the classification of plant gum samples with PCA methods. The developed method was a good alternative to other chromatographic methods for the analysis of polar monosaccharides.

15.6.7 Use of SFC for vitamins and provitamin analysis Fat-soluble vitamins (FSVs) are important for human health as part of lipid materials in the body [51]. There are different FSVs with different structures, properties, and functions, which make their simultaneous analysis difficult in food samples. Gong et al. [52] developed a fast method for the analysis of α-tocopherol (one species among several vitamin E components) in tropical fruits using “ultraperformance convergence chromatography,” which is a combination of SFC and UPLC and can benefit from the advantages of both techniques. α-Tocopherol appeared at a retention time shorter than 0.5 min on the chromatogram. Zhao et al. [53] developed a UHP-SFC method using acetonitrile as a modifier for the analysis of five retinol isomers (vitamin A components) in animal livers and liver products. Under optimized conditions, separation of the five isomers was performed within 20 min. Oberson et al. [51] analyzed several fat-soluble vitamins (A, D, E, and K) in food samples, including infant formulas and cereals and mixed meals using SFC-MS/MS.

398

Evaluation Technologies for Food Quality

Fig. 15.8 Analysis of enantiomeric pairs of a pyroglutamide derivative (compound 1). (A) By supercritical fluid chromatography (at 150 bar and 40°C) on a Chiralpak AS-H column using CO2 and ethanol at 4:1 ratio (v/v) at a flow rate of 3 mL/min. (B) By high-performance liquid chromatography (at 25°C) on a Chiralpak AS column using heptane and ethanol at a ratio of 4:1 (v/v), respectively, at a flow rate of 0.8 mL/min. From D. Baudelet, et al., Enantioseparation of pyroglutamide derivatives on polysaccharide based chiral stationary phases by high-performance liquid chromatography and supercritical fluid chromatography: a comparative study. J. Chromatogr. A. 1363 (2014) 257–269, with permission from Elsevier.

The developed method had high throughput, used less solvent, and had recoveries higher than 90%. Carotenoids are fat-soluble pigments that are considered provitamin A [36]. They cannot be synthesized in the body and as a consequence the body should receive them from daily foods for regular metabolisms in the body. Li et al. [36] developed a quick and simple UHP-SFC-photodiode array method for simultaneous determination of carotenoids in dietary supplements using a sub-2 μm

Supercritical fluid chromatography

399

particle-size column. Under optimized conditions, nine carotenoids, especially α-carotene and β-carotene, lutein, and zeaxanthin, which are two pairs of structural isomers, were separated. Abrahamsson et al. [37] developed an SFE-SFC method for the extraction and determination of carotenoids in the extracts of microalgae (Scenedesmus sp.). The developed SFC method was a good alternative to the HPLC system using C18 or C30 columns applying toxic organic solvents. Matsubara et al. [38] developed an SFC-MS/MS method for the determination of trace amounts of epoxy carotenoids in the presence of carotenoids within 20 min from human serum and lowdensity lipoprotein. The total separation time on the ODS column was 20 min, while the required times for HPLC and UPLC were longer than 85 and 46 min, respectively.

15.6.8 Use of SFC for the analysis of nonclassified compounds Yanshan et al. [54] developed an online SFE-SFC method for the measurement of four major aromatic components in vanilla. Since SFE uses CO2 as the extraction solvent and SFC uses this fluid as the mobile phase, they can be tandomly coupled and provide benefits of automatic analysis, reduced error, higher sample throughput, and higher extraction efficiency [54]. Tang et al. [55] developed a rapid, sensitive, and efficient SFC-MS method for the analysis of phenolic acids in extra virgin olive oil. The developed method was three times faster than HPLC and showed better selectivity. While the C18 column was satisfactory for the separation of phenolic acids with HPLC, it did not show satisfactory results with SFC. However, Platisil CN (a polar column) had higher retention and better baseline separation for these compounds [55]. Yoshioka et al. [56] developed a rapid and sensitive SFC-APCI-MS method for the analysis of 16 polycyclic aromatic hydrocarbons in coffee beverages and dark beer. Backpressure programming was used instead of the regular fixed back-pressure to provide larger sample introduction into the MS system and to achieve higher sensitivity. Table 15.2 also lists major instrumental conditions applied for the analysis of several hydrophobic compounds under sub- and supercritical fluid chromatography. Chiral separation of trans-stilbene oxide ((R,R)- and (S,S)-forms) has been studied by Funazukuri et al. [39] using SFC analysis. They reported that at conditions away from those of the critical point, there was a direct relationship between the isothermal compressibilities of a mixture of CO2 and the modifier with solvent density and temperature, but at conditions near the critical point such a relation was complicated. Enantioseparation of 24 asymmetric sulfoxides using 7 columns with polysaccharidebased chiral stationary phases were studied by West et al. [19]. Among them, chlorinated cellulosic columns showed best performance. They reported that molecules with folded U-shaped conformation were separated more efficiently than others, suggesting that the mechanism of enantioseparation was based on both the steric effect and analyte-stationary phase interactions. Yang et al. [27] developed an SFC method for the separation of 10 structurally similar furostanol saponins and their hydroxyl derivatives using the polar diol column in 22 min. This method was not able to separate the isomers of furostanol saponins. Lesellier et al. [17] compared the efficiency of the ELSD with that of a UV detector in an SFC system for the analysis of eight pentacyclic triterpenoids from apple pomace

400

Evaluation Technologies for Food Quality

and reported that ELSD had higher responses when compared to a UV detector. An effort to identify 11 unknown compounds did not result in a good separation among the peaks. Schaffrath et al. [40] developed an SFC method for the analysis of nonpolar racemic spirocyclic norisoprenoids using polysaccharide chiral stationary phases and carbon dioxide as the mobile phase without any modifier.

