Food aroma evolution : during food processing, cooking and aging [1st edition.] 9781138338241, 1138338249

Table of contents :
SECTION 1 - AROMA, TASTE, AND FLAVORChapter 1: Aroma and odorChapter 2: Flavors and tasteChapter 3: Chemical senses and flavor perceptionChapter 4: Aroma compounds (description, biosynthesis and regulation)Chapter 5: Orthonasal and retronasal olfactionSECTION 2: ANALYTICAL TECHNIQUESChapter 6: Extraction methods of volatile compounds from food matricesChapter 7: The role of the gas chromatography based methodologies for the understanding of food aromasChapter 8: Monitoring food aroma during processing and storage by rapid analytical methods - a focus on electronic noses and mass spectrometry-based systemsChapter 9: Hyphenated electronic nose technique for aroma analysis of foods and beveragesChapter 10: Food aroma compounds by capillary electrophoresisChapter 11: Proton-transfer-reaction mass spectrometry (PTR-MS)Chapter 12: Stable Isotope Dilution AssaySECTION 3: PRINCIPLES OF PROCESSING, EVOLUTION AND MODIFICATION Chapter 13: Food processing, cooking, and aging, a practical case studyChapter 14: The Maillard reactionChapter 15: Production of food aroma compounds (microbial and enzymatic methodologies)Chapter 16: Novel and emerging technologies (benefits and limitations)SECTION 4: AROMA COMPOUNDS IN FOOD MATRICES Chapter 17: DistillatesChapter 18: Evolution of beer aromaChapter 19: Coffee flavorChapter 20: Aroma evolution in the chocolate productionChapter 21: BakeryChapter 22: Recent advances in the study of grape and wine volatile composition: Varietal, fermentative and ageing aroma compoundsChapter 23: Milk/dairyChapter 24: MeatChapter 25: FishChapter 26: Fruits and vegetablesChapter 27: Spices and herbsChapter 28: Off-flavors in alcoholic beverages. An OverviewSECTION 5: INFLUENCES ON FLAVOR PERCEPTIONChapter 29: Interactions between food matrix and aroma compounds in relation with perceptionChapter 30: Food emulsions as flavor delivery systemsChapter 31: Relationship between structure and odorChapter 32: Bioactive potential of sesquiterpenes

Citation preview

Food Aroma Evolution During Food Processing, Cooking, and Aging

Food Analysis & Properties Series Editor Leo M.L. Nollet University College Ghent, Belgium This CRC series Food Analysis and Properties is designed to provide state-ofthe-art coverage on topics related to the understanding of the physical, chemical, and functional properties of food: including (1) recent analysis techniques on the choice of food components; (2) developments and evolutions in analysis techniques related to food; (3) recent trends in analysis techniques of specific food components and/or a group of related food components. Flow Injection Analysis of Food Additives Edited by Claudia Ruiz-Capillas and Leo M.L. Nollet Marine Microorganisms Extraction and Analysis of Bioactive Compounds Edited by Leo M.L. Nollet Multiresidue Methods for the Analysis of Pesticide Residues in Food Edited by Horacio Heinzen, Leo M.L. Nollet, and Amadeo R. Fernandez-Alba Spectroscopic Methods in Food Analysis Edited by Adriana S. Franca and Leo M.L. Nollet Phenolic Compounds in Food Characterization and Analysis Edited by Leo M.L. Nollet and Janet Alejandra Gutierrez-Uribe Testing and Analysis of GMO-Containing Foods and Feed Edited by Salah E.O. Mahgoub and Leo M.L. Nollet Fingerprinting Techniques in Food Authenticity and Traceability Edited by K.S. Siddiqi and Leo M.L. Nollet Hyperspectral Imaging Analysis and Applications for Food Quality Edited by Nrusingha Charan Basantia, Leo M.L. Nollet, and Mohammed Kamruzzaman Ambient Mass Spectroscopy Techniques in Food and the Environment Edited by Leo M.L. Nollet and Basil K. Munjanja Food Aroma Evolution: During Food Processing, Cooking and Aging Edited by Matteo Bordiga and Leo M.L. Nollet For more information, please visit the Series Page: https​://ww​w.crc​press​.com/​Food-​A naly​sis-​ Prope​rties​/ book​-seri​es/CR​C FOOD​A NPRO​

Food Aroma Evolution During Food Processing, Cooking, and Aging

Edited by

Matteo Bordiga

Dipartimento Di Scienze Del Farmaco

Leo M.L. Nollet

University College Ghent (Retired)

CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2020 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works International Standard Book Number-13: 978-1-138-33824-1 (Hardback) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www. copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-7508400. CCC is a not-for-profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe.

Library of Congress Cataloging‑in‑Publication Data Names: Bordiga, Matteo, editor. | Nollet, Leo M.L., 1948- editor. Title: Food aroma evolution : during food processing, cooking, and aging / by Matteo Bordiga and Leo M.L. Nollet. Description: 1st edition. | Boca Raton : CRC Press, 2019. | Series: Food analysis & properties, 2475-7551 | Includes bibliographical references. | Summary: “Of the five senses, smell is the most direct and food aromas are the key drivers of our flavor experience. They are crucial for the synergy of food and drinks. Up to 80% of what we call taste is actually aroma. This book deals with how food aromas are developed and affected during food processing, cooking, and aging.”-- Provided by publisher. Identifiers: LCCN 2019026347 (print) | LCCN 2019026348 (ebook) | ISBN 9781138338241 (hardback) | ISBN 9780429441837 (pdf) Subjects: LCSH: Food--Biotechnology. | Food--Odor. | Food--Sensory evaluation. | Flavor. Classification: LCC TP248.65.F66 F645 2019 (print) | LCC TP248.65.F66 (ebook) | DDC 664/.024--dc23 LC record available at https://lccn.loc.gov/2019026347 LC ebook record available at https://lccn.loc.gov/2019026348

Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

Contents Preface

ix

Editors

xi

Contributors

xiii

Section I  AROMA, TASTE, AND FLAVOR Chapter 1

Aroma and Odor

3

Christophe B.Y. Cordella Chapter 2

Flavors and Taste

15

Christophe B.Y. Cordella Chapter 3

Chemical Senses and Flavor Perception

23

Han-Seok Seo Chapter 4

Aroma Compounds (Description, Biosynthesis, and Regulation)

57

Mónika Valdenegro Espinoza, María Fernanda Flores Echeverría, and Lida Fuentes Viveros Chapter 5

Orthonasal and Retronasal Olfaction

99

Pengfei Han and Thomas Hummel

Section II  ANALYTICAL TECHNIQUES Chapter 6

Extraction Methods of Volatile Compounds from Food Matrices 123 Arthur Luiz Baião Dias, Francisco Manuel Barrales, and Philipe dos Santos

Chapter 7

The Role of Gas Chromatography-Based Methodologies for the Understanding of Food Aromas

141

Cátia Martins, Ângelo C. Salvador, and Sílvia M. Rocha

v

vi

Chapter 8

Contents

Monitoring Food Aroma during Processing and Storage by Rapid Analytical Methods: A Focus on Electronic Noses and Mass Spectrometry-Based Systems 159 Aoife Power, Vi Khanh Truong, James Chapman, and Daniel Cozzolino

Chapter 9

Hyphenated Electronic Nose Technique for Aroma Analysis of Foods and Beverages

177

Adriana Marcia Graboski, Sandra Cristina Ballen, Juliana Steffens, and Clarice Steffens Chapter 10

Food Aroma Compounds by Capillary Electrophoresis

193

Raffaella Colombo and Adele Papetti Chapter 11

Proton-Transfer-Reaction–Mass Spectrometry

217

Iuliia Khomenko and Brian Farneti Chapter 12

Stable Isotope Dilution Assay

241

Hans-Georg Schmarr

Section III  PRINCIPLES OF PROCESSING, EVOLUTION, AND MODIFICATION Chapter 13

Food Processing, Cooking, and Aging: A Practical Case Study 261 Emmanuel Bertrand

Chapter 14

The Maillard Reaction

281

Joseph Provost Chapter 15

Production of Food Aroma Compounds (Microbial and Enzymatic Methodologies)

293

Lorena de Oliveira Felipe, Bruno Nicolau Paulino, Adones Sales, Gustavo Molina, and Juliano Lemos Bicas Chapter 16

Novel and Emerging Technologies (Benefits and Limitations) 307 Mohammad Hassan Kamani, Hanieh Amani, Amir Yeganehshakib, and Amin Mousavi Khaneghah

Section IV  AROMA COMPOUNDS IN FOOD MATRICES Chapter 17

Distillates 337 Paul S. Hughes

Chapter 18

Evolution of Beer Aroma

353

Iztok Jože Košir and Miha Ocvirk Chapter 19

Coffee Flavor Sergio Pérez-Burillo, Matteo Bordiga, Silvia Pastoriza, and José A. Rufián-Henares

365

Contents

Chapter 20

Aroma Evolution in Chocolate Production

vii

383

Roberta Ascrizzi, Luisa Pistelli, and Guido Flamini Chapter 21

Bakery Products

415

Joana Pico and Juan Carlos Diego Chapter 22

Recent Advances in the Study of Grape and Wine Volatile Composition: Varietal, Fermentative, and Aging Aroma Compounds 439 Pilar Rubio-Bretón, Maria Rosario Salinas, Ignacio Nevares, Eva Pilar Pérez-Álvarez, Maria del Álamo-Sanza, Sandra Marín-San Román, Gonzalo Luis Alonso, and Teresa Garde-Cerdán

Chapter 23

Milk/Dairy 465 Kieran Kilcawley

Chapter 24

Meat 487 Mónica Bueno, Thais Devincenzi, and Virginia Celia Resconi

Chapter 25

Fish 519 Asghar Amanpour, Gamze Guclu, and Serkan Selli

Chapter 26

Fruits and Vegetables

543

Rajnibhas Sukeaw Samakradhamrongthai Chapter 27

Spices and Herbs

569

Alejandro Hernández, Emilio Aranda, Rocío Casquete, Cristina Pereira, and Alberto Martín Chapter 28

Off-Flavors in Alcoholic Beverages: An Overview

595

Rosa Perestrelo, Catarina Silva, and José S. Câmara

Section V INFLUENCES ON FLAVOR PERCEPTION Chapter 29

Interactions between the Food Matrix and Aroma Compounds in Relation to Perception

625

Elisabeth Guichard Chapter 30

Food Emulsions as Flavor Delivery Systems

651

Like Mao, Yrjö H. Roos, Costas G. Biliaderis, and Song Miao Chapter 31

Relationship between Structure and Odor

679

Valentina Villalobos Coa, Vito Lubes, Johannes Polster, Maria Monteiro de Araújo Silva, and Giuseppe Lubes Chapter 32

Bioactive Potential of Sesquiterpenes

695

Iramaia Néri-Numa, Kele A.C. Vespermann, Carlos H. Carvalho, Bruno Nicolau Paulino, Maria J. Macedo, Gláucia M. Pastore, Juliano Lemos Bicas, and Gustavo Molina Index

719

Preface This book focuses on the description of aroma evolution in several food matrices. Of the five senses, smell is the most “direct.” When we smell the aroma of a delicious dish, the odorous molecules reach the nasal cavity and are captured by the mucous, which contains olfactory receptors. These transmit a message to a specific area of our brain, through the olfactory nerve. This direct contact between the nose and brain explains how a simple smell can trigger an emotion. Furthermore, the olfactory experience influences the taste of food. This is due to two processes, called “orthonasal olfaction” and “retronasal olfaction,” which transform the olfactory signal into one of taste, thus enhancing our perception of flavor. For example, the Maillard reaction helps create smells that are particularly welcome to the palate, on condition that the food contains as little water as possible before being cooked. Actually, boiled foods create some of the least appetizing cooking smells. Conversely, grilled or oven-baked foods are characterized by “good smells”: high temperatures in fact diffuse odorous molecules more effectively. Not just cooking, but also processing (such as fermentation) and aging are responsible for food aroma evolution. A comprehensive evaluation of food requires that analytical techniques keep pace with the available technology. As a result, a major objective in the chemistry of food aroma is concerned with the application and continual development of analytical methods. This aspect, appearing particularly important, is discussed in depth in a dedicated section of the book. The book results in a good mix of referenced research with practical applications, also reporting case studies of the various applications of novel technologies. In the last few years, there have been numerous publications focused on food processing technology. Furthermore, numerous texts and reference books are available on fermented foods such as wine, beer, and cheese. However, none of those sources deal with food aroma evolution during different treatments (such as food processing, cooking, and aging) in a broad spectrum, including the proper analytical methods. This text represents a comprehensive reference book for students, educators, researchers, food processors, and food industry personals providing up-to-date insight. The range of techniques and materials covered provides engineers and scientists working in the food industry with a valuable resource for their work. The editors are very happy to thank all contributors for their appreciated contributions. They value all the time and energy spent to write the different chapters. Matteo Bordiga, PhD Leo M.L. Nollet, PhD I’m honored to give a special thanks to Matteo for giving me the opportunity to work with him on this project. This work will be a very respected volume in my book series Food Analysis & Properties. Leo M.L. Nollet

ix

Editors Matteo Bordiga, PhD, received a PhD in Food Science from the Università del Piemonte Orientale, Novara, Italy in 2010. He received his MS in Chemistry and Pharmaceutical Technologies from the same university. The main research activity of Dr. Bordiga concerns food chemistry and investigating the different classes of polyphenols from an analytical, technological, and nutritional point of view. More recently, he moved his research interests toward wine chemistry, focusing his attention on the entire production processss—from vine to glass. He has published more than 40 research papers in peer-reviewed international journals. Since 2013, he has been an editorial board member of the International Journal of Food Science & Technology. He has edited the books Valorization of Wine Making By-Products (CRC Press, 2016) and Post-Fermentation and -Distillation Technology: Stabilization, Aging, and Spoilage (CRC Press, 2018). All these research activities have also been developed through important collaborations with foreign institutions, including the Foods Science & Technology Department and Foods for Health Institute, University of California, Davis, United States; Fundación Parque Científico y Tecnológico de Albacete, and Instituto Regional de Investigación Científica Aplicada, Universidad de Castilla-La Mancha, Ciudad Real, Spain. Leo M.L. Nollet, PhD, earned an MS (1973) and PhD (1978) in Biology from the Katholieke Universiteit Leuven, Belgium. He is an editor and associate editor of numerous books. He edited for Marcel Dekker, New York—now CRC Press of the Taylor & Francis Group— the first, second, and third editions of Food Analysis by HPLC and Handbook of Food Analysis. The last edition is a two-volume book. Dr. Nollet also edited the Handbook of Water Analysis (first, second, and third editions) and Chromatographic Analysis of the Environment, third and fourth editions (CRC Press). With F. Toldrá, he coedited two books published in 2006, 2007, and 2017: Advanced Technologies for Meat Processing (CRC Press) and Advances in Food Diagnostics (Blackwell Publishing—now Wiley). With M. Poschl, he coedited the book Radionuclide Concentrations in Food and the Environment, also published in 2006 (CRC Press). Dr. Nollet has also coedited with Y.H. Hui and other colleagues on several books: Handbook of Food Product Manufacturing (Wiley, 2007), Handbook of Food Science, Technology, and Engineering (CRC Press, 2005), Food Biochemistry and Food Processing (first and second editions; Blackwell Publishing—now Wiley—2006 and 2012), and the Handbook of Fruits and Vegetable Flavors (Wiley, 2010). In addition, he edited the Handbook of Meat, Poultry, and Seafood Quality, first and second editions (Blackwell Publishing—now Wiley—2007 and 2012). From 2008 to 2011, he published five volumes on animal product-related books along with F. Toldrá: Handbook of Muscle Foods Analysis, Handbook of Processed Meats and Poultry Analysis, Handbook of Seafood and Seafood Products Analysis, Handbook of Dairy Foods Analysis, and Handbook of Analysis of Edible Animal By-Products. Also, in 2011, with F. Toldrá, he coedited two volumes for CRC Press: Safety Analysis of xi

xii

Editors

Foods of Animal Origin and Sensory Analysis of Foods of Animal Origin. In 2012, they published the Handbook of Analysis of Active Compounds in Functional Foods. In a coedition with Hamir Rathore, Handbook of Pesticides: Methods of Pesticides Residues Analysis was marketed in 2009; Pesticides: Evaluation of Environmental Pollution in 2012; Biopesticides Handbook in 2015; and Green Pesticides Handbook: Essential Oils for Pest Control in 2017. Other finished book projects include Food Allergens: Analysis, Instrumentation, and Methods (with A. van Hengel; CRC Press, 2011) and Analysis of Endocrine Compounds in Food (Wiley-Blackwell, 2011). Dr. Nollet’s recent projects include Proteomics in Foods with F. Toldrá (Springer, 2013) and Transformation Products of Emerging Contaminants in the Environment: Analysis, Processes, Occurrence, Effects, and Risks with D. Lambropoulou (Wiley, 2014). In the series Food Analysis & Properties, he edited (with C. Ruiz-Capillas) Flow Injection Analysis of Food Additives (CRC Press, 2015) and Marine Microorganisms: Extraction and Analysis of Bioactive Compounds (CRC Press, 2016). With A.S. Franca, he edited Spectroscopic Methods in Food Analysis (CRC Press, 2017) and with Horacio Heinzen and Amadeo R. Fernandez-Alba, he edited Multiresidue Methods for the Analysis of Pesticide Residues in Food (CRC Press, 2017). With J.A. Gutierrez-Uribe, he edited Phenolic Compounds in Food: Characterization and Analysis (CRC Press, 2018); with Salah Mahgoub, he edited Testing and Analysis of GMO-Containing Foods and Feed (CRC Press, 2018); with K.S. Siddiqi, he edited Fingerprinting Techniques in Food Authentication and Traceability (CRC Press, 2018); with N.C. Basantia and Mohammed Kamruzzaman, he edited Hyperspectral Imaging Analysis and Applications for Food Quality (CRC Press, 2018); and with Basil K. Munjanja, he edited Ambient Mass Spectroscopy Techniques in Food and the Environment (CRC Press, 2019).

Contributors Maria del Álamo-Sanza Grupo UVaMOX, E.T.S. Ingenierías Agrarias Universidad de Valladolid Palencia, Spain Gonzalo Luis Alonso Cátedra de Química Agrícola, E.T.S.I. Agrónomos y Montes Universidad de Castilla–La Mancha Albacete, Spain Hanieh Amani Department of Grain and Industrial Plant Technology Faculty of Food Science Szent István University Budapest, Hungary Asghar Amanpour Department of Food Engineering Faculty of Agriculture Cukurova University Adana, Turkey Emilio Aranda School of Agronomics Engineering University Institute of Agronomics Resources (INURA) University of Extremadura Badajoz, Spain Roberta Ascrizzi Department of Pharmacy University of Pisa Pisa, Italy

Sandra Cristina Ballen Department of Food Engineering URI—Campus of Erechim Erechim, Rio Grande do Sul, Brazil Francisco Manuel Barrales Department of Food Engineering School of Food Engineering University of Campinas Campinas, São Paulo, Brazil Emmanuel Bertrand INRA, UMR1163 Biodiversité et Biotechnologie Fongiques Marseille, France Juliano Lemos Bicas Department of Food Science School of Food Engineering University of Campinas Campinas, São Paulo, Brazil Costas G. Biliaderis Department of Food Science Faculty of Agriculture, Forestry and Natural Environment Aristotle University of Thessaloniki Thessaloniki, Greece Mónica Bueno Laboratory of Foodomics Institute of Food Science Research (CIAL, CSIC-UAM) Madrid, Spain

xiii

xiv

Contributors

José S. Câmara CQM—Centro de Química da Madeira and

Daniel Cozzolino School of Science RMIT University Melbourne, Victoria, Australia

Departamento de Química Faculdade de Ciências Exatas e Engenharia Universidade da Madeira Campus da Penteada Funchal, Portugal

Thais Devincenzi Programa Carne y Lana Instituto Nacional de Investigación Agropecuaria (INIA) Tacuarembó, Uruguay

Carlos H. Carvalho Department of Food Science School of Food Engineering University of Campinas Campinas, São Paulo, Brazil

Arthur Luiz Baião Dias Department of Food Engineering School of Food Engineering University of Campinas Campinas, São Paulo, Brazil

Rocío Casquete School of Agronomics Engineering University Institute of Agronomics Resources (INURA) University of Extremadura Badajoz, Spain

Juan Carlos Diego Instrumental Techniques Laboratory University of Valladolid Valladolid, Spain

James Chapman School of Medical and Applied Sciences CQUniversity Rockhampton, Queensland, Australia

María Fernanda Flores Echeverría Sociedad Agroadvance Ltda Peñaflor, Chile

Valentina Villalobos Coa Laboratorio de Equilibrios en Solución Universidad Simón Bolívar (USB) Caracas, Venezuela

Mónika Valdenegro Espinoza Escuela de Agronomía Facultad de Ciencias Agronómicas y de los Alimentos Pontificia Universidad Católica de Valparaíso Quillota, Chile

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

Brian Farneti Research and Innovation Centre Fondazione Edmund Mach Trento, Italy

Christophe B.Y. Cordella INRA, UMR 0914 Physiologie de la Nutrition et du Comportement Alimentaire Groupe Chimiométrie pour la Caractérisation de Biomarqueurs—C2B Paris, France

Lorena de Oliveira Felipe Graduate School of Life and Environmental Sciences University of Tsukuba Tsukuba, Ibaraki, Japan Guido Flamini Department of Pharmacy University of Pisa Pisa, Italy

Contributors

Teresa Garde-Cerdán Grupo VIENAP Instituto de Ciencias de la Vid y del Vino (CSIC, Universidad de La Rioja, Gobierno de La Rioja) Logroño, Spain Adriana Marcia Graboski Department of Food Engineering Universidade Regional Integrada do Alto Uruguai e das Missões Erechim Campus Erechim, Rio Grande do Sul, Brazil Gamze Guclu Department of Food Engineering Faculty of Agriculture Cukurova University Adana, Turkey Elisabeth Guichard Centre des Sciences du Goût et de l’Alimentation, AgroSup Dijon, CNRS, INRA Université Bourgogne Franche-Comté Dijon, France Pengfei Han Key Laboratory of Cognition and Personality Ministry of Education Faculty of Psychology Southwest University Chongqing, China Alejandro Hernández School of Agronomics Engineering University Institute of Agronomics Resources (INURA) University of Extremadura Badajoz, Spain Paul S. Hughes Department of Food Science and Technology Oregon State University Corvallis, Oregon

Thomas Hummel Smell and Taste Clinic Department of Otorhinolaryngology TU Dresden Dresden, Germany Mohammad Hassan Kamani Young Researchers and Elite Club Sabzevar Branch Islamic Azad University Sabzevar, Iran Amin Mousavi Khaneghah Department of Food Science Faculty of Food Engineering University of Campinas Campinas, São Paulo, Brazil Iuliia Khomenko Research and Innovation Centre Fondazione Edmund Mach Trento, Italy Kieran Kilcawley Food Quality & Sensory Science Teagasc Food Research Centre Moorepark Fermoy, Ireland Iztok Jože Košir Department for Agrochemistry and Brewing Slovenian Institute of Hop Research and Brewing Žalec, Slovenia Giuseppe Lubes Laboratorio de Equilibrios en Solución Universidad Simón Bolívar (USB) Caracas, Venezuela Vito Lubes Laboratorio de Equilibrios en Solución Universidad Simón Bolívar (USB) Caracas, Venezuela

xv

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Contributors

Maria J. Macedo Department of Food Science School of Food Engineering University of Campinas Campinas, São Paulo, Brazil

Ignacio Nevares Grupo UVaMOX E.T.S. Ingenierías Agrarias Universidad de Valladolid Palencia, Spain

Like Mao Teagasc Food Research Centre Moorepark Fermoy, Ireland

Miha Ocvirk Department for Agrochemistry and Brewing Slovenian Institute of Hop Research and Brewing Žalec, Slovenia

and School of Food and Nutritional Sciences University College Cork Cork, Ireland Alberto Martín School of Agronomics Engineering University Institute of Agronomics Resources (INURA) University of Extremadura Badajoz, Spain Cátia Martins QOPNA & LAQV-REQUIMTE Department of Chemistry University of Aveiro Campus Universitário de Santiago Aveiro, Portugal

Adele Papetti Department of Drug Sciences University of Pavia Pavia, Italy Gláucia M. Pastore Laboratory of Food Biotechnology School of Food Engineering Institute of Science and Technology Universidade Federal dos Vales do Jequitinhonha e Mucuri Diamantina, Minas Gerais, Brazil Silvia Pastoriza Facultad de Farmacia Campus Universitario de Cartuja Granada, Spain

Song Miao Teagasc Food Research Centre Moorepark Fermoy, Ireland

Bruno Nicolau Paulino Faculty of Pharmaceutical Sciences Federal University of Amazonas Manaus, Amazonas, Brazil

Gustavo Molina Laboratory of Food Biotechnology Institute of Science and Technology—UFVJM Diamantina, Minas Gerais, Brazil

Cristina Pereira School of Agronomics Engineering University Institute of Agronomics Resources (INURA) University of Extremadura Badajoz, Spain

Iramaia Néri-Numa Laboratory of Food Biotechnology School of Food Engineering Institute of Science and Technology Universidade Federal dos Vales do Jequitinhonha e Mucuri Diamantina, Minas Gerais, Brazil

Rosa Perestrelo CQM—Centro de Química da Madeira Universidade da Madeira Campus da Penteada Funchal, Portugal

Contributors

Eva Pilar Pérez-Álvarez Grupo VIENAP Instituto de Ciencias de la Vid y del Vino Universidad de La Rioja Logroño, Spain and Centro de Edafología y Biología Aplicada del Segura (CEBAS-CSIC) Departamento de Riego Campus Universitario de Espinardo Murcia, Spain Sergio Pérez-Burillo Facultad de Farmacia Campus Universitario de Cartuja Granada, Spain Joana Pico Food Innovation, Structure and Health Lab School of Engineering University of Guelph Guelph, Canada Luisa Pistelli Department of Pharmacy University of Pisa Pisa, Italy Johannes Polster Nestlé Product Technology Centre Orbe, Switzerland Aoife Power School of Medical and Applied Sciences CQUniversity Rockhampton, Queensland, Australia Joseph Provost University of San Diego San Diego, California Virginia Celia Resconi Dep. Producción Animal y Ciencia de los Alimentos Facultad de Veterinaria Universidad de Zaragoza Instituto Agroalimentario de Aragón IA2—CITA Zaragoza, Spain

xvii

Sílvia M. Rocha QOPNA & LAQV-REQUIMTE Department of Chemistry University of Aveiro Campus Universitário de Santiago Aveiro, Portugal Sandra Marín-San Román Grupo VIENAP Instituto de Ciencias de la Vid y del Vino Universidad de La Rioja Logroño, Spain Yrjö H. Roos School of Food and Nutritional Sciences University College Cork Cork, Ireland Pilar Rubio-Bretón Grupo VIENAP Instituto de Ciencias de la Vid y del Vino Universidad de La Rioja Logroño, Spain José A. Rufián-Henares Facultad de Farmacia Campus Universitario de Cartuja Granada, Spain Adones Sales Department of Food Science School of Food Engineering University of Campinas Campinas, São Paulo, Brazil Maria Rosario Salinas Cátedra de Química Agrícola E.T.S.I. Agrónomos y Montes Universidad de Castilla-La Mancha Albacete, Spain Ângelo C. Salvador QOPNA & LAQV-REQUIMTE, Department of Chemistry University of Aveiro, Campus Universitário de Santiago Aveiro, Portugal

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Contributors

Rajnibhas Sukeaw Samakradhamrongthai Department of Food Technology Faculty of Agro-Industry Prince of Songkla University Songkhla, Thailand Philipe dos Santos Department of Food Engineering School of Food Engineering University of Campinas Campinas, São Paulo, Brazil Hans-Georg Schmarr Dienstleistungszentrum Ländlicher Raum (DLR) Rheinpfalz Institute for Viticulture and Oenology Neustadt an der Weinstraße, Germany and

Maria Monteiro de Araújo Silva Nestlé Product Technology Centre Food Singen, Germany Juliana Steffens Department of Food Engineering Universidade Regional Integrada do Alto Uruguai e das Missões Erechim Campus Erechim, Rio Grande do Sul, Brazil Clarice Steffens Department of Food Engineering Universidade Regional Integrada do Alto Uruguai e das Missões Erechim Campus Erechim, Rio Grande do Sul, Brazil

Faculty for Chemistry University Duisburg-Essen Essen, Germany

Vi Khanh Truong School of Medical and Applied Sciences CQUniversity Rockhampton, Queensland, Australia

Serkan Selli Department of Food Engineering Faculty of Agriculture Cukurova University Adana, Turkey

Kele. A.C. Vespermann Department of Food Science School of Food Engineering University of Campinas Campinas, São Paulo, Brazil

Han-Seok Seo Department of Food Science University of Arkansas Fayetteville, Arkansas

Lida Fuentes Viveros Centro Regional de Estudios en Alimentos Saludables Valparaíso, Chile

Catarina Silva CQM—Centro de Química da Madeira Universidade da Madeira Campus da Penteada Funchal, Portugal

Amir Yeganehshakib School of Biomedical Sciences Symbiosis International University Pune, India

Section

I

Aroma, Taste, and Flavor

Chapter

1

Aroma and Odor Christophe B.Y. Cordella CONTENTS 1.1 Smell and Taste: Historical Aspects 3 1.2 Odor of Perfume: Some Social Aspects of Smell 4 1.3 The Scent of Molecules, Vectors of Smell, and Taste: A Molecular Point of View 5 1.4 Perception of Smells—The Sense of Smell 9 References 11

1.1  SMELL AND TASTE: HISTORICAL ASPECTS Smell and flavor (more commonly known as taste) are two popular words in science, particularly since the award of the Nobel Prize in Physiology or Medicine in 2004 to Linda Buck and Richard Axel for their work that led to the discovery of the gene coding for the synthesis of olfactory receptors. But this has not always been the case, quite the contrary. Of the five human senses, taste and smell have long been considered as having minor effects compared to vision or hearing, probably because of their apparent uselessness. Basically, humans need sight to move and see where they are going, and hearing to communicate and protect themselves. In humans, few critical situations involve taste and smell. In the Middle Ages, and the Renaissance, we paid more attention to masking odors than studying them, and therefore there was no interest in understanding the mechanisms that underlie their perception. This state of thinking has long prevailed and led to a total misunderstanding of how smell and taste work. It was not until the early 20th century, in the 1930s, that a scientific interest in odor and its perception was born. Dyson (Malcolm Dyson, 1938) was one of the first to try to formalize a theoretical framework of smell. Curiosity and scientific work on smell and the mechanisms of olfaction also developed from ideas proposed by Moncrieff (1949, 1954), who created models of steric interaction to explain the perception of odors. This was just the beginnings of a molecular understanding of smell and its molecular receptors. Scientists became interested in the olfactory membrane, in odor receptors, the stereochemical aspects of olfaction (two molecules that perfectly reflect one another in a mirror can result from a different perception of smell), and in the physiological mechanisms of the translation of smell, called transduction, which transforms chemical information into electrical information interpreted by our brain. We now understand how much effort and investigation is necessary to open the door to a clearer understanding of olfaction. Thanks to a better understanding of olfaction, we know that these senses are intimately linked to each other and cannot fully express themselves independently. By highlighting the genetic mechanisms of olfaction, and by identifying the gene set coding for the

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synthesis of receptors (proteins) present in the neurons of the olfactory epithelium of the same name, L. Buck and R. Axel further revisited knowledge on how olfaction works (Buck and Axel, 1991). Olfaction is currently believed to be based on the interaction between volatile molecules forming odor and olfactory receptors. The work of Buck and Axel blows away older representations of odor and perception such as the vibrational or wave vision of smell (Zhu et al., 2016), or the theory of odotopes and olfactophores (Mori and Shepherd, 1994). The vibrational theory of smell considered the olfactory receptors as a vibrational spectroscope. Molecules should be easily recognized by their vibrational spectrum. The odotope theory was based on the shape of the molecular fragments implying a purely geometric recognition of the olfactory receptors. Today, it is understood that an odorant must possess certain molecular features in order to provide sensory properties. It must have some water solubility, a sufficiently high vapor pressure, low polarity, some ability to dissolve in fat (lipophilicity), a surface activity, and a molecular weight lower than 300 Da. It does not need to have particular functional chemical groups or be chemically active (Fernandez and Chemat, 2012). Odorant compounds can have all the major functions of organic chemistry: alcohols, carbonyl compounds (mainly aldehydes and ketones), esters, phenols, and sulfur or nitrogen derivatives. Terpenes (C10 or C15 hydrocarbons) and terpenoids (functionalized terpenes) are nevertheless the most widespread and the most abundant. The olfactory sense is able to distinguish between a practically infinite number of chemical compounds at very low concentrations. This number has been estimated at 400,000 (Mori and Yoshihara, 1995). Recently, Bushdid et al. (2014) claimed that humans can discriminate more than one trillion odors, but this assumption, based on the results of psychophysical testing, is still debated in the scientific community. The actual capacity of humans to discriminate between odor mixtures and also if any other animal can discriminate between them is still unknown (Grabe and Sachse, 2018).

1.2  ODOR OF PERFUME: SOME SOCIAL ASPECTS OF SMELL Even if we understand more about what a smell is and how we perceive it, the need to mask some odors and especially body odors is not new. Since antiquity, we have used many different scented waters, extracts, and balms, often derived from natural plant extracts (e.g., geranium and vetiver on the island of Reunion or ylang-ylang in Mayotte) or of animal origin (e.g., castoreum and civette, substances from the glandular secretions of small mammals). Ancient civilizations, such as those of Egypt, Greece, Persia, and Rome, are rich in examples of how perfumes and spices have been intricately woven into the fabric of various societies. The Enuma Elish, a cuneiform text dating from thousands of years before Christ, indicated that fragrant oils were widely used throughout the Middle East to provide skincare and protection from the hot and dry environment, and spices and fragrances were added to wine (Heidel, 1949). The development of tanneries in France (especially in Grasse, France) in the Middle Ages, and then their decline, paved the way for modern perfumes. Thanks to a microclimate favorable to the growth of scented plants, tanners evolved in the 18th century, initially becoming glovers and perfumers, and then perfumers exclusively. Gloves and leather garments were often associated with fragrance. The idea was thus to present to our taste and olfactory papillae another image that Mother Nature had sent us from these skins. In public, we do not accept smells more now than we did before, but we have learned to study them. Odors are recorded, analyzed, represented, and artificially synthesized to mimic the originals found in nature. With a better understanding of the

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molecular aspects of odors, we now know better how to satisfy our sensory organs and how to show what we prefer. Odor control requires knowledge for measuring, differentiating, quantifying, and comparing them, which is the main purpose of the electronic nose. Meanwhile, considerable interest has also developed in the study and understanding of the mechanisms of the perception of taste sensations and, in this area, the electronic tongue is the complementary tool of the e-nose.

1.3  THE SCENT OF MOLECULES, VECTORS OF SMELL, AND TASTE: A MOLECULAR POINT OF VIEW Leaning on a flower, a rose for example, to experience its scent or smell in places that we pass every day is so familiar that we do not imagine living otherwise. One rarely wonders how this is possible and how the perception of smell helps us to live and understand our environment. Yet behind this simple act lie extraordinary biochemical processes juggling with nuances and diversity. In this area, many theories (Ohloff, 1994; Sell, 1999) have been developed in order to explain the chemical and biochemical mechanisms that enable a fragrant compound to generate a particular signal interpretable by the brain as an odor. However, none of these theories have been able to report any experimental evidence. Indeed, we have known since the early 20th century that some compounds, such as carvone or menthol, have similar organoleptic properties even though they have a different chemical formula. Carvone (Figure 1.1) is a very important monoterpene ketone for the flavor industry. S-(+)-carvone is the main component of caraway oil and dill, with an odor resembling these herbs. The other isomer (R-(−)-carvone) occurs at high concentrations (70–80%) in spearmint oil and is also the major component responsible for its aroma. The aroma perception threshold for carvone is between 6.7–820 ppb, for S-(+)-carvone, and 2.7–600 ppb for the R-(−)-isomer (Paula Dionísio et al., 2012). Conversely, we know of many molecules whose enantiomers cause different organoleptic sensations even though they have the same chemical formula. Ohloff (Rienäcker and Ohloff, 1961) was the first to publish results on the enantioselective perception of chiral odorous compounds: (+)-β-citronellol was described as having a typical smell of lemon while (–)-β- citronellol produced an odor of geraniums. Since then, many optically active odorous compounds have been identified and are commonly used in perfumes and food flavorings (see Table 1.1) .