15.7

Summary and outlook

SFC is a green technology that is basically moving away from the use of organic solvents such as hexane, chloroform, methanol, acetonitrile, and tetrahydrofuran, which are dominantly used as parts of mobile phases in LC and are also destructive to the environment. Despite the possible breakdowns or reactions that might occur with some compounds being analyzed by GC and HPLC, SFC conditions, when CO2 is applied as the supercritical medium, are not usually destructive to the sample components. This is mainly due to the fact that the temperature applied for the SFC analysis can be as low as 32°C, just a little above the critical temperature of CO2, which has been paid exclusive attention for the SFC analysis, and also because CO2 (as an inert compound) provides extra support for maintaining the integrities of the sample components. SFC can be applied for the analysis of a variety of components with adequate separation/resolution that can be achieved with minimal changes in operational conditions. Application of detectors such as MS and ELSD, typically used with GC and HPLC systems, respectively, has resulted in higher sensitivity levels for the analysis of numerous compounds. The latest developments in the use of different columns along with the possibility of using several of them in series has opened new horizons for the use of SFC for the analysis of a wide range of components from nonpolar to highly polar properties. With emerging technologies introduced on a daily basis, our prediction is that possibility of adopting SFC for the separation of heavy compounds such as polysaccharides and proteins is fast-approaching.

Acknowledgment The authors would like to acknowledge the assistance provided by the Research Council of the College of Agriculture and Natural Resources of the University of Tehran (Karaj, Iran).

References [1] K. Hori, et al., High-throughput and sensitive analysis of 3-monochloropropane-1,2-diol fatty acid esters in edible oils by supercritical fluid chromatography/tandem mass spectrometry, J. Chromatogr. A 1250 (2012) 99–104. [2] K.A. Rezaei, F. Temelli, Using supercritical fluid chromatography to determine diffusion coefficients of lipids in supercritical CO2, J. Supercrit. Fluids 17 (1) (2000) 35–44. [3] M. Kondo, et al., On-line extraction reaction of canola oil with ethanol by immobilized lipase in SC-CO2, Ind. Eng. Chem. Res. 41 (23) (2002) 5770–5774.

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[4] J.L. Martinez, K. Rezaei, F. Temelli, Effect of water on canola oil hydrolysis in an online extraction reaction system using supercritical CO2, Ind. Eng. Chem. Res. 41 (25) (2002) 6475–6481. [5] K. Rezaei, F. Temelli, Changes in enzyme efficiency during lipase-catalyzed hydrolysis of canola oil in a supercritical bioreactor, Iran. J. Chem. Chem. Eng. (IJCCE) 25 (4) (2006) 25–35. [6] H. Abbasi, K. Rezaei, L. Rashidi, Extraction of essential oils from the seeds of pomegranate using organic solvents and supercritical CO2, J. Am. Oil Chem. Soc. 85 (1) (2008) 83–89. [7] H. Kazazi, K. Rezaei, Effect of various parameters on the selective extraction of main components from hyssop using supercritical fluid extraction (SFE), Food Sci. Technol. Res. 15 (6) (2009) 645–652. [8] F. Temelli, et al., Tocol composition and supercritical carbon dioxide extraction of lipids from Barley pearling flour, J. Food Sci. 78 (11) (2013) C1643–C1650. [9] S. Đurđevi
c, et al., Antioxidant and cytotoxic activity of fatty oil isolated by supercritical fluid extraction from microwave pretreated seeds of wild growing Punica granatum L, J. Supercrit. Fluids 133 (2018) 225–232. [10] Y. Huang, et al., Development and validation of a fast SFC method for the analysis of flavonoids in plant extracts, J. Pharm. Biomed. Anal. 140 (2017) 384–391. [11] S. Qu, Z. Du, Y. Zhang, Direct detection of free fatty acids in edible oils using supercritical fluid chromatography coupled with mass spectrometry, Food Chem. 170 (2015) 463–469. [12] V. Cutillas, et al., Evaluation of supercritical fluid chromatography coupled to tandem mass spectrometry for pesticide residues in food, J. Chromatogr. A 1545 (2018) 67–74. [13] C. Foulon, P. Di Giulio, M. Lecoeur, Simultaneous determination of inorganic anions and cations by supercritical fluid chromatography using evaporative light scattering detection, J. Chromatogr. A 1534 (2018) 139–149. [14] J. Duval, et al., Hyphenation of ultra high performance supercritical fluid chromatography with atmospheric pressure chemical ionisation high resolution mass spectrometry: part 1. Study of the coupling parameters for the analysis of natural non-polar compounds, J. Chromatogr. A 1509 (2017) 132–140. [15] J. Duval, et al., Contribution of supercritical fluid chromatography coupled to high resolution mass spectrometry and UV detections for the analysis of a complex vegetable oil— application for characterization of a Kniphofia uvaria extract, Compt. Rend. Chim. 19 (9) (2016) 1113–1123. [16] E. Lesellier, A. Latos, A.L. de Oliveira, Ultra high efficiency/low pressure supercritical fluid chromatography with superficially porous particles for triglyceride separation, J. Chromatogr. A 1327 (2014) 141–148. [17] E. Lesellier, et al., Fast separation of triterpenoids by supercritical fluid chromatography/ evaporative light scattering detector, J. Chromatogr. A 1268 (2012) 157–165. [18] H. Segawa, et al., Enantioseparation of methamphetamine by supercritical fluid chromatography with cellulose-based packed column, Forensic Sci. Int. 273 (2017) 39–44. [19] C. West, et al., Enantioseparation of novel chiral sulfoxides on chlorinated polysaccharide stationary phases in supercritical fluid chromatography, J. Chromatogr. A 1499 (2017) 174–182. [20] P. Subra, A. Vega, Retention of some components in supercritical fluid chromatography and application to bergamot peel oil fractionation, J. Chromatogr. A 771 (1) (1997) 241–250.

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[21] T. Bamba, et al., High throughput and exhaustive analysis of diverse lipids by using supercritical fluid chromatography-mass spectrometry for metabolomics, J. Biosci. Bioeng. 105 (5) (2008) 460–469. [22] J.W. Lee, et al., Profiling of regioisomeric triacylglycerols in edible oils by supercritical fluid chromatography/tandem mass spectrometry, J. Chromatogr. B 966 (2014) 193–199. [23] W. Huber, et al., Determination of cholesterol in milk fat by supercritical fluid chromatography, J. Chromatogr. A 715 (2) (1995) 333–336. [24] J.W. Lee, et al., Application of supercritical fluid chromatography/mass spectrometry to lipid profiling of soybean, J. Biosci. Bioeng. 113 (2) (2012) 262–268. [25] I. Franc¸ois, P. Sandra, Comprehensive supercritical fluid chromatography
reversed phase liquid chromatography for the analysis of the fatty acids in fish oil, J. Chromatogr. A 1216 (18) (2009) 4005–4012. [26] D. Speybrouck, et al., The effect of high concentration additive on chiral separations in supercritical fluid chromatography, J. Chromatogr. A 1510 (2017) 89–99. [27] J. Yang, et al., Separation of furostanol saponins by supercritical fluid chromatography, J. Pharm. Biomed. Anal. 145 (2017) 71–78. [28] D. Baudelet, et al., Enantioseparation of pyroglutamide derivatives on polysaccharide based chiral stationary phases by high-performance liquid chromatography and supercritical fluid chromatography: a comparative study, J. Chromatogr. A 1363 (2014) 257–269. [29] M.A. Patel, et al., Supercritical fluid chromatographic resolution of water soluble isomeric carboxyl/amine terminated peptides facilitated via mobile phase water and ion pair formation, J. Chromatogr. A 1233 (2012) 85–90. [30] P. Ramı´rez, et al., Separation of rosemary antioxidant compounds by supercritical fluid chromatography on coated packed capillary columns, J. Chromatogr. A 1057 (1) (2004) 241–245. [31] S. Li, et al., Efficient and scalable method in isolation of polymethoxyflavones from orange peel extract by supercritical fluid chromatography, J. Chromatogr. B 846 (1) (2007) 291–297. [32] M.R. Garcı´a-Risco, et al., Fractionation of thyme (Thymus vulgaris L.) by supercritical fluid extraction and chromatography, J. Supercrit. Fluids 55 (3) (2011) 949–954. [33] M. Ganzera, Supercritical fluid chromatography for the separation of isoflavones, J. Pharm. Biomed. Anal. 107 (2015) 364–369. [34] D. Wolrab, P. Fr€uhauf, C. Gerner, Direct coupling of supercritical fluid chromatography with tandem mass spectrometry for the analysis of amino acids and related compounds: comparing electrospray ionization and atmospheric pressure chemical ionization, Anal. Chim. Acta 981 (2017) 106–115. [35] V. Pauk, et al., Ultra-high performance supercritical fluid chromatography-mass spectrometry procedure for analysis of monosaccharides from plant gum binders, Anal. Chim. Acta 989 (2017) 112–120. [36] B. Li, et al., Application of ultra-high performance supercritical fluid chromatography for the determination of carotenoids in dietary supplements, J. Chromatogr. A 1425 (2015) 287–292. [37] V. Abrahamsson, I. Rodriguez-Meizoso, C. Turner, Determination of carotenoids in microalgae using supercritical fluid extraction and chromatography, J. Chromatogr. A 1250 (2012) 63–68. [38] A. Matsubara, et al., Highly sensitive and rapid profiling method for carotenoids and their epoxidized products using supercritical fluid chromatography coupled with electrospray ionization-triple quadrupole mass spectrometry, J. Biosci. Bioeng. 113 (6) (2012) 782–787.

Supercritical fluid chromatography

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[39] T. Funazukuri, et al., Density dependence of retention factors of trans-stilbene oxide for chiral separation by supercritical fluid chromatography, J. Chromatogr. A 1527 (2017) 91–96. [40] M. Schaffrath, V. Weidmann, W. Maison, Enantioselective high performance liquid chromatography and supercritical fluid chromatography separation of spirocyclic terpenoid flavor compounds, J. Chromatogr. A 1363 (2014) 270–277. [41] Z.-J. Jiang, et al., Fast determination of alkylphenol ethoxylates in leafy vegetables using a modified quick, easy, cheap, effective, rugged, and safe method and ultra-high performance supercritical fluid chromatography–tandem mass spectrometry, J. Chromatogr. A 1525 (2017) 161–172. [42] T. Uchikata, et al., High-throughput phospholipid profiling system based on supercritical fluid extraction–supercritical fluid chromatography/mass spectrometry for dried plasma spot analysis, J. Chromatogr. A 1250 (2012) 69–75. [43] K. Rezaei, et al., Characterization of free and bound lipids among four corn genotypes as affected by drying and storage temperatures, J. Am. Oil Chem. Soc. 89 (7) (2012) 1201–1210. [44] Z. Taghvaee, et al., Determination of polycyclic aromatic hydrocarbons (PAHs) in olive and refined pomace olive oils with modified low temperature and ultrasound-assisted liquid–liquid extraction method followed by the HPLC/FLD, Food Anal. Methods 9 (5) (2016) 1220–1227. [45] D. Guillarme, et al., What are the current solutions for interfacing supercritical fluid chromatography and mass spectrometry? J. Chromatogr. B 1083 (2018) 160–170. [46] Y. Jiang, et al., High-fast enantioselective determination of prothioconazole in different matrices by supercritical fluid chromatography and vibrational circular dichroism spectroscopic study, Talanta 187 (2018) 40–46. [47] C. Lou, et al., Simultaneous determination of 11 phthalate esters in bottled beverages by graphene oxide coated hollow fiber membrane extraction coupled with supercritical fluid chromatography, Anal. Chim. Acta 1007 (2018) 71–79. [48] Y. Tao, et al., Supercritical fluid chromatography-tandem mass spectrometry-assisted methodology for rapid enantiomeric analysis of fenbuconazole and its chiral metabolites in fruits, vegetables, cereals, and soil, Food Chem. 241 (2018) 32–39. [49] P. Donato, et al., Supercritical fluid chromatography
ultra-high pressure liquid chromatography for red chilli pepper fingerprinting by photodiode array, quadrupole-timeof-flight and ion mobility mass spectrometry (SFC
RP-UHPLC-PDA-Q-ToF MS-IMS), Food Anal. Methods 11 (2018) 3331–3341. [50] K. Ikeda, et al., Global analysis of triacylglycerols including oxidized molecular species by reverse-phase high resolution LC/ESI-QTOF MS/MS, J. Chromatogr. B 877 (25) (2009) 2639–2647. [51] J.-M. Oberson, et al., Application of supercritical fluid chromatography coupled to mass spectrometry to the determination of fat-soluble vitamins in selected food products, J. Chromatogr. B 1086 (2018) 118–129. [52] X. Gong, et al., A new method for determination of α-tocopherol in tropical fruits by ultra performance convergence chromatography with diode array detector, Food Anal. Methods 7 (8) (2014) 1572–1576. [53] H. Zhao, et al., Determination of five retinol isomers in animal livers using ultra-high performance supercritical fluid chromatography, Chromatographia 11 (2018) 1173–1180. [54] L. Yanshan, et al., Determination of major aromatic constituents in vanilla using an on-line supercritical fluid extraction coupled with supercritical fluid chromatography, J. Sep. Sci. 41 (7) (2018) 1600–1609.