FIGURE 1.1  Carvone (2-me​thyl-​5-(1-​methy​lethe​nyl)-​2-cyc​lohex​en-1-​one).​ Member of a

family chemicals called terpenoids. Carvone has two mirror image forms or enantiomers R(–)-carvone and S(+)-carvone.

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TABLE 1.1  Classical Example of Enantiomers Showing Different Odor Properties Compounds (See chemical structures in Figure 1.2) 7-Hydroxy-6,7-dihydro-citronellal (4) Linalool (5) Carvone (6) Nootkatone (7) Nerol oxide (8) Androstenone (9) Menthol (10) Limonene (11)

Odor description (+): Lily of the valley with green minty notes (−): Sweet lily of the valley note (+): Sweet, petitgrain (−): Woody, lavender (+): Caraway (−): Spearmint (+): Grapefruit (−): Woody, spicy (+): Green, floral (−): Green, spicy, geranium (+): Odorless (−): Sweaty, urine, strong, musky (−): Sweet, fresh, minty, strong cooling effect (+): Dusty, vegetable, less minty, less cooling (+): Orange (−): Turpentine

Source: Extracted from Brenna et al., 2003.

FIGURE 1.2  Classical enantiomers showing different odors properties. (Extracted from

Brenna et al., 2003.)

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Thus, enantiomers differ either in the nature and quality of the odor (difference in the sensations of smell) or in the intensity of the smell. Certain structural features of molecules tend to increase the strength of odorants. Moncrieff listed the clearest of these: (1) polar functional groups (OH, C=O, CN, SH, –O–, etc.) increase intensity, (2) unsaturation generally increases intensity, (3) steric shielding of a functional group decreases intensity, and (4) when two hydrogen bond acceptors are present, the odorant is stronger when they are close to each other (Ohloff’s bifunctional rule (Ikan, 1995)). The perception of smell is physiologically limited, and this limit is called the odor perception threshold. This can be expressed as the mass of odorant per unit volume of air and is measured with various techniques such as olfactometry, in solution using Guadagni’s method (Schimmer and Guadagni, 1962), or with the triangle test. Usually, a scent is classified into two categories, depending on its use: fragrances and flavorings. As reported by Brenna et al. (2003), in a significant review, fragrances are chemical compounds used in functional and fine perfumery and flavorings are contained in or added to foods and beverages. These can be natural or identical to natural to comply with the regulations. Note that the enantiomeric excess of a compound, that is, the ratio between the two optically active forms, plays an important role in the perception of odor. The absolute configuration of chiral centers seems to have a particular influence on the perception of odor. This involves a wide range of olfactory receptors, leading to a highly generic form of the adaptability of protein receptors with multiple configurations of enantiomeric odorants (see Figure 1.3 and Table 1.2). Finally, the concentration of an odorant is also a key parameter that significantly changes the perception of the smell. A classic example is thioterpineol which has a scent of passion fruit at low concentrations, a smell of grapes at moderate concentrations, and produces a totally unpleasant or unsustainable odor at high concentrations (Brenna et al., 2003; Malnic et al., 1999). As indicated by Zampini and Spence (2012), smell and taste are part of a group of perceptions that follow the natural order, and in the case of food we first find the visual

FIGURE 1.3  Irones 104–106 are the odoriferous principles of natural Orris root

oil. They were first isolated from the iris rhizome in 1893. (Extracted from Brenna et al., 2003.)

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TABLE 1.2  Odor Descriptions of Irones Samples According to Brenna et al., 2003 Compound (see Figure 1.3) (+) 104a (–) 104a (+) 104b (–) 104b (+) 106a (–) 106a

(+) 106b (–) 106b (+) 105

(–) 105

a b

Odor description Violet-like, with woody, methylionone undertones Slightly stronger with a distinct “orris-butter” character It was described as the weakest of the α-isomers It shows a weak violet/wood/red berry character. Neither (+)- nor (−)-trans- α -irone possesses the characteristic “orris” odor It shows a floral, fatty, sweet, and woody odor character, an ionone-type odor with slightly sweet aspects It shows a β-ionone-type odor of warm floral-woody tonality. Green aspects are present, too. It shows some fruity nuances, reminiscent of pineapples. The odor is linear, and it can be considered a dry-down note It is very weak, of a woody odor tonality It is not very powerful, but it possesses a soft “orris-butter”-type of odor It possesses a β-ionone-type odor of warm floral-woody tonality with green and anisic aspects. The odor is linear, and the tenacity of the note is good. It can be considered a dry-down note It has a woody odor with a distinct honey note, that is quite sweet. Furthermore, it shows floral ionone-type facets, and a fruity tonality, but also an unpleasant smoky character. It belongs to the β-ionone-type family, without being very close to β-ionone

Odor threshold 100 ppma 10 ppma

0.75 ng/lb

113.5 ng/lb 26.35 ng/lb

Odor threshold by triangular test. Odor threshold by GC olfactometry.

properties, tactile and kinesthetic perceptions (via hands and cutlery), the smell, sensations in the mouth: flavor, texture, aroma, and finally the residual perception (after swallowing). Generally, this succession of perceptions often leads novice consumers to confuse aroma (information from the olfactory epithelium) with flavor (information from gustatory bulbs present mainly under the tongue). Having the feeling that perception is localized in the mouth, one refers to this perception by using the generic term “taste,” a term that can encompass information on texture. In fact, the combination of aroma and taste is the flavor of food. We see here that flavor and smell are closely linked and play an essential role in the sensory representation of objects around us. The odorous compounds that form flavors are organic molecules of low molecular weight; they have a partial ­pressure of vapors that is sufficiently high at atmospheric pressure and ambient temperature to allow a fraction to be released into the headspace surrounding their original media (e.g., food). During breathing, the molecules reach the olfactory mucosa and cause a stimulus. In a food, natural aroma represents a very small percentage (>> acid >> sweet > salty. The taste receptors operate on the principle of cell membranes and exploit the concentration gradients phenomena between the internal environment (gustatory neurons directly connected to brain areas of taste through three cranial nerves: the facial nerve, the glossopharyngeal nerve, and the trigeminal nerve) and the external environment (the external surface of the tongue and especially the cells forming the taste buds). The concentration difference between the two media varies over time and in terms of the substances that are placed in the mouth. These changes in concentration will trigger the release of neurotransmitters in the sensory dendrites of taste cells, causing a depolarization of tastes neurons’ extremity. This electrochemical disturbance is the starting point of the chain of taste perception. Neurobiologists have discovered that our taste buds are not limited to transmitting five flavors: sweet, salty, sour, bitter, and umami. We actually perceive a taste continuum that results from many flavors, but we have few words to express their diversity. Language does not allow us to describe all the different sensations from one person to another; the sensitivity to taste varies considerably. The rapid growth of research on the topic of flavor perception in recent years (e.g., Verhagen, 2007; Verhagen and Engelen, 2006) is linked to the idea that gaining a better understanding of how the multisensory integration taking place in the context of food perception might impact the theories of multisensory integration in general (e.g., Simons and Noble, 2003). On the other hand, it is also widely believed that the study of the multisensory processes involved in flavor perception will have a number of important consequences for the food and beverage industries, such as, for example, a better understanding of the processes used by people to assess the acceptability and flavor of new products. One robust finding to have emerged from recent psychophysical research on flavor perception is that odors can elicit changes in the perceived sweetness of foodstuffs (e.g., Stevenson et al., 1999). One of the main consequences of a better understanding of the multisensory processes involved in the flavor perception is the potential benefits to an international flavoring company, as it can reduce the concentration of the typically more expensive aroma added to a flavor by changing the amount of tastant (typically much cheaper) that is added, while still keeping the flavor profile delivered to their customer constant. Another area of intense commercial interest currently revolves around seeing whether the consumer’s brain can, in some sense, be tricked into perceiving tastes/flavors without the need to include all the unhealthy ingredients that so many of us seem to crave. There are also some interesting commercial opportunities here around exploiting genetic differences in taste perception. Some people have 16 times more gustatory receptors on their tongues than others. In a very real sense, then, we may well live in different taste worlds. While some of the most profound differences in taste perception involve certain bitter-tasting compounds, recent research has demonstrated that “supertasters” are also more sensitive to the oral-somatosensory attributes of foodstuffs (for example, to the fat in a salad dressing), and possibly also to certain olfactory stimuli, while at the same time being less influenced by visual cues when judging taste/flavor. Interestingly, Gary Pickering and colleagues have just published a paper suggesting that wine experts, if not “foodies,” tend to be more sensitive to certain bitter-tasting compounds than the rest of the population (Pickering et al., 2013).

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The link between the perception of taste and behavior is also a topic of interest and an important area of research. A fundamental question in this area is what the important functions of taste for animals and particularly for humans are. Research has shown that taste is involved at many levels in the development of mammals and humans. For food intake, taste sensations influence our thinking, our deciding, and our behavior toward food both consciously and unconsciously. The sensory experience of food is a determining factor in the control of food intake, often attributed to the positive hedonic response associated with certain sensory signals. Sensory cues based on the sight, smell, taste, and texture of a food are operational before, during, and after a meal (McCrickerd and Forde, 2016). A number of studies on consumer preferences have demonstrated that taste has a social sense. S. Højlund related in an interesting review that By moving the attention of taste as a physiological stimulus-response of individuals to tasting as a shared cultural activity, it is possible to recognize the taster as a reflexive actor that communicates, performs, manipulates, senses, changes and embodies taste— rather than passively perceives a certain experience of food. (Højlund, 2015)

There is evidence that taste plays a role in social communication in humans, but is it a general characteristic in mammals and vertebrates? Probably it is. For many vertebrates, physical, social contacts, including the licking of nonvolatile social chemical compounds from the genitals of a related animal, urine, sweat, or saliva, help to route compounds to the vomeronasal organ of many species of vertebrates that respond to conspecific compounds of social communication (Chamero et al., 2012). In the case of invertebrates, the social function of taste has been demonstrated in male drosophila which use taste to differentiate males and females as well as to recognize the maturity status of females for mating (Bray and Amrein, 2003; Koganezawa et al., 2010). The honeybee is another example of an invertebrate where it is known that smell and taste are of great importance in the social organization of the species (Robinson, 1996). The world of the honeybee is populated by pheromones, and taste plays a key role because their survival depends on the collection and consumption of nectar and pollen, as well as other natural products (de Brito Sanchez et al., 2014). The honeybee and its chemical means of communication is an active field of research because the insect is a good model for the study of interactions between individuals via volatile (pheromones produced by the queen) or nonvolatile (liquid substances secreted or deposited by the honeybees) chemical compounds. Studies have shown that the honeybee is sensitive to a large number of organic substances such as glucose, fructose, maltose, sucrose, sodium chloride, potassium chloride, or lithium chloride. Concerning taste perception more specifically, it has been demonstrated that the honeybee has gustatory molecular receptors not only at the level of the head but also at the level of the antennae, and it is now known that these taste receptors are useful for identification of the chemical signature of the colony (de Brito Sanchez, 2011; Erickson, 1982; Whitehead and Larsen, 1976; Wright, 2009). To conclude this introductory chapter on taste and its molecular perception (flavors) and the intimate link between the perception of taste and the perception of smell (see Chapter 1), one can highlight the capital role played by the olfaction and taste senses in the evolution of man and hominids at all levels (Breslin, 2013; Breslin and Spector, 2008). Indeed, the role of these two types of sensory perception (in combination with others like sight, hearing, and touch) is major and determining in the behaviors of living species and therefore in their survival and adaptation to the environments in which they evolve. Therefore, smell and taste perception also depend on social behaviors (ingestion or non-ingestion of food—food safety), choice of food according to the associated metabolic

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consequences (the search for the nutritional optimum), research and recognition of sexual partners, organization, and management of the group (in insects such as the bee or in groupliving animals such as wolves, lions etc.). The multimodal nature of taste perception requires more studies implying a combinatorial approach of our senses. Only when true multimodal flavor experiments are explored will we begin to understand how the human brain forms the supramodal sensation of flavor (Lundstrom et al., 2011). At the molecular level, fundamental questions are about to be solved, such as how the odor, taste, and trigeminal perception are formed and how are they collated to create the perception of the flavor. Others are not yet answered, such as those relating to social taste assessments occurring during human interactions; kissing, for example (Breslin, 2008).

GLOSSARY Flavor: The sensory impression of food or other substances, determined primarily by the chemical senses of taste and smell. The “trigeminal senses,” which detect chemical irritants in the mouth and throat, as well as temperature and texture, are also important to the overall gestalt of flavor perception. Gestalt: The psychology of form or gestaltism is a psychological, philosophical, and biological theory, according to which the processes of perception and mental representation spontaneously treat phenomena as global forms, structured or not, rather than as addition or juxtaposition of simple elements. Perception: T he conscious or unconscious awareness of things through the physical senses (smell, taste, touch, sight, hearing) that give rise to experience. Taste: A perception that results from stimulation of a gustatory nerve. Taste belongs to the chemical sensing system. Tasting begins when molecules stimulate special cells in the mouth or throat. These special cells transmit messages through nerves to the brain, where specific tastes are identified.

REFERENCES Beare, J. I. (1906). Greek Theories of Elementary Cognition from Alcmaeon to Aristotle, Clarendon Press, Oxford University. Bray, S. and Amrein, H. (2003). A putative Drosophila pheromone receptor expressed in male-specific taste neurons is required for efficient courtship. Neuron 39, 1019–29. Breslin, P. A. S. (2008). Multi-modal sensory integration: Evaluating foods and mates. Chemosensory Perception 1, 92. Breslin, P. A. S. (2013). An evolutionary perspective on food and human taste. Current Biology 23, R409–18. Breslin, P. A. S. and Spector, A. C. (2008). Mammalian taste perception. Current Biology 18, R148–55. Carterette, E. C. and Friedman, M. P. (1978). History of taste research. In Handbook of Perception (E. C. Carterette and M. P. Friedman, eds.), pp. xi. Academic Press, London. Chamero, P., Leinders-Zufall, T., and Zufall, F. (2012). From genes to social communication: Molecular sensing by the vomeronasal organ. Trends in Neurosciences 35, 597–606. de Brito Sanchez, M. G. (2011). Taste perception in honey bees. Chemical Senses 36, 675–92.

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de Brito Sanchez, M. G., Lorenzo, E., Su, S., Liu, F., Zhan, Y., and Giurfa, M. (2014). The tarsal taste of honey bees: Behavioral and electrophysiological analyses. Frontiers in Behavioral Neuroscience 8, 25. Deppenweiler, A. (2014). Le glutamate monosodique comme ehauster de goût: Confiance ou méfiance? Sciences pharmaceutiques. Available at: https://dumas.ccsd.cnrs.fr/ dumas-01011277/. Erickson, E. H., Jr. (1982). Evidence for electrostatic enhancement of odor receptor function by worker honeybee antennae. Bioelectromagnetics 3, 413–20. Gruner, O. C. (1973). A Treatise on the Canon of Medicine of Avicenna: Incorporating a Translation of the First Book, AMS Press, New York, NY. von Haller, A. and Cullen, W. (1803). First Lines of Physiology, pp. 215–21. O. Penniman & Co. Henning, H. (1915). Der Geruch I. Zeitschrift fur Psychologie und Physiologie der Sinnesorgane 78, 161–267. Højlund, S. (2015). Taste as a social sense: Rethinking taste as a cultural activity. Flavour 4, 6. Koganezawa, M., Haba, D., Matsuo, T., and Yamamoto, D. (2010). The shaping of male courtship posture by lateralized gustatory inputs to male-specific interneurons. Current Biology 20, 1–8. Lundstrom, J. N., Boesveldt, S., and Albrecht, J. (2011). Central processing of the chemical senses: An overview. ACS Chemical Neuroscience 2, 5–16. McCrickerd, K. and Forde, C. G. (2016). Sensory influences on food intake control: Moving beyond palatability. Obesity Reviews 17, 18–29. Pickering, G. J., Jain, A. K., and Bezawada, R. (2013). Super-tasting gastronomes? Taste phenotype characterization of foodies and wine experts. Food Quality and Preference 28, 85–91. Robinson, G. E. (1996). Chemical communication in honeybees. Science 271, 1824–5. Schiffman, S. S. and Erickson, R. P. (1971). A psychophysical model for gustatory quality. Physiology & Behavior 7, 617–33. Schiffman, S. S. and Gatlin, C. A. (1993). Clinical physiology of taste and smell. Annual Review of Nutrition 13, 405–36. Siegel, R. E. (1970). IV. Organ and perception of taste. In Galen on Sense Perception. His Doctrines, Observations and Experiments on Vision, Hearing, Smell, Touch and Pain, and Their Historical Sources, pp. 158–73. Karger Publishers, Basel. Simons, C. T. and Nobel, A. C. (2003). Challenges for the sensory sciences from food and wine industries. Nature Reviews Neuroscience 4, 599–605. Small, D. M. and Prescott, J. (2005). Odor/taste integration and the perception of flavor. Experimental Brain Research 166, 345–57. Stevenson, R. J., Prescott, J., and Boakes, R. A. (1999). Confusing tastes and smells: How odors can influence the perception of sweet and sour tastes. Chemical Senses 24, 627–35. Vicq D’Azyr, F. (1787). Encyclopédie Méthodique, Médecine (Panckoucke, ed.), Vol. 1, p. 931. F. Vicq D’Azyr, Liège. Verhagen, J. V. and Engelen, L. (2006). The neurocognitive bases of human food perception: Sensory integration. Neuroscience and Biobehavioral Reviews 30, 613–50. Whitehead, A. T. and Larsen, J. R. (1976). Electrophysiological responses of galeal contact chemoreceptors of Apis mellifera to selected sugars and electrolytes. Journal of Insect Physiology 22, 1609–16. Wright, G. A. (2009). The “sweet tooth” of the honeybee: The perception of nectar and its influence on honeybee behaviour. SEB Experimental Biology Series 63, 183–204.

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Chemical Senses and Flavor Perception Han-Seok Seo CONTENTS 3.1 Introduction 23 3.2 Chemosensory System Associated with Flavor Perception 25 3.2.1 Gustatory System 25 3.2.1.1 The Sense of Taste 25 3.2.1.2 Anatomy and Physiology 27 3.2.2 Olfactory System 29 3.2.2.1 The Sense of Smell 29 3.2.2.2 Orthonasal and Retronasal Olfaction 30 3.2.2.3 Anatomy and Physiology 30 3.2.3 Oral Somatosensory System 32 3.2.3.1 Somesthesis/Chemesthesis 32 3.2.3.2 Anatomy and Physiology 32 3.3 Multisensory Flavor Perception 34 3.3.1 Interactions between Chemosensory Cues in Flavor Perception 34 3.3.1.1 Crossmodal Correspondence 34 3.3.1.2 Lateralization/Localization 34 3.3.1.3 Intensity and Pleasantness 35 3.3.2 Effects of Tactile or Temperature Cues of a Stimulus Medium on Flavor Perception 37 3.3.3 Effects of Visual or Auditory Cues on Flavor Perception 40 3.4 Conclusion 41 References 41

3.1 INTRODUCTION Consider this scenario: Hannah and two friends, James and Olivia, have dinner at a jazz club restaurant in New Orleans, Louisiana. They experience perceptions and acceptances that may vary dynamically over a span of consumption that starts at the point of ordering meal items, perhaps even from just entering the restaurant, up to the point of completing their entire meal. Ingredient descriptions and images of menu meal items lead Hannah, James, and Olivia to all order the same item: spicy crispy chicken tenders. Subsequently, when the chicken tenders are served, Hannah, James, and Olivia experience a variety of

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sensory cues that include appearance, aroma, taste, aromatics, irritations, mouthfeel, and sounds, all originating from the spicy chicken tenders. While Hannah likes her spicy crispy chicken tenders, James and Olivia do not enjoy their portions. Because Olivia suffers from a stuffy nose caused by a bad cold, the chicken tenders taste too bland to her. James is quite sensitive to spicy components, so he is unable to continue eating the spicy chicken tenders because of pain and irritation in his mouth. Hannah suggests that James try drinking a milk beverage because she had previously read an article suggesting that such a beverage could be effective in decreasing residual spiciness (Samant et al., 2016). When James tries eating the spicy chicken tenders with a milk beverage, he feels better because it aids in reducing residual burning. After consuming their main dishes, they decide to order strawberry mousse cake desserts served on white plates in the presence of jazz music. These desserts taste sweeter and more flavorful than they had expected, increasing their acceptance (see Spence and Shankar, 2010; Piqueras-Fiszman et al., 2012; Fiegel et al., 2014; Spence, 2015a). This scenario illustrates that the perception of flavor during meal consumption can be dynamic and complex. The perception of flavor can be influenced by sensory characteristics of the meal items, as well as non-sensory factors such as socio-demographics, personality traits, emotional states, and surrounding contexts (Delwiche, 2004; Spence, 2015a,b). The term “flavor” has been conceptualized and used differently by various authors depending on their areas of expertise (e.g., flavor chemists, food scientists, and culinary experts) and their socio-demographic profiles (e.g., gender, age group, language, and culture) (Delwiche, 2003). For example, in a survey where 140 professionals working in various areas (agriculture, food science, sensory evaluation, and chemical senses) were questioned about their concept of flavor, they differed with respect to which attributes they rated as essential in contributing to the flavor of food. However, there was a consensus that smell and taste are considered to be the most essential sensations involved in the perception of flavor (Delwiche, 2003). Amerine et al. (1965) defined flavor as “the sum of perceptions resulting from stimulation of the sense ends that are grouped together at the entrance of the alimentary and respiratory tracts.” Hall (1968) further specified flavor as “the sensation produced by a material taken in the mouth, perceived principally by the senses of taste and smell, and also by the general pain, tactile and temperature receptors in the mouth. Flavor denotes the sum of the characteristics of the material which produce that sensation.” More recently, the International Standards Organization (2008; ISO 5492) characterized flavor as a “complex combination of the olfactory, gustatory and trigeminal sensations perceived during tasting. The flavor may be influenced by tactile, thermal, painful and/or kinesthetic effects.” Some researchers, however, have suggested that sensations derived from other sensory modules (e.g., the senses of vision and hearing) should also be added to the definition of flavor (Spence, 2015a). Furthermore, others have proposed that all sensory inputs related to a particular food should be considered as flavor components (Verhagen and Engelen, 2006; Spence, 2015a). In this chapter, only sensory modules evoked from ingested substances in the mouth, including gustatory, olfactory, and oral somatosensory (focusing on trigeminal) sensations, will be considered as primary components of flavor. This chapter introduces concepts, roles in daily life, anatomy and physiology, and other influential factors related to olfactory, gustatory, and oral somatosensory (in particular, trigeminal) systems predominantly associated with a perception of flavor. This chapter will also address multisensory flavor perception, with an emphasis on both (1) interactions of chemosensory cues and (2) effects of non-chemosensory cues on chemosensory perception.

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3.2  CHEMOSENSORY SYSTEM ASSOCIATED WITH FLAVOR PERCEPTION 3.2.1 Gustatory System 3.2.1.1 The Sense of Taste Taste is composed of sensations elicited by tasting compounds through an anatomically and physiologically defined gustatory system (Bachmanov and Beauchamp, 2007). Because other sensations such as olfaction and somatosensation, along with taste sensations, may have been evoked in the mouth during eating and drinking, many people use the word “taste” to describe sensations arising from the oral cavity (Bachmanov and Beauchamp, 2007; Seo and Hummel, 2011a). In this sense, people with olfactory disorders often report that they have lost their sense of taste. The sense of taste plays an important role in judging whether something may be beneficial or dangerous for humans to eat or drink (Massler, 1980; Seo and Hummel, 2011a). Generally speaking, a bitter taste is associated with sensing of diverse natural toxins, and abnormal bitterness is likely to be equated with dietary danger (Drewnowski and Gomez-Carneros, 2000). For example, aversive bitterness can be detected from rancid fats, hydrolyzed proteins, microbial fermentation, and plant-derived alkaloids, although all toxic species do not necessarily produce humanly detectable bitter-taste signals (e.g., Dioscorea dumetorum, also known as the bitter yam and found in Africa, is almost tasteless) (Hladik and Simmen, 1996). Even though there is no general rule relating concentrations of bitter-tasting compounds and their toxicities, bitter-tasting compounds are detected by humans at much lower levels than are other tasting compounds (Hladik and Simmen, 1996; Drewnowski and Gomez-Carneros, 2000). The current consensus on taste quality is that human taste sensation can be classified into five basic taste qualities: sweet, salty, sour, bitter, and umami (or savory). Human neonates exhibit differential facial expressions in response to qualities such as sweet, salty, sour, and bitter tastes (Steiner, 1974; Rosenstein and Oster, 1988; Forestell and Mennella, 2017). The sweet taste is related to signals reflecting the presence of energy and nutrients in foods and beverages. Salty taste, mainly detected from sodium salts, is thought to be attractive to people’s diets for electrolyte balance (Bachmanov and Beauchamp, 2007). Sour tastes, along with bitter tastes, may be associated with signals related to spoiled substances (Bachmanov and Beauchamp, 2007). Like an infant’s dislike for sour-tasting items, initial aversive responses may tend to change to positive responses over time (Bossfeld et al., 2007; Forestell and Mennella, 2017). Finally, the umami taste commonly detected from l-glutamate is associated with signals reflecting the presence of protein (Bachmanov and Beauchamp, 2007). As shown in Figure 3.1, basic taste qualities can interact with one another, leading to enhancement, suppression, or no effect on perceived intensity (Keast and Breslin, 2003). It should be noted that binary taste interactions are compound-specific as well as intensity/concentration specific (Keast and Breslin, 2003). There is also growing interest in other taste qualities (i.e., the existence of a sixth basic taste). These include a “fat taste” (Fukuwatari et al., 1997; Khan and Besnard, 2009; Mattes, 2009a,b; Keast and Costanzo, 2015) and a “starchy taste” (Sclafani, 2004; Lapis et al., 2014, 2016). CD-36 and G protein-coupled receptor 120 are considered to potentially allow humans to be able to detect free fatty acids varying in chain length (C6 –C18) and saturation at low concentrations (Mattes, 2009a,b). However, it still

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FIGURE 3.1  Schematic review of binary taste interactions at (a) low intensity/concentra-

tion, (b) medium intensity/concentration, and (c) high intensity/concentration levels of taste qualities. (Reprinted from Food Quality and Preference, 14(2), Keast and Breslin, “An overview of binary taste-taste interactions,” pp. 111–24, 2003, with permission from Elsevier.) remains unclear as to whether there is a recognizable perception of fat independent of other taste qualities (Keast and Costanzo, 2015). Recent studies have also demonstrated that humans can detect glucose oligomers (average degree of polymerization: 7 and 14), but not glucose polymers (average degree of polymerization: 44) (Lapis et al., 2016). The detection of glucose oligomers seems to be independent of the sweet taste receptor, that is, hT1R2/hT1R3 (Lapis et al., 2014, 2016). If there is an independent mechanism apart from the sweet taste for glucose oligomers, “starchy taste” would be the most likely to be associated with identifying a source of energy (Lapis et al., 2016). Hartley et al. (2019) suggested the following comprehensive list of criteria for determining the appropriateness of other new tastes: 1. A unique class of effective substances must exist. 2. Detection of effective substances must have an evolutionary perspective (e.g., the body’s electrolyte balance for salty taste). 3. Unique receptors and neural transmission of the effective substances must exist. 4. Neurotransmission of electrical signals to taste processing regions of the brain must occur. 5. The perceptual quality of the effective substances must be independent from other taste qualities.

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6. Hedonic responses to the effective substances must exist. 7. Physiological and/or behavioral responses to the effective substances must exist. It has been found that 4 to 10% of patients consulting a specialized smell and taste clinic in the United States and Germany report impairment in their sense of taste (Deems et al., 1991). Among the general population, the percentage of people with “hypogeusia” (i.e., gustatory hyposensitivity compared to young, healthy subjects) is estimated to be about 5%. People with “ageusia” (i.e., complete or pronounced reduction of the sense of taste or a loss of sensitivity in a single taste quality) are considered very rare (Welge-Lüssen et al., 2011; Fark et al., 2013). With respect to qualitative taste disorder, “parageusia” (i.e., inadequate or wrong taste sensation elicited by a taste stimulus) and “phantogeusia” (i.e., perception of taste in the absence of a taste stimulus) have been reported and often occur together (Fark et al., 2013). Although the main reasons for taste disorders include craniocerebral injury, infection of the upper respiratory tract, iatrogenic causes, side effects of medication, exposure to toxic substances, and burning mouth syndrome (Hummel et al., 2011; Fark et al., 2013), approximately 30% of taste disorder cases state no specific cause (Fark et al., 2013). 3.2.1.2 Anatomy and Physiology Each of the five basic taste qualities is mediated by distinct transduction pathways expressed in specific subsets of taste receptor cells (Bachmanov and Beauchamp, 2007). Sweet and umami tastes are specifically mediated by a family of three class-C G proteincoupled receptors (GPCRs): T1R1, T1R2, and T1R3. Although T1R3 alone serves as a taste receptor for sucrose and other sugars (Nelson et al., 2001; Zhao et al., 2003), the heterodimers of T1R2 and T1R3 serve as sweet taste receptors for a variety of sweettasting substances (Breslin and Huang, 2006; Temussi, 2009). A heterodimeric complex of T1R1 and T1R3 also serves as a taste receptor for umami-tasting substances (Zhao et al., 2003). Full-length mGluR1 and mGluR4 (Toyono et al., 2002) and a variant of mGluR1 (i.e., taste-mGluR1, San Gabriel et al., 2009) are also considered candidates for umami taste receptors (Yasumatsu et al., 2012). Bitter-tasting substances are bound to the T2R family, a large family of class A GPCRs (Adler et al., 2000; Chandrashekar et al., 2006). Salty-tasting (sodium) and sour-tasting (protons) substances are mediated by specific ion channels (DeSimone and Lyall, 2006). With respect to the salty taste, the selective epithelial amiloride-sensitive sodium channel (ENaC) is considered to be involved, at least in rodents (Bachmanov and Beauchamp, 2007). Finally, multiple candidates have been found to mediate responses to sour-tasting substances. These include acid-sensing ion channels (ASICS; Ugawa et al., 1998), neuronal amiloride-sensitive cation channel 1 (ACCN1), HCN1, and HCN4 from a family of hyperpolarization-activated cyclic nucleotide-gated channels (HCNs; Stevens et al., 2001), transient receptor potential channels PKD1L3 and PKD2L1 (Huang et al., 2006; Ishimaru et al., 2006), and TASK-1, and Na+-H+-exchanger isoform 1 (NHE-1) (Bachmanov and Beauchamp, 2007). Taste receptor cells are mainly found in garlic- or rosebud-shaped multicellular clusters called “taste buds.” Humans normally have between 5000 and 10,000 taste buds (Loper et al., 2015). Taste buds have been observed not only on the tongue’s surface, but also on the soft palate, the pharyngeal and laryngeal regions of the throat, the stomach, and the gastrointestinal tract (Breslin and Huang, 2006; Rozengurt, 2006; Trivedi, 2012a; Rober and Chaudhari, 2017). Each taste bud contains a small opening, a “taste pore,” on the upper surface of the taste papillae (Just et al., 2005). Through this taste pore, substances to be tasted come into contact with taste receptor cells (Breslin and

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Huang, 2006; Seo and Hummel, 2011a). There are four principal types of cells, types I, II, III, and IV, within a taste bud (Feng et al., 2014). They can be classified by distinct ultrastructural features and immuno-histochemical characteristics (Kataoka et al., 2008). Type I (dark) cells with several long microvilli seem to have supportive or glia-like functions in taste buds; they express enzymes and transporters required to remove extracellular neurotransmitters (e.g., the neurotransmitter adenosine 5´-triphosphate (ATP)) (Bartel et al., 2006; Roper and Chaudhari, 2017). A subset of type I cells is considered to be involved in salty-taste reception (Vandenbeuch et al., 2008; Feng et al., 2014). Type II (light) cells, polarized cells with short microvilli, function as taste receptors for sweet, bitter, umami, and possibly salty-tasting substances (Trivedi, 2012b). Most type II cells express one class of taste GPCRs, that is, T1R or T2R, that respond to only one taste quality (Roper and Chaudhari, 2017). However, since T1R1, T1R2, and T1R3 are often co-expressed in taste bud cells, each taste bud responds to multiple tasting substances (Roper and Chaudhari, 2017). Type III (dark) cells, thin spindle-shaped cells with single microvillus, form synapses with gustatory nerve endings (Chaudhari and Roper, 2010; Feng et al., 2014). Their subsets are considered to be involved in salty taste (Kataoka et al., 2008; Oka et al., 2013; Feng et al., 2014) and/or sour taste perception (Huang et al., 2006; Trivedi, 2012b). Type III cells are also likely to serve in integrating and transmitting signals from type II cells to gustatory nerves (Tomchik et al., 2007; Feng et al., 2014). Finally, type IV cells (also called basal cells), located near taste bud bases, are newly generated taste precursor cells differentiating into mature taste cells (Trivedi, 2012b; Feng et al., 2014). There are four types of taste papillae: fungiform, circumvallate, foliate, and filiform. Fungiform (“mushroom-like”) papillae are distributed both on the anterior two-thirds (Konstantindis, 2009) and on the side (Kullaa-Mikkonen et al., 1987) of the tongue. Their densities are related to taste-intensity perception (Miller and Reedy, 1990) or 6-n-propylthiouracil (PROP) status (Eldeghaidy et al., 2018). For example, people with a higher density of fungiform papillae may perceive some tastes as more intense than those with a lower density of fungiform papillae (Miller and Reedy, 1990). However, little or no association between fungiform papillae density and perceived taste intensity has been observed (Feeney and Hayes, 2014; see also Dinnella et al., 2018). Women have been found to possess more fungiform papillae than men (Bartoshuk et al., 1994; Dinnella et al., 2018). Fungiform papillae density increases from childhood to adulthood (Correa et al., 2013) then decreases with age (Dinnella et al., 2018). While circumvallate (“walllike”) papillae are present at the posterior third of the tongue, foliate (“leaf-like”) papillae are positioned along its posterior lateral edges (Konstantindis, 2009). Finally, filiform (“thread-like”) papillae with no taste buds are found on the tongue surface. They seem to be involved in a somatosensory function, manipulation of food bolus, and/or saliva distribution on the tongue and food bolus (Konstantindis, 2009; Seo and Hummel, 2011a). Interestingly, salivary secretion, flow, and profiles have been found to affect taste functions, such as taste sensitivity (Christensen et al., 1987; Mese and Matsuo, 2007; Stolle et al., 2017). Three primary cranial nerves (CNs), facial (CN VII), glossopharyngeal (CN IX), and vagus (CN X), are involved in the transmission and processing of taste signals to the gustatory cortex. The facial nerve delivers taste signals via the chorda tympani nerve and the greater superficial petrosal nerve (Spector, 2000; Breslin and Huang, 2006); the chorda tympani nerve innervates the anterior two-thirds of the tongue and the greater superficial petrosal nerve innervates taste buds on the soft palate (Konstantindis, 2009). The glossopharyngeal nerve innervates the majority of the circumvallate papillae of the posterior