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[55] G. Tang, et al., Determination of phenolic acids in extra virgin olive oil using supercritical fluid chromatography coupled with single quadrupole mass spectrometry, J. Pharm. Biomed. Anal. 157 (2018) 217–225. [56] T. Yoshioka, et al., Development of an analytical method for polycyclic aromatic hydrocarbons in coffee beverages and dark beer using novel high-sensitivity technique of supercritical fluid chromatography/mass spectrometry, J. Biosci. Bioeng. 126 (1) (2018) 126–130.

Mass spectrometry for food quality and safety evaluation

16

Xinzhong Zhang Tea Research Institute, Chinese Academy of Agricultural Sciences, Hangzhou, People’s Republic of China

16.1

Introduction of mass spectrometry

In physics, mass refers to the quantity of matter that a body contains and is measured in kilograms using the International System of Units. Ions are atoms, molecules, or fragments of molecules that carry one or more positive or negative electrical charges. When the number of protons in the nucleus of a molecule is no longer balanced by the number of negatively charged electrons present, an ion is created by the addition of a proton or the removal of an electron. The mass-to-charge ratio of an ion is the number obtained by dividing the mass of the ion (m) by the number of electrical charges (z) acquired by the sample during the ionization process. The m/z of an ion is a dimensionless number; m and z are always written in italics. Because of the lack of dimensions, no equals sign should be used when specifying an ion, e.g., m/z 201 and not m/z ¼ 201. A mass spectrometer is an m/z analyzer that does not directly measure mass. It is an instrument for separating and detecting different isotopes, based on the principle that charged particles can be deflected in the electromagnetic field, separated and detected by the composition of the material according to the mass difference of the atoms, molecules, or molecular fragments. The most common mass spectrometer analyzers currently available are the quadrupole (Q) mass spectrometer, ion-trap (IT) mass spectrometer, time-of-flight (TOF) mass spectrometer, magnetic mass spectrometer, and other combination types of these analyzers such as IT-TOF, Q-TOF, or triple-quadrupole (QqQ). However, these tandem mass spectrometry (MS) instruments are more expensive than the single-stage mass analyzers. There are six major components (seen in Fig. 16.1) that make up a mass spectrometer, which carry out the processes of ionization, mass separation, and detection: (1) sample introduction system (may contain a chromatographic separation system); (2) ion source, where the analytes are vaporized and ions are produced; (3) mass analyzer, where the ions are separated according to their m/z ratios; (4) ion detector, where the signal intensities of each separated m/z value are determined; (5) vacuum system, which is needed to prevent the loss of ions through collisions with neutral gas molecules as well as with the walls of the mass analyzer, the detector, and sometimes the ion source; and (6) computer, to control the instrument operation, and record and process the generated data. Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00016-0 © 2019 Elsevier Inc. All rights reserved.

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Sample introduction system DESI, LC, GC, CE

Mass analyzer

Ion source EI, APCI, CI, ESI... ...

Ion detector

TOF, DFMP, Q, ITMS... ...

Vacuum system Computer

Fig. 16.1 The major components of a mass spectrometer.

Different analytes can be ionized in different ways in different types of ion sources, and then the gaseous ions are detected. According to the different ion sources, compound ionization modes are generically classified in two ways: hard ionization and soft ionization. Generally, hard ionization refers to electron bombardment ionization (EI). EI involves the molecules entering a stream of electrons fired at 70 electron volts (eV) within a high vacuum, where they become charged to generate a molecular ion and fragments in a reproducible and reliable manner related to the structure of the molecule. If in the EI process there is more energy introduced than the formation of molecular ions needed, then the compound bonds break, creating a variety of fragmented ions, which can be used for qualitative and quantitative analysis of vaporized and volatile compounds coupled with gas chromatography (GC). This consistent fragmentation means that mass spectral fragmentation libraries can be created and searched, and there are several commercial libraries available for use on any GC-MS instrument generating data at 70 eV. Soft ionization, such as chemical ionization (CI), electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), atmospheric pressure photoionization, matrix-assisted laser desorption ionization (MALDI), desorption electrospray ionization (DESI), and direct analysis in real-time mass spectrometry (DART-MS), is not high in ionization energy. It is usually only sufficient to generate adding/reducing protons or other ions, such as ammonium, chloride, etc., to form quasimolecular ions both positive and negative, such as [M+H]+ or [M–H]. CI is a soft ionization technique for GC-MS, involving the collision of analyte molecules with charged “reagent” ions, such as methane or ammonia, contained in the ion source area for subsequent charge transfer to the target molecule. This charge transfer method generally provides only limited fragmentation and a more intense molecular ion, which enables better determination of the molecular weight of the target species. ESI, APCI, and other ionization modes are the development of suitable ionization sources for interfacing liquid chromatography (LC) with MS, which has enabled MS to be used to measure a much wider range of compounds, from low molecular weight pesticides to high molecular weight intact proteins, not just volatiles or semivolatiles such as for GC-MS. Generally, it is commonly used to determine the exact molecular weight of compounds, or for selecting parent ions for tandem mass spectrometric fragmentation analysis to obtain neutral loss and fragment ions, which can be used for qualitative and quantitative analysis of

Mass spectrometry for food quality and safety evaluation

407

pollutants coupled with LC or capillary electrophoresis (CE) and other instruments, possibly after sample pretreatments. The origins of MS go back to the characterization of “positive rays” by J.J. Thompson in the early 1900s. MS has earned a number of Nobel Prizes, starting with J.J. Thompson, who received the physics prize in 1906 for the development of positive rays. Next, the chemistry prize went to Francis W. Aston in 1922 for his research elucidating the existence of isotopes. Moving to the modern era, Wolfgang Paul and Hans G. Demelt were awarded the physics prize in 1989 for developing ways to trap ions, while John B. Fenn and Koichi Tanaka received the chemistry prize in 2002 for the development and application of ESI and MALDI to the analysis of proteins, respectively. Over the years MS has become more and more important in people’s lives [1].

16.2

Procedures, advantages, and limitations of mass spectrometry

Food quality and safety assurance in the food supply chain are crucial for public health and world sustainability. MS has been in the domain of physicists and physical chemists for some time, and there have been significant advances in both instrumentation and applications over the past few decades. Over the last 20 years, there have been an ever-increasing number of scientists, physicians, and technicians coming in contact with the technique. MS is defined as a detection technique that uses the difference in m/z of ionized molecules to separate them from each other, which is useful for quantification of molecules and determining their molecular weight, as well as giving chemical and structural information. In many modern analytical and testing technologies, MS is a universal method that combines high sensitivity, specificity, and rapid responsive. So, mass spectrometers can be used to analyze an amazing range of compounds from simple gases to complex biopolymers, both qualitatively and quantitatively. The process of which type of mass spectrometer to choose to analyze a sample, including the sample inlet system and operational experimental conditions to be undertaken, is based on several factors, such as molecular mass, polarity of the analyte, and complexity of the sample mixture. The information sought, be it structural or quantitative or both, also influences the selection of instruments and experimental setups. When food quality and safety are determined and evaluated by MS, they can be divided into two ways: direct MS analysis and indirect MS analysis. Direct MS analysis currently includes MADLI TOFMS and real-time MS such as DESI-MS, DART-MS, and so on [2]. Fig. 16.2 shows schemes of food quality and safety assurance in the food supply chain with DART-MS and other ambient ionization MS methods. Usually, there is no need for sample pretreatment and chromatographic separation, the direct analysis time is short and fast, and the result is intuitionistic. However, the disadvantages do not include the simultaneous analysis of various components, especially isomers. An indirect MS analysis method often contains chromatographic separation, including capillary electrophoresis mass spectrometry (CE-MS), GC-MS, liquid

Indicator

Microbe

Toxin

Contact material

Migrant

Processing

Additive Intended

MS analyzer

DART, DESI, ASAP, EESI, etc.

Q, TOF, orbitrap, IT, QqQ, QqTOF, LIT-orbitap, etc.

Tube/rod Plate

Adulterant Accidental

Ion source

By-product

Porous surface Clamp

Sampler

Category

AI-MS

Evaluation Technologies for Food Quality

Storage and transportation

408

Customized-holder Pesticide

Animal

Veterinary drug

Penning ionization Proton transfer Authenticity

Electron capture

Dissociative electron capture

Component Traceability

Mechanism

Production

Standardized-holder Plant

Cation/anion attachment

Fig. 16.2 Food quality and safety assurance in the food supply chain with direct analysis in realtime mass spectrometry and other ambient ionization mass spectrometry schemes [2].

chromatography mass spectrometry (LC-MS), and supercritical fluid chromatography MS analysis. All of these technologies need sample pretreatment to extract and purify, and then be separated by different chromatography methods. The different types of samples and compounds may need different suitable chromatographic methods, and suitable analytical compounds are different due to the different ways of ionization. Especially in recent years, the continuous rapid development of chromatography technology, such as fast gas chromatography (FGC), ultrahighperformance liquid chromatography (UPLC), and other techniques, means that the analysis time has gradually shortened, and it can be constantly adapt to the needs of development and followed by MS analysis. After separation, isomers can be distinguished, qualitative and quantitative more accurate. For example, ESI facilitated the coupling of LC with MS, allowing the online analysis of polar and bioactive compounds, such as pesticides and their metabolites.

16.3

Applications of mass spectrometry in different food areas

Food quality and safety is a major issue related to people’s livelihoods. It has evolved into an international problem, especially because of globalization. The impact of food safety on people’s lives and health is often linked to the phenomenon of technical barriers and trade frictions, and the threat of both endogenous and exogenous substances.