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tongue via the lingual-tonsillar branch (Spector, 2000; Konstantindis, 2009). Finally, the vagus nerve innervates taste buds located on the laryngeal surface of the epiglottis, the larynx, and the proximal part of the esophagus via the superior laryngeal nerve (Spector, 2000; Konstantindis, 2009). The axons of gustatory neurons terminate in the rostral part of the nucleus of the solitary tract in the medulla (Beckstead and Norgren, 1979). These neurons project into the parvicelluar part of the posteromedial ventral thalamus through the central tegmental tract (Huart et al., 2009; Seo and Hummel, 2011a; Iannilli and Gudziol, 2019). Neurons from the thalamus project into the primary taste cortex in the frontal operculum and adjoining insula areas (Huart et al., 2009) involved in taste intensity, identification, and memory (Small, 2006; Iannilli and Gudziol, 2019). Each half of the human tongue was found to be innervated by ipsilaterally ascending cranial nerves. However, the pathway of taste laterality from the oral cavity to the primary taste cortex in humans remains unclear (for a review, Iannilli and Gudziol, 2019). The primary taste cortex projects into the secondary taste cortex. It includes the caudolateral orbitofrontal cortex, the cingulate gyrus, the amygdala, the hypothalamus, and the basal ganglia (Sewards, 2004; Breslin and Huang, 2006; Small, 2006; Huart et al., 2009; Seo and Hummel, 2011a). Detection and suprathreshold intensity-related perception of gustatory stimuli are associated with processing in the operculum/insula, while affective response is likely to be related to processing in the orbitofrontal cortex (Small, 2006). 3.2.2 Olfactory System 3.2.2.1 The Sense of Smell The sense of smell plays a more important role than commonly thought in a broader range of daily tasks. First, the sense of smell serves as a detector of hazardous substances (e.g., spoiled foods, poisonous fumes, gas leaks, etc.). Second, the sense of smell helps us detect (Porter et al., 2007) and identify edible food sources (Fallon and Rozin, 1983). It also influences food acceptability (Aschenbrenner et al., 2008), food preparation (Seo and Hummel, 2009), eating behavior (Aschenbrenner et al., 2008), and nutritional status (Duffy et al., 1995), although such effects have not been observed in all studies (Mattes, 2002). Finally, the sense of smell can serve as a social communicator (Stevenson, 2010) by influencing reproductive behavior such as inbreeding avoidance, fitness detection in prospective mates (Stevenson, 2010; Pause, 2016), and communication of emotion via body odors (Zhou and Chen, 2009). Prevalence of olfactory disorder in the general population is common, but most individuals are unaware of olfactory loss (Nordin et al., 2004; Croy et al., 2014). About 20% of the general population have reported at least one type of olfactory disorder (Croy et al., 2014), although the prevalence rate of such disorders varies in earlier studies (Nordin et al., 2004; Croy et al., 2014; Noel et al., 2017). Olfactory disorders can be classified into two major groups, quantitative and qualitative. A quantitative olfactory disorder may include (1) “anosmia” (lack of ability to perceive odors), (2) “congenital anosmia” (condition in which people are born with an inability to perceive odors), (3) “specific anosmia” (inability to perceive specific odor(s) due to the lack of certain olfactory receptors), (4) “functional anosmia” (significantly reduced ability to perceive odors), (5) “hyposmia” (reduced ability to perceive odors), and (6) “hyperosmia” (heightened ability to perceive odors), based on the level of olfactory performance that can be quantified through clinical testing (Hummel et al., 2016). It is estimated that the prevalence rate of congenital

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anosmia is one individual out of every 5000 to 10,000 (Croy et al., 2012). A qualitative olfactory disorder includes (1) “parosmia” (also called “troposmia”; inadequate or wrong perception of odor stimulus) and (2) “phantosmia” (perception of smell in the absence of an odor stimulus) (Hummel et al., 2016). Both parosmic and phantosmic sensations are typically described as distorted or unpleasant. For example, odors are often described as “burned,” “rotten,” “chemical,” or “fecal” smells (Leopold, 2002; Frasnelli et al., 2004; Hummel et al., 2016). In the general population, the prevalence rates of parosmia and phantosmia have been reported as less than 4% (Nordin et al., 2007) and 0.8–2.1% (Landis et al., 2004), respectively. The most common etiologies of olfactory disorder are post-viral upper respiratory infection (18–45% of the clinical population) and nasal/ sinus disease (7–56%), followed by head trauma (8–20%) and exposure to toxins/drugs (2–6%) (Nordin and Bramerson, 2008; Croy et al., 2014). Olfactory disorders can significantly affect daily living activities and quality of life (Miwa et al., 2001; Santos et al., 2004; Seo et al., 2009; Nordin et al., 2011; Croy et al., 2014). Miwa et al. (2001) reported (1) safety-related activities such as reduced ability to detect spoiled food (75% of 1407 clinical patients with olfactory disorder), gas leaks (61%), or smoke (50%), and (2) eating-related activities such as eating (53%) or cooking (49%) as the most frequently cited daily activities impaired by olfactory disorder. Similarly, Santos et al. (2004) reported that cooking-related incidents (45% of 445 clinical patients with olfactory disorder) and ingestion of spoiled food (25%) are the most common hazardous events associated with patients’ olfactory disorders. The incidence of hazardous events increases with the degree of olfactory disorder (Pence et al., 2014). 3.2.2.2 Orthonasal and Retronasal Olfaction People perceive volatile aromatic compounds via two different pathways. First, volatile aromatic compounds can be perceived via the external nares during sniffing or nasal inhalation. This is referred to as “orthonasal olfaction,” providing external information associated with a food source, edibility, toxicity/danger, or social communication (Rozin, 1982; Shepherd, 2006; Seo and Hummel, 2011a). The other pathway is the mouth (oral cavity or nasopharynx) via which volatile aromatic compounds can be perceived during eating, drinking, or exhalation, a condition referred to as “retronasal olfaction.” It provides information about what is consumed in the mouth (Rozin, 1982; Shepherd, 2006; Seo and Hummel, 2011a; Goldberg et al., 2018). Retronasal odors from the food or beverage matrix are typically released from the mouth during consumption (Shepherd, 2006). Since they are localized to the mouth (Lim and Johnson, 2012), people appear to frequently confuse retronasal stimulation with taste (Seo and Hummel, 2011a); that is, they experience “taste–smell confusion” (Murphy et al., 1977; Murphy and Cain, 1980; Rozin, 1982). 3.2.2.3 Anatomy and Physiology Through either orthonasal or retronasal pathways, volatile aromatic compounds of substances reach the olfactory epithelium, a layered structure residing in the upper part of the nasal cavity. More specifically, it resides bilaterally within the olfactory cleft and extends into the superior turbinate and superior part of the middle turbinate (Rawson et al., 1997; Rawon and Yee, 2006; Seo and Hummel, 2011a). The olfactory epithelium is composed of different cell types, including Bowman’s gland cells, horizontal basal cells, globose basal cells, both immature and mature olfactory neurons, and sustentacular cells (Lavoie et al., 2017). Basal cells are capable of proliferating and differentiating into either neural or non-neural cells throughout adulthood. Both mature and immature olfactory

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neurons are positioned in the intermediate layer of the olfactory epithelium (Lavoie et al., 2017). The apical layer also contains sustentacular cells and sensory cilia that can be projected from the dendrites of olfactory neurons (Lavoie et al., 2017). When volatile aromatic compounds bind to the olfactory receptors on the sensory cilia, the membrane-bound protein structure changes, allowing extracellular calcium ions to enter a cell (Hornung, 2006). It then creates a generator potential that produces an electronic signal flowing ipsilaterally from the axons of the olfactory neurons to the olfactory bulb located in the anterior cranial fossa, above the cribriform plate of the ethmoid (Hornung, 2006; Huart et al., 2009). The olfactory bulb is the first relay station in the olfactory system (Freiherr, 2016). It exhibits relatively large individual size variation, ranging from 37 to 98 mm3 with respect to its volume in healthy adults (Buschhüter et al., 2008). The volume of the olfactory bulb, larger in males than in females (Buschhüter et al., 2008), has been found to be correlated with olfactory function both in healthy people (Buschhüter et al., 2008; Hummel et al., 2013) and people with olfactory disorder (Haehner et al., 2008; Rombaux et al., 2010). The olfactory bulb has sphere-shaped glomeruli that contain synapses between olfactory receptor neurons and dendrites of the mitral cells (Huart et al., 2009), relaying olfactory information to second-order neurons, mitral and tufted cells (Imai, 2014). The mitral and tufted cells are modulated by intrabulbar circuits and centrifugal inputs, with the neurons generating a unique odor code (Imai, 2014). From the olfactory bulb, olfactory information is transmitted via the lateral olfactory tract to other cortical olfactory structures. These include the piriform cortex, the entorhinal cortex, the amygdala, and periamygdaloid cortex, the olfactory tubercle, and the anterior olfactory nucleus (Freiherr, 2016). Although the anterior and posterior sub-regions of the piriform cortex appear to be histologically identical, they provide different functions (Gottfried, 2006). The anterior part of the piriform cortex is associated with initial neural representation of an odorant and encoding of its molecular features (Davison and Ehlers, 2011). The posterior or temporal part of the piriform cortex is related to perceptual information related to odor quality and categorization (Gottfried et al., 2006; Howard et al., 2009; Freiherr, 2016). The entorhinal cortex is considered a gateway to the hippocampus responsible for memory processes (Insausti et al., 2002; Freiherr, 2016). The amygdala and periamygdaloid cortex are considered responsible for cognitive evaluation of olfactory information (Freiherr, 2016) and intensity coding of odors with emotional salience (Winston et al., 2005; Grabenhorst et al., 2007; Freiherr, 2016). Olfactory information further projects into brain areas responsible for cognitive odor processing and perception. These include the orbitofrontal cortex, the insular, the hippocampus, the thalamus, the hypothalamus, the cingulate cortex, the ventral striatum, and the cerebellum (Freiherr, 2016). While olfactory bulb volume is related to odor identification performance (Buschhüter et al., 2008), the gray matter volume of the orbitofrontal cortex (OFC) is associated with odor threshold and odor discrimination performances (Seubert et al., 2013; Freiherr, 2016). The insular has been found to be involved in an integration of flavor perception, a perception of unpleasant olfactory or intranasal trigeminal stimuli (Albrecht et al., 2010), and a multisensory integration of negatively valenced stimuli (e.g., unpleasant odor and disgusted face) (Seubert et al., 2010; Freiherr, 2016). The hippocampus is responsible for emotional and memory-related processing of olfactory stimuli and multisensory integration of olfactory inputs (Freiherr, 2016). Olfactory information is transmitted to cortical areas without a thalamic relay, but there is an indirect pathway of olfactory processing through the mediodorsal thalamus to the neocortex (Freiherr, 2016). The mediodorsal thalamic nucleus receives direct inputs from primary

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olfactory areas that include the piriform cortex, the enthorhinal cortex, and the amygdala, and has reciprocal connections with the OFC (Courtiol and Wilson, 2014, 2015). It is considered to be a higher-order olfactory thalamus (Freiherr, 2016). The mediodorsal thalamic nucleus appears to be related to olfactory functions, including odor perception and discrimination, attention to odors, and coding of the hedonic valence of odor stimuli (Courtiol and Wilson, 2015). 3.2.3 Oral Somatosensory System 3.2.3.1 Somesthesis/Chemesthesis During eating or drinking, people often perceive various kinds of physical or chemical sensations such as “softness,” “roughness,” “stickiness,” “irritation,” “burning,” “stinging,” “cooling,” or “tickling,” along with olfactory and gustatory sensations in the mouth. These other sensations, neither olfactory nor gustatory, are characterized as “oral somesthesis.” They provide information about the physicochemical constituents and characteristics of substances taken into the mouth (Lim, 2016). In particular, chemosensory sensations, neither olfactory nor gustatory, were described as “common chemical senses” by George H. Parker in 1912. He reported that animals have three distinct types of chemosensory receptors: olfactory, gustatory, and common chemical (Slack, 2016). However, the term itself and the concept of “common chemical senses” were actively debated (Green, 2016). The term “chemesthesis” was introduced to define the chemical sensibility of the skin and mucous membranes as senses other than chemical (Green et al., 1990; Green and Lawless, 1991; Green, 2016). The term chemesthesis refers to any somatosensory response to an irritant or a noxious chemical (Slack, 2016). Chemesthetic sensations have been found to be mediated via thermoreceptors, nociceptors, and mechanoreceptors of the somatosensory nerves (Green, 1996, 2016; Lim, 2016). Those evoked within the nose and the mouth are primarily mediated via the trigeminal nerve (CN V) that is innervated in the nasal mucosa and the anterior regions of the oral cavity. Thus, chemicals that evoke sensations other than olfactory or gustatory sensations are often described as “trigeminal stimuli” (Green, 2016). The glossopharyngeal and vagal nerves of the somatosensory nerves are involved in chemesthetic sensations in response to irritants present in the posterior regions of the oral cavity (Green, 1996; Rentmeister-Bryant and Green, 1997; Slack, 2016). 3.2.3.2 Anatomy and Physiology Somatosensory nerves contain three types of somesthesis-related receptors: mechanoreceptors, thermoreceptors, and nociceptors (Vallbo et al., 1979). Oral mechanoreceptors responsible for sensory perceptions of pressure, slip, vibration, and movements occurring in the mouth play an important role in the safe manipulation of food bolus (Engelen, 2012). More specifically, orofacial mechanoreceptors are involved in multiple activities that include (1) position and movement of the tongue, (2) manipulation of food bolus suitable for chewing and swallowing, (3) prevention of biting of the tongue and cheek during oral processing, and (4) swallowing without chocking (Engelen, 2012). In orofacial areas, there are three types of receptors: (1) slow-adapting (SA) type I receptors (SA I) that end in Merkel cells, (2) slow-adapting type II receptors (SA II) that end in Ruffling corpuscles, and (3) fast-adapting (FA) type I afferents (FA I) that end in Meissner corpuscles (Engelen, 2012); however, no fast-adapting type II afferents (FA II) that end in the Pacinian corpuscles have been found in orofacial areas (Trulsson and Essick, 2010; Engelen, 2012). In the mouth, SA I receptors with a high degree of spatial resolution are

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associated with texture and shape perceptions of food bolus in the processes of chewing and manipulation (Engelen, 2012). SA II receptors in the mouth are associated with size perception of food bolus as well as shape and position perceptions of the tongue (Engelen, 2012). While FA I afferents seem to be involved in the perception of vibratory sensation in the mouth (Engelen, 2012), further research is needed to clarify their roles in the mouth. Proprioceptors in the face and periodontal mechanoreceptors have also been found to play a crucial role in the perception of mechanical characteristics of food bolus during eating (Engelen, 2012; Higaki et al., 2014). Nociception, the sensation of pain, is mediated by free nerve endings (nociceptors). Myelinated Aδ mechanical and thermal fibers mediate fast and sharp pain stimuli. Unmyelinated C-fibers transmit slow and dull pain (Silberstein, 2003; Engelen, 2012). Because nociceptors are polymodal neurons, they are involved in a variety of mechanical, chemical, and thermal stimuli (Engelen, 2012) as well as auditory and visual stimuli (Mickle et al., 2016). Cutaneous thermal information is processed via multiple classes of afferent nerve fibers (Schepers and Ringkamp, 2010). While warm stimuli are processed by warm fibers (unmyelinated C-fibers) at an average rate of 1.0 m/s (LaMotte and Campbell, 1978; Darian-Smith et al., 1979), cold stimuli are thought to be processed by either thinly-myelinated Aδ fiber conducting at 9 to 15 m/s or unmyelinated C-fibers (Schepers and Ringkamp, 2010; Engelen, 2012). The activity of several transient receptor potential (TRP) ion channels has been found to depend on surrounding temperatures ranging from noxious cold to noxious heat (Voets et al., 2004). In particular, among 28 different TRP channels in humans, three TRP families, that is, TRP vanilloid channels (TRPV), ankyrin transmembrane protein channels (TRPA), and melastatin or long TRP channels (TRPM), seem to be temperature-sensitive (Schepers and Ringkamp, 2010). The TRPV1 channel is activated not only by noxious heat above 43°C, but also by multiple chemical stimuli such as capsaicin (hot peppers), eugenol (cloves), gingerol (ginger), allicin (garlic), and acids, evoking perceptions of tingling, sharpness, and burning (Caterina et al., 1997; Frasnelli and Manescu, 2016). The TRPV3 channel is activated by noxious warm stimuli above 39°C and chemical stimuli such as monoterpenoid phenol present in high concentrations in the essential oils of oregano, thymol (thyme), eugenol (cloves), and carveol (Viana, 2011; Frasnelli and Manescu, 2016). In contrast, the TRPA1 and TRPM8 channels are activated by cold stimuli. The TRPA1 channels are activated by noxious cold below 17°C, as well as allyl thioisocyanate (mustard), cinnamaldehyde, allicin, and gingerol (Viana, 2011; Frasnelli and Manescu, 2016). Volatile organic compounds present in cigarette smoke and smog, triggering eye irritation, coughing, and mucous secretion, are also TRPA1 agonists (Viana, 2011). The TRPM8 channels are activated by cold stimuli between 8 and 25°C, as well as natural and synthetic cooling stimuli such as menthol and eucalyptol (Viana, 2011; Engelen, 2012). The trigeminal nerve (CN V), the largest cranial nerve, has three major branches: the ophthalmic nerve (CN V1), the maxillary nerve (CN V2), and the mandibular nerve (CN V3) (Doty and Cometto-Muñiz, 2003). The ophthalmic nerve branch is involved in sensory inputs from the upper part of the head, the forehead, the upper eyelid, the nasal mucosa, and the tip of the nose (Huart et al., 2009; Bathla and Hegde, 2013). The maxillary nerve branch is associated with sensory inputs originating from the middle third of the head, the lower eyelid, the cheek, the nares, the upper lip, and the upper teeth (Huart et al., 2009; Bathla and Hegde, 2013). Finally, the mandibular nerve carries both sensory and motor information coming from the lower third of the head, the chin, the lower lip, the lower teeth, and the jaw (Huart et al., 2009; Seo and Hummel, 2011a; Bathla and

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Hegde, 2013). These three branches converge on the trigeminal ganglion (also referred to as Gasserian Ganglion) located within the Meckel’s cave (Huart et al., 2009; Bathla and Hegde, 2013). The axons of the first-order neurons from the trigeminal ganglion enter the brainstem at the level of the pons. Within the brainstem, fibers segregate into three different sensory nuclei: the principal sensory nucleus, the mesencephalic nucleus, and the spinal trigeminal nucleus (Walker, 1990; Bathla and Hegde, 2013). The principal sensory nucleus, positioned in the pontine tegmentum, mediates tactile sensations, in particular pressure and light touch from V1–V3, while the mesencephalic nucleus, located at the junction of pons and mid-brain, mediates proprioceptive sensations from V3 (e.g., masticatory muscles, teeth, hard palate, periodontium, and the temporomandibular joint) (Walker, 1990; Bathla and Hegde, 2013). The spinal trigeminal nucleus, extended from the pontomedullary junction to the upper cervical cord, mediates pain and temperature sensations from V1–V3 (Walker, 1990; Doty and Cometto-Muñiz, 2003; Huart et al., 2009; Bathla and Hegde, 2013). The second-order neurons transmit to the contralateral side and ascend through the trigemino-thalamic tract toward the ventro-postero-medial nucleus of the thalamus (Huart et al., 2009). Via the posterior arm of the internal capsule, the third-order neurons project from the thalamus to the primary somatosensory cortex, the brain area responsible for somatosensation (Huart et al., 2009; Frasnelli and Manescu, 2016). Other important processing areas include the secondary somatosensory cortex, the amygdala, and the hippocampus (Huart et al., 2009; Seo and Hummel, 2011a; Han et al., 2018).

3.3  MULTISENSORY FLAVOR PERCEPTION 3.3.1 Interactions between Chemosensory Cues in Flavor Perception 3.3.1.1 Crossmodal Correspondence Crossmodal correspondence, characterized as “a compatibility effect between attributes or dimensions of a stimulus (i.e., an object or event) in different sensory modalities (be they redundant or not)” (Spence, 2011), is innate and developed by perceptual learning (Gilbert et al., 1996; Schifferstein and Tanudjaja, 2004; Spence, 2011). Crossmodal correspondence has been found to exist between chemosensory cues. People often confuse retronasal stimulation with taste (Murphy et al., 1977; Murphy and Cain, 1980; Rozin, 1982) because taste cues are often perceived with retronasal odors during eating and drinking. Based on frequent experiences of smell and taste co-occurrence in the mouth, people are accustomed to matching certain orthonasal or retronasal odors with specific taste qualities (Stevenson and Boakes, 2004). For example, while aromas/flavors of strawberry, vanilla, or caramel are likely to be matched with a sweet taste, aromas/ flavors of lemon or citrus fruits are often paired with a sour taste. Crossmodal correspondences between olfactory and trigeminal stimuli have also been reported. Bensafi et al. (2013), for example, showed that participants matched intranasal carbon dioxide (CO2) with orange odor, but not with rose odor, probably due to their previous experience with a mixture of orange odor and carbon dioxide in soft drinks. 3.3.1.2 Lateralization/Localization Humans can lateralize trigeminal stimuli, such as carbon dioxide with a high degree of accuracy (Kobal et al., 1989; Kleemann et al., 2009). However, it seems to be difficult for humans to lateralize pure odor stimuli without accompanying trigeminal stimulation

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when they are presented only to the left or the right nostril (Schneider and Schmidt, 1967; Kobal et al., 1989; Radil and Wysocki, 1998; Frasnelli et al., 2009; Kleemann et al., 2009; Tremblay and Frasnelli, 2018). Although contradictory findings have also been reported (Negoias et al., 2013), olfactory lateralization seems to occur only when an odor stimulus triggers the trigeminal somatosensory system. In a study conducted by Kleemann et al. (2009), participants were unable to localize a pure odor stimulus (hydrogen sulfide, H 2S), stimulating the olfactory system only at low concentrations. They were able, however, to localize the odor stimulus accompanying trigeminal stimulation (isoamyl acetate) or the trigeminal stimulus (carbon dioxide, CO2). It should also be noted that a majority of odorants (also tasting substances) typically produce trigeminal stimulation at a high concentration level (Doty et al., 1978; Hummel and Livermore, 2002). Von Békésy (1964) suggested that olfactory stimuli could be localized by their differences with respect to concentration and time to reach the nostril. He showed that the perceived location of orthonasal odors, depending on the time interval between the odor and taste stimulation, changes from the nose to the mouth. However, it should be noted that, in his study, only odorants capable of evoking a trigeminal sensation (e.g., benzol, cloves, lavender, and eucalyptus) were used and participants (N = 3) had some previous experiences with localization tasks (Kleemann et al., 2009). In addition, in a study by Negoias et al. (2013), olfactory stimuli presented to only one nostril could not be correctly lateralized by untrained participants, while their ability to lateralize olfactory stimuli was improved after completing four training sessions in olfactory lateralization. Several studies have demonstrated that humans can determine whether an olfactory stimulus comes either from the tip of the nose or from the back of the mouth (Heilmann and Hummel, 2004; Small et al., 2005). In other words, nasal airflow direction to the olfactory epithelium (i.e., anterior delivery of orthonasal stimulation versus posterior delivery of retronasal stimulation) is likely to help humans in localizing odor stimulation (Mozell, 1970; Seo and Hummel, 2016). It is unlikely, however, that determining nasal airflow direction is necessary for localizing the olfactory stimulation when a gustatory stimulus is also present in the mouth (Seo and Hummel, 2016). For example, combined olfactory and gustatory stimuli (e.g., clove odor and acid solution) were perceived as a single sensation occurring in the mouth when they were presented “simultaneously” (Von Békésy, 1964). Such a single perception was also found even when the odor stimulus was presented via an orthonasal route (Stevenson et al., 2011). Since a gustatory stimulation may induce participants’ selective attention toward tasting substances, participants are likely to be less attentive to placing an odor stimulus in an orthonasal odor referral to the nose (Stevenson et al., 2011; Seo and Hummel, 2016). While oral somatosensory stimulation of taste stimuli may also be involved in taste-smell confusion (Murphy and Cain, 1980), such stimulation alone seems to be relatively ineffective in eliciting retronasal odor localization (referral) to the mouth because it constantly presents in the mouth during involuntary swallowing, breathing, and talking (Lim and Johnson, 2011; Stevenson et al., 2011; Seo and Hummel, 2016). For retronasal odor localization to the mouth, the presence of congruent taste in the mouth has been found to be crucial (Lim and Johnson, 2011, 2012; Lim et al., 2014; Fondberg et al., 2018), while oral tactile stimulation alone contributed no retronasal odor referral to the mouth (Lim and Johnson, 2011). 3.3.1.3 Intensity and Pleasantness Bimodal congruency between chemosensory stimuli plays a vital role in modulating perceived intensity and pleasantness of either single or mixed stimuli (Small and Prescott, 2005; Seo and Hummel, 2011a). More specifically, congruent odor stimuli are likely

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to enhance the perceived intensity and/or pleasantness of corresponding taste stimuli (Frank and Byram, 1988; Bingham et al., 1990; Small and Prescott, 2005). However, the enhancement of perceived taste intensity was not observed in some earlier studies (Bingham et al., 1990; Frank and Van der Klaauw, 1992; Frank et al., 1993). For example, Frank and Byram (1988) showed that strawberry retronasal odor but not peanut butter retronasal odor was found to increase the perceived sweetness of whipped cream, while the strawberry odor did not enhance the saltiness of sodium chloride solution. Congruent gustatory cues can also modulate olfactory perception (Dalton et al., 2000; Green et al., 2012; Fujimaru and Lim, 2013; Lim et al., 2014). As shown in Figure 3.2, Dalton et al. (2000) showed that participants were more sensitive to cherry/almondlike odor (benzaldehyde) in the presence of a subthreshold concentration of congruent taste (saccharin) in the mouth, but not in the presence of an incongruent taste (monosodium glutamate) or of deionized water. Using olfactory event-related potentials (ERPs), Welge-Lüssen et al. (2005) also showed that a congruent combination (e.g., vanillin odor and sweet taste) could produce higher amplitudes and shorter latencies of N1 and P2 peaks than an incongruent combination (e.g., vanillin odor and sour taste). This suggests that gustatory–olfactory interaction occurs at relatively early levels of neural processing. Neuroimaging studies have also found that the insula/operculum, the caudal orbital frontal cortex, and the anterior cingulate cortex are involved in interactions between gustatory and olfactory cues for flavor perception (Small et al., 1997; CerfDucastel and Murphy, 2001; Small et al., 2004, 2005; Small, 2006; Seo and Hummel, 2011a; Seo et al., 2013).

FIGURE 3.2  Congruent taste-enhanced odor sensitivity. Participants were more sensitive

(positive numbers represent increases in sensitivity) to cherry/almond-like odor (benzaldehyde) in the presence of a subthreshold concentration of congruent taste (saccharin) in the mouth. This was not observed in the presence of an incongruent taste (monosodium glutamate, MSG) or of deionized water. (Reprinted from Nature Neuroscience, 3(5), Dalton, Doolittle, Nagata, and Breslin, “The merging of the senses: integration of subthreshold taste and smell,” pp. 431–432, 2000, with permission from Springer Nature.)

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Congruency-enhanced pleasantness of chemosensory stimuli between olfactory and trigeminal stimuli has also been observed. Specifically, a congruent mixture of olfactory and trigeminal stimuli (e.g., orange odor and carbon dioxide) was rated as more pleasant than an incongruent mixture (e.g., rose odor and carbon dioxide) (Bensafi et al., 2013). The congruency-enhanced pleasantness in the mixture of olfactory and trigeminal stimuli was found to be related to increased neural activities in the hippocampus and anterior cingulate gyrus (Bensafi et al., 2013). Apart from a congruency between olfactory and trigeminal stimuli, it has been found that olfactory and trigeminal stimuli interact by mutually enhancing or suppressing one another at peripheral, central, or perceptual levels (Cain and Murphy, 1980; Hummel and Livermore, 2002; Frasnelli et al., 2004; Brand, 2006; Pellegrino et al., 2017; Tremblay and Frasnelli, 2018). 3.3.2 Effects of Tactile or Temperature Cues of a Stimulus Medium on Flavor Perception Tactile cues of a stimulus medium have been found to influence chemosensory or flavor perception (Delwiche, 2004; Verhagen and Engelen, 2006). Typically, as the viscosity of a flavor medium increases, both sensitivity to (Mackey and Valassi, 1956; Stone and Oliver, 1966) and perceived intensity of taste, odor, or flavor stimulus (Moskowitz and Arabie, 1970; Pangborn et al., 1973, 1978; Pangborn and Szczesniak, 1974; Christensen, 1980; Bult et al., 2007) are likely to diminish. The mechanism for suppression of flavor perception by enhanced viscosity remains unclear. It could be that the viscous characteristic of a flavor medium evokes trigeminal stimulation, reducing flavor perception (Bayarri et al., 2006) or inducing selective attention to the medium (Seo and Hummel, 2016). The viscous characteristics of the medium may also modulate the pathways of releasing and/ or transporting flavor components from the medium matrix to receptors (Bayarri et al., 2006; Mao et al., 2017). Hardness (or firmness) of a flavor medium has been found to affect perceived flavor intensity in semi-solid and solid matrices (Seo and Hummel, 2016). In general, perceived flavor intensity decreases with increasing hardness (or firmness) of semi-solid and solid matrices (Weel et al., 2002; Visschers et al., 2006). Weel et al. (2002) showed that the hardness of a whey protein gel system could change the perceived flavor intensity, probably because of a change in mouthfeel, without affecting the in-nose flavor concentration. The crispness level of a food matrix has also been found to modulate flavor perception. For example, as shown in Figure 3.3, Luckett et al. (2016) showed that flavors were rated as more intense and maximum flavor perception occurred sooner with an increase in crispness level of potato chips. Interestingly, the impact of crispness level on flavor perception was more pronounced for older participants than for younger or middle-aged groups, probably due to an association with mastication patterns, such as the number of chews. In fact, the textural properties of semi-solid or solid foods can change consumers’ mastication patterns. These include chewing rate, chewing duration, and the number of chews, all of which can affect temporal flavor dynamics as well as general flavor perception (Blissett et al., 2006; Tarrega et al., 2008; Luckett et al., 2016; Luckett and Seo, 2017). The temperature of a stimulus medium is another important factor influencing taste, odor, or flavor perception (Olson et al., 1980; Kähkönen et al., 1995; Cruz and Green, 2000; Engelen et al., 2003; Ventanas et al., 2010). Although there were conflicting results among them, previous psychophysical studies using basic taste solutions have shown effects of solution temperature on taste intensity (Hahn and Günther, 1932; McBurney

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FIGURE 3.3  Mean time-intensity curves of potato chips as a function of crispness level (low, medium, and high), flavor type (plain, cheese, and spicy), and age group [younger (20–25 years old), middle-aged (40–45 years old), and older (65+ years old) groups]. Flavors were rated as more intense and maximum flavor perception occurred quicker with an increase in crispness level of potato chips. The effect of crispness level on flavor perception was more clearly observed in the older participants than in the younger or middle-aged groups. (Reprinted from Food Quality and Preference, 51, Luckett, Meullenet, and Seo, “Crispness level of potato chips affects temporal dynamics of flavor perception and mastication patterns in adults of different age groups,” pp. 8–19, 2016, with permission from Elsevier.)

et  al., 1973; Moskowitz, 1973; Bartoshuk et al., 1982; Prescott et al., 1984; Calviño, 1986; Green and Frankmann, 1987, 1988; Schiffman et al., 2000; Green and Andrew, 2017; for a review, Lemon, 2017). One plausible explanation for such inconsistency is that tongue temperature had not been carefully controlled over the testing interval (Green and Frankmann, 1987; Delwiche, 2004). In other words, variations in oral temperature could have possibly altered the perceived intensity of basic taste solutions (Cruz and Green, 2000; Green, 2002). The effects of oral temperature on taste, odor, or flavor perception have also been studied using more complex matrices such as food and beverage models (Drake et al., 2005; Ross and Weller, 2008; Mony et al., 2013; Kim et al., 2015; Stokes et al., 2016; Steen et al., 2017; Adhikari et al., 2018; Pramudya and Seo, 2018a,b; Chapko and Seo, 2019), in which the effects of oral or product temperature were dependent on the type of food (Mela et al., 1994; Engelen et al., 2003; Mony et al., 2013; Kim et al., 2015) and dietary behaviors (Kim et al., 2015). For example, Kim et al. (2015) asked both trained panelists and untrained consumers to rate saltiness intensities in sodium chloride (NaCl) solution, chicken broth, and miso soup at five different temperatures: 80, 70, 60, 50, and

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40°C. Neither trained nor untrained consumer panelists were able to find a significant difference in saltiness intensity among the NaCl solutions. However, untrained consumers, not trained panelists, rated chicken broth and miso soup as less salty when served at 70 and/or 80°C compared to when there were served at 40 to 60°C. Consumers accustomed to frequently consuming hot dishes also rated the soup samples served at 60°C significantly saltier than those less accustomed to consuming hot dishes. Recent studies have also demonstrated the effect of serving/consumption temperature on flavor perception of brewed coffee (Stokes et al., 2016; Steen et al., 2017; Adhikari et al., 2018; Chapko and Seo, 2019). Chapko and Seo (2019) asked six trained panelists to rate intensities of 32 sensory attributes (3 appearance, 12 aromas, 13 flavors, 2 tastes, and 2 mouthfeels) with respect to three varieties (Colombian, Ethiopian, and Kenyan) of brewed coffee presented at four different serving temperatures: 75, 55, 40, and 25°C, respectively. Interestingly, as shown in Figure 3.4, a principal component analysis (PCA) showed that serving temperature could account for the greater amount of data variation (63.28% of total variation) than coffee variety (21.24%). For example, aromas and flavors of “roasted” and “coffee impression” were more associated with brewed coffee served at higher temperatures (70 and 55°C), while “stale” flavor was more likely to be associated with brewed coffee served at lower temperatures (40 and 25°C). These results emphasize that sensory attributes of brewed coffee should be evaluated at multiple s­ erving temperatures, for example, both higher (70 to 55°C) and lower (40 to 25°C) ones (Chapko and Seo, 2019). Taken together, the effects of oral or product temperature on taste, odor, or flavor perception must be interpreted from multiple perspectives.

FIGURE 3.4  Two plots of principal component analysis with respect to three coffee varieties (Colombian, Ethiopian, and Kenyan) of brewed coffee samples evaluated at four serving temperatures: 75, 55, 40, and 25°C. (A) Represents 24 sensory attributes used for data analysis and (B) represents 12 brewed coffee samples (three varieties × four serving temperatures). “A,” “T,” and “F” represent aroma, taste, and flavor, respectively. (Reprinted from Food Research International, In Press, Chapko and Seo, “Characterizing product temperature-dependent sensory perception of brewed coffee beverages: Descriptive sensory analysis.” https​://do​i.org ​/10.1​016/j​.food​res.2​018.1​2 .026, 2019, with permission from Elsevier.)

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3.3.3 Effects of Visual or Auditory Cues on Flavor Perception Some researchers have proposed that all sensory inputs related to substances consumed during eating or drinking could be interactive. This will affect flavor perception (Verhagen and Engelen, 2006; Spence, 2015a), although chemosensory cues, that is, gustatory, olfactory, and trigeminal sensations, are considered to be major components of flavor. Earlier studies provided empirical evidence that certain gustatory or olfactory cues could be matched with specific attributes of non-chemosensory cues. These would include color hue (Gilbert et al., 1996; Zellner et al., 2008; for a review, Spence et al., 2015), color brightness (Von Hornbostel, 1931; Fiore, 1993), shapes (Hanson-Vaux et al., 2013), symbols (Seo et al., 2010), pitch (Crisinel and Spence, 2010; Wang et al., 2016), and timbre (Crisinel and Spence, 2011, 2012). Bimodal congruency between chemosensory and non-chemosensory cues (e.g., visual or auditory) has been found to play a crucial role in modulating chemosensory or flavor perception (Delwiche, 2004; Zampini et al., 2007, 2008; Seo et al., 2010; Shankar et al., 2010; Seo and Hummel, 2014). More specifically, congruent colors are more likely than incongruent colors to increase flavor discrimination (Zampini et al., 2007) or identification (DuBose et al., 1980; Stillman, 1993; Philipsen et al., 1995). Interestingly, when white wines are colored red using an odorless dye, individuals describe them using odor-related terms more associated with red wines than with white wines (Morrot et al., 2001). Also, when the color of a tasting substance is more intense, individuals tend to rate taste, odor, or flavor cues as more intense (DuBose et al., 1980; Johnson et al., 1982; Johnson and Clydesdale, 1982; also see Frank et al., 1989; Lavin and Lawless, 1998; Delwiche, 2004). Such effects of color cues on taste/odor/flavor perceptions are influenced by many other factors, including participant age (Philipsen et al., 1995; Lavin and Lawless, 1998), gender, cultural background (Shankar et al., 2010), odor stimulus delivery route (Koza et al., 2005), and experimental contexts (Seo and Hummel, 2011a). For example, the effect of darker red colors on sweetness intensity was found to be present in adults, but not in children aged between 5 and 10 (Lavin and Lawless, 1998). Koza et al. (2005) also demonstrated that when colored odorous solutions were sniffed, the colors increased the perceived intensities of the odor stimuli, but an opposite effect of color on odor intensity was found when solutions were smelled retronasally (from the mouth). Moreover, in a study conducted by Shankar et al. (2010), 70% of British participants, when presented with a brown colored drink, matched the brown color with a cola flavor, while 49% of Taiwanese participants associated the same color with a grape flavor. Such examples illustrate that the effect of a visual cue such as color on chemosensory or flavor perception should be carefully approached because there may be many influential factors (Spence et al., 2010). Although auditory cues have been considered to represent a “forgotten” flavor sense (Delwiche, 2004; Spence, 2012), there is a rapidly growing body of empirical evidence that auditory cues can be matched with gustatory, olfactory, or flavor stimuli (for a review, Spence, 2012). For example, Crisinel and Spence (2010) showed that 12 different taste and flavor stimuli were matched with different levels of pitch. Low-pitched sounds generated by brass instruments were frequently matched with the bitter taste of a caffeine solution, while high-pitched sounds produced by a piano were often matched with the sweet taste of a sucrose solution. Auditory cues can also affect perceived intensities and likings (Ferber and Cabanac, 1987; Woods et al., 2011; Stafford et al., 2012; Fiegel et al., 2014; Yan and Dando, 2015; Kantono et al., 2016, 2018; Reinoso Carvalho et al., 2016, 2017) as well as discrimination

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(Pellegrino et al., 2015) of chemosensory or flavor stimuli, although these effects were not consistently reported in earlier studies. Woods et al. (2011) showed that loud background noise (75–85 dB) more than quiet background noise (45–55 dB) could decrease perceived intensities of sweetness and saltiness (see also Stafford et al., 2012). Similarly, cabin noise presented at a level of 80 to 85 dB was found to decrease sweetness intensity of a sweet sucrose solution, while it increased the umami intensity of an umami monosodium glutamate (MSG) solution (Yan and Dando, 2015). A hedonic tone of auditory stimuli is also likely to be transformed into hedonic ratings of odor, flavor, and food/drink stimuli (Seo and Hummel, 2011b; Fiegel et al., 2014, 2019; Kantono et al., 2016, 2018; Reinoso Carvalho et al., 2016).