Mass spectrometry for food quality and safety evaluation

409

Generally, endogenous substances refer to the internal nutrients in food, or the effects of pesticide abuse and environmental pollution on the raw food materials during animal and plant growth processes; exogenous substances are mainly derived from improper use of food additives, or hazardous substances introduced from food production, packaging, storage, transportation, and marketing processes. MS technology plays an increasingly important role in these areas and has attracted more and more attention. In 2013, Wang et al. [3] reviewed the latest developments and applications of MS in food safety and quality analysis, introduced fundamental principles and applications of the different types of mass detector, and highlighted novel advances in newly developed MS methods, including MALDI-TOF/MS imaging and ambient ionization MS for direct food analysis. They also discussed and compared the advantages and limitations of different MS techniques in their applications to food safety and quality, and also commented on the future. Nowadays, food quality and safety has become one of the focus topics all over the world. Illegal addition of additives and chemical contaminants is the prominent problem. With the advantages of MS, the combination of MS with LC and GC has been increasingly used in food quality and safety analysis. In recent years, MS has played an increasingly important role in the detection of intrinsic and extrinsic components. There are many qualities, such as proteins, amino acids, and other characteristics of nutrients in food, as well as the aroma components in tea and coffee. Different foods have different quality grades because of their different nutrients. The content of the ingredients determines the level, quality, and grade of the food. The pesticide residues, mycotoxins, and persistent pollutant residues affect the safety of food. Here, recent studies regarding the applications of MS from these aspects in food quality and safety evaluation are reviewed based on papers mainly published from 2010 to 2018, which can be helpful in defining potential risk compounds in food analysis for the future [4].

16.3.1 Proteins and amino acids Protein is one of the main components of biological cells in animals, plants, and microorganisms. It is a general term for a group of organic compounds containing nitrogen. Amino acids are the basic units of protein. All the important components of the body require protein participation; without protein there is no life. Different food samples contain different types of protein. Usually, we can identify the source and quality of primary food by determining the special and characteristic proteins and amino acids. With the development of proteomics and metabonomics, MS has been applied more and more to these fields to ensure food quality. For example, Cemile Yılmaz and Vural G€ okmen [5] developed an analytical method for the determination of tryptophan and its derivatives in the kynurenine pathway by tandem MS in various fermented food products, such as bread, beer, red wine, white cheese, yoghurt, kefir, and cocoa powder, using aqueous extraction and reversed-phase separation with a pentafluorophenyl chromatographic column. Kynurenine in beer samples was within the range of 28.7  0.7 and 86.3  0.5 μg/L, and from 30.3 to 763.8 μg/kg dw in yoghurt, white cheese, and kefir dairy products,

410

Evaluation Technologies for Food Quality

and the highest amounts of kynurenic acid (4486.2  165.6 μg/kg dw) were in cacao powder. Amelie Charissou et al. [6] reported an accurate method for the quantification of carboxymethyllysine (a useful marker of protein damage in severely heated foods) in food samples by GC-MS, which stipulated double dramatization of amino acids and quantification by selected ion monitoring. Marı´a Mateos-Vivas et al. [7] described a simultaneous determination method of unmodified nucleosides and nucleotide mono-, di-, and triphosphates by CE-MS, using hexafluoro-2-propanol in the separation medium and as an additive to the sheath liquid of the ESI interface; the limits of detection (LODs) were in the range of 14–53 ng/mL for nucleosides and 7–23, 20–49, and 64–124 ng/mL for nucleotide mono-, di-, and triphosphates, respectively. Lipid transfer protein (LTP) is an extremely stable protein that is resistant to both proteolytic attack and food processing, which permits allergens to reach the gastrointestinal immune system in an immunogenic and allergenic conformation, allowing sensitization and induction of systemic symptoms. David R. Albers et al. [8] first developed a quantification and characterization LC-UV-MS method to determine a major food allergen maize LTP that was capable of inducing specific IgE as well as eliciting severe symptoms. An endogenous  9 kDa LTP from maize kernels was purified and characterized; the maize LTP consisted of 93 amino acid residues and had an Mr of 9046.1 Da, determined by ESI-MS. The results showed that LTP over a concentration range from 29 to 1030 μg/g in maize kernel samples had relative standard deviations (RSDs) of method recoveries not exceeding 14.4% at three concentrations. Vincenzo Cunsolo et al. [9] reviewed that the improved performance and versatility of mass spectrometers together with the increasing availability of gene and genomic sequence databases led MS to become an indispensable tool for either protein or proteome analyses in cereals, including rice, barley, and wheat, in the last decade. Mass spectrometric works on prolamins have rapidly evolved from the determination of molecular masses of proteins to the proteomic approaches aimed at large-scale protein identification and the study of functional and regulatory aspects of proteins. MS coupled with electrophoresis, chromatographic methods, and bioinformatics tools is currently making significant contributions to better knowledge of the composition and structure of cereal proteins and their structure-function relationships. Results obtained using MS are summarized, including characterization of prolamins, investigation of gluten toxicity, identification of proteins responsible for cereal allergies, determination of protein pattern and its modification under environmental or stress effects, and investigation of genetically modified varieties by proteomic approaches to illustrate current trends, analytical issues and challenges, and suggest possible future perspectives.