3.4 CONCLUSION The flavor is a complex and dynamic combination of gustatory, olfactory, and oral somatosensory (in particular trigeminal) sensations evoked by ingested substances during eating and drinking. Although gustatory, olfactory, and oral somatosensory (trigeminal) systems are different at the peripheral level, each system has its own role in daily life. They often interact at the peripheral and central nervous levels. In addition, flavor perception is influenced by many other factors that include non-chemosensory cues, demographic profiles, and environmental eating contexts. Flavor perception should therefore be considered to be a Gestalt concept.

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Aroma Compounds (Description, Biosynthesis, and Regulation) Mónika Valdenegro Espinoza, María Fernanda Flores Echeverría, and Lida Fuentes Viveros CONTENTS 4.1 Introduction 58 4.1.1 Concept of Flavor: Taste and Aroma 58 4.1.2 Impact Compounds of Natural Aromas: Odor Threshold 59 4.2 Chemistry and Organoleptic Properties of Volatile Aroma Compounds 61 4.2.1 Volatile Compounds 61 4.2.2 Esters 62 4.2.3 Lactones 63 4.2.4 Terpenoids 65 4.2.5 Aldehydes and Ketones 66 4.2.5.1 Aldehydes 66 4.2.5.2 Ketones 67 4.2.6 Phenylpropenes and Other Aromatic Derivatives 67 4.2.7 Alcohols 68 4.2.8 Furans and Its Derivatives 68 4.2.9 Pyrazines 69 4.2.10 Sulfur Compounds 69 4.3 Biosynthesis 70 4.3.1 Fatty Acid-Derived and Lipophilic Compounds 70 4.3.1.1 Lipoxygenase Pathway (in Chain Oxidation) 71 4.3.1.2 α and β-Oxidation 73 4.3.2 Amino Acid Derivates 75 4.3.2.1 Acids, Alcohols, Aldehydes, Esters, Lactones, and N- and S-Containing Flavor Molecules 75 4.3.2.2 Phenylpropenes and Other Aromatic Derivatives 76 4.3.3 Carbohydrate-Derived Flavor Compounds 78 4.3.3.1 Furanones and Pyrones 78 4.3.3.2 Terpenoids 79 4.3.3.3 Apocarotenoids 80 References 81

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4.1 INTRODUCTION 4.1.1 Concept of Flavor: Taste and Aroma The acceptance of a food depends on many factors, which highlight their sensory properties such as color, appearance, taste, aroma, texture, and even the sound that is generated during chewing (Ponce, 2006; Singh et al., 2013; Parker, 2015; Forney and Song, 2018). When food is consumed, the interaction of taste, odor, and textural feeling provide an overall sensation which is best defined by the word “flavor.” The taste is the result of two classes of compounds: some of them are nonvolatile compounds associated and responsible for the taste (El Hadi et al., 2013) and others are aroma substances responsible for odors. However, there are compounds which provide both sensations. When chemical compounds interact with taste receptors, located in the taste buds of the tongue, four important basic taste perceptions are provided: sour, sweet, bitter, and salty; the fifth basic taste is stimulated by glutamate substances. Aroma substances are volatile compounds, which are perceived by the odor receptor sites of the smell organ, that is, the olfactory tissue of the nasal cavity. They reach the receptors when drawn in through the nose (orthonasal detection) and via the throat after being released by chewing (retronasal detection) (Taylor et al., 2000). The concept of aroma substances, like the concept of taste substances, should be used loosely, since a compound might contribute to the typical odor or taste of one food, while in another food it might cause a faulty odor or taste, or both, resulting in an off-flavor. The aroma of a food can provide various functions, among them can be linked emotionally with past experiences, stimulate our appetite, or can alert us about the safety of such foods, in the presence of a rancid product for example (Parker, 2015). The aroma of food has several functions, not only conveying the essential character of the food and providing variety and interest to what we consume but also alerting us to rancid and unsafe food, stimulating the appetite as well as providing an emotional link to past experiences. The compounds that are responsible for aroma are highly volatile, low-molecular weight compounds that are present in foods at low levels (Parker, 2015). The food habits of people are determined to a large extent by the aroma and flavor of the products that they consume and that allow their development and survival (Ponce, 2006). Innovations in the knowledge of the generation of aromas and flavors have made the development of new foods possible. These new food products will be accepted or rejected by the consumers primarily based on their characteristics, aroma, and flavor, regardless of nutritional quality, toxicology, or their advantages. Fruit quality includes both its preharvest development, such as changes in color, flavor, and texture as fruits develop, grow, and ripen, as well as its maintenance following harvest as the perishable tissues senesce (Ogundiwin et al., 2009). Flavor consists both of the perception in the mouth (sweetness, acidity or bitterness) and of the odor, produced by several volatile compounds. All plants are able to emit volatile organic compounds (VOCs), and the content and composition of these molecules show both genotypic variation and phenotypic plasticity (Maffei, 2010, Vrhovsek et al., 2014). As aroma is one of the most appreciated fruit characteristics, volatile flavor compounds are likely to play a key role in determining the perception and acceptability of products by consumers. Identification of key volatile flavor metabolites that carry the unique character of the natural fruit is essential, as it provides the principal sensory identity and characteristic flavor of the fruit (Cheong et al., 2010; El Hadi et al., 2013).

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4.1.2 Impact Compounds of Natural Aromas: Odor Threshold Compounds defined as aromas are highly volatile substances, with low molecular weight and present in low concentrations in food (ca. 10–15 mg/kg), and they have a fundamental effect on their quality and acceptance (Belitz et al., 2008; Parker, 2015; Forney and Song, 2018, Pernolle and Briand, 2004). In general, however, a great variety of compounds is often present in fruits and vegetables as well (Belitz et al., 2008, Rodríguez et al., 2013). In processed foods this variety can be high, comprising a large number of compounds, especially when foods are made by thermal processes alone (e.g., coffee) or in combination with fermentation (e.g., bread, beer, cocoa, or tea) (Gasser and Grosch, 1988). Only a limited number are important for aroma (Belitz et al., 2008). A threshold concentration is defined as the concentration at which an individual first perceives the stimulus. For aroma, this can be either a detection threshold—the point at which the individual can sense an aroma, or a recognition threshold—the point at which an individual can recognize the aroma (Parker, 2015). The threshold value corresponds to the lowest concentration of a compound that is just enough for the recognition of its odor, and is called the odor threshold (recognition threshold) (Table 4.1). The volatile profile of most foods contains many odor-active compounds, but very few of these actually give character to the food. For example, cooked meat contains hundreds of odor-active compounds (Cerny, 2012); many of which impart generic savory notes when roasted, toasted, or TABLE 4.1  Odor Threshold Values in Water of Some Aroma Compounds (20°C) Compound Ethanol Maltol Furfural Hexanol Benzaldehyde Vanillin Raspberry ketone Limonene Linalool Hexanal 2-Phenylethanal Methylpropanal Ethyl Butyrate (+)-Nootkatone (−)-Nootkatone Filbertone Methylthiol 2-Isobutyl-3-methoxypyrazine 1-p-Menthene-8-thiol

Threshold Value (mL−1) 100 9 3.0 2.5 0.35 0.02 0.01 0.01 0.006 0.0045 0.004 0.001 0.001 0.001 1.0 0.00005 0.00002 0.000002 0.0000002

Source: Data from Belitz et al. (2008). Aroma compounds. In: Food Chemistry. Springer. Belitz H., Grosch W., Schieberle, P. (Eds.) 339–402, doi: 10.1007/978-3-540-69934-7_6.

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fried, but they are also present in snacks, fries, nuts, and so on. Others impart seemingly unrelated aromas such as herbaceous or vegetal, rose, mushroom, and candy floss (cotton candy). However, there are only a few compounds that impart a characteristic meaty aroma, and the most common examples are 2-methyl-3-furanthiol and bis-(2-methyl3-furan) disulfide, called the “character impact compounds” of meat because, without these, the food would be unrecognizable (Parker, 2015). The threshold values are frequently determined by smelling (orthonasal value) and tasting the sample (retronasal value). The threshold concentrations (values) for aroma compounds are dependent on their vapor pressure, which is affected by both temperature and medium. The “aroma value” of a compound is calculated considering the ratio between the concentration of the compound in the food and its odor threshold. The evaluation of volatile compounds on the basis of the aroma value provides only a rough pattern at first, and once the odor threshold of a compound and its concentration in the extract have been determined, the odor-activity value (OAV) can be calculated. The OAV is defined as the concentration of the aroma compound divided by its odor threshold (Doty, 1991). Flavor and aroma are important characteristics that contribute to the quality of different foods, especially fresh fruits and vegetables (Tomiyama et al., 2012; Pérez et al., 1992; 1996a; 1996b). Forney and Song (2018) point out that “It is estimated that more than 480 species of fruits and 600 species of vegetables are consumed worldwide.” However, in Western markets, about 25 fruit species and 40 vegetable species dominate, with an additional 80 or so “specialty” fruits and vegetables available seasonally (CPMA, 2012). Each of these fruits and vegetables has a unique flavor and aroma profile, with a physical and metabolic process that occurs as fruits and vegetables mature both prior to and following harvest, as constituents responsible for flavor and aroma continually alter. The flavor and aroma of foods are physiological phenomena that are closely related; however, the compounds responsible in each case have different physical and chemical properties. In the first case, they are substances of higher molecular weight, nonvolatile, soluble in water and in smaller numbers than those related to the aroma, which must necessarily be volatile for reaching the olfactory centers. Both flavor and aroma are a human perception of a complex combination of volatile, nonvolatile, and structural components contributing to aroma and taste, as well as appearance and texture (Drewnowski, 1997). Volatile compounds are responsible for the aroma of fruits and vegetables and provide the unique flavor characteristics that distinguish different commodities. A desirable complement of volatile compounds is required to ensure consumer acceptance. Volatile aroma compounds are perceived through the human sense of smell when these compounds are detected by receptors on the olfactory epithelium, located in the nasal cavity. These nerve receptors have a wide range of sensitivity depending on the compound and it is estimated that there are about 1000 different olfactory receptors (Pernollet and Briand, 2004). Signals from these receptors are interpreted by the brain, resulting in a perception of aroma and flavor. Smelling volatile compounds through the nose (orthonasal) provides a different perception of the aroma than when volatiles are perceived through the back of the throat (retronasal), which occurs during chewing and swallowing (Voirol and Daget, 1987). Another important characteristic is the chiral nature of these compounds because the aroma and taste chemoreceptors have the ability to distinguish between the various enantiomeric forms (Ponce, 2006). The chemical constituents of fruits and vegetables that provide the stimulus that results in the perception of flavor and aroma are diverse. Volatile compounds are typically

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analyzed by gas chromatography, which has identified over 8000 different volatile compounds in foods (VCF Online, 2016; Song and Forney, 2008) and each commodity has a unique volatile profile, which varies within a species depending on cultivar, maturity, and other factors (Forney, 2001; Baldwin et al., 2007). In addition to primary volatile compounds that are present in fruits and vegetables, secondary volatile compounds are formed when plant tissues are disrupted by processes such as cutting or cooking (Kays and Wang, 2000). Physical actions including bruising, cutting, chewing, freezing, and heating result in cell rupture, the mixing of enzymes and substrates, and chemical and physiological responses that initiate the production of a variety of compounds, many of which contribute to flavor (Beaulieu and Baldwin, 2002).

4.2  CHEMISTRY AND ORGANOLEPTIC PROPERTIES OF VOLATILE AROMA COMPOUNDS Volatile aroma compounds are responsible for odor and contribute to the overall flavor of the fresh and processed fruit (Brackmann et al., 1993). In the case of fruits and vegetables, the volatile compounds that determine the aroma and contribute to the flavor are found in low concentrations (rarely exceeding 30 ppm), and the concentration of individual volatile compounds can vary widely (Forney and Song, 2018). However, the most abundant volatile compounds present in a product may not be the most important contributors to the flavor. The odor threshold properties of individual compounds are highly variable and this aspect is the reason that the most abundant volatile compounds present in a product may not be the most important contributors to the flavor. Among volatile compounds typically found in fresh fruits and vegetables, the odor threshold may differ by 108-fold or more (Forney, 2001; Forney and Song, 2018). Although different fruits often share many aromatic characteristics, each fruit has a distinctive aroma that depends upon the combination of volatiles, the concentration and the perception threshold of individual volatile compounds (Tucker, 1993; Misry et al., 1997). 4.2.1 Volatile Compounds Volatile compounds present in fruit and vegetables are mainly comprised of diverse classes of chemicals, including esters, alcohols, aldehydes, ketones, lactones, and terpenoids (Forney and Song, 2018). However, some sulfur compounds, such as S-methyl thiobutanoate, 3-(methylthio) propanal, 2-(methylthio) ethyl acetate, 3-(methylthio) ethyl propanoate, and 3-(methylthio) propyl acetate, also contribute to the flavor of fruit such as melons (Cucumis melo L.) (Song and Forney, 2008). The contribution of each compound to the specific aroma profile of each cultivar depends on the activity and substrate specificity of the relevant enzymes in the biosynthetic pathway, the substrate availability, the odor threshold above which the compound can be detected by smell, and the presence of other compounds (Rizzolo et al., 2006). For example, more than 285 volatile aroma compounds have been reported from various mango cultivars, including monoterpenes, sesquiterpenes, esters, aldehydes, ketones, alcohols, carboxylic acids, aliphatic hydrocarbons, and aromatics (Preethi et al., 2014, Uji et al., 2015). Hydrocarbon monoterpenes and sesquiterpenes contribute 70–90% of the total volatile aroma compounds (Winterhalter, 1991). Examples of different groups of volatile compounds can be observed in Figures 4.1 through 4.4.

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FIGURE 4.1  Examples of odor-active compounds containing carbon, hydrogen, and

oxygen. (Reprinted and modified with permission from Parker J.K. (2015). Introduction to aroma compounds in foods. In: J.K. Parker, S. Elmore, and L. Methven (Eds.), Flavour Development, Analysis and Perception in Food and Beverages. Woodhead Publishing: Cambridge.) 4.2.2 Esters Esters are formed by the esterification of carboxylic acid derivatives and alcohols. Esters are the predominant volatile compound in many fruits (for example melons, apples, pineapples, and strawberries) and typically contribute fruity aromas and flavors. Esters also contribute to the more delicate aromas found in cured ham (Theron et al., 2010) and some cheeses. Ethyl butanoate and ethyl hexanoate are key odorants in Parmigiano Reggiano (Qian and Reineccius, 2003) and blue cheese (Qian et al., 2002). Fruits and vegetables are able to utilize a wide variety of these substrates, resulting in a vast array of esters having a variety of distinctive aromas. In apples, alkyl esters comprise about 90% of the volatile compounds (Dixon and Hewett, 2000). The most abundant compound is ethyl acetate, present in most ripe or ripening fruits. Gonzalez et al. (2009) showed in strawberries a marked increase in the abundance of acetates, promoted by an increase in the alcohol acyl transferase that brings about the esterification of acyl-CoAs and alcohols. In this sense, ethyl esters are major components of fruit aroma, particularly in ripe fruit where the production of ethanol has boosted their formation. Esters are the most abundant volatile compounds emitted by apple and, together with α-farnesene, have been proposed for cultivar classification (Young et al., 2005). Ethyl 2-methyl butanoate, 2-methyl butyl acetate, and hexyl acetate contribute mostly to the characteristic aroma of “Fuji” apples, while ethyl butanoate and ethyl 2-methyl butanoate are the active odor compounds in “Elstar” apples, and ethyl butanoate, acetaldehyde, 2-methyl butanol, and ethyl methyl propanoate in “Cox Orange” (Berger, 2007). The longer chain ethyl esters become soapy,

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FIGURE 4.2  Examples of odor-active compounds, oxygen heterocycles, and phenols.

(Reprinted with permission from Parker J. K. (2015). Introduction to aroma compounds in foods. In: J.K. Parker, S. Elmore, and L. Methven (Eds.), Flavour Development, Analysis and Perception in Food and Beverages. Woodhead Publishing: Cambridge.) cheesy, and waxy. Some esters can be quite characteristic of specific fruits: 3-methylbutyl acetate is characteristic of pear or pear drops, allyl hexanoate, is typically in pineapple, cis-3-hexenyl butanoate imparts the green leafy aroma of the parent alcohol, and the C9esters are important for melon aroma. 4.2.3 Lactones Lactones are cyclic organic (or intramolecular) esters that are potent aroma compounds formed from the corresponding hydroxy acid and contribute to the flavor of some fruits and vegetables. Those based on a furan ring are γ-lactones (e.g., γ-octalactone (or 4-octanolide) and γ-decalactone (4-decanolide)) and tend to impart peachy, creamy, and coconut aromas. Consequently, they are very popular in tropical flavors; for example, γ-decalactone is the major lactone in both peaches and nectarines (Engel et al., 1988a) and has a threshold of 11 μg/kg in water. The δ-lactones, which are based on a pyran ring, are less odor-active than their furanyl isomers (Parker, 2015). Both γ- and δ-lactones contribute to the aroma of peach. Eduardo et al. (2010) identified six lactones that are important to peach aroma: γ-decalactone, γ-dodecalactone, γ-octalactone, δ-decalactone, γ-nonalactone, and δ-dodecalactone. γ-Decalactone is described as having a “creamy, fruity, peach-like” odor. In celery, the three most potent odorants are the phthalides 3-butylphthalide, sedanenolide, and sedanolide, the former being described as “green spicy” and the latter two as “sweet spicy” (Kurobayashi et al., 2006). When

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FIGURE 4.3  Examples of odor-active nitrogen compounds. (Reprinted with permission from Parker J. K. (2015). Introduction to aroma compounds in foods. In: J.K. Parker, S.  Elmore, and L. Methven (Eds.), Flavour Development, Analysis and Perception in Food and Beverages. Woodhead Publishing: Cambridge.)

FIGURE 4.4  Examples of odor-active sulfur compounds. (Reprinted with permission

from Parker J. K. (2015). Introduction to aroma compounds in foods. In: J.K. Parker, S.  Elmore, and L. Methven (Eds.), Flavour Development, Analysis and Perception in Food and Beverages. Woodhead Publishing: Cambridge.)

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these compounds were added to chicken broth, they were found to enhance the perceived intensities of “umami” and “sweet” flavor (Kurobayashi et al., 2008). Several lactones were identified in an extract of sweet cream butter, of which δ-decalactone had the highest OAV and was believed to contribute to the sweet cream aroma. γ-Nonalactone, δ-decalactone, and the two unsaturated lactones (5-hydroxyoct-2-enoic acid lactone and 5-hydroxydec-2-enoic acid lactone) were found to have relatively high OAVs in milk chocolate (Schnermann and Schieberle, 1997). All of them were also found in cocoa, but the 5-hydroxydec-2-enoic acid lactone had been used in the production of the chocolate. Jasmine lactone provides a floral petal-like aroma to green tea (Katsuno et al., 2014), and lactones also make a significant contribution to the volatile profile of Bourbon whiskey, with δ-nonalactone having a FD factor of 2048, and cis-3-methyl-4-octanolide (which is also known as whiskey lactone) and γ-decalactone also contributing to the aroma. The odor thresholds of lactones decrease significantly as the number of constituent carbons increases (Parker, 2015; Forney and Song, 2018). Sensory important lactones usually possess 8–12 carbon atoms and some of them are very potent flavor components for a variety of fruits (Basear and Demirci, 2007). All lactones originate from their corresponding 4‐ or 5‐hydroxy carboxylic acids, which are formed by either (i) reduction of oxo acids by nicotinamide adenine dinucleotide (NAD)‐linked reductase, (ii) hydration of unsaturated fatty acids, (iii) epoxidation and hydrolysis of unsaturated fatty acids, or (iv) reduction of hydroperoxides (Schöttler and Boland, 1996; Schwab et al., 2008). 4.2.4 Terpenoids Terpenoids are the largest and most diverse group of volatile compounds that contribute to fruit and vegetable flavor, constituting one of the most diverse families of natural products, with over 40,000 different molecular structures (Schwab et al., 2008; Forney and Song, 2018). Some nonvolatile terpenoids are recognized as phytohormones—gibberellins, abscisic acid, brassinosteroids—involved in important plant processes, such as membrane structure, photosynthesis, redox chemistry, and growth regulation, that regulate fruit development and have relevant importance in non-climacteric fruit ripening (Croteau et al., 2000; Cherian et al., 2014). The volatile terpenoids—monoterpenoids (C10) and sesquiterpenoids (C15)—have been associated with the flavor profiles of most fruits and the scent of flowers at varying levels. Citrus fruit aroma consists mostly of these terpenes, which accumulate in specialized oil glands in the flavedo (the external part of the peel) and oil bodies in the juice sacs. The monoterpene R-limonene normally accounts for over 90% of the essential oils of the citrus fruit (Weiss, 1997). The sesquiterpenes valencene and α‐ and β-sinensal, although present in minor quantities in oranges, play an important role in the characteristic flavor and aroma of an orange fruit (Vora et al., 1983; Weiss, 1997; Maccarone et al., 1998). Nootkatone, a putative derivative of valencene, is a small fraction of the essential oils, but has a dominant role in the flavor and aroma of grapefruit (Shaw and Wilson, 1981), while the monoterpene S-linalool was found to be an important general strawberry aroma compound (Larsen and Poll, 1992; Aharoni et al., 2004, Van de Poel et al., 2014) and is found in many other fruits including peaches, guavas, nectarines, papayas, mangoes, passion fruits, tomatoes, litchi, oranges, prickly pears, and koubos (Flath and Takahashi, 1978; Idstein et al., 1985; Bernreuther and Schreier, 1991; Visai and Vanoli, 1997; Ong and Acree, 1998; Baldwin et al., 2000; Ninio et al., 2003). The combination of the monoterpenes geraniol, citronellol, and rose oxide is a key component of the characteristic aroma of aromatic Muscat grapes as well

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as the special scent of roses (Dunphy and Allcock, 1972; Bayrak, 1994; Luan et al., 2005). C13 norisoprenoids, which are derived from carotenoids, are the most abundant volatiles in raspberry fruit and comprised 64% to 94% of the total volatile content among nine raspberry genotypes (Forney et al., 2015; Forney and Song, 2018). Primary terpenes contributing significantly to aroma as determined by gas chromatography–olfactory detection (GCO) include myrcene, terpinolene, sabinene, and 1,3,8-p-menthatriene (Fukuda et al., 2013), which have “herbaceous and woody,” “sweet and piney,” “woody and spicy,” and “camphoraceous and herbal” aroma notes, respectively (Forney and Song, 2018). The terpenoid geosmin is responsible for the “earthy” aroma found in red beet (Acree et al., 1976). In tomato, the terpenoids geranial, β-ionone, β-damascenone, and 6-methyl-5-heptene-2-one contribute “fruity” and “floral” aromas (Klee and Tieman, 2013). 4.2.5 Aldehydes and Ketones 4.2.5.1 Aldehydes Aldehydes and ketones are organic compounds which incorporate a carbonyl functional group, C=O. The carbon atom of this group has two remaining bonds that may be occupied by hydrogen or alkyl or aryl substituents. If at least one of these substituents is hydrogen, the compound is an aldehyde; if neither is hydrogen, the compound is a ketone (Azzara and Campbell, 1992; Schwab et al., 2008; El Hadi et al., 2013; Forney and Song, 2018). Aldehydes are common components of many foods or flavorings, having an interesting low odor threshold. The straight-chain unbranched aldehydes are present in different food and plant tissues. Therefore, acetaldehydes impart fruity ester notes and are one of the main components of flavoring of fruit, whereas propanal, butanal, and pentanal (C3 –C5 aldehydes) tend to have a rather chemical/malty/green note of hard define (Foney and Song, 2018). Many volatile aldehydes have remarkable odor properties (Van Gemert, 2000). This kind of compound can be formed from alcohols and other precursors by oxidation processes, so the changes in aroma properties linked to oxidation are very often related to the formation of aldehydes (Azzara and Campbell, 1992). Many aldehydes with two double bonds have low odor thresholds, for example, the 2,4-alkadienals have great importance in fried aromas and have some characteristic fried notes, for example, spicy touches could be present in fried chicken (Gasser and Grosch, 1990) or in cooked beef Gasser and Grosch, 1988). Also, the aroma threshold in water is 0.2 µg k−1 (Belitz et al., 2004). Another close series of aldehydes, trans-4,5-epoxy-(E)2-alkenals are frequently founded in different foods. They all have a metallic odor being the most potent trans-4,5-epoxy-(E)-2-decenal in soy milk (Kaneko et al., 2011), potato chips (Kasuga et al., 2008), and in black tea (Kumazawa et al., 2008). Saturated and unsaturated volatile C6 and C9 aldehydes and alcohols are key contributors to the characteristic flavors of fruits, vegetables, and green leaves (Schwab et al., 2008; El Hadi et al., 2013), being widely used as food additives that grants a characteristic “fresh green” odor. Aldehydes and alcohols are found ubiquitously in the plant kingdom at high concentrations, and they are derived from the degradation of branched-chain and aromatic amino acids or methionine constitute (Schwab et al., 2008). The C6 aldehydes hexanal, (Z)-3-hexenal, and (E)-2-hexenal contribute “green” aroma notes, which are emitted when tissues are disrupted by cutting or chewing in both vegetables and fruits. These aldehydes are important flavor components of many fruits and vegetables including apples (Defilippi et al., 2005; Villatoro et al., 2008), oranges (Gonzalez-Mas

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et al., 2011), raspberries (Klesk et al., 2004), peaches (Engel et al., 1988; Aubert et al., 2003; Aubert and Milhet, 2007; Wang et al., 2009), strawberries (Jetti et al., 2007), cherries, tomato, cucumber, and spinach (Belitz et al., 2009; Forney and Song, 2018). In some cases, hexanal and related C6 aldehydes decreased as fruit matures on the plant (Engel et al., 1988). The C9 aldehydes contribute to green-bean and cut-grass impressions in foods when assessed alone and to fresh green aromas; (E,Z)-2,6-nonadienal and (E)-2-nonenal are key aroma components of cucumber, contributing “cucumber-like” and “green, fatty” aromas (Schieberle et al., 1990), and (Z)-3-nonenal contributes to the “green apple” note in apples (Belitz et al., 2009). Benzaldehyde is also an important aroma compound in cherry and other stone fruit (Belitz et al., 2009; Forney and Song, 2018). Aldehydes containing an aromatic ring, such as benzaldehyde, are present in cherries and almonds; phenylacetaldehyde (rose and honey) and cinnamaldehyde are important components of foods and flavorings. One example of these is vanillin (4-hydroxy-3-metoxybenzaldehyde). 4.2.5.2 Ketones This type of compound contributes characteristic aromas to different types of fruits and vegetables, especially C5, C7, and C8 ketones (Forney and Song, 2018). The straight-chain methylketones, that contains one carbonyl group in the 2-position, like 2-heptanone, impart both a blue cheese and fruit pear aroma, while 3-octanone has earthy, mushroom notes (Cho et al., 2006). Another type of compound in this group are the α-dicarbonyl compounds; 2,3 pentanedione and 2,3-butanedione or diacetyl have lower fat thresholds and bring butter and creamy notes to many cooked foods. In fruits, this type of compound contributes to floral aromas, like in grapefruit, where they bring a “geranium-like” odor (Buettner and Schieberle, 2001). In raspberry, 1-(p-hydroxyphenyl)-3-butanone is a great contributor to fruit flavor, but Klesk et al. (2004) showed in aroma extract dilution analysis of “Meeker” raspberries that this type of compound was a minor contributor to the aroma. 4.2.6 Phenylpropenes and Other Aromatic Derivatives Phenylpropanoid and benzenoid volatile compounds contribute to the aromas and scents of many plant species and play important roles in plant communication with defense pathways (Dudareva and Pichersky, 2006; Knudsen and Gershenzon, 2006; Pichersky et al., 2006; Naoumkina et al., 2010.). Benzyl alcohol derivative, 1,3,5-trimethoxybenzene, has been identified as a key component of the odor of Chinese rose (Yomogida, 1992). This volatile is an effective sedative and has been used as a cosmetic additive (Shoji et al., 2000). The biosynthesis pathway is thought to begin with phloroglucinol and includes three methylation steps. Many modern rose varieties synthesize a related compound, 3,5-dimethoxytoluene (Flament et al., 1993), from orcinol (3,5-dihydroxytoluene) by two successive methylations. Vanillin (4-hydroxy-3-methoxybenzaldehyde) is the most widely used flavor compound in the world. It is the principal flavor component of the vanilla extract obtained from cured pods (beans) of the orchid Vanilla planifolia Andrews. Vanillin accumulates in the secretion around the seeds in the mature fruits. A unique secretory tissue composed of closely packed unicellular hairs is located in three gaps between the placentas along the central fruit cavity. These cells seem to be responsible for vanillin secretion (Joel et al., 2003). Vanilla extract is valued as a natural flavor, but, because of its cost and limited availability, less than 1% of the annual world demand

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for vanillin is isolated from its natural source (Walton et al., 2003). Most of the vanillin used by the flavor industry originates from chemical methods that use guaiacol, eugenol, or lignin as starting materials (Rao and Ravishankar, 2000). 4.2.7 Alcohols With respect to alcohol volatile compounds, this type of compound is important in alcoholic beverages and in most fruits and vegetables but tends to play a minor role in aroma and flavor. The release of volatile flavor materials from alcoholic beverages depends not only on the concentration of volatiles in the solution but also by interactions between volatiles through the presence of various nonvolatile materials and ethanol concentration. Ethanol is the most abundant of the volatile compounds in wine and it can modify both the sensory perception of aromatic attributes as well as the detection of volatile compounds (Goldner et al., 2009). In another food, the presence of butanol has been reported to contribute to an apple aroma (Young et al., 1996). In watermelon, the C9 compound alcohol (Z,Z)-3,6-nonadienol is one of the compounds with major contribution to flavor, being described as “watermelon, fruity, fresh, cucumber” aroma (Xisto et al., 2012). In other fruits and vegetables, C6 compound alcohols such as hexanol, (Z)-3-hexenol and (E)-2-hexenol bring “herbaceous” aromas (Belitz et al., 2009). 4.2.8 Furans and Its Derivatives Furan and its derivatives are naturally occurring compounds found at very low levels in many foods and drinks, and they are associated with the flavor of foods. These include commercially prepared foods as well as homemade foods. Furans are a major class of compounds formed during the Maillard reaction and their presence in foods is well documented (Maga, 1979). Literature data indicate multiple sources of furan formation originating from (i) thermal degradation/Maillard reaction reducing sugars, alone or in the presence of amino acids, (ii) thermal degradation of certain amino acids, and the thermal oxidation of (iii) ascorbic acid, (iv) polyunsaturated fatty acids, and (v) carotenoids (Yaylayan, 2006, Pérez and Yaylayan, 2004). The primary source of furan in food is the thermal degradation of carbohydrates, such as glucose, lactose, and fructose (Vranová and Ciesarová, 2009). A wide range of compounds can be formed during thermal processing of food, some of which are relevant for aroma (e.g., furfural), while others are of great health concern (e.g., furan). The formation of furan under pyrolytic conditions has been studied in simple model systems revealing more precursor classes, that is, (i) ascorbic acid and related compounds; (ii) Maillard type systems containing amino acids and reducing sugars; (iii) lipid oxidation of unsaturated fatty acids or triglycerides; and (iv) carotenoids (Pérez and Yaylayan, 2004; Becalski and Seaman, 2005; Yaylayan, 2006). Furthermore, the effect of ionizing radiation on furan formation in real (apple and orange juice) and model systems has been studied (Fan, 2005a,b). Furan and its derivatives were identified in a small number of heat-treated foods back in the 1960s and 1970s. Furans are important compounds contributing to fruit aroma. Those present in fresh produce are mainly furanoid terpenes. One example is linalool oxide, which brings a floral herby note, but in this type of product it is a signal of an oxidation process and loss of quality. In other foods like grapes, wine, and tea, some compounds like theaspirane, another bicyclic furanoid derived from carotenoids, exist as four diastereoisomers (Collin et al.,

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2011). Each of them has different aroma properties: camphoraceous notes (the 2R and the 2S,5S isomers), blackcurrant (the 2R,5S isomer) or smells like naphthalene (the 2S,5R isomer). Furaneol brings a “sweet, caramel, floral, strawberry-like” aroma and contributes to the flavor of fruits including strawberry, blackberry, raspberry, pineapple, mango, and tomato (Du and Qian, 2008; Forney and Song, 2018). In strawberry, both furaneol and mesifuran (2,5-dimethyl-4-methoxy-2H-furan-3-one) have been shown to increase during fruit ripening (Pérez et al., 1996a, 1996b). Most of the furans are formed during the thermal processing of ingredients. Those found in fresh produce tend to be furanoid terpenes, such as linalool oxide, which brings a floral herby note but tends to appear during storage and is indicative of oxidation. 4.2.9 Pyrazines Pyrazines are heterocyclic nitrogen-containing compounds that contribute to flavor in several vegetables and specifically in some processed products like wine (Ryona et al., 2008), contributing “green bell peppers” notes (Forney and Song, 2018). Most pyrazines are generated during the thermal processing of foods at temperatures >100°C. The simple unsubstituted or monosubstituted pyrazines have a roasted, biscuity aroma and relatively high aroma thresholds, but as substitution increases, the odor threshold decreases (Parker, 2015). They have very strong odors. One example is 3-isobutyl-2-methoxypyrazine, reported to have an odor threshold of only 2 parts per 1012 parts of water (Buttery et al. (1969) cited by Forney and Song (2018)). Hinterholzer et al. (1998) analyzed volatile profiles in different raw vegetables, and they detected 3-isobutyl 2-methoxypyrazine in green and red bell peppers and French beans. They also quantified the presence of 3-secbutyl 2-methoxypyrazyne in carrots, parsnips, and beets, and 3-isopropil 2- methoxypyrazine in peas, broad beans, asparagus, and cucumbers. The pyrazines found in uncooked potatoes and vegetables are methoxy-substituted, and powerful odorants. For example, 2-methoxy-3-isobutylpyrazine is the character impact compound in green bell pepper and is identified as the most potent odorant in raw French beans (Hinterholzer et al., 1998). The homologous 2-isopropyl-3-methoxypyrazine is known as bean pyrazine because it imparts earthy, pea, and beany notes to soy milk (Kaneko et al., 2011), earthy notes to potato (Buttery and Ling, 1973), and was also found to be odor-active in parsley leaves (Jung et al., 1992) and gravies containing vegetables (Christlbauer and Schieberle, 2009). 4.2.10 Sulfur Compounds Volatile organic compounds (VOCs) comprise a wide diversity of low-molecular weight secondary metabolites, with an appreciable vapor pressure under ambient conditions (McGorrin, 2011). Although some VOCs are probably common to almost all plants, others are specific to only one or a few related taxa. To the first type belong the so-called “green leaf” volatiles (GLVs) because of their “fresh green” odor. This group comprises short-chain (C6) acyclic aldehydes, alcohols, and their esters, produced by plants from most taxa as a wound response via the enzymatic metabolism of polyunsaturated fatty acids. However, species- or genus-specific VOCs have been described in some species, such as the sulfur-containing VOCs of Alliaceae and Brassicaceae (Qualley and Dudareva, 2001). In these cases, many strong aromas are the response of sulfur-containing compounds. Glucosinolates are sulfur-rich, anionic natural products that upon hydrolysis

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by endogenous thioglucosidases called myrosinases produce several different products (e.g., isothiocyanates, thiocyanates, and nitriles) (Halkier and Gershenzon, 2006). They contain sulfur groups and are present in numerous species belonging to the Brassicaceae family (Verkerk et al., 2009, Agerbirk and Olsen, 2012). Chemically, glucosinolates are composed of thiohydroximate-O-sulfonate group linked to glucose, and an alkyl, aralkyl, or indolyl side chain (R) (Barba et al., 2016). It is known that the enantiomers of chiral flavor compounds often have different sensory properties (McGorrin, 2011). In some cases, one chiral form may exhibit a lower flavor threshold relative to its epimer. In other situations, the aroma may change flavor character between the two enantiomeric forms, or shift in character from odor to odorless. The sensory properties of enantiomers of sulfur volatile compounds have been described and compared (Bernreuther et al., 1997; Engel et al., 2001).

4.3 BIOSYNTHESIS Many plant flavor compounds are accumulated and biosynthesized in specialized anatomical structures (Bagchi, 2000; Croteau et al., 2000; Schwab et al., 2008). Therefore, these tissues contain many of the biosynthetic enzymes, showing a high expression of genes involved in the production of such metabolites (Schwab et al., 2008). This section describes the biogenetic origin of plant-derived flavor molecules, with emphasis on biosynthesis process described on fruit. 4.3.1 Fatty Acid-Derived and Lipophilic Compounds The main part of plant volatiles is synthesized from saturated and unsaturated fatty acids. Fatty acid-derived straight-chain alcohols, aldehydes, acids, ketones, esters, and lactones (C1–C20) are found extensively in the plant kingdom at high concentrations, being the major building blocks of many straight-chain volatile flavor compounds of fresh fruit flavors (Sanz et al., 1997; Schwab et al., 2008; Aragüez and Valpuesta, 2013; El Hadi et al., 2013; Forney and Song, 2018). These compounds are fundamentally formed by three biosynthesis processes: α-oxidation, β-oxidation, and the lipoxygenase (LOX) pathway (Schwab and Schreier, 2002; Conde et al., 2008; Schwab et al., 2008; Espino-Díaz et al., 2016). Aroma volatiles in intact fruit are formed via the β-oxidation biosynthetic pathway, whereas when fruit tissue is disrupted, volatiles are formed via the lipoxygenase pathway (Lea, 1995; Vogt et al., 2013; 160Wu et al., 2018). However, identification of a number of oxylipin containing phosphatidylglycerols, monogalactosyldiacylglycerols, and digalactosyldiacylglycerols reveal that direct oxidation of the fatty acid side chain in acylglycerides is possible (Buseman et al., 2006; Schwab et al., 2008). Therefore, some studies suggest that increased availability of fatty acid, along with higher membrane permeability, during fruit ripening might allow the LOX pathway to become active in intact plant tissue and to function as an alternative to β-oxidation (Guadagni et al., 1971). Many of the aliphatic esters, alcohols, acids, and carbonyls found in fruits are derived from the oxidative degradation of linoleic and linolenic acids (Reineccius, 2006). In addition, some of the volatile compounds derived from the enzyme-catalyzed oxidative breakdown of unsaturated fatty acids may also be produced by autoxidation (Chan, 1987). Autoxidation of linoleic acid produces 9,13-hydroperoxides, whereas linolenic acid also produces 12,16-hydroperoxides (Berger, 2007). Hexanal and 2,4-decadienal are the

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primary oxidation products of linoleic acid, while autoxidation of linolenic acid produces 2,4-heptadienal as the major product. Further autoxidation of these aldehydes leads to the formation of other volatile products (Chan, 1987). It was reported that the constitutive over-expression of a yeast (Saccharomyces cerevisiae) Δ9-desaturase, to catalyze the conversion of linoleic acid to linolenic acid, in tomato produced a modified oxidation pattern of lipids, with the result that the concentration of some short-chain alcohols and aldehydes increased derived from fatty acids, such as (Z)-3-hexenol, 1-hexanol, hexanal, and (Z)-3-hexenal (Wang et al., 1996). The over-expression of ω‐3 fatty acid desaturases (FAD) from Brassica napus (BnFAD3) and potato (StFAD7) produced an increase in linolenic/linoleic acid ratio in tomato leaves and fruits. In addition, the transgenic lines presented an increase of the (Z)-3-hexenal/hexanal ratio and were more tolerant to chilling (Domínguez et al., 2010). 4.3.1.1 Lipoxygenase Pathway (in Chain Oxidation) The metabolism of polyunsaturated fatty acids, via the first LOX catalyzed step and the subsequent reactions, is commonly known as the LOX pathway (Schwab et al., 2008; El Hadi et al., 2013). Four enzymes have been described in this biosynthetic pathway: lipoxygenase (LOX), hydroperoxide lyase (HPL), 3Z,2E-enal isomerase, and alcohol dehydrogenase (ADH) (Schwab et al., 2008; El Hadi et al., 2013). In intact fruit, enzymes in the LOX pathway and their substrates have different subcellular locations, preventing the formation of volatile compounds (El Hadi et al., 2013). During ripening, cell walls and membranes may become more permeable, allowing the LOX pathway to become active without tissue disruption (El Hadi et al., 2013), providing substrates for ester production (De Pooter et al., 1983), and acting as an alternative to β-oxidation of fatty acids during LOX biosynthetic activation by ripening. Conversely, when the fruit is homogenized, linoleic and linolenic acid are oxidized to various C6 and C9 aldehydes by the effect of free enzymes (Lea, 1995). Therefore, it is important that the studies of volatile compounds by destructive techniques take into account the biochemical changes involved during the extraction process. LOX is a non-heme, iron-containing dioxygenase that catalyzes the regio- and enantioselective dioxygenation of unsaturated fatty acids such as linoleic and α-linolenic acid containing one or more 1Z,4Z-pentadienoic moieties (Liavonchanka and Feussner, 2006; Schwab et al., 2008). Several LOX have been characterized in plants, being essential components of the oxylipin pathway, converting fatty acids into hydroperoxides for the generation of flavors such as 3Z-hexenol, 2E-hexenal, and 2E,6Z-nonadienal. The LOX enzyme classification is on base to their specificity of the hydrocarbon backbone of fatty acid oxygenation, that is, oxygenation at C9 (9-LOX) or at C13 (13-LOX) leads to the (9S) and (13S)-hydroperoxy derivatives, respectively. Plant LOX have been grouped according to their sequence similarity into two gene sub-families. Enzymes carrying no plastidic transit peptide (type-1 LOX) show a high sequence similarity (>75%) between them, while the type-2 LOX have only a moderate sequence similarity (approximately 35%) (Liavonchanka and Feussner, 2006; Schwab et al., 2008). The action of the HPL enzyme involves the cleaving of fatty acid hydroperoxides generated from the LOX reaction, resulting in the formation of ω-oxo acids and volatile C6 and C9 aldehydes. Similar to LOX, HPL can be classified into two groups according to substrate specificity (Noordermeer et al., 2001). HPL is a member of the cytochrome P450 family CYP74B/C that acts on a hydroperoxy functionality in a lipid peroxide without any co-factor, being responsible for the emission of small aldehydes that act as green leaf volatiles (Lee et al., 2008). The study of HPL role in LOX pathway, shows that

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down-regulation of HPL in potato plants (Salas et al., 2005) induced an increase in LOX activity but a decrease of most of the C6 volatile compounds. On the other hand, regarding compartmentalization of these enzymes, the heterologous expression of grapevine (Vitis vinifera) hydroperoxide lyase (VvHPL1) merged to green fluorescent protein (GFP) in tobacco BY-2 cells show that the potential location of HPL is in plastids (Akaberi et al., 2018). The ADH enzyme catalyzed the metabolism of aldehydes (C6 and C9) to form the corresponding alcohols. ADH genes related to aroma production are expressed and regulated during fruit ripening (Manríquez et al., 2006). Over-expression of the tomato SlADH2 gene led to the improved flavor of the fruit by modifying the levels of shortchain aldehydes and alcohols, particularly 3Z-hexenol (Speirs et al., 1998; Prestage et al., 1999). On the other hand, when a tomato short-chain ADH (SlscADH1) was silenced specifically in fruit, these tomatoes accumulated higher concentrations of C5 and C6 volatile compounds of the LOX pathway (Moummou et al., 2012). In contrast, the grapevine ADH (VvADH2), over-expressed and silenced in grape berries, didn’t affect the content of free or bound volatile compounds, except benzyl alcohol and 2-phenylethanol (Torregrosa et al., 2008). The higher contents of fatty acids and fatty acid-derived volatiles present in tomato fruit, in parallel with the induced expression of TomloxC, HPL, ADH2, and increased activities of LOX, ADH, and HPL, were observed in abscisic acid (ABA)-treated tomato fruit. Promoter region analysis shows that the cis-acting elements involved in ABA responsiveness (ABREs) can be found in the 2000 bp region upstream of TomloxC and HPL, suggesting that the accumulation of fatty acid volatiles is regulated by ABA, among other factors (Wu et al., 2018). Some genetic efforts have been made at an aroma improvement breeding program for apples (Dunemann et al., 2009; Marconi et al., 2018). Although, putatively, a quantitative trait locus (QTLs) for a LOX candidate gene was mapped (Dunemann et al., 2009), the authors suggest that it would need to be validated using more apple cultivars and species to better understand the genetics of apple aroma and to develop molecular markers involved in volatile biosynthesis for the development of a breeding program. Thus, big data analysis correlating quality parameters and molecular markers has been useful to differentiate a particular locality gene of apple varieties from other European locations (Marconi et al., 2018). In grapes, the RNA sequencing (RNA-Seq) analysis of berries treated with CPPU [forchlorfenuron N-(2-chloro-4-pyridyl)-N-phenylurea], a synthetic cytokine-like plant regulator that promotes grape berry set and development, shows that differentially expressed genes (DEGs) were associated with the formation of volatile compounds. These DEGs were associated with fatty acid degradation and biosynthesis, phenylpropanoid metabolism and biosynthesis, and carotenoid biosynthesis genes, such as CCDs (carotenoid cleavage dioxygenase), LOX, GGDP reductase (geranylgeranyl diphosphate reductase), PAL (phenylalanine ammonia-lyase) (Wang et al., 2017). With regard to volatile compound biosynthesis in bananas (Musa sp), genes that encode enzymes for the important steps of aroma production include BanLOX, BanPDC, BanHPL, BanBCAT, BanADH, and BanAAT (Beekwilder et al., 2004; Yang et al., 2011a) A recent study has revealed genes involved in ester formation from amino acids, saturated fatty acids, and unsaturated fatty acids such as lipoxygenases, transferases, and acetyltransferases (Asif et al., 2014). During the storage of bananas, the expressions of a subset of aroma biosynthetic genes including MaOMT1, MaMT1, MaGT1, MaBCAT1, MaACY1, MaAGT1, and BanAAT were down-regulated when pre-stored

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at 7°C compared to those at 22°C during the ripening process of bananas. Furthermore, two transcriptional factors—MabZIP4 and MabZIP5—were found to be regulators of gene expression from these aroma biosynthetic genes (Guo et al., 2018). 4.3.1.2 α and β-Oxidation Although the degradation of straight-chain fatty acids by α and β-oxidation is an important process for the formation of flavor molecules, many aspects of plant biosynthesis must be understood (Schwab et al., 2008; El Hadi et al., 2013). The α-oxidation mechanism involves enzymatic degradation of free fatty acids (C12 –C18) via one or two intermediates producing C(n−1) long-chain fatty aldehydes and CO2 (Hamberg et al., 1999; Schwab et al., 2008), described in the plant catalysis by an enzyme with dual function: α-dioxygenase/peroxidase and NAD+ oxidoreductase (Saffert et al., 2000). On the other hand, the β-oxidation results in the successive removal of C2 units (acetyl-CoA) from the parent fatty acid (Goepfert and Poirier, 2007), being the primary biosynthetic process providing alcohols and acyl coenzyme A (CoAs) for ester formation (Sanz et al., 1997). Fatty acid acyl-CoA derivatives are converted to shorter chain acyl-CoAs by losing two carbons in every round of the β-oxidation cycle, a process that requires NAD, flavinadenine dinucleotide (FAD) and free CoA. Acyl-CoAs are reduced by the action of acyl-CoA reductase to aldehyde that in turn is reduced by ADH to alcohol for use by alcohol acyl transferases (AAT) to produce esters (Bartley et al., 1985). Pear and apple aromas are two classic examples of volatile formation through the β-oxidation pathway (Paillard, 1990). The biosynthesis of lactones, key aroma components in fruits such as peach and nectarine (γ-decalactone and γ-dodecalactone), pineapple (δ-octalactone), or coconut (Cocos nucifera L.) (γ-octalatcone), is also associated with the β-oxidation pathway (Tressl and Albrecht, 1986), without ruling out its association with the LOX pathway. Aliphatic short and medium-chain aldehydes and alcohols have been described in different plant parts, being probably formed by enzymatic reduction of the parent acylCoAs (Flamini et al., 2007). Particularly, alcohols are less important as flavor molecules due to their high odor thresholds in comparison with their aldehyde homologous. Alternatively, alcohols can also be formed by ADH. This enzyme-mediated hydrogenation of aldehydes, and medium-chain aldehydes are intermediates of the α-oxidation cycle, starting with common fatty acids (Hamberg et al., 1999). An ADH with specific substrate preference has been isolated from melons (Manríquez et al., 2006). Specifically, flavor ester production relies upon the supply of acyl-CoAs formed during β-oxidation and alcohols. Alcohol acyl transferases (AAT) are capable of combining various alcohols and acyl-CoAs, resulting in the synthesis of a wide range of esters, thus accounting for the diversity of esters. Several AAT genes have been isolated and characterized in fruit and vegetables (Aharoni et al., 2000; Beekwilder et al., 2004; El-Sharkawy et al., 2005; Gonzalez et al., 2009; Balbontin et al., 2010). Aliphatic esters are emitted by vegetative tissues and contribute to the aroma of nearly all fruits, being some of them responsible for a particular fruit or floral aroma. AAT enzymes catalyze the last step in ester formation by transacylation from an acylCoA to an alcohol, for example, the biosynthesis of volatile butanoates and hexanoates in strawberry fruit (Aharoni et al., 2000; Cumplido-Laso et al., 2012). Combinations between different alcohols and acyl-CoAs will result in the formation of a range of esters in different fruit species. The most likely precursors for the esters are lipids and amino acids (Beekwilder et al., 2004). For example, in strawberry, the amino acid Ala has been implicated in the formation of ethyl esters during ripening (Pérez et al., 1992). However, it has been suggested that selectivity of enzymes preceding AATs in the biosynthetic

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pathway also determined the ester profile of fruits (Wyllie and Fellman, 2000). In addition, the oxidative degradation of linoleic and linolenic acids results in the production of volatile aldehydes, which in turn are utilized by alcohol dehydrogenases to form alcohols. The latter is subsequently converted to hexyl and hexenyl esters (Pérez et al., 1996; Shalit et al., 2001). The activity of AAT enzymes and gene expression pattern has been subject of several investigations on extracts of various fruit species, including banana, strawberry, melon (Cucumis melo), apple, grape, apricot, and mountain papaya (Vasconcellea pubescens) (Aharoni et al., 2000; Wyllie and Fellman, 2000; Shalit et al., 2001; Pérez et al., 2002; Beekwilder et al., 2004; Wang and De Luca, 2005; Gónzalez et al., 2009; GonzálezAguero et al., 2009; Balbontin et al., 2010). In strawberry and banana, the AAT activity showed a broad substrate specificity (alcohols and acyl-CoAs) and an activity dependent on maturation (Beekwilder et al., 2004). In the last decades, fruit expressed genes encoding enzymes with AAT activity have been isolated and characterized from strawberry, melon, banana, and mountain papaya (Aharoni et al., 2000; Yahyaoui et al., 2002; Beekwilder et al., 2004; Balbontin et al., 2010). Aharoni et al. (2000) utilized cDNA microarrays for gene expression profiling during strawberry (Fragaria ananassa) fruit development and identified the SAAT gene, which showed a strong induction upon ripening. Moreover, the recombinant SAAT enzyme could catalyze the formation of esters typically found in strawberry, using aliphatic, medium-chain alcohols (e.g., octanol) in combination with various chain length (up to C10 tested) acyl-CoAs as substrates. The activity of SAAT was much lower for aromatic substrates, and no activity could be detected with the monoterpene alcohol, linalool. Yahyaoui et al. (2002) reported on the molecular and biochemical characterization of a ripening-induced and ethylene-regulated AAT gene (CM-AAT1) from ripe melon fruit. As in the case of SAAT, the recombinant CM-AAT1 enzyme was capable of producing esters from a wide range of alcohols and acyl-CoAs. While CM-AAT1 and the SAAT proteins share only 21% sequence identity, they show a similar preference toward alcohols. The comparison of recombinant AAT isolated from fruit of wild strawberry (Fragaria vesca) and banana (Musa sapientum) with SAAT enzyme, showed that the substrate preference of recombinant enzymes was not necessarily reflected in the representation of esters in the corresponding fruit volatile profiles. The above suggests that the specific profile of a given fruit species is to a significant extent determined by the supply of precursors (Beekwilder et al., 2004). Furthermore, the obtention of transgenic petunia (Petunia hybrida) plants overexpressing the SAAT gene showed that the volatile profile was found to be unaltered while the expression of SAAT and the activity of AAT was detected in transgenic plants. Feeding of isoamyl alcohol to explants of transgenic lines resulted in the emission of the corresponding acetyl ester and confirmed that the availability of alcohol substrates is an important parameter to consider for engineering volatile ester formation in plants (Beekwilder et al., 2004). Esters have been described as the main volatile compounds produced during fruit ripening of mountain papaya (Vasconcellea pubescens), and most of them are dependent on ethylene. The transcript accumulation pattern and increase in AAT activity showed that the VpAAT1 gene is expressed exclusively in fruit tissues and that a high level of transcripts is accumulated during ripening, it is induced by ethylene and it is avoided by 1-methylcyclopropene (1-MCP) treatment (Balbontin et al., 2010). The data indicate that VpAAT1 is involved in aroma formation and that ethylene plays a major role in regulating its expression. On other hands, the over-expression of VpAAT1 gene in yeasts and in vitro assay with different precursors suggest that this enzyme has a preference for the formation of benzyl acetate (Balbontin et al., 2010).

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4.3.2 Amino Acid Derivates Aldehydes and alcohols derived from the degradation of branched-chain and aromatic amino acids or methionine are abundant plant volatiles. However, many aspects of their biosynthesis pathways are still unknown. Many branched-chain esters give their characteristic to many fruits; for example, 2-methyl-butyl acetate has a strong apple scent and is associated with apple varieties that are rich in aroma such as “Gala,” “Fuji,” and “Golden Delicious” (Dixon and Hewett, 2000; Holland et al., 2005); isoamyl acetate is one of the key constituents of banana flavor (Surburg and Panten, 2005) and grants a strong fruity odor described as similar to banana or pear; methyl 2-methyl butanoate determines the characteristic aroma of prickly pear (Weckerle et al., 2001), while a combination of isoamylacetate and 2-methyl-butyl acetate and other volatiles imparts the unique aroma of melons (Beaulieu and Grimm, 2001; Jordan et al., 2001; Shalit et al., 2001). Branched amino acid metabolism plays an important role not only in flavor and volatile biosynthesis but also as an important nutrient for human health (Klee and Tieman, 2013). 4.3.2.1 Acids, Alcohols, Aldehydes, Esters, Lactones, and N- and S-Containing Flavor Molecules The knowledge of amino acids catabolism in microorganisms proposes three biochemical reactions: (i) formation of 2-ketoacids by aminotransferases, (ii) decarboxylation to aldehydes, and (iii) reduction to 2-hydroxyacids (Marilley and Casey, 2004; Schwab et al., 2008). Compounds derived from leucine such as 3-methylbutanal, 3-methylbutanol, and 3-methylbutanoic acid, as well as phenylacetaldehyde and 2-phenylethanol formed from phenylalanine, are abundant in tomato, strawberry, and grape varieties, among other fruits (Aubert et al., 2005). Alcohols and acids derived from amino acids can also be esterified to compounds with a large impact on fruit odor, such as 3-methylbutyl acetate and 3-methylbutyl butanoate in banana (Nogueira et al., 2003). The isolation of gene encoding enzymes responsible for the direct decarboxylation of phenylalanine from tomato, petunia, and rose was one of the first signs that alternative catabolic pathways exist in plants (Kaminaga et al., 2006; Tieman et al., 2006; Schwab et al., 2008). Studies of amino acid catabolism performed in tomato (Solanum lycopersicum L., Solanaceae) fruit indicated that the catabolism of l-phenylalanine into aroma volatiles is initially mediated by decarboxylation, followed by deamination (Tieman et al., 2006). In contrast, in petunia (Petunia hybrida, Solanaceae) and rose (Rosa hybrida, Rosaceae) petals, it has been observed that one enzyme is able both to decarboxylate and to deaminate l-phenylalanine to release phenylacetaldehyde (Kaminaga et al., 2006), which indicates that different biosynthetic routes can be used. Although the enzymes display subtle differences in sequences and enzymatic properties, their down-regulation led to reduced emission of 2-phenylacetaldehyde and 2-phenylethanol. Transgenic tomato lines have shown that the over-expression of the amino acid decarboxylase resulted in fruits with up to 10-fold increased levels of 2-phenylacetaldehyde, 2-phenylethanol, and 1-nitro-2-phenylethane (Tieman et al., 2006). The modulation of the emission of these compounds can have different effects, observing that at low concentrations 2-phenylethanol and 2-phenylacetaldehyde are associated with pleasant, sweet, flowery notes, while at high levels, 2-phenylacetaldehyde is unpleasant (Tadmor et al., 2002). The essential amino acids, l-phenylalanine, l-methionine, l-leucine, l-isoleucine, and l-valine, are metabolized into aroma compounds in melon fruit (Gonda et al., 2010; Gonda et al., 2013; Gonda et al., 2018), and the action of aminotransferases (AT) has been suggested as a key step for generating the respective α-keto acids. In apple, BCAT,

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ArAT, and amino acid decarboxylases (AADC) were up-regulated during ripening and further enhanced by ethylene treatment (Yang et al., 2011b). The ethylene treatment at the pre-climacteric stage of apple growth showed an early burst of branched-chain volatiles, including 2-methylbutanol, 2-methylbutylacetate, and butyl-2-methylbutanaote increase (Song, 1994). In oriental sweet melon, it has been observed that ethylene regulated the metabolism of branched-chain amino acids, including valine, leucine, and isoleucine, as well as phenylalanine and cysteine, and enhanced the formation of aroma volatiles, especially methyl-branched and aromatic esters (Li et al., 2016). The activities of aminotransaminase BCAT, ArAT, and pyruvate dehydrogenase (PDH), as well as the expression patterns of CmBCAT1 and CmArAT1, showed a clear ethylene regulation (Li et al., 2016). On the other hand, a diverse catabolic pathway of amino acids to generate different aromatic compounds has been described in onion (Allium cepa), garlic (A. sativum) (Jones et al., 2004; Lanzotti, 2006), maize (Frey et al., 2000), cruciferous vegetables such as mustard, broccoli, cauliflower, kale, turnips, collards, brussels sprouts, cabbage, radish, and watercress (Chen and Andreasson, 2001; Bak et al., 2006; Bones and Rossiter, 2006). 4.3.2.2 Phenylpropenes and Other Aromatic Derivatives Phenylpropanoid and benzenoid volatile compounds, primarily derived from phenylalanine, contribute to the aromas and scents of many plant species and play important roles in plant communication with defense pathways (Dudareva and Pichersky, 2006; Knudsen and Gershenzon, 2006; Pichersky et al., 2006; Naoumkina et al., 2010.). Several enzymes that catalyze the final steps in the biosynthesis of these compounds have been isolated and characterized; however, there is still much to understand about the early steps leading to the formation of the benzenoid backbone (Beuerle and Pichersky, 2002; Schnepp and Dudareva, 2006; Wildermuth, 2006; Schwab et al., 2008). The biosynthesis of benzenoids from phenylalanine requires shortening of the carbon skeleton side chain by a C2 unit, which can be carried out by β-oxidative or non-oxidative pathway (Boatright et al., 2004). The existence of both routes has been demonstrated by experiments with stable isotope-labeled precursors in different plants. In tobacco (Nicotiana tabacum) leaves (Ribnicky et al., 1998), the studies suggested that benzoic acid is produced from phenylalanine derived cinnamic acid via the β-oxidative pathway, first yielding benzoyl-CoA, which can then be hydrolyzed by a thioesterase to free benzoic acid. Conversely, the same experiments in Hypericum androsaemum cell cultures (Ahmed et al., 2002), together with initial enzyme characterization, supported the existence of a pathway for non-oxidative conversion of cinnamic acid to benzaldehyde to obtain benzoic acid, which can be further converted to benzoyl-CoA (Beuerle and Pichersky, 2002). On the other hand, in vivo isotope labeling and metabolic flux analysis of the benzenoid network in petunia flowers revealed that both pathways yield benzenoid compounds and that benzyl benzoate is an intermediate between l‐phenylalanine and benzoic acid (Boatright et al., 2004). The generation of transgenic petunia plants with reduced or eliminated expression of benzoyl-CoA—phenylethanol/benzyl alcohol benzoyltransferase (BPBT) enzyme that uses benzoyl-CoA and benzyl alcohol to make benzyl benzoate—showed that the decrease or elimination of benzyl benzoate formation decreased the endogenous pool of benzyl acid and methyl benzoate emission but increased the emission of benzyl alcohol and benzylaldehyde, confirming the contribution of benzyl benzoate to benzoic acid formation (Orlova et al., 2006). The dilution of 2H-5-phenylalanine suggests an alternative pathway from a different precursor than phenylalanine, possibly phenylpyruvate (Schwab et al., 2008).

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Phenylpropenes such as trans-anethole, eugenol, and isoeugenol are produced by plants as defense compounds against animals and microorganisms and as floral attractants of pollinators (Schwab et al., 2008). Moreover, humans have used phenylpropenes since antiquity as medicinal agents and for food preservation and flavoring (Gross et al., 2002). Sweet basil (Ocimum basilicum) contains an enzyme that can use coniferyl acetate and NADPH to form eugenol (Koeduka et al., 2006) and petunia (Petunia hybrida cv. Mitchell) flowers possess an enzyme homologous to basil that also uses the same substrates but catalyzes the formation of isoeugenol. These enzymes are phenylpropene forming enzymes that belong to a structural family of NADPH dependent reductases that includes pinoresinol/lariciresinol reductase, isoflavone reductase, and phenylcoumaran benzylic ether reductase (Koeduka et al., 2006). Phenylpropenes and benzoids are further subjected to methylation catalyzed by plant O-methyltransferases (OMTs) (Ibrahim et al., 1998). Various OMTs involved in the biosynthetic pathways of floral scent components have been identified and characterized. For example, S-adenosyl-l-methionine (iso)eugenol OMT, which catalyzes the methylation of eugenol and isoeugenol to form the volatiles methyleugenol and isomethyleugenol, has been isolated from Clarkia breweri (Wang et al., 1997). Eugenol OMT and chavicol OMT, which convert eugenol and chavicol to methyleugenol and methylchavicol, respectively, have been identified in Ocimum basilicum (Lewinsohn et al., 2000; Gang et al., 2002). Similarly, enzymatic activities able to convert trans-anole to trans-anethole and chavicol to estagole have been demonstrated in Foeniculum vulgare tissues (Gross et al., 2002, 2006). Transient over-expression in tobacco showed that OMT gene isolated from apple (MdOMT1) utilized a range of phenylpropene substrates and catalyzed the conversion of chavicol to estragole (Yauk et al., 2015). The same study showed that multiple transgenic apple lines (cv. Royal Gala) with reduced MdOMT1 expression produced lower levels of methylated phenylpropenes, including estragole and methyleugenol indicate that MdOMT1 is required for the production of methylated phenylpropenes in apple and that phenylpropenes including estragole may contribute to ripe apple fruit aroma (Yauk et al., 2015). A phloroglucinol O-methyltransferase that methylates the first step to produce the intermediate 3,5-dihydroxyanisole has been isolated from rose petals, and the two previously described orcinol O-methyltransferases catalyze the subsequent steps (Wu et al., 2004). As studies of the enzymes responsible for the synthesis of aromatic compounds progress, the possibilities of joining different substrates increase. Therefore, recently, it has been shown that AATs from ripe strawberry (SAAT1) and tomato (SlAAT1) fruit can also utilize p-coumaryl and coniferyl alcohols, indicating that ripening-related AATs are likely to link volatile ester and phenylpropene production in many different fruits (Yauk et al., 2017). Methyl salicylate and methyl benzoate are common components of floral scent and are believed to be important attractants of insect pollinators (Dobson, 1994; Dudareva et al., 1998, 2000; Dudareva and Pichersky, 2000). Enzymes that catalyze the formation of methyl salicylate and methyl benzoate from salicylic acid (SA) and benzoic acid (BA), respectively, have been characterized from flowers of Clarkia breweri, snapdragon (Antirrhinum majus), petunia, Arabidopsis thaliana, and Stephanotis floribunda (Ross et al., 1999; Murfitt et al., 2000; Negre et al., 2002; Pott et al., 2002; Chen et al., 2003). While these enzymes use S-adenosyl-l-methionine as the methyl donor, as do many previously characterized methyltransferases that act on a variety of substrates (e.g., DNA, protein, phenylpropanoids), these SA and BA carboxyl methyltransferases have primary amino acid sequences that show no significant sequence identity to other

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methyltransferases. Interestingly, a group of N-methyltransferases involved in the biosynthesis of the alkaloid caffeine, including theobromine synthase, share sequence similarity with the SA and BA carboxyl methyltransferases (D’Auria et al., 2003). These enzymes were therefore grouped into a new class of methyltransferases designated the SABATH methyltransferases, and this family now also includes jasmonic acid methyltransferase (Seo et al., 2001), indole-acetic acid methyltransferase (Zubieta et al., 2003), and cinnamic/p-coumaric acid methyltransferase (Kapteyn et al., 2007). The three dimensional structure of C. breweri SA carboxyl methyltransferase (Zubieta et al., 2003), combined with in silico modeling of the active site pocket in the Nicotiana suaveolens and S. floribunda enzymes (Pott et al., 2004), also indicates that these enzymes have a unique structure that is distinct from those of unrelated methyltransferases found in plants (Noel et al., 2003). Different reports suggest that vanillin could be synthesized from phenylpropanoid precursors (Schwab et al., 2008), and a three-step pathway for vanillin biosynthesis from 4-coumaric acid has been proposed based on precursor accumulation and feeding cell cultures of V. planifolia with the proposed precursors (Havkin‐Frenkel et al., 1999). In this pathway, 4-coumaric acid is first converted to 4-hydroxybenzaldehyde via 4-hydroxybenzaldehyde synthase through a chain shortening step (Podstolski et al., 2002). Then, 4-hydroxybenzaldehyde synthase carries out the hydroxylation of position 3 on the ring of p-hydroxybenzyl alcohol converting it to 3,4-dihydroxybenzyl alcohol or aldehyde. The final enzymatic step was shown to be catalyzed by a multifunctional V. planifolia OMT that had a broad substrate range, including 3,4-dihydroxybenzaldehyde (Pak et al., 2004). 4.3.3 Carbohydrate-Derived Flavor Compounds 4.3.3.1 Furanones and Pyrones Only a limited number of natural volatiles originate directly from carbohydrates without prior degradation of the carbon skeleton; such compounds include the furanones and pyrones (Bood and Zabetakis, 2002), which are important fruit constituents and have been isolated from the bark and leaves of several tree species (Schwab and Roscher, 1997). Pyrone maltol and substituted 4-hydroxy-3-(2H)-furanones constitute flavor molecules with exceptional low odor thresholds. Furanones are emitted only by the fruits; conversely, maltol has been isolated from the bark and leaves of Larix deciduas, Evodiopanax innovans, Cercidiphyllum japonicum, and Pinaceae plants (Tiefel and Berger, 1993; Schwab et al., 2008). Regarding the precursor of furaneol, experiments using the labeled precursors revealed that d-fructose-1,6-diphosphate is an efficient biogenetic precursor of furaneol. In strawberry (Fragaria ananassa) and tomato (Solanum lycopersicum), the hexose diphosphate is converted to 4-hydroxy-5-methyl-2-methylene3-(2,H)-furanone, which serves as the substrate for an enone oxidoreductase isolated from ripe fruit (Raab et al., 2006; Klein et al., 2007). Furaneol is one of the key flavor compounds in the attractive aroma of fruits (Farine et al., 1994). In strawberry, furaneol is further metabolized by an O-methyltransferase (FaOMT) to methoxyfuraneol (Wein et al., 2002). The in vivo methylation of furaneol by FaOM has been demonstrated by the genetic transformation of strawberry with the FaOMT sequence in the antisense orientation, under the control of a constitutive promoter, resulting in a near-total loss of the methoxyfuraneol volatile (Lunkenbein et al., 2006). However, the reduced level of methoxyfuraneol was only perceived by one-third

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of the volunteer panelists, consistent with results obtained by aroma extract dilution assays. Norfuraneol and homofuraneol have been identified in tomato and melon fruits, respectively, but their biogenetic pathways and that of maltol remain unknown (Schwab and Roscher, 1997; Schwab et al., 2008). However, studies in tomato and yeast have identified phosphorylated carbohydrates as potential precursors of the furanones (Sasaki et al., 1991; Hauck et al., 2003). 4.3.3.2 Terpenoids These molecules are enzymatically synthesized de novo from acetyl-CoA and pyruvate available from the carbohydrate present in plastids and the cytoplasm, without an important contribution of acetyl-CoA from fatty acid oxidation in peroxisomes (Schwab et al., 2008). Despite their diversity, all terpenoids derive from the common building units isopentenyl diphosphate (IDP) and its isomer dimethylallyl diphosphate (DMADP) (Croteau and Karp, 1991; McGarvey and Croteau, 1995; Croteau et al., 2000). Both IDP and DMADP are synthesized via two parallel pathways in plants: (i) the mevalonate (MVA) pathway, which is active in the cytosol; and (ii) the methylerythritol 4‐phosphate (MEP) pathway, which is active in the plastids (Lichtenthaler, 1999; Rodriguez‐Concepción and Boronat, 2002; Rohdich et al., 2002; Rohmer, 2003). In recent years, there has been significant progress in understanding the subcellular distribution of substrates with differing chain length and cross-talk between the two pathways for substrate formation (Gutensohn et al., 2013; Rasulov et al., 2015; Dong et al., 2016; Pazouki and Niinemets, 2016). The cytosolic pathway is responsible for the synthesis of sesquiterpenes, phytosterols, and ubiquinone, whereas monoterpenes, gibberellins, abscisic acid, carotenoids and the prenyl moiety of chlorophylls, plastoquinone and tocopherol are produced in plastids (Lichtenthaler, 1999; Rodriguez‐Concepción and Boronat, 2002; Rohdich et al., 2002; Rohmer, 2003), but indications of cross‐talk between the plastidic and cytosolic pathways have been found in tobacco, Arabidopsis, and snapdragon petals (Ohara et al., 2003; Aharoni et al., 2004; Dudareva et al., 2005). The direct precursors of terpenoids, linear geranyl diphosphate (GDP, C10), farnesyl diphosphate (FDP, C15), and geranylgeranyl diphosphate (GGDP, C20), are produced by the activities of three prenyl transferases (PTs). Terpene synthases are the primary enzymes responsible for catalyzing the formation of hemiterpenes (C5), monoterpenes (C10), sesquiterpenes (C15) or diterpenes (C20) from the substrates DMADP, GDP, FDP, or GGDP, respectively. Prenyl transferases catalyze the addition of IDP units to prenyl diphosphates with allylic double bonds to the diphosphate moiety. Most of the PTs accept DMADP as the initial substrate, but they also bind GDP or FDP depending on the particular prenyltransferase (Tarshis et al., 1994, 1996; Greenhagen and Chappell, 2001; Withers and Keasling, 2007). The availability of GDP and FDP are often the key factor in the production of monoterpenes and sesquiterpenes in plants. In fact, multi-substrate enzymes can form monoterpenes with GDP as the substrate and sesquiterpenes with FDP as the substrate (Davidovich-Rikanati et al., 2008; Gutensohn et al., 2013; Pazouki et al., 2015; Pazouki and Niinemets, 2016). This problem was elegantly overcome in metabolic engineering experiments by the co‐expression of GDP and FDP synthases with appropriate monoterpene and sesquiterpene synthases over-expressed in tobacco (Wu et al., 2006). This strategy, together with the targeting of the over-expression to the plastid compartment, resulted in increased synthesis of the sesquiterpenes amorpha‐4,11‐diene and patchoulol and the monoterpene S‐limonene (Wu et al., 2006). The third phase of terpene volatile biosynthesis involves the conversion of the various prenyl diphosphates DMADP, GDP, FDP, and GGDP to hemiterpenes, monoterpenes, sesquiterpenes, and diterpenes,