16.3.2 Vitamins Vitamin, in general, is a kind of organic substance that maintains human life activities, and it is also an important active substance to maintain human health. There are very few vitamins in the body, but they are indispensable. Humans acquire the

Mass spectrometry for food quality and safety evaluation

411

different vitamins they need through food. According to their solubility, vitamins can be divided into two categories: fat soluble (VA, VD, VE, and VK) and water soluble (VB1, VB2, VB6, VB12, VP, VPP, and VC). The level of certain vitamins is one of the important indicators for evaluating the quality of agricultural products. Analysis of vitamins is a complex task. General procedures for vitamins analysis in different samples are: (1) decomposing the samples with acid, alkali, or enzyme to make the vitamins free; (2) extracting with solvent; (3) separating the interfering material and purifying the sample solution; and (4) determining vitamins by the proper instrumental analysis method. The commonly used instrumental analysis methods for vitamin determination are spectrographic analysis, fluorescence analysis, thin layer chromatography, high performance liquid chromatography (HPLC), and so on. In recent years, with the development of MS, MS as a detector with GC or LC for analysis of vitamins has gradually become a new method. For example, Alessandra Gentili et al. [10] established a liquid chromatography-diode array detector-tandem mass spectrometry (LC-DAD-MS) method to quickly and comprehensively evaluate six fat-soluble vitamins (α-tocopherol, δ-tocopherol, γ-tocopherol, ergocalciferol, phylloquinone, and menaquinone-4) and four carotenoids (lutein, zeaxanthin, β-cryptoxanthin, and β-carotene) in maize flour and green and golden kiwi, extracted by a matrix solid-phase dispersion method, separated on a C30 reversed-phase column and detected by APCI-MS/MS in selected reaction monitoring mode. The recoveries of all compounds under study exceeded 78% and 60% from maize flour and kiwi, respectively, with RSDs below 12%. Chen Meijun et al. [11] established a simultaneous determination method of 11 B vitamins in infant formula by UPLCMS/MS. Samples were dissolved in 0.1% formic acid and then added to zinc acetate to precipitate the proteins; they were then separated and analyzed by HSS T3 column followed by ESI+-MRM mode in 12 min. The spiked recoveries and RSDs were in the range of 85%–110% and 1.03%–6.75%, respectively. The limits of quantification (LOQs) for vitamin B1, vitamin B2, nicotinic acid, niacinamide, pantothenic acid, pyridoxal, pyridoxine, pyridoxamine, folic acid, vitamin H, and vitamin B12 were 40.0, 40.0, 30.0, 30.0, 50.0, 0.9, 1.2, 1.2, 2.0, 2.0, and 0.2 μg/100 g, respectively. This method also has good precision and accuracy and can be applied to detect 11 B vitamins in infant formula. Yan Lijuan et al. [12] developed an LC-APCI-MS/MS internal standard method for the determination of vitamin D2 and vitamin D3 in infant formula, followed by hexane extracted from a milk powder sample, with ProElut VDC cartridge cleanup, separated by a Kinetex C18 column. The LODs and LOQs for vitamin D2 and D3 in the formula were 2 and 5 μg/kg, and the spiked average recoveries of vitamin D in milk powder at 5, 10, and 100 μg/kg were between 85.2% and 105.3% with RSDs between 4.7% and 8.1%. Liang Rui-Qiang et al. [13] established an HPLC-MS/MS method for the simultaneous determination of 10 kinds of water-soluble vitamins (thiamine, riboflavin, calcium pantothenate, vitamin B6, biotin, folic acid, vitamin B12, nicotinic acid, nicotinamide, and ascorbic acid) in food supplements, performed using a UPLC HSS T3 column. All of the recoveries were between 80% and 120% with RSDs between 0.3% and 3%.

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Evaluation Technologies for Food Quality

16.3.3 Polyphenols Polyphenols, also known as flavonoids and containing many phenolic groups, are made up of more than 40 chemical constituents. Polyphenols are responsible for the taste and color of fruit, vegetable, wine, and other beverages, and confer astringency, bitterness, and structure to the food. Moreover, polyphenols are the main compounds related to the benefits of food consumption in the diet, because of their properties in the treatment of circulatory disorders, such as capillary fragility, peripheral chronic venous insufficiency, and microangiopathy of the retina. MS had and still has a very important role for research and quality control; its analytical power is relevant for structural studies on aroma and polyphenolic compounds. Nowadays, LC-MS is the best analytical approach to study polyphenols, and the most effective tool in the structural study of anthocyanins. LC-MS allows the characterization of complex structures of polyphenols, such as procyanidins, proanthocyanidins, prodelphinidins, and tannins, and provides experimental evidence for structures that were previously only hypothesized; MS/MS is a very powerful tool. The MALDITOF/MS technique is suitable for determining the presence of molecules of higher molecular weight with high accuracy, and it has been applied with success to study procyanidin oligomers up to heptamers in the reflectron mode, and up to nonamers in the linear mode [14]. Polyphenols in tea and fruit extracts are important due to their potential health benefits. Davy Guillarme et al. [15] developed an efficient and high-throughput analytical method by UHPLC-UV-MS/MS for the separation of seven predominant polyphenols, including (+)-catechin, ()-epicatechin, ()-catechin gallate, ()-epicatechin gallate, ()-gallocatechin gallate, ()-epigallocatechin gallate, ()-epigallocatechin, and gallic acid, also known as catechin derivatives present in tea extracts. First, a liquid-liquid extraction procedure was added prior to UHPLC-UV analysis to decrease the complexity of the sample. Second, UHPLC was coupled to ESI-MS/ MS to attain sufficient sensitivity and selectivity between catechin derivatives and other constituents of tea extract, as seen in Fig. 16.3. Ma Yue et al. [16] developed a method for analyzing flavonoids and polyphenols of mulberry extracts by HPLCQ-Orbitrap MS, including five flavonoids—rutin, isoquercerin, kaempferol-7-glucoside, taxifolin, and quercetin—and three polyphenols—3,4-dihydroxyphenylacetic acid, chlorogenic acid, and caffeic acid—performed using H2O-CH3CN containing 0.1% formic acid with 0.2 mL/min flow rate by a Syncronis C18 column identified by retention time, accurate molecular weight, tandem MS fragmentation information, and loss of glucose residue (162 u), CO (28 u), CO2 (44 u), and H2O (18 u), which are characteristic losses for the identification of flavonoids in mulberry. Mohd Nazrul Hisham Daud et al. [17] evaluated the antioxidant potential of Artocarpus heterophyllus L. J33 variety fruit waste (rind and rachis) from different extraction methods (maceration, percolation, and Soxhlet) and the identification of phenolic constituents by LC-TOF/MS. They identified two phenolic acids, protocatechuic acid, and chlorogenic acid derivatives as the major constituents responsible for the antioxidant activity of the active extracts.