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respectively, by the large family of the terpene synthases (TPSs) (Pazouki and Niinemets, 2016). Triterpenes (and sterols) and tetraterpenes (such as carotenoids) are derived from the condensation of two molecules of FDP or GGDP, respectively. Evolutionarily, plant hemiterpene, monoterpene, sesquiterpene, and diterpene synthases are related to each other and are structurally distinct from triterpene or tetraterpene synthases (Bohlmann and Keeling, 2008). Many TPSs have been isolated and characterized from various plant species (Bohlmann et al., 1998; Tholl, 2006; Davidovich-Rikanati et al., 2008; Gutensohn et al., 2013; Pazouki et al., 2015). In tomato and other plants, there is evidence that several TPSs are multi-substrate enzymes, capable of synthesizing terpenes of different chain length depending on corresponding substrate availability (Davidovich-Rikanati et al., 2008; Gutensohn et al., 2013; Pazouki et al., 2015). Among such multi-substrate enzymes, some can form monoterpenes with GDP as the substrate and sesquiterpenes with FDP as the substrate (Davidovich-Rikanati et al., 2008; Gutensohn et al., 2013; Pazouki et al., 2015; Pazouki and Niinemets, 2016). It has also been reported that the cultivated apple genome (Malus domestica) contains 55 putative terpene synthase TPS genes but only 10 are functional (Nieuwenhuizen et al., 2013). While many terpene volatiles are direct products of TPSs, many others are formed through transformation of the initial products by oxidation, dehydrogenation, acylation, and other reactions (Croteau and Karp, 1991; Croteau et al., 2000; Dudareva et al., 2004; Pichersky et al., 2006). For example, (–)-(1R,2S,5R)-menthol, the principal monoterpene of commercial peppermint essential oil is formed by eight enzymatic steps involving monoterpene synthases, isomerases, and reductases (Turner and Croteau, 2004; Ringer et al., 2005). The biosynthesis starts with the formation of 4S-limonene from GPP and ends with the reduction of (–)-menthone to (°)-menthol. 4.3.3.3 Apocarotenoids Apocarotenoids are commonly found in the flowers, fruits, and the leaves of many plants (Winterhalter and Rouseff, 2001), and possess flavor aroma properties together with low aroma thresholds. They are found among the potent flavor compounds in wines and contribute to floral and fruity attributes (Winterhalter and Schreier, 1994, 2002). Carotenoid cleavage dioxygenases catalyze the oxidative cleavage of carotenoids, resulting in the production of apocarotenoids (Schmidt et al., 2006). CCDs often exhibit a preference for several substrates, contributing to the diversity of apocarotenoids found in nature. The synthesis of β-ionone, geranyl acetone and 6-methyl-5-hepten-2-one in tomato fruits increase 10–20‐fold during fruit ripening, and these compounds were shown to be produced by the activity of the genes LeCCD1A and LeCCD1B that were isolated from tomato fruits (Simkin et al., 2004). In tomato fruit, β-ionone is present at very low concentrations (4 nLL −1), but due to its low odor threshold (0.007 nLL −1), it is the second most important volatile contributing to fruit flavor (Baldwin et al., 2000). Silencing of LeCCD1A and LeCCD1B resulted in a significant decrease in the β-ionone content of ripe fruits, implying a role for these genes in C13 norisoprenoid synthesis in vivo (Simkin et al., 2004). Reduction of Petunia hybrida CCD1 transcript levels in transgenic plants led to a 58–76% decrease in β‐ionone synthesis in the corollas of selected petunia lines, indicating a significant role for this enzyme in volatile synthesis (Simkin et al., 2004). Also, a potential CCD gene was identified among a Vitis vinifera L. EST collection, and recombinant expression of VvCCD1 confirmed that the gene encodes a functional CCD that cleaves zeaxanthin symmetrically yielding 3-hydroxy-β-ionone and a C14 aldehyde (Mathieu et al., 2005). CCDs were also found to be involved in the formation of important aroma compounds in melon (Cucumis melo). The product of the CmCCD1 gene,

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whose expression is up-regulated upon fruit development, was shown to cleave carotenoids, generating geranylacetone from phytoene, pseudoionone from lycopene, β-ionone from β-carotene, and α-ionone and pseudoionone from δ-carotene (Ibdah et al., 2006). A recent study of carotenoid biosynthesis in bilberry or European blueberry showed an increase of expression of the gene encoding for carotenoid cleavage dioxygenase (VmCCD1) and 9-cis-epoxycarotenoid dioxygenase (VmNCED), indicating enzymatic carotenoid cleavage and degradation during ripening (Karppinen et al., 2016). Instead, mature bilberry fruits responded specifically to red/far-red light wavelengths by inducing the expression of both the carotenoid biosynthetic and the cleavage genes indicating tissue and developmental stage-specific regulation of apocarotenoid formation by light quality (Karppinen et al., 2016).

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Chapter

5

Orthonasal and Retronasal Olfaction Pengfei Han and Thomas Hummel CONTENTS 5.1 5.2 5.3 5.4 5.5

Basics of Human Olfaction 99 Orthonasal and Retronasal Olfaction 100 Methods to Investigate Orthonasal Olfaction 101 Method to Study Retronasal Olfaction 102 Differences between Orthonasal and Retronasal Olfaction 102 5.5.1 Differences at the Psychophysical and Peripheral Levels 103 5.5.2 Differentiations at the Neural Level 103 5.6 The Role of Ortho- and Retronasal Olfaction in Food Perception 104 5.6.1 Orthonasal Food Odor Perception 105 5.6.2 Retronasal Food Odor Perception 105 5.7 Factors Influencing Olfactory Perception 106 5.7.1 Gender 106 5.7.2 Age 107 5.7.3 Genetic Factors 108 5.7.4 Oral Environment (Saliva and Microbiota) 109 5.7.5 Hormonal Levels 110 5.7.6 Olfactory Disorders 110 5.8 Conclusions 111 Acknowledgment 111 References 111

5.1  BASICS OF HUMAN OLFACTION Olfaction is phylogenetically considered the oldest sense, yet remains the least well understood (Philpott et al., 2008). For humans, the sense of smell is more important than is generally realized (McGann, 2017), as suggested by the important role it plays in the evolution of human diet, food ingestion, threat detection, and social behaviors (Shepherd, 2004; Stevenson, 2010). Olfaction comprises the chemosensory modality dedicated to detecting low concentrations of airborne, volatile chemical substances (Ache and Young, 2005). The ascending pathway for odor perception begins in the olfactory epithelium, where odor molecules make contact with sensory endings of olfactory receptor neurons. The olfactory epithelium comprises 6–30 million neurons. They express hundreds of receptor proteins which enable detection and discrimination of thousands of odorants (Menashe and Lancet, 99

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2006). The axons of sensory neurons in the olfactory epithelium project to the olfactory bulb—the first olfactory brain structure, in which different odor molecules are represented by different patterns of spatial activities. Axonal projections from these cells are conveyed via the lateral olfactory tract onto the primary and secondary olfactory cortex (Gottfried, 2010). Thus, an odor perception (e.g., banana or bacon) is the brain’s interpretation of the activation pattern of many peripheral sensory neurons that are differentially sensitive to a wide variety of odor molecules (Firestein, 2001). There are different dimensions commonly used to define odor perception, which include odor quality and intensity, as well as the affective (hedonic) dimension, or the familiarity with an odor. For humans, the most common feature of olfactory function to be quantified is the ability to identify an odor (odor identification), but there are also other aspects of olfactory functions, such as the ability to discriminate between odors (odor discrimination) or to detect odors at low concentrations (odor threshold).

5.2  ORTHONASAL AND RETRONASAL OLFACTION In humans and other similarly behaving animals, there are two ways to sample and transport odor molecules from the outside atmosphere to the olfactory epithelium. Orthonasal olfaction occurs when odor molecules are sampled via nasal inhalation or sniff; odors are delivered with inhaled air to the olfactory receptors through the nose. Retronasal olfaction is naturally initiated by the chewing and swallowing of foods. Volatile molecules are released into the back of the oral cavity and travel through the nasopharynx during exhalation and subsequently stimulate receptors on the olfactory epithelium in the nose (Figure 5.1). Because retronasal sensations are typically perceived through the mouth (Lim and Johnson, 2011), retronasal sensations are commonly referred to as “taste.” In addition, smell–taste confusions are so profound that there are even languages (e.g., Swiss German) that do not have a proper word for “smell” but largely refer to it as “taste.”

FIGURE 5.1  The orthonasal (dark gray) and the retronasal (light gray) route for olfaction.

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In this chapter, the methods used to investigate orthonasal and retronasal olfaction will be first introduced. Although the same set of olfactory receptor neurons are being activated, olfactory perceptions through the orthonasal and retronasal routes are different at both the psychophysical and neural levels. In addition, this chapter will compare and discuss the roles the two olfactory routes play in food perception and nutrition. Finally, factors that influence olfaction in humans will be summarized.

5.3  METHODS TO INVESTIGATE ORTHONASAL OLFACTION Among the most widely used orthonasal olfactory tests is the “Sniffin’ Sticks” battery (Burghart Messtechnik GmbH, Wedel, Germany) (Figure 5.2) (Kobal et al., 1996). The Sniffin’ Sticks test is based on felt-tip pen-like devices containing common odors selected specifically to be applicable in the general European population (Hummel et al., 1997). The classic test consists of three parts: odorant threshold, discrimination, and identification (TDI) abilities (Hummel et al., 1997), which allows a comprehensive description of the overall olfactory function. The Sniffin’ Sticks test was also modified into other versions; for example, the “Sniffin’ Kids” test for children between 6 to 17 years old (Schriever et al., 2014). Another version is the extended test for odor identification with 32 items, which was developed in order to create more precise tools enabling repeated, longitudinal testing of small changes of olfactory subfunction (Haehner et al., 2009; Sorokowska et al., 2015a). A major advantage of the “Sniffin’ Sticks” is the availability of normative data generated in large groups of healthy subjects which can be applied to both healthy people and patients with olfactory dysfunctions (Hummel et al., 2007). Another popular test for olfactory identification is the University of Pennsylvania Smell Identification Test (UPSIT), which contains 40 microencapsulated odorants on the paper of the test kits (Doty et al., 1984b). The identification scores of the UPSIT test have been

FIGURE 5.2  The “Sniffin’ Sticks” smell test with its three parts: Odor identification

(foreground tube rack, with list of verbal and graphical descriptors of odors); the odor discrimination test (middle tube rack); and the phenyl ethyl alcohol odor threshold test (back ground tube rack).

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shown to be comparable with the Sniffin’ Sticks identification test (Hugh et al., 2015). The UPSIT offers the advantage of being able to be self-administered by most participants. By microencapsulating odorants, olfactory testing can reach large amounts of subjects, for example, through mailing. In addition, normative data based on results from nearly 4000 people are available, and an individual’s percentile rank can be determined from tables based upon data from healthy people of the same age and gender (Doty, 1997).

5.4  METHOD TO STUDY RETRONASAL OLFACTION The key issue for the investigation of retronasal olfaction is the delivery of odorous stimuli to the back of the throat. Psychophysical tests to explore retronasal olfactory function include flavor identification tests such as the “taste powders test” (Heilmann et al., 2002; for an international version see Croy et al. [2014]), or the “candy smell test” (Renner et al., 2009). Taste powders are a test kit with 20 grocery-available food powders. The powders are administered to the middle of the tongue, and subjects are asked to identify the “taste” from a list of four items using a multiple forced choice procedure (Heilmann et al., 2002). Similarly, the candy smell test consists of 23 hard candies, each containing one unique aroma. The aromas are congruent to sweet taste. Both tests show good test– retest reliability and are commonly used in clinical measurement of retronasal olfaction. Although these tests address the retronasal application of odors, they cannot allow direct comparisons to stimuli that are presented in front of the nose, simply because the oral administration of odors may produce gustatory, thermal, and mechanical sensations which may interact with olfactory mediated sensations (Spence, 2013). To avoid this situation, some researchers have placed odors in containers and then placed them on the tongue to deliver odor retronasally (Sun and Halpern, 2005), while others asked subjects to sniff the headspace of an odorous liquid or inhale the same headspace through the mouth followed by nasal exhalation (Burdach et al., 1984; Voiol and Daget, 1986). However, these methods have limitations since the odor concentration in the oral cavity is non-predictable and the mechanical stimulation of intraoral surfaces is not fully suppressed (Bojanowski and Hummel, 2012). A more defined retronasal stimulation is possible when using a computer-controlled air-dilution olfactometer (Kobal, 1981), which allows researchers to control the concentration and flow rate of an odor stimulus. Under endoscopic control, Heilmann and Hummel inserted a tube below the lower turbinate of the nasal cavity and placed its opening in the epipharynx (Heilmann and Hummel, 2004). In this way, the odor stimuli could be presented to the olfactory epithelium retronasally without additional activation of sensors in the oral cavity. Using the computer-controlled olfactometer, this form of stimulation provides excellent control over both odor concentration and stimulus onset/offset.

5.5  DIFFERENCES BETWEEN ORTHONASAL AND RETRONASAL OLFACTION Although both routes deliver the odor molecules to the same receptor fields in the olfactory epithelium, sensation and perception from orthonasal or retronasal olfaction have been reported differently. Traditionally, retronasal smell has been considered equivalent to orthonasal smell, just coming from a different direction. However, the validity of such an assumption has come under question. Odors presented via the retronasal route appear to evoke

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different sensations compared to orthonasal presentation (Frasnelli et al., 2008). Orthonasal and retronasal olfaction differ in terms of odor threshold, odor intensity, ability to localize an odor, and the neuronal processing. Differences in airflow patterns, apart from cognitive factors, play an important role in perceptual differences between ortho- and retronasal presentations of odors (Mozell, 1964; Rebello et al., 2015; Scott et al., 2007, 2014). The direction of the odorized airstream (e.g., forward or backward) and the consequential differences in absorption patterns of odor molecules to the olfactory epithelium across the olfactory epithelium probably leads to differences in the processing of odorous information. 5.5.1 Differences at the Psychophysical and Peripheral Levels A number of studies have reported psychophysical differences between ortho- and retronasal olfaction. Thresholds to odor stimuli are typically lower for the orthonasal compared to the retronasal route; in other words, people are more sensitive to orthonasal than to retronasal stimuli (Duffy et al., 1999; Heilmann and Hummel, 2004; Melzner et al., 2011; Voiol and Daget, 1986). The reason for such differences has been postulated to be due to different stimulation patterns of the olfactory epithelium, or due to different temporal patterns of activation (Engen, 1982). On the suprathreshold level, Pierce and Halpern compared ortho- and retronasal odor identification abilities (Pierce and Halpern, 1996). They found odor identification to be significantly better when odors were presented orthonasally. In addition, intensity ratings were higher for orthonasal than for retronasal stimuli (Heilmann and Hummel, 2004). It has also been reported that the odor identification is more efficient when stimuli are presented orthonasally (Burdach et al., 1984). Thus, retronasal olfactory stimulation appears to lead to a lower degree of activation of the olfactory system than orthonasal stimulation. At the same time, measures of odor concentrations in the olfactory cleft appeared to indicate that both absolute odor concentrations and time course of the stimuli were similar, regardless of whether odors were presented in the back or in the front of the nose. Intranasal air flow exhibited subtle differences in relation to stimulus presentation. For example, numerous studies indicate that pure olfactory stimuli cannot be lateralized when the stimulus is applied to one of the two nostrils (Kobal et al., 1989; von Skramlik, 1925); however, subjects are able to differentiate between ortho- and retronasal presentation of an odorant (Hummel, 2008). Differences between ortho- and retronasal olfactory functions also affect the salivary response. Bender et al. observed recovery from salivary habituation (e.g., reduced saliva secretion upon repeated exposure to a certain odor stimulus) upon presentation of the same odor via a novel route (e.g., changed from orthonasal to retronasal or vice versa) (Bender et al., 2009). A similar finding was reported from a recent study where adaptation was observed to odors delivered orthonasally but not retronasally (Pierce and Simons, 2018). This lack of cross-adaptation demonstrates that presenting an odor via different pathways represents a distinct sensory signal (Bender et al., 2009). 5.5.2 Differentiations at the Neural Level In humans, when recording electrical potentials at the olfactory epithelium (electroolfactograms, EOGs) in response to orthonasal or retronasal stimulation with phenylethyl alcohol, smaller electro-olfactogram signal amplitudes were observed for retronasal

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as compared to orthonasal stimulation, indicating that the perceptual difference starts at the level of the olfactory epithelium (Hummel et al., 2017a). Gautam and Verhagen (Gautam and Verhagen, 2012) showed that in the olfactory bulb, retronasally presented odors induced not only smaller response amplitude but also longer response onset latencies when compared to orthonasally presented odors in a rat model. Electroencephalogram-derived olfactory event-related potentials (OERPs) also indicated differences between orthonasal and retronasal odor perception. For example, a prolonged OERP latency and smaller amplitude were shown when a fruity odor stimulus was delivered retronasally as compared to orthonasally, which was in accordance to a stronger perceived intensity through the orthonasal route (Ishii et al., 2008). In addition, the OERP seemed to be odor- and context-dependent. When a non-food odorant (e.g., lavender) was presented in a contextually unusual site, that is, retronasally, the peak amplitude of OERP was larger compared to the presentation of the same odorant at an orthonasal site. This was the other way around for a food-related odor (e.g., chocolate). These findings clearly indicate the contextual differences in information processing depending on the route of odor presentation (Hummel and Heilmann, 2008). Human brain imaging studies demonstrate similar yet different patterns of brain activation in response to orthonasal and retronasal odor stimulation. By applying odorants in aqueous solution, previous studies have found that multiple brain regions, including the piriform cortex, amygdala, orbitofrontal cortex (OFC), insular cortex, and hippocampus, known as the regions activated by orthonasal stimulation, were also activated in response to retronasal odor stimulation (Cerf-Ducastel and Murphy, 2001; de Araujo et al., 2003). However, activation by retronasal odor stimulation was found at the base of the central sulcus, corresponding to the primary representation of the oral cavity-related stimulation, possibly reflecting that retronasal odors are referred to the mouth (Boling et al., 2002; Pardo et al., 1997; Yamashita et al., 1999). When the same set of odors were perceived through either orthonasal and retronasal routes, larger brain activation in the insula, opercula, thalamus, hippocampus, amygdala, piriform, and caudolateral OFC was found in response to orthonasal odor stimuli, whereas the pregenual cingulate, posterior cingulate, medial OFC, and superior temporal gyrus extending into the temporal operculum show greater activation after retronasal olfactory stimulation (Small et al., 2005). Importantly, this difference was only found for food-related odors (Small et al., 2005). Taken together, these findings indicate that orthonasal and retronasal olfaction represent two qualitatively distinct systems. This finding supports one of the first theories of orthonasal versus retronasal differences coined by Rozin (1982), stating that there are different behavioral consequences depending on the two types of information. In short, orthonasal stimulation represents information from odor sources in the environment ranging from animals over plants to fire. Retronasal stimulation signals information from odor sources in the oral cavity, which is generally an object that has been previously selected as food.

5.6  THE ROLE OF ORTHO- AND RETRONASAL OLFACTION IN FOOD PERCEPTION For food odors, orthonasal perception represents information from the food sources in the environment, while retronasal stimulation signals information from odor sources in the oral cavity. An increasing body of literature describes how the perceptions of orthonasal and retronasal food odor presentation routes differ.

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5.6.1 Orthonasal Food Odor Perception A major role of orthonasal odor perception is to identify and spatially localize the food sources. Human neonates tended to move toward a pad scented with their mother’s breast odor in contrast to a clean control pad (Varendi and Porter, 2001), indicating the early development of orthonasal olfaction to guide food- and nutrient-seeking behavior (Adam-Darque et al., 2017; Varendi et al., 1994). In addition, orthonasal odors are needed to identify a food’s suitability for ingestion, reflecting prior learning about the food’s immediate and delayed consequences. Food-related odors can be categorized according to their “taste” attributes, which may be utilized to estimate the dominant macronutrients (e.g., carbohydrate, protein or fat) in certain foods (Denzer-Lippmann et al., 2017). For example, sweet food odors usually emanate from food high in sugar content. In addition, through orthonasal olfaction, humans can discriminate high concentrations of long-chain fatty acids in vapor phase (Bolton and Halpern, 2010) and were able to discriminate between skimmed, medium, and full fat milk samples with an overall accuracy of 40–55% correct trials in three consecutive experiments, a value that is significantly above the expected chance level (33.3%) (Boesveldt and Lundstrom, 2014). Thus, the orthonasal olfaction may function as a detection system for nutrients content within natural food sources. The presence of food odor typically triggers the “cephalic phase response” (CRR)—a series of anticipatory physiological regulations related to feeding, including salivation and secretion of hormones and digestive enzymes. CRS is to prepare the body for ingestion and digestion of specific foods or macronutrients (Smeets et al., 2010). In addition, food odors may stimulate appetite for food consumption (Ramaekers et al., 2014; Zoon et al., 2016). The odor-induced appetite seems to follow a sensory-specific manner; for example, savory food odor increased the appetite for savory food, but decreased appetite for sweet foods, and vice versa after exposure to sweet food odors. In addition, exposure to food odors can direct choice toward the food that is signaled by the odor specifically. For example, it has been reported that sub-threshold exposure to fruit odors prior to a meal led participants to choose more fruit and vegetable-based foods at a subsequent meal (Gaillet et al., 2013; Gaillet-Torrent et al., 2014; Marty et al., 2017). However, Zoon et al. (2014) found that exposure to ambient odors that signaled high or low energy-dense sweet and savory foods had no effect on the consumption of these foods, and this did not vary with hunger state. In contrast to the appetizing effect, another study reported that orthonasal exposure to dark chocolate odor suppressed hunger and reduced the ghrelin concentration level (Massolt et al., 2010). Taken together, orthonasal food odor can direct people toward food sources; however, the extent to which these odors are utilized to regulate appetite (including both its stimulation and inhibition) or subsequent food choices remains unclear and requires more research. 5.6.2 Retronasal Food Odor Perception In real life, retronasal perception of food odors occurs when food is chewed in the mouth. Retronasal odor perception often, if not always, interacts with signals from other sensory modalities during food consumption, such as taste, somatosensation, or audition, and is considered a fundamental factor in the formation of food flavor (Shepherd, 2006). Retronasal olfaction is often referred to the mouth, which is augmented in the presence of

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a congruent taste (Lim and Johnson, 2011,2012). When retronasal olfaction connects to other sensations in the mouth, for example, taste or somatosensation, the unified percept is projected to the brain’s mouth area, eventually leading to the well-known sensory confusion between taste and retronasal olfaction (Murphy and Cain, 1980; Rozin, 1982). Loss of retronasal olfaction reduces the food-induced sensations related to gustation, texture, and chemesthesis. As a result, the ability to identify flavors is severely limited (Running, 2018). As compared to orthonasal odor perception, which is related to food or nutrient anticipation, retronasal odor perception is suggested to be related to food reward (Small et al., 2005). Possibly, retronasal odors are linked to nutrient intake and become satiating. In addition, oral exposure to odors may also affect sensory-specific satiety because cephalic phase responses are generally stronger during oral than during odor stimulation. A series of studies suggested that retronasal odor stimulation may be used to induce satiety during consumption and ultimately may contribute to a decrease in food intake (Ruijschop et al., 2009a). Indeed, Ruijschop et al. (2008) demonstrated that participants felt significantly more satiated and had less desire to eat congruent (sweet) foods if they were stimulated with a longer retronasal odor profile during (sweet) milk consumption. However, in that study, there was no effect of retronasal odor exposure on the subsequent milk intake, while a later study established a 9% lower food intake when participants were exposed to a high retronasal condition versus other conditions with lower concentrations or shorter exposure duration (Ramaekers et al., 2013). In another study, a negative correlation was observed between the extent of retronasal aroma release and the ad libitum food consumption (Ruijschop et al., 2009b). Collectively, these results indicated that changing the release of retronasal aroma through modulation of concentration and exposure time may affect perceived satiety and food intake, but perhaps not in all situations or for all participants. In addition, the composition of the odor seems to affect the satiating effects of retronasal perception, that is, the more complex an aroma, the higher the satiation (Ruijschop et al., 2010).

5.7  FACTORS INFLUENCING OLFACTORY PERCEPTION Human olfactory perception differs enormously between individuals, with large reported perceptual variations in the intensity and pleasantness of a given odor. Like in other senses, humans vary widely in their olfactory sensitivity and quality perception. This variability includes differences in general olfactory acuity and in the sensitivity toward particular odorants. 5.7.1 Gender In terms of odor detection, there is no conclusive empirical evidence for gender difference (Doty and Cameron, 2009; Larsson et al., 2000). Nevertheless, where gender differences exist, women are usually more sensitive to odors compared to men (Dalton et al., 2002; Kobal et al., 2001; Koelega, 1994). Females are better at odor discrimination and identification (Cain, 1982; Ferdenzi et al., 2013), which could be related to gender differences in verbal proficiency (Larsson et al., 2003). In neuroimaging measurement with odor perception, females exhibited stronger responses in the frontal brain cortex, including the OFC (Royet et al., 2003; Yousem et al., 1999), a brain structure that is involved in odor identification (Kjelvik et al., 2012). For retronasal olfaction, women also outperformed

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men (Heilmann et al., 2002). Overall, gender differences in olfactory ability appear to be due to aspects of olfactory processing that require higher-level cognition, such as odor identification or odor memory. There is also evidence suggesting gender differences in odor intensity or pleasantness. For example, with a large cross-cultural sample size (n = 772), Ferdenzi et al. (2013) showed that females rated odors more intense compared to males. In another study, Olofsson and Nordin (2004) found that females, as compared to males, provided higher intensity and unpleasantness ratings for olfactory/trigeminal stimuli, which was reflected as more identifiable early components, larger amplitudes and shorter latencies for the relatively exogenous late OERP component. During repeated exposure to an odor, women showed smaller decrease in liking as compared to men (Triscoli et al., 2014). 5.7.2 Age The human olfactory system is well-formed during fetal development and olfactory functions are already present at birth (Adam-Darque et al., 2017; Dominguez, 2011). Overall olfactory performance increases with age and peaks at the age of 20 to 30 years, and then declines with aging (Figure 5.3). In adults, aging often involves various transformations of the olfactory system, which leads to a decreased olfactory function (for review see Doty and Kamath, 2014). The overwhelming evidence indicates a decline of odor sensitivity with aging, pronouncedly so in older people (Cain et al., 1990; Griep et al., 1995; Larsson et al., 2009, 2000; Stuck et al., 2006). The decline of sensitivity toward retronasal odors has also been described (Duffy et al., 1999; Stevens and Cain, 1986). Some authors suggested that

FIGURE 5.3  Age-related changes of the “Sniffin’ Sticks” test TDI scores in healthy subjects (n = 479). With the exception of the youngest subjects, TDI scores decreased as a function of age. (Adapted from Kobal et al., 2000.)

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aging impairs detection thresholds of different odors in a similar way (Cain and Gent, 1991; Cowart, 1989; Stevens et al., 1988). Others, however, referred to the stimulus specificity in olfactory aging, suggesting that some flavors might fade faster than others (Cain et al., 1990). For example, Flaherty and Lim showed decreased retronasal responsiveness among older participants for vanilla and soy sauce odors but not for strawberry or chicken odors (Flaherty and Lim, 2016). Odor discrimination and recognition, which engage more cognitive load (Hedner et al., 2010) are developed through learning and cognitive development. Children showed an age-related increase in their olfactory abilities (Sorokowska et al., 2015b), and they were poorer in discriminating or recognizing odors as compared to adults, which may result from the lesser experiences with odors, relative to adults (Stevenson et al., 2007). For adults, significant age-related alterations are generally observed for odor identification (Doty et al., 1984a; Evans et al., 1995; Larsson et al., 2000; Sorokowska et al., 2015b) or discrimination (Cain et al., 1990). Age-related changes in olfaction are also indicated by ERP measures. For adults, the decreased olfactory performance with aging was associated with longer latencies or smaller amplitudes for the OERP components (Evans et al., 1995; Hummel et al., 1998; Stuck et al., 2006). Evans et al. (1995) also reported a significant life-span prolongation of the latency for late OERP components. In addition, one fMRI study found reduced activation in the olfactory brain areas in response to odors among older participants (Suzuki et al., 2001). Aging is also related to decreased odor pleasantness (Joussain et al., 2013). Taken together, the age-related changes in olfaction are observed at all levels of information processing. 5.7.3 Genetic Factors Individual variation of olfactory perception is largely due to genetic polymorphisms; these include olfactory receptor genes, signal transduction genes, as well as genes involved in propagation and processing of the olfactory input, such as those underlying the development of olfactory epithelium and olfactory bulb (Hasin-Brumshtein et al., 2009). Qualitatively, the genetic variations of the olfactory receptor genes explain much of the differences in sensitivity for odors, and, consecutively, flavor experience of foods. For example, the rs6591536 single-nucleotide polymorphism (SNP) variation of the olfactory receptor OR5A1 explains perceptual sensitivity to the odorant β-ionone (Jaeger et al., 2013). Several other food odors have been associated with genetic variation in odorant receptor genes. Sensitivity to odors associated with SNPs in specific genes includes isovaleric acid (cheesy, sweaty), β-ionone (floral), cis-3-hexen-1-ol (green, grassy), and guaiacol (smoky) (Jaeger et al., 2013; Mainland et al., 2014; Menashe et al., 2007). Sensitivity to other food-related odors, such as the isobutyraldehyde (malty), β-damascenone (floral), and 2-heptanone (banana), has only been associated with regions known to encode odor receptors (McRae et al., 2013). Genetic variations also contribute to qualitative odor perception. The smell of the steroid androstenone, which can be found in the skin and adipose tissue of male pigs, varies among individuals with some experiencing no sensation, others a sweet, florallike sensation, and others an unpleasant sweaty, urinous sensation. Gene association studies in humans indicate that SNPs in the OR7D4 olfactory receptor gene are at least partially responsible for perceptual differences for the odor of androstenone (Keller et al., 2007). The unpleasant perception (e.g., “soapiness”) for cilantro is also suggested to

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be genetically determined (Eriksson et al., 2012; Mauer, 2011). An SNP in the OR4N5 OR6A2 variation has been implicated in cilantro soapiness and disliking, as well as one near the bitter taste receptor gene TAS2R1, may contribute to the pleasantness of cilantro odor, as individuals homozygous for both minor alleles at these locations disliked cilantro (Mauer, 2011). However, assessing the genetic variability is difficult to separate from cultural exposure to the herb, as this varies widely among ethnic groups. Additionally, a well-documented individual odor perceptual difference due to mutations in odor receptor genes is the inability to smell a particular chemical compound, called “specific anosmia” (Amoore, 1977). Such specific anosmias have been reported for many food-related odors, for example, isobutyric acid (Amoore et al., 1968) or trimethylamine (Amoore and Forrester, 1976), among others. It is also assumed that these idiosyncrasies are based on specific genetic differences between individuals (Croy et al., 2015; Keller et al., 2007). 5.7.4 Oral Environment (Saliva and Microbiota) The perception of quality and intensity for an odor are mainly linked to the chemical structure (Stevens, 1960) and the concentration of the molecule (Chastrette, 1997), respectively. In addition, salivary proteins and microbiota have been shown to influence the release of odor molecules in the oral cavity (Canon et al., 2018; Muñoz-González et al., 2018). The salivary proteins (e.g., mucins and enzymes) have been shown to interact with aroma compounds, consequently affecting aroma concentration and retronasal perception (Buettner, 2002a,b; Friel and Taylor, 2001; Muñoz-González et al., 2018; PagèsHélary et al., 2014). The addition of artificial saliva to reconstituted red wines decreased the release of the more hydrophilic molecules but enhanced that of hydrophobic molecules (Mitropoulou et al., 2011). Compared to artificial saliva, human saliva affects the release of ketones and esters differently (Pagès-Hélary et al., 2014), which is possibly due to different types and amounts of protein found in natural human saliva. Furthermore, one study showed how the individual differences in saliva composition (such as protein content and total antioxidant capacity) could be responsible for differences in aroma release (Muñoz-González et al., 2018). They found that centrifugation tends to reduce the effect of saliva and its interindividual variability (Muñoz-González et al., 2018). Another study showed that saliva from obese individuals presented a diminished aroma release compared with normal-weight subjects, and this fact was again related to the total protein content and the total antioxidant capacity determined in saliva (Piombino et al., 2014). Moreover, enzymes present in saliva can metabolize aroma compounds (MuñozGonzález et al., 2018) or glycosidic aroma precursors (Muñoz-González et al., 2015) which could modify the temporal aroma perception (Muñoz-González et al., 2015). The microbiological composition in saliva also has an impact on odor perception (Muñoz-González et al., 2015; Piombino et al., 2014). For example, Muñoz-González et al. found obese people to have more Firmicutes and Actinobacteria, while they had less Proteobacteria and Fusobacteria, as compared to the normal-weight participants, which also contributed to the suppressed aroma release from the wine sample (Piombino et al., 2014). However, until now most of the studies have been carried out in well-controlled in vitro situations that could not have represented the complexity and dynamics occurring at the human mouth level. Future studies are needed with sensory evaluation by human participants in order to better understand the impact of the oral environment on odor perceptions.

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5.7.5 Hormonal Levels The olfactory system is considered to be tightly intertwined with the endocrine system in the regulation of chemical state and nutritional needs (Palouzier-Paulignan et al., 2012). Olfaction is modulated by both orexigenic (appetite-stimulating) and anorexigenic (appetite-suppressing) signals, of which the production and site of action are located in the periphery (e.g., gastrointestinal tract, adipose tissue, and others) and/or in the central nervous system (e.g., hypothalamus, reward circuits) (Soria-Gomez et al., 2014a). It has been shown that ghrelin increases exploratory sniffing and enhances odor detection in both humans (Tong et al., 2011) and rodents (Loch et al., 2015; Tong et al., 2011), possibly by acting in the olfactory cortex. The endocannabinoids and exogenous cannabinoids increased odor detection in fasted mice, which linking the feeling of hunger to stronger/ more effective odor processing (Soria-Gomez et al., 2014b). A lower olfactory capacity is related to higher circulating concentrations of endocannabinoid 2-arachidonoylglycerol and higher body mass index in women (Pastor et al., 2016). In humans, administration of the opioid remifentanil raised olfactory thresholds while having little or no influence on odor discrimination and odor identification performance (Lötsch et al., 2001), and pharmacological blocking of opioid receptors was found to attenuate olfactory hedonic responses (Yeomans and Wright, 1991). Thus, there is abundant evidence that orexigenic factors enhance olfactory performance. Conversely, satiety and hypophagic molecules induce a decrease in olfactory sensitivity. Two of the main hormones involved in satiety processes are leptin and insulin (Coll et al., 2007; Morton et al., 2006). Importantly, both hormones and their receptors are also locally expressed in olfactory circuits, mainly in the olfactory mucosa and olfactory bulb. In rodents, a decreased performance in olfactory detection was observed upon the central administration of insulin (Aimé et al., 2012) or leptin (Julliard et al., 2007), mimicking the satiation state. In humans, reduced olfactory sensitivity was observed with elevated insulin levels (Brünner et al., 2013; Ketterer et al., 2011), or with increased visceral fat, which is linked to enhanced leptin concentrations (Fernandez-Garcia et al., 2017). In addition, elevated leptinemia is reflected by low ratings of black pepper-odor pleasantness (Trellakis et al., 2011). However, a recent study showed intranasal insulin application significantly decreased the sensitivity to n-butanol but not to peanut odor, and was observed only in females, which suggested odor- and gender-dependent effects on odor thresholds (Rodriguez-Raecke et al., 2018). In addition, blood leptin concentration is correlated, in a gender-specific way, to odor identification (Karlsson et al., 2002). Altogether, these data show that the nutritional status and its hormonal components modulate olfactory circuits, consequently changing the olfactory behaviors related to food consumption. 5.7.6 Olfactory Disorders Olfactory dysfunction includes qualitative and quantitative disorders (Hong et al., 2012; Hummel et al., 2017b). Quantitative olfactory dysfunction refers to a decreased, weaker odor perception, and includes hyposmia (the sense of smell is partially reduced) and anosmia (the sense of smell is lost completely or reduced to an extent that is not useful in daily life) (Hummel et al., 2017b). Qualitative olfactory dysfunction refers to distorted perceptions of odor quality such as parosmia (distorted odor perceptions in the presence of an odor source) and ­phantosmia (reported odor sensation in the absence of an odor) (Frasnelli et al., 2004).