100

%

%

100

8

m/z 457.4

7

457.4 > 169.3

8

%

8

457.4 > 169.3

Mass spectrometry for food quality and safety evaluation

100

7 2.00

3.00

4.00

5.00

100

441.4 > 169.3

5

2.00

1.00

6.00

4.00

m/z 441.4

5.00

6.00

5

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3.00

4.00

5.00

1.00

6.00 100

6

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4.00

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4.00

5.00

3.00

4.00

5.00

1.00

6.00

%

m/z 289.3

2.00

3.00

4.00

5.00

1.00

6.00

2.00

3.00

4.00

5.00

1.00

6.00

1.00

2.00

3.00

4.00

5.00

6.00

4.00

5.00

6.00

289.3 > 109.3

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3.00

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3

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169.1 > 125.2

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%

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3

169.1 > 125.2

6.00

1

100

3

5.00

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0 1.00

4.00

2

1

6.18

100

3.00

305.3 > 125.3

2.00

100

2

289.3 > 109.3

0

2.00

6

m/z 305.3

2.00

100

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0 1.00

6.00

%

1

3.00

6.00

%

100

2.00

5.00

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100

0 1.00

441.4 > 169.3

1.00

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6

305.3 > 125.3

0

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%

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Fig. 16.3 Ultrahigh-performance liquid chromatography-tandem mass spectrometry analysis of seven-catechin derivatives and gallic acid. (1) C, (2) EC, (3) AC, (4) CG, (5) ECG, (6) EGC, (7) GCG, (8) EGCG [15].

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Evaluation Technologies for Food Quality

16.3.4 Volatile components and polycyclic aromatic hydrocarbons According to the definition of the World Health Organization (WHO), volatile organic compounds (VOCs) are a class of organic compounds whose boiling point is 50°C– 250°C, saturated vapor pressure is more than 133.32 Pa, and which exist in the air in the form of vapor at room temperature. According to their different chemical structures, they can be divided into eight categories: alkanes, aromatics, alkenes, halides, esters, aldehydes, ketones, and others. Some are characteristic ingredients in food, such as aroma components in tea; while others are harmful substances in food, such as polycyclic aromatic hydrocarbons (PAHs). VOCs can be produced in food storage, processing, or contaminants. The most effective method for the determination of VOCs is GC-MS. The combination of multiple extraction methods and GC-MS analysis can enhance the accuracy of identification, and provide a reference for further study on the flavor of foods. Cao et al. [18] reviewed the developments and applications of MS as a powerful tool for the quality and safety assessment of cooking oil and highlighted its increasing applications in authentication, aging, and market detection of used cooking oil; they also provided the current technical challenges and future prospects associated with these methodologies. Chang Yufei et al. [19] used a combination of solid-phase microextraction (SPME) and simultaneous distillation extraction (SDE) to extract the VOCs, and GC-MS along with Kovats indices and authentic standard compounds to accurately identify the volatile compounds in codfish. The results showed that a total of 86 volatile compounds were identified in codfish, and of them 24 were extracted by SDE, 69 by SPME, and 10 by both SDE and SPME. Seventy volatile compounds were found to have specific odors, and of them seven typical compounds contributed significantly to the flavor of codfish. Alcohols (i.e., (E)-2-penten-1-ol and 2-octanol), esters (i.e., ethyl butyrate and methyl geranate), and aldehydes (i.e., 2-dodecenal and pentadecanal) contributed the most to fresh flavor, while nitrogen compounds, sulfur compounds, furans, as well as some ketones (i.e., 2-hydroxy-3-pentanone) brought unpleasant odors, such as fishy and earthy odors. Tea contains characteristic VOCs, polyphenols, caffeine, and catechins, and is therefore the most widely consumed beverage all over the world. VOCs are present in minimal quantities in tea, approximately 0.01% of the total dry weight, but due to their low threshold values, they have a high impact, and result in high odor units. The VOCs of tea are classified in two groups: group I consisting mainly of nonterpenoids (hexenols), which provide fresh green flavor, whereas group II, including monoterpene alcohols (linalool and geraniol), imparts highly desirable sweet flowery aromas. Dong Bok Jeon et al. [20] studied the VOCs extracted from semifermented tea by simultaneous distillation-solvent extraction and analyzed by GC-MS, and from fresh Jukro tea leaves collected from Damyang-gun (Jeollanam-do) at 40-, 60-, and 90-day growth stages. A total of 159 VOCs was identified in the analyzed Jukro tea leaves. Comparatively, the increase in the concentrations of VOCs was high in 60-day leaves. Based on the results, the 60-day leaves were found to be the most suitable and useful for making semifermented Jukro tea.

Mass spectrometry for food quality and safety evaluation

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PAHs are harmful to human health and are important pollutants with teratogenicity and carcinogenicity, especially in aquatic and smoked food samples, because of their fat solubility. There is a lot of research in this area. For example, Wang et al. [21] applied accelerated solvent extraction (ASE) and GC-MS analysis to analyze 16 PAHs from biological samples, such as fish tissues and ground pork. They applied this extraction and quantitation method to the determination of PAHs in several smoked meat samples obtained from a local market, and up to 12 PAHs were found to be present at concentrations ranging from 3 to 52 ng/g wet sample. Chen et al. [22] developed an anticontamination method for the simultaneous determination of 16 PAHs in tea using acetonitrile extraction, simultaneous dispersive solid-phase extraction (D-SPE), liquid-liquid extraction (LLE) purification, and GC-MS/MS determination at MRM mode. The LOQs of the 16 PAHs were