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The incidence of anosmia in the general population is about 5%, and is more frequent among older people (Bramerson et al., 2004; Landis et al., 2004; Murphy et al., 2002; Vennemann et al., 2008). The major causes of olfactory dysfunctions include chronic rhinosinusitis with or without nasal polyps, acute infections of the upper respiratory tract, head trauma, and idiopathic olfactory loss (Temmel et al., 2002). Other causes include neurodegenerative disorders (such as Parkinson’s disease and Alzheimer’s disease). Olfactory loss can influence both orthonasal and retronasal olfaction. The presence of nasal polyposis influences orthonasal but typically to a lesser degree retronasal olfactory function (Landis et al., 2003). In addition, olfactory loss has a clear influence on food odor perception and eating behaviors (Aschenbrenner et al., 2008). Despite the colloquial meaning of the word “taste,” much of a food’s overall flavor actually comes from odor: specifically, retronasal olfaction. When nasal passages are inflamed or otherwise blocked, the movement of air is restricted and results in a lack of sensation. Loss of retronasal olfaction leads to reduced eating-related sensations, including gustation, texture, and chemesthesis. As a result, the ability to identify flavors is severely limited. For example, people with olfactory loss rated food odors as less intense (Seo and Hummel, 2009). Olfactory loss could further result in difficulties related to eating, preparing food/cooking, and reduced appetite. Interestingly, however, these eating problems do not lead to a general pattern of reduced food intake (Aschenbrenner et al., 2008; Temmel et al., 2002). In a study by Ferris and Duffy, 18% of the patients with olfactory loss described an increased food consumption while 20% showed a decrease, and the majority reported no change in food consumption (Ferris and Duffy, 1989).

5.8 CONCLUSIONS Orthonasal and retronasal olfaction are linked but also separated, particularly in food odor perception, suggesting a dual sensory process (Rozin, 1982). The difference not only lies in the perceptual level, but also in the neural level. In addition, individual differences in terms of olfactory perception are modulated by age, sex, genetic, and hormonal factors. More research is necessary to better understand the mechanism of orthonasal and retronasal olfactory perception and their differential roles in health and diseases.

ACKNOWLEDGMENT Special thanks go to Cornelia Hummel for preparing Figure 5.1.

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Small D. M., Gerber J. C., Mak Y. E., and Hummel T. 2005. Differential neural responses evoked by orthonasal versus retronasal odorant perception in humans. Neuron 47: 593–605. Smeets P. A., Erkner A., and de Graaf C. 2010. Cephalic phase responses and appetite. Nutr Rev 68: 643–55. Soria-Gomez E., Bellocchio L., and Marsicano G. 2014a. New insights on food intake control by olfactory processes: The emerging role of the endocannabinoid system. Mol Cell Endocrinol 397: 59–66. Soria-Gomez E., Bellocchio L., Reguero L., Lepousez G., Martin C., Bendahmane M., Ruehle S., Remmers F., Desprez T., Matias I., Wiesner T., Cannich A., Nissant A., Wadleigh A/, Pape H. C., Chiarlone A. P., Quarta C., Verrier D., Vincent P., Massa F., Lutz B., Guzman M., Gurden H., Ferreira G., Lledo P. M., Grandes P., and Marsicano G. 2014b. The endocannabinoid system controls food intake via olfactory processes. Nat Neurosci 17: 407–15. Sorokowska A., Albrecht E., Haehner A., and Hummel T. 2015a. Extended version of the “Sniffin’ Sticks” identification test: Test–retest reliability and validity. J Neurosci Methods 243: 111–4. Sorokowska A., Schriever V. A., Gudziol V., Hummel C., Hahner A., Iannilli E., Sinding C., Aziz M., Seo H. S., Negoias S., and Hummel T. 2015b. Changes of olfactory abilities in relation to age: Odor identification in more than 1400 people aged 4 to 80 years. Eur Arch Otorhinolaryngol 272: 1937–44. Spence C. 2013. Multisensory flavour perception. Curr Biol 23: R365–9. Stevens J. C. and Cain W. S. 1986. Smelling via the mouth: Effect of aging. Percept Psychophys 40: 142–6. Stevens J. C., Cain W. S., and Burke R. J. 1988. Variability of olfactory thresholds. Chem Senses 13: 643–53. Stevens S. S. 1960. The psychophysics of sensory function. Am Scientist 48: 226–53. Stevenson R. J. 2010. An initial evaluation of the functions of human olfaction. Chem Senses 35: 3–20. Stevenson R. J., Mahmut M., and Sundqvist N. 2007. Age-related changes in odor discrimination. Dev Psychol 43: 253–60. Stuck B. A., Frey S., Freiburg C,. Hormann K., Zahnert T., and Hummel T. 2006. Chemosensory event-related potentials in relation to side of stimulation, age, sex, and stimulus concentration. Clin Neurophysiol 117: 1367–75. Sun B. C. and Halpern B. P. 2005. Identification of air phase retronasal and orthonasal odorant pairs. Chem Senses 30: 693–706. Suzuki Y., Critchley H. D., Suckling J., Fukuda R., Williams S. C., Andrew C., Howard R., Ouldred E., Bryant C., Swift C. G., and Jackson S. H. 2001. Functional magnetic resonance imaging of odor identification: The effect of aging. J Gerontol A Biol Sci Med Sci 56: M756–60. Temmel A. F., Quint C., Schickinger-Fischer B., Klimek L., Stoller E., and Hummel T. 2002. Characteristics of olfactory disorders in relation to major causes of olfactory loss. Arch Otolaryngol Head Neck Surg 128: 635–41. Tong J., Mannea E, Aimé P., Pfluger P. T., Yi C. X., Castaneda T. R., Davis H. W., Ren X., Pixley S., Benoit S., Julliard K., Woods S. C., Horvath T. L., Sleeman M. M., D’Alessio D., Obici S., Frank R., and Tschop M. H. 2011. Ghrelin enhances olfactory sensitivity and exploratory sniffing in rodents and humans. J Neurosci 31: 5841–6.

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Section

II

Analytical Techniques

Chapter

6

Extraction Methods of Volatile Compounds from Food Matrices Arthur Luiz Baião Dias, Francisco Manuel Barrales, and Philipe dos Santos CONTENTS 6.1 Introduction 123 6.2 Extraction Methods for Liquid Samples 124 6.2.1 Liquid–Liquid Extraction 124 6.2.2 Liquid–Solid Extraction 125 6.2.3 Solid-Phase Extraction 125 6.3 Extraction Methods for Liquid or Solid Samples 126 6.3.1 Static Headspace Extraction 126 6.3.2 Dynamic Headspace Extraction (Purge and Trap) 127 6.3.3 Solid-Phase Microextraction 129 6.4 Extraction Methods for Solid Samples 129 6.4.1 Conventional Methods 130 6.4.2 Ultrasound-Assisted Extraction 130 6.4.3 Microwave-Assisted Extraction 132 6.4.4 Supercritical and Pressurized Fluid Extraction 133 References 134

6.1 INTRODUCTION Volatile compound fractions in food matrices are associated with the aromas and flavors that are constructed during the preparation, cooking, and consumption of the food. A multifaceted series of factors can affect the perception, quantification, and analysis of the food’s flavor. Generally, the analysis involves the identification and quantification of an analyte or a group of analytes. According to Sides, Robards, and Helliwell (2000), odor analysis involves the determination of a physiological concept via physicochemical measurements of a complex system aimed to the detection of a wide range of compounds and this introduces some unique analytical constraints. Generally, the food flavor is composed of volatile organic compounds (VOCs), as well as their derivatives, and other nonvolatile compounds. Several factors can complicate the analysis of the food’s flavor. First, a recurrent problem is identifying the compounds contributing to the food aroma, which is generally represented by many classes of compounds and may be present in low or trace concentrations (Sides, Robards, and Helliwell, 2000). Another group of

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factors that can influence an analysis are the differences in the physical proprieties of those compounds, since each class of compound or each compound has specific thermodynamic proprieties and can interact with the matrix and solvents in many ways, which affects the extraction and separation. However, the use of an adequate technique can facilitate the separation of a specific compound or a group. Thus, the objective of this chapter is to provide an overview of the different methodologies to extract, isolate, and purify volatile compounds from food matrices. The methods are arranged according to the sample’s physical state.

6.2  EXTRACTION METHODS FOR LIQUID SAMPLES 6.2.1 Liquid–Liquid Extraction Liquid–liquid extraction (LLE) is based on the relative solubility of two different immiscible liquids, usually a polar and a nonpolar solvent; therefore, this technique has also been referred to as immiscible solvent extraction. Usually, one phase is aqueous, and the other is an organic solvent, where the analyte is transferred from one phase to the other liquid immiscible phase, due to the chemical potential of the solutes. The immiscibility of the solvents allows for an easy separation of the phases in a separatory funnel, where the lower layer can be collected through the bottom, and the upper layer can be removed through the top, since the position of each phase in the funnel depends on the densities of the liquids (Wells, 2003). The LLE method has disadvantages compared to other food flavor extraction methods, such as the exposure to large volumes of organic solvents and the formation of emulsions. Moreover, this method has a limitation when extracting and isolating flavor compounds from food matrices containing a significant content of lipids, because this method also extracts lipids. Therefore, in these cases, further purification steps are required for the separation and isolation of the flavor compounds. Despite the simplicity of the method, there is a tendency to replace LLE with other techniques, due to, on the one hand, trace analysis requiring expensive high-purity solvents, and on the other hand the elevated use of environmentally damaging and unhealthy solvents associated with this technique (Augusto, Lopes, and Zini, 2003; Wells, 2003). To overcome these drawbacks, other sample preparation techniques have been developed and implemented; for example, solid-phase extraction (SPE) and solid-phase microextraction (SPME). Meanwhile, there are several papers that have used this technique for food aroma analysis, such as for wine and grape varieties (López and Gómez, 2000; Rocha et al., 2000; Mestres, Busto, and Guasch, 2000), and for the isolation of volatile compounds in fruits and vegetables, for example cherry tomatoes (Selli et al., 2014), and oranges (Kelebek and Selli, 2011), and for the extraction of rose water (Canbay, 2017). The wide applications of this technique is evidence of its success for food aroma analyses; however, according to Varlet, Prost, and Serot (2007), LLE is not recommended for recovering the volatile aldehydes in smoked fish. Moreover, according to Marsili (2016), researchers identified 80 neutral volatiles in raw milk from different species using vacuum distillation and liquid–liquid extraction followed by high-resolution gas chromatography (HRGC). Also, despite the significant number of volatiles identified, the results of their work did not identify which compounds were the most important to good milk flavor. Therefore, this method has some restrictions and limitations. However, it is an excellent preliminary test for research of food flavoring compounds due to the simplicity and low cost.

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6.2.2 Liquid–Solid Extraction Liquid–solid extractions (LSEs) were used to concentrate semi-volatile compounds from liquids into a solid. In summary, the liquid sample is placed in contact with the bulk solid extracting phase, and after a determined period an equilibrium is established between the two phases. Next, the physical separation of the solid and liquid phases is performed, by decanting or filtering, and finally the extraction from the solid is conducted with a suitable solvent to isolate the volatile compounds. Different modifications to the LSE of volatile organic compounds have developed several extraction techniques; for example, solid-phase extraction, solid-phase microextraction (SPME), and stir bar sorptive extraction (SBSE) (Wells, 2003). 6.2.3 Solid-Phase Extraction Solid-phase extraction was a technique developed in the mid-1970s and was introduced in the market in the 1980s (Panighel and Flamini, 2015). This methodology of sample preparation is based on the sorption of the analyte onto a cartridge and its recovery by elution using a suitable solvent, resulting in a concentration and purification/isolation of the target compound or a class of compounds. The capability of the cartridge to extract some target compound depends on the bed sorbent, sample volume loaded, and the characteristics and volume of the solvents and eluents used in the analysis. The performance of the method can also be affected by the breakthrough volume, which is defined as the maximum volume of sample that can be introduced into the sorbent (Panighel and Flamini, 2015; Wells, 2003). The SPE technique is composed of four stages: (i) column preparation; (ii) sample loading; (iii) column post-wash; and (iv) sample desorption, as shown in Figure 6.1. However, recent advances in sorbent technology removed the column preparation step. The objective of the pre-wash or column preparation and post-wash

FIGURE 6.1  Four basic steps for solid-phase extraction: (1) Conditioning the sorbent prior to sample application ensures reproducible retention of the compound of interest; (2) Retention of the adsorbed isolate, undesired matrix, and other undesired matrix compounds; (3) Rinse the column(s) to remove undesired components; (4) Elution of desired components remain.

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stage is a condition of the stationary phase and removal of undesirable contaminants, respectively. Generally, the target compounds are retained on the sorbent, the interferers go through the stationary phase, and an elution solvent with an appropriate solvent recovers the adsorbed analytes. Usually, in analytical procedures, SPE is carried out using small columns or cartridges containing the solid stationary phase (sorbent). In volatile compounds analysis, the most common sorbent used is octadecyl (C18); this material allows a reversed-phase extraction of mid-polar to nonpolar analytes (Wells, 2003). SPE has been widely used in grape and wine volatiles analysis (Campone et al., 2018; Picard et al., 2018; Weldegergis et al., 2011; Williams et al., 1982). SPE has also been applied to characterize butter flavor (Vreuls et al., 1999), to aromatic compounds from fruit pulps (Boulanger and Crouzet, 2000), to the analysis of flavor-related to alkylbenzenes in cigarette smoke (Stanfill and Ashley, 2000), and to other nonvolatile compounds present in food matrices. For more details and examples, the papers published by Andrade-Eiroa and collaborators (Andrade-Eiroa et al., 2016a,b) give an excellent review of SPEs.

6.3  EXTRACTION METHODS FOR LIQUID OR SOLID SAMPLES 6.3.1 Static Headspace Extraction This technique is over 30 years old and has been applied to a variety of food matrices for the extraction of volatile organic compounds, such as fish products (Fukami et al., 2002; Girard and Nakai, 1991; Duflos et al., 2006; Li et al., 2013) and vegetables (Colina-Coca et al., 2013; Molina-Calle, Capote and de Castro, 2007), among others. The versatility of this method allows the sample to be solid or liquid. Meanwhile, its low sensitivity does not allow for the analysis of trace or high boiling point compounds (Pico et al., 2016). The most significant advantage of the static headspace extraction technique is the simplicity of the liquid samples preparation. The preparation involves only transferring the sample into the vial, typically of 10 to 20 mL, and sealing it immediately. Meanwhile, for solids, the sample must be ground to increase the superficial area available for the analyte volatilization to the headspace, and occasionally the solid sample might be dissolved or suspended in a liquid to attain equilibrium inside the vial faster. Once the sample is inside the vial and hermetically sealed, it is incubated at a controlled temperature. The volatile analytes diffuse to the headspace of the vial. When the equilibrium between the gas and liquid (or solid) phase is achieved, an aliquot of the headspace gas is injected into a gas chromatograph (GC) for analysis, as illustrated in Figure 6.2a. The last step may be manual or automatic (Slack, Snow, and Kou, 2003). The automatic system consists of three stages: (i) Equilibrium; (ii) Pressurization; and (iii) Sampling, as illustrated in Figure 6.2b. (i) Equilibrium: This is the most critical stage. Special attention must be given to equilibrium temperature and time when developing a method. The equilibrium time is considered once the sample is inserted into the vial and sealed, until the insertion of the sample needle into the vial. Each compound migrates from the sample matrix to the gas phase at its own rate, according to the temperature. Therefore, the minimum equilibrium time depends on the slowest diffusion compound. High volatility compounds might start the migration to the gas phase during the vial preparation, occasioning the loss of some volatile compounds before they arrive in the vial. In these cases, it is recommended to keep the preparation of the samples at a low temperature. In addition, there are different migration

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FIGURE 6.2  Schematic diagram of headspace extraction autosampler (a) and the steps

for balanced pressure sampling in GC headspace analysis (b). (Adapted from Kolb 1999.) rates from the sample matrix to the headspace, since each compound has its own solubility in the sample matrix, which also depends on the temperature. Usually, the rise of temperature diminishes the solubility of the VOCs, increasing the concentration of the analyte in the headspace at higher temperatures. It is recommended to use a temperature of at least 15°C over room temperature to ensure the correct temperature control. Special attention must be given to higher temperatures, which may cause degradation of thermolabile compounds. (ii) Pressurization: Once equilibrium is attained, the headspace gas is ready to be transferred into a GC. The most common transference mechanism involves the pressurization of the vial headspace with an inert gas, using a heated hollow needle, followed by a pressure release into the pneumatic sampler, through the same needle. Another strategy is to use a system with a sampler loop. In this case, the pressure inside the vial will transfer the sample into an internal sampler loop, from where the sample accesses the GC inlet by actioning the sampler valve. (iii) Sample transference: After pressurization, the gas sample into the vial flows to the pneumatic sampler through the needle, moved by the pressure gradient established during pressurization. 6.3.2 Dynamic Headspace Extraction (Purge and Trap) Dynamic headspace extraction (DHE) may be used for liquid as well as for solid samples. It is mainly used when there is a small quantity of the analyte in the sample, at trace level, or when an exhaustive extraction is needed, and it has been applied for a variety of matrices, such as sponge cake (Pozo-Bayón et al., 2007), fruit (Mamede and Pastore, 2006), and meat (Madruga et al., 2009), among others. However, it must be considered that DHE only allows for measurement of the ratio of specific volatile compounds

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FIGURE 6.3  Schematic diagram of a typical purge and trap–GC system (a), and the

needle sparger for purge and trap (b). (Adapted from Zang et al. 2017.) concentrated over the sorbent surface. Meanwhile, static headspace extraction (SHS) allows the measurement of the ratio of all of the volatile compounds contained in the gaseous phase inside the vial (Pico et al., 2016). DHS is similar to SHS; however, the VOCs are removed continuously from the sample by a continuous inert gas flow. In this way, a concentration gradient that favors the process exists. The system is composed of a purge vessel, a sorbent trap (usually Tenax-TA®), a six-way valve, and transference lines, as illustrated in Figure 6.3a. The sample is inserted into the purge vessel, and a purge gas (usual helium) passes through the sample continuously carrying the VOCs into the trap, where the sorbents retain them. When the purge ends, the trap is heated to desorb the analytes into the GC (Soria, García-Sarrió, and Sanz, 2015). There are three kinds of purge vessels: (i) frit spargers; (ii) fritless spargers; and (iii) needle spargers. Frit spargers (Figure 6.3a) are used for liquids that are relatively clear, that are not prone to foam, and that have solid particles that may clog the system. Fritless spargers and needle spargers (Figure 6.3b) are recommended for particulate systems and liquids with proteins that are prone to foam. The trap is usually a stainless-steel tube of 3 mm (ID) and 25 mm long, packed with different layers of sorbents. The trap must retain the analyte and not introduce impurities. The system operation consists of a series of steps: (i) Purge, a period of 10 to 15 minutes, wherein the carrier gas extracts the VOCs and leads them to the trap, the samples might be heated by an electrical heater or by microwaves to accelerate the mass transference from the matrix to the gas phase; (ii) Dry purge, wherein the carrier gas only passes through the trap, and not through the sample in order to eliminate the water that might be carried out into the trap along with the analyte; (iii) Desorption preheating, wherein the carrier gas is turned off and the trap is heated to 5 to 10°C below the desorption temperature, to accelerate desorption; (iv) Desorption, wherein the trap is heated to 180 to 250°C and the GC gas carrier is turned on in a reversed flow, for about 1 to 4 min. The gas carrying the VOCs is conducted into a GC inlet; (v) Trap bake, the gas flow returns to the initial direction and the trap temperature rises to 15°C above the desorption temperature to eliminate possible contamination in the trap and gets the system ready for the next sample (Slack, Snow, and Kou, 2003).

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6.3.3 Solid-Phase Microextraction This technique was first developed by Belardi and Pawaliszyn (1989) while looking to reduce the costs and time employed to perform liquid–liquid extraction and solid-phase extraction. This method has been commercially available since 1993 and has been widely applied since then on several matrices, such as fish products (Jiménez-Martín et al., 2015), fruit (Chen et al., 2018; Sdiri et al., 2017), off-flavors (Marsili, 1999; Matsushita et al., 2017), and spices (Korkmaz, Hayaloglu, and Atasoy, 2017), among others. Solid-phase microextraction (SPME) is a microscale approach to solid-phase extraction for the extraction and preconcentration of analytes. The SPME methodology is based on the partitioning of analytes between a reusable coated fiber (a stationary phase) and a sample. In SPME, the analyte molecules must migrate from the sample and penetrate into the fiber coating, so the mass transfer resistance must be overcome to reach the equilibrium or adsorption equilibrium, and then the fiber is inserted into the GC inlet to desorb and analyze the analytes (Merkle, Kleeberg, and Fritsche, 2015). There are two possible approaches for the analyte adsorption process into the fiber, direct and headspace (HS-SPME). In direct adsorption, the fiber is immersed into the sample matrix or in a solution containing the sample. Thus, the fiber might be exposed to nonvolatile compounds that will contaminate the sample and may affect the chromatography and reduce the number of times that the fiber might be reused. On the other hand, in HS-SPME, the fiber is inserted into the vial’s headspace, which contains the sample, the vial is heated, and the volatile compounds are transferred from the liquid phase to the gaseous phase, and then they are absorbed into the fiber. Therefore, only volatile compounds reach for the fiber, avoiding undesirable compounds (Slack, Snow, and Kou, 2003). There are several fiber coatings on the market, which may be arranged in three groups, polar, semi-polar, and nonpolar. To choose a fiber coating, one has to consider the nature of the analytes. A fiber coating with similar polarity to that of the analyte will favor its adsorption. Thus, the extraction will be selective, reducing the chance of extracting contaminant compounds (Valente and Augusto, 2000). SPME exhibits several advantages over traditional extraction methods, such as to be a rapid, simple, sensitive, and solvent-free method, and have linear results for a wide range of concentrations and analytes (Nerín et al., 2009). However, the disadvantages of SPME are the limited number of commercially available stationary phases (fiber coatings), low recommended operating temperature (240–80°C), the instability and swelling in organic solvents, breakage of the fiber, stripping of coatings, bending of the needle, and the cost. Other disadvantages are the limited lifetime of the fiber and the low extraction efficiencies (Nerín et al., 2009; Merkle, Kleeberg, and Fritsche, 2015). SPME has been extensively applied to the sampling and analysis of aroma in several raw materials and food products. Optimization processes for this method involve the selection of the fiber coating, as well as the fiber diameter, and the extraction conditions, stirring, temperature, direct immersion or headspace, vial volume, sample volume, equilibrium time, fiber exposure time, and fiber preparation and conservation.

6.4  EXTRACTION METHODS FOR SOLID SAMPLES In general, analysis of aromas or volatile compounds from plants or foods involves two steps: extraction and analytical methods. Extraction of analytes aims to separate the target compounds from the matrix and increase their concentration level. The sample preparation is an important step which determines the quality of the analysis, and it is

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also the primary source of systematic errors and the lack of precision of analytical methods (Armenta, Garrigues, and de la Guardia, 2015). A pre-treatment is required before extraction, aiming at the reduction of the particle size and an increase in the diffusion of analytes from the sample to the solvent. The choice of appropriate parameters (e.g., solvent, sample size, pressure, temperature, number of cycles, and extraction time) is necessary to optimize the process. Moreover, extraction efficiency is mainly influenced by three factors: the solubility of an analyte on a solvent, the mass transfer properties, and the matrix effects (Kou and Mitra, 2003). 6.4.1 Conventional Methods Conventional extraction methods, such as hydro-distillation (HD), simultaneous distillation extraction (SDE), Soxhlet extraction, and ultrasound-assisted extraction (UAE) are operated under atmospheric pressure and under heating. These methods consume a large amount of solvent and may have a long extraction time. On the other hand, there are other “green” and “innovative” extraction methods, such as supercritical fluid extraction (SFE), pressurized fluid extraction (PLE), and microwave-assisted extraction (MAE) that are faster, demand less consumption of solvent, and require less energy to operate (Kou and Mitra, 2003). Soxhlet is a benchmark method used for the extraction of semi-volatile organics from solid samples. The main advantages are the independence of the matrix, the low cost of the equipment, and that further filtration is not required. However, the disadvantages are the long extraction time and the relatively large amount of solvent consumed. Study of this methodology to quantify aroma compounds of promising application in food industries has been conducted over the years. Recent works have identified volatile substances from wheat breadcrumb and gluten-free flours (Pico et al., 2016) and cornstarch (Pico et al., 2018). Soxhlet extraction was also used to identify volatile compounds from grape seed oils (Al Juhaimi and Özcan, 2018). 6.4.2 Ultrasound-Assisted Extraction UAE can enhance the extraction yield since it increases the mass transfer between the solvent and plant matrix. The cavitation bubbles lead to a cell disruption near the solid surface, which improves the solvent penetration and can also break the cell walls. Among the advantages of UAE are less dependency of the solvent, better solvent penetration, extraction at lower temperatures, reduced extraction time, a higher yield of extracted compounds, and faster start-up. Disadvantages are the amount of solvent required and possible extraction evaporation. A study compared the extraction of volatile compounds from tea leaves using Soxhlet, ultrasound-assisted extraction, and simultaneous distillation extraction. It was observed that Soxhlet obtained the highest extraction yield (Gao et al., 2017). In another study, the quantification of volatile compounds from Schinus terebinthifolius Raddi fruits was performed by UAC. The authors noticed that high yields of the extracts might be due to the extraction of high-molecular weight compounds (e.g., triterpenes and carotenoids) (Silva et al., 2017). Ultrasonic waves are mechanical vibrations applied to solids, liquids, or gases with frequencies exceeding 20 kHz. Such waves are different from electromagnetic waves since they need matter to propagate. Their frequency and wavelength characterize them, and the mathematical product of these parameters results in the wave velocity through the medium (Wang and Weller, 2006).

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Ultrasonic waves cause effects of expansion and compression on the matter. The expansion can create bubbles in a liquid and produce negative pressure, while the collapse of the formed bubbles can cause cavitation. The collapse of bubbles near the cellular walls produces cellular disruption, and as a result, there is better penetration of the solvent into the cells, and consequently an increase in the mass transfer (Esclapez et al., 2011). Figure 6.4 is a schematic representation of the effects of the ultrasound process on the interface of a plant matrix. In Figure 6.4, the formation of bubbles can be seen in the first stage, (a). Such bubbles undergo expansion and compression (b), which will cause their collapse or implosion (c). Eventually, should this collapse occur near the array interface, it can generate shock waves that will disturb the wall of the matrix, consequently releasing the intraparticular material into the solvent (d) (Esclapez et al., 2011). An extremely important factor in ultrasonic-assisted extraction is the extractor configuration, which may have the transducer coupled to the extraction vessel or have an ultrasonic probe immersed in the solvent/matrix medium. Figure 6.5 shows some extractor geometries schematically with ultrasonic waves. Currently, the most widely used laboratory scale extraction systems are ultrasonic baths or direct sonification. Ultrasonic baths consist of a transducer coupled to a tank containing a liquid responsible for transferring the waves to a container containing the extractive matrix, according to Figure 6.5a. The disadvantages of this geometry are the lack of uniformity of ultrasound wave distribution. Thus, direct sonification emerged as an alternative to improve the distribution of the ultrasonic waves through the extraction medium. This system consists of inserting an ultrasonic probe directly into the solvent/matrix mixture, as shown in Figure 6.5b and c (Esclapez et al., 2011). The use of ultrasound was conducted in the extraction of aroma compounds from aged brandies, tea, wine, and garlic, in the extraction of antioxidants from pomegranate peel, and carotenoids from tomato waste (Chemat et al., 2017). Moreover, some studies extracted lycopene from tomatoes,

FIGURE 6.4  Schematic representation of the effects of the ultrasound process on the

matrix vegetable. (Adapted from Esclapez et al., 2011; and Capote and de Castro, 2007.)

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FIGURE 6.5  Typical extractor configurations: (a) transducer coupled to a vessel; transducer (probe) immersed in solvent/plant matrix medium, (b) in batch and in continuous (considering the fluid/solvent) (c). (Adapted from Esclapez et al., 2011.)

phenolics from strawberries, citrus peel, and coconut shell powder, anthocyanins from red raspberries, and capsaicinoids from peppers (Chemat and Khan, 2011). 6.4.3 Microwave-Assisted Extraction Microwave-assisted extraction works with the dissipation of the electromagnetic waves in the irradiated medium through heat. Among the main advantages of this technique are the reduced costs, easy equipment manipulation, higher purity of the final products, reduced extraction time, and energy consumption. However, MAE requires a post-extraction step (e.g., cooling and filtration) which can extend the process and it is quite an exhaustive process including interfering species that need cleanup before the analysis. For food production, it is worth mentioning that this method allows the reduction of the equipment size, a faster response to heating processes, and increases of the production and elimination of post-treatment steps (Chemat et al., 2017). Solvent-free microwave extraction (SFME) is an adaption of MAE that uses a combination of microwave heating and dry distillation under atmospheric pressure without the use of solvent or water. This process has been used to extract aroma compounds from citrus fruit (e.g., orange [Ferhat et al., 2006] and lemon [Ferhat et al., 2007a]), aromatic herbs (e.g., basil, mint, and thyme) and spices (e.g., cumin and anise [Lucchesi, Chemat, and Smadja, 2004]). In a recent study comparing SFME with a conventional hydro-distillation extraction to obtain essential oil from Origanum vulgare L, it was observed that SFME was more efficient with a higher extraction yield, and in a shorter extraction time (Bayramoglu, Sahin, and Sumnu, 2008). Another study compared microwave-assisted simultaneous distillation-solvent extraction (MW-SDE) with conventional SDE in the extraction of volatile compounds from fresh aromatic herb (Zygophyllum album L.). It was noticed that MW-SDE was faster, required less solvent amounts and less energy in comparison to conventional SDE extraction (Ferhat et al., 2007b).

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6.4.4 Supercritical and Pressurized Fluid Extraction Among the innovative extraction processes, SFE appears as an alternative to the use of conventional methods. SFE is considered a green methodology that aims to obtain compounds with reduced energy requirements, shorter extraction times, and using solvents generally recognized as safe (GRAS). Supercritical fluids enhance transport properties, due to their high diffusivity and low viscosity, being able to readily diffuse through solid materials with faster extraction rates. Supercritical carbon dioxide (SC-CO2) is the most common solvent used since it is a non-toxic, non-flammable, non-polluting, and low-cost solvent; it is relatively inert and can be recovered (Brunner, 2005). Moreover, it works at moderate critical conditions (Tc = 31.05°C and Pc = 7.38 MPa) that are crucial in the preservation of aroma compounds from extracts. However, the equipment costs can be a disadvantage of this technique when scale-up is aimed for. Another SC-CO2 limitation is its low polarity making the process more appropriate for extracting nonpolar compounds. The process of extraction with a supercritical fluid, specifically using CO2 , occurs in two stages: the extraction of the solutes and the separation of the solute from the solvent. The first step is to manipulate the carbon dioxide in the binomial pressure/temperature in order to obtain the highest solvation of the target solutes. The solvent flows into the extractor and through the entire plant matrix, solubilizing the solutes. The solvent/solute then mixture goes to the second step, in which the pressure is reduced below the critical point value. In this way, the solvent changes its state of supercritical aggregation to gas, reducing its power of solvation, and consequently precipitation of the solute occurs. Thus, the solute is recovered, and the gas is redirected into a recycle (Raventós, Duarte, and Alarcón, 2002; Martínez, 2007). Figure 6.6 shows the supercritical fluid extraction process. The recycle conducts the gaseous carbon dioxide into a condenser, where the gas

FIGURE 6.6  Flow diagram of a supercritical extraction process from solid matrices. (Adapted from Rosa et al., 2008.)

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is liquefied. Thereafter, the CO2 has its pressure increased above the pressure of the critical point, as a result of the work of a pump, and its elevated temperature up to the desired operating temperature by a heater (Rosa et al., 2008). The use of SFE to obtain extracts enriched in a specific compound of interest for the food industries has been carried out throughout the years, such as the extraction of caffeine from green coffee beans, free fatty acids from rice bran oil, tocopherol from wheat germ oil, and beta-carotene from crude palm oil. SFE has been used in the fractionation of fatty acid ethyl esters from fish oil, extraction of essential oils from seeds of a pomegranate, walnut oil, carrot fruit, and carotenoids from pumpkins. The application of SFE was also studied before analysis of volatile compounds in beverages and sugar cane (Herrero et al., 2010). The use of SFE assisted by ultrasound (SFE-US) has emerged as an alternative for extracting and quantifying bioactive compounds from food samples, since the ultrasound waves can increase the rupture of the cell walls of the matrix enhancing solvent penetration and extraction yield. SFE-US was studied for the extraction of aroma compounds from ginger (Balachandran et al., 2006), essential oil from almond (Riera et al., 2004), antioxidants from blackberry (Pasquel Reátegui et al., 2014), tocopherol and tocotrienols from passion fruit (Barrales, Rezende, and Martínez, 2015), and capsaicinoids from peppers (Santos et al., 2015; Dias et al., 2016). Pressurized liquid extraction (PLE) is an alternative technique that uses liquid, like ethanol or/and water, as solvents at elevated pressure and temperature, enhancing the solubility and mass transfer properties of an analyte in a solvent. Moreover, the higher temperature diminishes the solvent’s viscosity and surface tension, improving the solvent penetration in the matrix sample. PLE works with a reduced extraction time and has less solvent consumption, and it does not require a filtration step when compared to conventional processes, which is important taking into account the use of automated and online systems. Furthermore, the use of polar solvents, such as water and ethanol, covers the extraction of compounds of high and intermediate polarity. The main drawback of this technique is the cost of equipment. The use of PLE to obtain food aroma compounds was recently performed in the quantification of anthocyanins, phenolics, antioxidants, isoflavonoids, carotenoids, and capsaicinoids (Mustafa and Turner, 2011).

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Chapter

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The Role of Gas Chromatography-Based Methodologies for the Understanding of Food Aromas Cátia Martins, Ângelo C. Salvador, and Sílvia M. Rocha CONTENTS 7.1 Introduction 141 7.2 Sample Preparation toward Gas Chromatographic Analysis 142 7.3 The Role of Gas Chromatographic-Based Tools: Principles and Potentialities 143 7.3.1 One-Dimensional Gas Chromatography (1D-GC) 144 7.3.2 Comprehensive Two-Dimensional Gas Chromatography (GC×GC) 145 7.3.3 Olfactometry (GC-O) 150 7.4 Concluding Remarks and Future Trends 152 Acknowledgments 153 References 153

7.1 INTRODUCTION Food aroma is of major concern to food researchers and industrial teams as it represents a significant factor influencing the public’s food-buying decisions, as well as being associated with food quality and safety. Odorous compounds are typically volatile or semi-volatile in nature and have a low molecular weight (the majority below 300 amu). Despite this apparently limited range, odorous stimuli belong to a broad variety of s­ ubstance classes that comprise diverse structural moieties such as ester, alcohol, aldehyde, or ketone functions, among others, having aromatic or aliphatic forms, or comprising of thio and other heteroatomic groups (Baldovini, 2017). A detailed analysis of the literature allows us to infer that there is some misconception between characterizing volatile food composition and understanding its aroma, as the study of a food aroma is much more than the analysis of its volatile composition. Indeed, the human nose’s perception of aromas depends, among other factors, on the extension of the volatiles released from the matrix and the odor properties of the compounds, in which these molecules do not contribute equally to the overall flavor profile of a sample; thus, a higher chromatographic area generated by a chemical detector does not

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necessarily correspond to higher odor intensities (Zellner et al., 2008). The aroma perception also depends on the characteristics of the nasal and oral physiology of mucosa and microbiota. The experiences and memories of the individual and their sensory acuity are other significant parameters critical to aroma recognition. Aroma characterization has been an issue of concern for a long time and new devices and innovative approaches have been developed, such as gas chromatography-olfactometry (GC-O) that combines sensorial and instrumental data (Giungato et al., 2018; Song and Liu, 2018), or highly sensitive equipment, for instance, comprehensive two-dimensional gas chromatograph coupled to a mass spectrometer with a time-of-flight analyzer (GC×GC-ToF-MS). The determination of aroma molecules, as well as the knowledge of their origin and possible evolution, for instance, during ripening, processing, storage, or aging, are crucial for understanding and modulating the aroma properties of foods and their perception by the consumer. These challenges are intimately connected with the use of gas chromatographic methodologies. Thus, this chapter aims to highlight the role of gas chromatographic-based techniques to understand the aroma of foods. The 1-D, GC×GC, and GC-O will be presented, as well as the concepts and main potentialities and challenges related to the use of these tools. First, the phases preceding chromatographic analysis will be briefly presented.

7.2  SAMPLE PREPARATION TOWARD GAS CHROMATOGRAPHIC ANALYSIS Food volatile characterization should be carefully planned, and the construction of a workflow that includes the sequence of all steps, that is, sampling, extraction, instrumental analysis and data processing and interpretation, is recommended (Figure 7.1). This chapter will focus on instrumental analysis using gas chromatographic-based techniques; nevertheless, there are several considerations with sampling and extraction that the authors want to emphasize as they can contribute to misinterpretations of the data. In fact, food aroma scientists face enormous challenges regarding this topic, and, as extraction techniques were discussed in detail in a previous chapter, this section only highlights some concerns that are antecedent to aroma chromatographic analysis per se. The sampling and extraction steps should be carefully established in order to generate volatile-related data that may be useful for food item aroma comprehension. Therefore,

FIGURE 7.1  Workflow that summarizes the main steps that may be used for food vola-

tile characterization, including the odorant molecules, here illustrated by beer as a case study: (1) sampling, (2) extraction, (3) instrumental analysis, and (4) data processing and interpretation.

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it is extremely important to have a representative aliquot of the food item (even in size/ amount), whose analysis may reflect the active odor volatiles released from the food item, as close as possible. Also, another crucial factor is the number of samples, which should be representative and statistically significant of the overall food population, and independent sample aliquots may be preferred over analytical replicates (Giungato et al., 2018; Schieberle and Molyneux, 2012). The perceived aroma of food items usually reflects the free volatile compounds. However, an in-depth study may also comprise the analysis of aroma precursor compounds, such as in the form of glycones, or high-molecular weight molecules. For instance, monoterpenic compounds may be present in fruits linked to sugar molecules (glycones); thus, these compounds, as nonvolatile, do not contribute to aroma perception. However, during ripening or technological processes, the glycosidic-linkage may be broken, and the free released compounds may consequently contribute for aroma (Rocha et al., 2010). Thus, to estimate the potential impact of the compounds present in the form of glycones, and to mimic the effect of physiologic or technological processes that may contribute to their release, the sampling step may include an acid and/or enzymatic hydrolysis before the extraction step. Food aroma compounds are generally present in low concentration, being a common practice to concentrate volatiles extracted from foods before their analysis. Many extraction techniques are often used to extract and/or to simultaneously extract and concentrate volatile compounds from foods, such as headspace, solvent, or sorptive based techniques. For instance, after extraction with solvents, and before chromatographic analysis, the solvent should be removed to concentrate the volatile fraction. Otherwise, sorptive techniques, such as solid-phase microextraction (SPME), promote the simultaneous extraction and concentration of volatiles. A food’s volatile composition can be significantly changed particularly during pre-treatment techniques if an adequate procedure is not performed. For instance, compounds may be lost or modified during the extraction steps. Factors such as concentration of the solvent extracts before analysis, storage before analysis, or cross-contamination with storage and/or analysis materials may influence the volatile composition (Nongonierma et al., 2007). Hence, the main requirements of an extraction protocol are to ensure that volatiles are extracted in a representative way; their variability, as a result of the extraction step, is minimized; and compounds are not modified due to the extraction method (Rodrigues et al., 2016).

7.3  THE ROLE OF GAS CHROMATOGRAPHIC-BASED TOOLS: PRINCIPLES AND POTENTIALITIES The volatile characterization of food items is usually performed using gas chromatographic techniques. One-dimensional gas chromatography (1D-GC) is the most common and widely used analytical method, not only by food researchers but also by industrial teams. Nevertheless, the complexity of the volatile composition of foods usually exceeds the capacity of one-dimensional separation. Co-elutions may compromise reliable identifications. Thus, there is a need to constantly seek for more sensitive and selective analytical tools that are able to study targeted and untargeted compounds from these complex matrices, with short instrumental analysis and data processing times. Noteworthy improvements have been occurring such as the development of new GC column stationary phases (namely ionic liquids), enhancement of instrumental technologies (e.g., new detectors, such as high resolution mass spectrometers), and more powerful data analysis through new software and/or algorithms (Prebihalo et al., 2018; Wong, Hayward, and Zhang, 2013). Also, instrumentation has evolved from 1D-GC to multidimensional gas chromatography

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(MDGC), namely comprehensive two-dimensional gas chromatography (GC×GC) that has been applied to determine the volatile profile from a wide variety of foods and beverages, such as beer (Martins et al., 2015, 2018), clams (Rocha et al., 2013), elderflowers (Salvador, Silvestre, and Rocha, 2017), elderberries (Salvador et al., 2016), grapes (Rocha et al., 2007), aromatic plants (Petronilho et al., 2013, 2011; Jalali et al., 2012, 2013), sea salt (Silva et al., 2015, 2010), and wine (Santos et al., 2015, 2013; Perestrelo et al., 2010, 2011). Furthermore, instrumental devices that combine the chromatographic and sensorial evaluation of food items, such as gas chromatography-olfactometry (GC-O), are very powerful tools for unveiling food aromas. This technique combines both olfactometry and mass spectrometry fields, where the obtained data complement the information of each detection mode, used for rapid mapping of aroma-active compounds, identification of key aroma-active compounds, cluster analysis based on the aroma-active compounds, relationship between odorants and sensory properties, and elucidation of formation mechanism of important odorants. In the following sections, the main gas chromatographic-based tools used for the understanding of food aromas will be presented. 7.3.1 One-Dimensional Gas Chromatography (1D-GC) Gas chromatographic separation processes foresee that the compounds separate ­according to partitioning (dispersion and specific interactions) between two immiscible phases, the mobile phase (e.g., helium, hydrogen, or nitrogen) and the stationary phase (Grob and Barry, 2004). 1D-GC schematic representation is shown in Figure 7.2, where the GC column is connected in between an injector and a detector, within a temperature programmable oven. As food volatile components exhibit a high diversity of chemical structures, there are a wide range of stationary phases for GC columns, such as polyethylene glycol (polar) and 5% of phenylmethylpolysiloxane or equivalents (nonpolar), which are frequently used for foods volatile profile determination. Different types of detectors may also be connected with the gas chromatograph, the flame ionization detector (FID) is commonly used, which is cheaper and more used for target analysis than the mass spectrometry detector (MS). This last type of detector allows for powerful and effective compound identification, based on a mass spectrum fragmentation pattern, and possible comparison with MS databases, also promoting high sensitivity and sensibility. GC-MS equipment has robust software that facilitates data processing, either for quantitative or qualitative purposes (Dettmer, Aronov, and Hammock, 2007; Milman, 2015). Indeed, different approaches may be used to acquire and process the MS data: fullscan (scanning in a m/z range), single or multiple ion monitoring (SIM or MIM—data

FIGURE 7.2  Schematic illustration of the one-dimensional gas chromatographic system

(1D-GC).

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acquisition using specific m/z as diagnostic ions), and ion extraction mode (IEC—use of specific m/z as diagnostic ions to process MS data). As peaks co-elution may occur in the full-scan data acquisition mode, SIM and IEC modes may be used as strategies to overcome this problem, since they can increase the specificity and sensitivity, particularly in target analysis (Tranchida et al., 2004; Petronilho, Coimbra, and Rocha, 2014; Martins, Almeida, and Rocha, 2017). IEC allows the minimization of the contribution of co-eluted peaks, contributing to increasing the target compound’s peak area. Thus, the combination of the data obtained through full-scan mode acquisition (which gives the food’s global volatile profile) and the data achieved by IEC processing (the use of specific m/z as diagnostic ions that highlights a particular chemical family) allows in-depth information about a food’s volatile composition, particularly in the improvement of the target compound’s data. For quantitative or semiquantitative purposes, the chromatographic area is used to estimate the amount of each analyte. Raw data may be normalized versus an internal (or external) standard, and analyte amount can be estimated through calibration curves or the standard addition method. Different types of internal standards may be used for quantification purposes, depending on the applied methodologies and the target compounds. The use of specific isotope-labeled internal standards is recommended once they assure an equal response factor for the analytes; however, they can be quite expensive. Accurate analyte identification is only confirmed through the co-injection of authentic standards; however, they can be expensive or not commercially available. For that reason, the use of the commercial/open-access mass spectra databases can be decisive tools for compound putative identification (Dettmer, Aronov, and Hammock, 2007; Milman, 2015). Confidence in the identification of the compound may be improved using other strategies, namely through the use of retention indices (RI) values. Basically, a n-alkanes series (typically ranging from C6 to C20) is injected in the same GC program of the sample, and retention time of each n-alkane is recorded. An RI value of 100 times the number of carbon is attributed to each n-alkane. Then, a compound’s retention time is normalized using the retention times of the adjacent eluting n-alkanes, through the van Den Dool and Kratz equation (van Den Dool and Dec. Kratz, 1963). The calculated RI can be compared with RIs available in the literature or open sources and that were achieved with a similar GC column to the column used experimentally. This previous fact is possible because RIs are not dependent on several GC instrumental features (e.g., carrier gas type and velocity, column’ diameter and length, film thickness, among others), which allows for comparison with other analytical laboratories (Babushok, ­ 2015; Grob and Barry, 2004). Although 1D-GC is widely used in the qualitative and quantitative analysis of a wide range of foods, providing high-quality analytical data, sometimes its complexity exceeds the separation capacity of a single chromatographic column, producing overloaded chromatograms. In such cases, co-elution peaks might occur, which makes compound identification and quantification difficult. Therefore, in order to increase chromatographic resolution, independent techniques have been developed, namely comprehensive two-dimensional gas chromatography (GC×GC) that is a powerful and high throughput solution (Mondello et al., 2008; Tranchida et al., 2004). 7.3.2 Comprehensive Two-Dimensional Gas Chromatography (GC×GC) Multidimensional gas chromatography (MDGC) plays a key role in the determination of a sample’s volatile profile, namely in the comprehensive mode (GC×GC). The separation of chromatographic peaks in complex matrices (Marriott et al., 2012; Mondello et al.,

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FIGURE 7.3  Schematic illustration of a comprehensive two-dimensional gas chromatographic system coupled with mass spectrometry and a time-of-flight analyzer (GC×GC-ToF-MS).

2008), as well as possible enantiomeric recognition (Cao et al., 2011), are important features of MDGC. In-depth and detailed characterization of a food’s volatile profile can be achieved by GC×GC (Figure 7.3), comprising two orthogonal separation mechanisms that combine the use of two GC columns, which are coated with specific stationary phases ruled by different properties, namely volatility, chirality, or polarity. The column combination most commonly used is a nonpolar 1D column (separation ruled by volatility, generally 30 m) and a polar 2D column (separation ruled by polarity, usually 1–2 m) (Cordero et al., 2018). A specific interface, called a modulator, makes the connection between the two GC columns. A modulator is responsible for the transfer of small portions of the eluate (2–8 s) from the primary 1D column, and then for re-injecting it into the secondary 2 D column, preserving the integrity of the 1D separation, once each peak is modulated several times. Different categories of modulators are available, namely valve-based, flow, and thermal. The collection of the eluate is made by a loop or channel, either valve-based or through flow modulation (often called the same); while in thermal modulation, the eluate is trapped using temperature. This last modulator type is the most used, particularly the cryomodulator, where a fast cooling of the eluate occurs using a cryogenic jet (for instance with liquid nitrogen) followed by a fast heating applied using a hot-gas jet, this allows the immobilization and then remobilization of the compounds (Mondello et al., 2008; Marriott et al., 2012; Prebihalo et al., 2018). Narrow peaks are produced once the collected fractions are no bigger than ¼ of the width peak; thus, they are “flash” separated before the end of the modulation time. However, the wraparound phenomenon might occur when the separation of an analyte exceeds the modulation time, and it is not finished before the next modulation (Figure 7.4). This phenomenon may promote co-elutions, interfering in the precise quantification of the compounds (Marriott, Massil, and Hügel, 2004, 2012; Mondello et al., 2008), being necessary to formerly optimize the modulation time to avoid wraparound. The obtained GC×GC peaks are very narrow, with a typical width at half-height of 0.1 s or less, and several data points are needed to record these peaks, which imply higher data acquisition rates (ca. 100 full mass-range spectra per second) that are only possible with a time-of–flight mass spectrometer (ToF-MS) (Dallüge, Beens, and Brinkman, 2003; Mondello et al., 2008). Furthermore, reliable spectra deconvolution of overlapped peaks is possible due to full mass spectra acquisition and continuity (the same ion abundance ratios are observed in all the point of the chromatographic peak for the different masses in the spectrum) of the ToF-MS, even at trace levels (Górecki, Panić, and Oldridge, 2006;

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FIGURE 7.4  Schematic representation of the wraparound phenomenon: the separa-

tion of an analyte exceeds the modulation time, and it is not finished before the next modulation. Grob and Barry, 2004). This data complexity can be handled through specific software that allows GC×GC-ToF-MS data processing. One example is ChromaTOF® that includes different algorithms (True Signal Deconvolution® and automated peak finding), able to perform the acquisition, processing, and data report. This software also creates and allows the visualization of the GC×GC chromatograms, in a contour plot or a three-dimensional plot (Dallüge, Beens, and Brinkman, 2003). Regarding analytes quantification, the same strategies reported for 1D-GC analysis, could also be implemented for GC×GC data. For identification purposes, the same criteria described previously for 1D-GC, namely the co-injection of standards (when it is possible); MS spectral similarity, calculated RI and comparison with literature are other possibilities for identification. Furthermore, GC×GC provides an extra powerful tool for analyte identification, namely the formation of structured chromatograms. The orthogonal separation mechanism of GC×GC (promoted by the combination of a nonpolar with a polar column) lets the twodimensional spatial distribution of the compounds be ruled by their chemical properties, that is, chemically related compounds are organized in the same two-dimensional chromatographic space, determining a two-dimensional “chemical map.” This feature is particularly helpful in the achievement of reliable identifications, especially in the analysis of complex matrices like foods, and when chemical standards are absent; it can also simplify the data analysis, and consequently decrease the analysis time (Cordero et al., 2015; Marriott et al., 2012). Figure 7.5 shows an example of a structured chromatogram that highlights one of the most important tools of GC×GC compared to 1D-GC. For instance, hydrocarbons present a lower retention time for the second dimension (2tR) once they are nonpolar compounds, contrary to organic acids that present higher polarity, and therefore exhibit the highest 2tR. In recent years, GC×GC-ToF-MS has been widely applied in food research (Nolvachai, Kulsing, and Marriott, 2017; Dymerski, 2018). For instance, several studies have been performed to determine specific volatile components or chemical families of different foods, namely beer (Martins et al., 2018), elderflowers (Salvador, Silvestre,

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FIGURE 7.5  Three-dimensional GC×GC-ToF-MS total ion chromatogram plot obtained

from a lager beer analysis. A structured chromatogram is shown though the bands and clusters that are formed by structurally related compounds of each beer volatile chemical family. and Rocha, 2017), elderberries (Salvador et al., 2016), grape (Rocha et al., 2007), or aromatic plants (Jalali et al., 2013, 2012; Petronilho et al., 2013, 2011). Figure 7.6 shows a practical example of the use of GC×GC-ToF-MS to access the evolution of elderberries’ volatile composition (Salvador et al., 2016), namely through the volatile profile of the Sambucus nigra variety during the ripening process (before and after a ripe state). The clusters delineated on the contour plots (Figure 7.6) allow for elucidation of the volatile profile of the chemical families (monoterpenic and sesquiterpenic compounds, and norisoprenoids) for each type of elderberry (unripe and ripe), and reveals the utility of the compound’s relative spatial position within the same chemical family. The achieved GC×GC-ToF-MS contour plots are snapshots of the actual ripening stage of the elderberry under study, and their visual analysis allows for observation of the differences between the two stages of ripening. A visual chromatogram inspection of Figure 7.6 reveals an increase in the diversity of detected chemical compounds during the elderberries’ ripening, and a decrease of their content, particularly of monoterpenic compounds. These differences in elderberry plots allow for the conclusion that the analysis of chromatogram contour plots demonstrate the potentiality for rapid assessment of the volatile varietal profiles of elderberries, which may have a further impact on volatile composition of elderberry-based products, and eventually, on their aroma.

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FIGURE 7.6  A blowup of a part of a GC×GC-ToF-MS chromatogram contour plot

obtained for a Sambucus nigra variety in IEC mode (m/z 93, 169, 204) for unripe and ripe elderberry. The chromatographic spaces corresponding to monoterpenic compounds (C10), norisoprenoids (C13), and sesquiterpenic compounds (C15) are highlighted. The advantages of GC×GC application in the compound’s separation that have the same volatility can be shown through the practical example shown in Figure 7.7a, in which β-ocimene and 1,1,3,5-tetramethylcyclohexane present similar volatility (same retention time for the first dimension—1t R : 528 s); therefore, co-eluting on the 1D column (Equity-5). Nevertheless, they can be separated by the 2D column (DB-FFAP), within only around half of a second, once they present different polarities (2t R of 0.530 and 0.590 s, respectively). Also, the lower detection limits and spectral quality of GC×GC-ToF-MS gives a huge advantage for the determination of trace compounds, which remains to be one of the main challenges for food volatile composition. In fact, the narrow peak (ca. 39 ms) achieved for β-ocimene in beer volatile determination (Figure 7.7b) had an appropriate spectral quality of the mass spectrum (similarity value 926/1000) compared with a commercial database. Hence, β-ocimene can be putatively identified based on the combination of several parameters: its retention times (1t R and 2t ), RI calculation, comparison with RIs, its mass spectrum pattern, and comparison R with commercial databases. GC×GC-ToF-MS overcomes several drawbacks of the conventional 1D-GC, namely shorter run times, enhanced resolution and peak capacity, lower detection limits, and

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FIGURE 7.7  (a) Blowup of a part of a GC×GC-ToF-MS total ion chromatogram contour plot obtained from a lager beer analysis, illustrating the separation of (1) 1,1,3,5-tetramethylcyclohexane, (2) β-ocimene, and (3) γ-terpinene; (b) β-ocimene, a beer trace component, presenting a peak of 39 ms-wide, is easily identified with a very high mass spectrum similarity (926 of 1000), based on the comparison with Wiley MS database.

higher mass sensitivity and selectivity as a result of the peak focusing in the modulator (Seeley and Seeley, 2013; Tranchida et al., 2016; Mondello et al., 2008; Marriott, Massil, and Hügel, 2004). Also, signal-to-noise ratio is enhanced for GC×GC, compared to 1D-GC (Dallüge, Beens, and Brinkman, 2003). However, GC×GC has some disadvantages, namely the high costs related to the equipment acquisition, and its operation, and maintenance. Also, the instrumentation is complex; thus, an expert technician is required, the data generated are quite complex, and their alignment, integration, and processing are a time-consuming task (Mondello et al., 2008). 7.3.3 Olfactometry (GC-O) For many decades, the in-depth characterization of a food’s volatile compounds was accomplished through extraction and gas chromatographic techniques, which revealed the identification of more than 10,000 volatile compounds. Although only a few of them

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FIGURE 7.8  Schematic illustration of a gas chromatography-olfactometry system (GC-O).

are aroma-active and affect the overall aroma profile of foods (Song and Liu, 2018). This illustrates one of the main drawbacks of GC-MS-based techniques, that is, the chemical structures of the volatile compounds present in foods may be elucidated; however, these techniques are incapable of determining the odor properties and contribution to the food samples (Chen and Ho, 2006; Song and Liu, 2018). Gas chromatography-olfactometry (Figure 7.8) complements the aforementioned drawback of MS-based techniques using the combination of compound separation through the GC capillary column and the olfactometer detector (Zellner et al., 2008). When coupled with analytical techniques, GC-O becomes a precise, descriptive approach to characterize stimuli, evaluating and measuring impressions, which enables the comprehension and quantification of a sensorial characteristic (Zellner et al., 2008). GC-O was invented in 1964 (Fuller, Steltenkamp, and Tisseiand, 1964) and has undergone several improvements over the years. Even so, the concept was maintained in which the GC effluent is mixed with humid air or inert gas and sent to the olfactometer for sniffing (Figure 7.8). The GC system is equipped with a common detector, such as FID or MS, plus a non-destructive thermal conductivity detection system with the outlet connected to a sniffing port (Song and Liu, 2018). The identification of aroma-active compounds is conducted by the same criteria previously reported for 1D-GC and GC×GC. Additionally, the sensorial detector gives the aroma descriptor of the detected analyte, as well as its intensity, which should be compared with the odor descriptions of reference compounds (Song and Liu, 2018). GC-O data may be collected and processed through different olfactometric methods: dilution, time-intensity, detection frequency, and posterior intensity methods. The most common method is dilution analysis, in which the aroma extract is successively diluted until there is no odor perception by panelists, and Charm (combined hedonic aroma response method) or AEDA (aroma extraction dilution analysis) represent the most common strategies for this (Zellner et al., 2008). More recently, Osme (timeintensity method) has been used as a popular method for measuring the potency of flavor components (Chen and Ho, 2006). Despite the huge utility of the GC-O analysis, the implementation of this technique represents several challenges. The chemical complexity of food flavor implies the occurrence of co-elutions that may occur both in nonpolar and polar stationary phases, leading to the inaccurate identification of odor-active compounds (Zellner et al., 2008). Key odor-active compounds may be present in the matrix at a trace level, and the co-elution of compounds may easily occur making the correlation between the chromatographic peaks and the perceived aroma difficult to assess (Giungato et al., 2018). Theoretically, this drawback may be overcome by using multidimensional GC combined with olfactometry (MDGC-O), being, for instance, already carried out, on a

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fresh puree of kiwi (Jordán et al., 2002) which allowed for the identification and report of several components for the first time in this matrix. Nevertheless, GC×GC-O remains to be a quite a challenging technique as the human breathing cycle is slower than the separation, that is, the formation of very narrow chromatographic peaks (e.g., 100–400 ms) during one modulation time period (Giungato et al., 2018). Another drawback of GC-O systems is due to the fact that the chromatographic separation implies that volatile components are assessed individually and their impact on the food versus the isolated component is not linear (due to the masking and synergistic effects that might occur between the volatile components), thus the overall perceived aroma cannot be fully elucidated (Hallier et al., 2004). The human factor is another limitation of the GC-O system, as olfactory capacity and odor thresholds may vary significantly both within and between people, and also cases of specific anosmia may occur, that is, some people with an otherwise normal sense of smell are unable to detect families of similar smelling compounds (Delahunty, Eyres, and Dufour, 2006). Additionally, the lack of concentration, breathing cycle, health status of the individual, and the natural variation of the olfactory response over the time are factors of biased results (Plutowska and Wardencki, 2008; Delahunty, Eyres, and Dufour, 2006). Thus, the number of assessors (panelists that will sniff at the olfactometric port) should be significant and appropriate, in order to guarantee the representativeness of sensory evaluation and to preserve the reliability of the results (Giungato et al., 2018). GC-O appears as a powerful tool for investigations in the food industry, through the detection and analysis of key aroma contributing components that are used in a wide range of applications in flavor analysis (Giungato et al., 2018). For instance, Grosch summarized the use of GC-O and the character-impact odorants identified in olive oil, butter, cheese, meat, bread, beer, green tea, spices, and also the study of off-flavors from several food items (Grosch, 1994). Other food items analyzed by GC-O involving either Charm analysis or the AEDA techniques included fruit and fruit products, namely grape juice (Baek et al., 1997), alcoholic beverages, such as wine (Campo et al., 2011), chocolate and cocoa (Schnermann and Schieberle, 1997), roasted coffee beans and brewed coffee (Semmelroch et al., 1995), dairy products (Curioni and Bosset, 2002), among numerous other food and food products (Chen and Ho, 2006; Zellner et al., 2008).

7.4  CONCLUDING REMARKS AND FUTURE TRENDS The aroma of a food can be studied through different techniques, choosing the most suitable equipment is crucial for the achievement of reliable data due to the food’s complexity and the analytes’ concentration (which may be in different orders of magnitude). Nevertheless, the authors would like to reinforce the idea that food aroma is much more than its volatile components, since most of them are not aroma-active, and may not contribute to the overall aroma properties. Gas chromatography is extremely important for the determination of a food’s volatile composition and for monitoring the food aroma evolution, 1D-GC being the most inexpensive and widely used equipment. However, the co-elution frequency in 1D-GC, mainly in the study of food samples, is high, which may cause misidentifications and respective incorrect quantifications, particularly for trace compounds. Therefore, MDGC techniques, for instance, GC×GC, assume a key role in the study and reliable, in-depth characterization of the food’s volatile composition, particularly due to the presence of structured chromatograms. The instrumental improvement of gas chromatographic techniques has led to long lists of volatiles that may or may not have a sensory relevance to food. Olfactometry techniques, specifically

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GC-O, have been challenged to enlighten and complement this avalanche of data by understanding the role of odor in the volatile components present in foods. MDGC techniques are essential: for the in-depth characterization of a food’s volatile composition; for the detection of key odorants and elucidation of their formation; for the discrimination of enantiomers and co-eluting compounds that can be trace elements with peculiar odor characteristics (Cordero et al., 2018). Nevertheless, the relation between a food’s volatile composition and flavor perception is still challenging, and requires other techniques as a complement, such as olfactometry. In recent years, significant improvements have occurred in GC systems, in order to increase resolution and sensitivity. The development of new stationary phases of GC columns have been growing (e.g., ionic liquid stationary phases) (Prebihalo et al., 2018), as well as the development of simpler and effective modulators (Prebihalo et al., 2018; Cordero et al., 2018). The huge amount of data generated by GC×GC-ToF-MS brings challenging problems, that can be overcome through the ­development of new, effective, and powerful GC×GC-ToF-MS data processing methods, as well as new data analysis methods (Cordero et al., 2015, 2018; Prebihalo et al., 2018). Moreover, the development of new mass spectra libraries is required (Dymerski, 2018). In the near future, developments will occur on faster and miniaturized solutions (even portable devices), with reduced costs for the production and performance of the equipment (Dymerski, 2018) that will allow real-time monitoring of food aroma evolution, and that can be routinely used in labs. Several challenges still remain in food aroma analysis, such as the development of faster and more powerful instrumental techniques, which will access the flavor compounds with higher accuracy and sensitivity. Also, the development of specific flavor-isolation techniques will allow more reliable and accurate identification of specific groups of flavor compounds. Furthermore, suitable techniques that mimic the interaction between humans and foods are still needed, which will provide knowledge about which compounds have the greatest impact on how a food tastes. Altogether, when analyzing a food product, it is crucial to plan and measure all the parameters that will possibly have an impact on its odor, and the nature of the food product and the link of sensory data should always be considered and required to fully understand the impact of the analytical results.

ACKNOWLEDGMENTS Thanks are due to FCT/MEC for the financial support to the QOPNA and LAQVREQUIMTE Research Unit (FCT UID/QUI/00062/2019), through national funds and where applicable co-financed by the FEDER, within the PT2020 Partnership Agreement.

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Chapter

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Monitoring Food Aroma during Processing and Storage by Rapid Analytical Methods: A Focus on Electronic Noses and Mass Spectrometry-Based Systems Aoife Power, Vi Khanh Truong, James Chapman, and Daniel Cozzolino CONTENTS 8.1 Introduction 159 8.2 Sensors 160 8.3 Data Integration and Analysis 163 8.4 Examples and Applications in Food Systems 163 8.4.1 Electronic Noses and Mass Spectrometry-Based Systems 163 8.4.2 Electronic Tongues 168 8.5 Conclusion 169 References 169

8.1 INTRODUCTION Flavor perception is influenced by a multitude of sensory modalities which are in turn the product of a complex combination of chemical compounds in the food matrix. For example, in wine and other alcoholic beverages, it is their aroma (or smell), that contributes significantly to their taste, similar trends can also be observed in other foods such as meat and cheese. Consequently, the flavor and/or taste of a product is ultimately determined by the presence and concentration of chemical volatile compounds (CVC) (Polášková et al., 2008). Many of the analytical methods used to study aroma in foods typically involve the preparation of an extract (or the collection of volatiles with traps) followed by chromatographic separation and detection (Linforth, 2000; Taylor and Linforth, 2000; KressRogers and Brimelow, 2001; Polášková et al., 2008; Ebeler and Thorngate, 2009; Ross, 2009; Lesschaeve and Noble, 2010; Heaven and Nash, 2012; Sáenz-Navajas et al., 2012). However, other aspects of the food process involved in the perception of aroma and taste, such as the temporal dimension of the eating/drinking process, which has an influence

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on the release and transport of chemical compounds to the olfactory epithelium, are not considered in such analyses, although the temporal dimension is a central feature of the eating and/or drinking process (Linforth, 2000; Kress-Rogers and Brimelow, 2001; Polášková et al., 2008; Ebeler and Thorngate, 2009; Ross, 2009; Lesschaeve and Noble, 2010; Heaven and Nash, 2012; Sáenz-Navajas et al., 2012). Typically, human senses do not react to the absolute intensity of a stimulus, but they do respond to the rate of change of that particular stimulus (Linforth, 2000; Kress-Rogers and Brimelow, 2001; Polášková et al., 2008; Ebeler and Thorngate, 2009; Ross, 2009; Lesschaeve and Noble, 2010; Heaven and Nash, 2012; Sáenz-Navajas et al., 2012). Therefore, the temporal dimension associated with the sensorial response should be measured using methods that can relate to time and the intensity of such compounds in the food. This approach has also led to the development of mathematical models to describe how perception is affected by temporal changes in breath aroma concentration (Linforth, 2000; Kress-Rogers and Brimelow, 2001; Polášková et al., 2008; Ebeler and Thorngate, 2009; Ross, 2009; Lesschaeve and Noble, 2010; Heaven and Nash, 2012; Sáenz-Navajas et al., 2012).

8.2 SENSORS When food is stored, it releases specific volatile compounds, such as hydrocarbons, ethanol, aldehydes, acids, ethers, and esters (Gallo and Ferranti, 2016; Ghasemi-Varnamkhasti and Lozano, 2016; Matindoust et al., 2016). The presence and concentration of these volatile compounds are different and depend on other factors like the raw material, brand and type of food, storage conditions (e.g., temperature, humidity), and issues associated with spoilage (e.g., presence of microorganisms) (Gallo and Ferranti, 2016; Ghasemi-Varnamkhasti and Lozano, 2016; Matindoust et al., 2016). These volatile compounds have been proposed as being like fingerprints of the factors affecting food during storage and processing (Gallo and Ferranti, 2016; Ghasemi-Varnamkhasti and Lozano, 2016; Matindoust et al., 2016). The ideal flavor or aroma analysis tool would be able to monitor changes in the temporal dimension, and thus make objective measurements related to perception (Linforth, 2000; Polášková et al., 2008; Lesschaeve and Noble, 2010). To achieve this in a timely manner and at low cost, sensitive analytical systems which have a selectivity and sensitivity comparable with the human olfactory receptor are required (Linforth, 2000; Polášková et al., 2008; Lesschaeve and Noble, 2010). Several limitations in the use of existing sensors are evident; for example, while electroencephalography (EEG) and magnetic resonance imaging (MRI) can both be used to monitor a participants brain activity directly when they are encountering a taste or aroma, the instrumentation itself is not practical from an industrial point of view (e.g., excessive cost, lack of portability, too time-consuming) (Linforth, 2000; Polášková et al., 2008; Lesschaeve and Noble, 2010). Alternatively, the use of purely instrumental techniques to follow changes in breath volatile concentration during eating or drinking, relating to the observed patterns associated with sensory perception, could be implemented by the food industry; the best representation of such modern tools are the so-called electronic noses (ENs) (Moseley and Norris, 1991; Baek et al., 1999; Hines et al., 1999; Hurst, 1999; Citterio and Suzuki, 2008; Lesschaeve and Noble, 2010; Brattoli et al., 2011; Croissant et al., 2011; Li-Chan et al., 2011). The EN employs arrays of sensors (in much the same way as the olfactory epithelium has arrays of receptors) which can be used to monitor volatile compounds (Hurst, 1999; Linforth, 2000; Kress-Rogers and Brimelow, 2001; Polášková et al., 2008; Ebeler and Thorngate, 2009; Ross, 2009; Lesschaeve and Noble, 2010; Heaven and Nash,

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2012; Sáenz-Navajas et al., 2012). All the sensors have a certain specificity, such that one sensor responds more to esters than aldehydes, whereas the opposite might be true for another type of sensor. To date, however, no sensor array can respond in a manner comparable with that of the human olfactory system. Moreover, they lack in both sensitivity (ppm/ppb concentration) and in some cases selectivity. Coupled with their comparatively slow response rate, it makes existing instrumentation incapable of appropriately monitoring rapid changes in breath volatile concentration (Mielle, 1996; Lindinger and Jordan, 1998; Hines et al., 1999; Hurst, 1999; Wilson et al., 2001; Pearce et al., 2006; Citterio and Suzuki, 2008; Röck et al., 2008; Brattoli et al., 2011; Heaven and Nash, 2012). Several studies have reported the application of ENs for quality control in several foods (e.g., grapes, wine, saffron, medicinal plants). However, a limited number of reports can be found in the literature on the application of the EN technology for the online or real-time monitoring of aroma during food processing and storage. Electronic noses can also be based on conducting organic polymers (COP) (Suppes et al., 2008; Kukla et al., 2009; Sanaeifar et al., 2017). These are a set of active mechanisms which detect odors and convert chemical vapors into electrical signals. Conducting polymer characteristics depend on doping levels, ion size of the dopant, water content, and protonation levels. These sensors may be classified into a mode of transduction and application (Suppes et al., 2008; Kukla et al., 2009; Sanaeifar et al., 2017). The other group of EN instruments available on the market are those based on a metal oxide semiconductor, which is a form of gas sensor that can detect the early signs of meat spoilage (Sanaeifar et al., 2017). It has been demonstrated that the ideal instrumental method must be objective, cost effective, and provide rapid, reproducible results, with continuous operation. However, to date, instrumental methods for sensory analysis lack the ability to reliably perceive all of these key sensory attributes, and depending on the food matrix being analyzed current methods have been inconsistent in their predictive relationships between sensory and instrumental measurements (Martens, 1999). Real-time quantitative detection of volatiles can be achieved using an electron impact source with a membrane separator between the source and the external environment coupled with a mass spectrometer (Linforth, 2000; Kress-Rogers and Brimelow, 2001). The advantages of the mass spectrometer (relative to the electronic nose) are its fast response rate and greater sensitivity (Suppes et al., 2008; Kukla et al., 2009; Sanaeifar et al., 2017). However, often the separator membrane reduces the technique’s overall sensitivity, while its selective permeability reduces its application potential. In recent years, the further development of mass spectrometer systems, such as atmospheric pressure chemical ionization–mass spectrometry (APCI-MS) and proton transfer reaction–mass spectrometry are envisioned to be capable of the real-time detection of compounds at ppb concentrations (Hurst, 1999; Linforth, 2000; Kress-Rogers and Brimelow, 2001; Martı ́ et al., 2005; Sanaeifar et al., 2017). As the demands of public health and consumer safety authorities increase, the development of rapid screening techniques to determine the quality characteristics of foods and beverages has become of great interest to the modern food industry. Ideally, these techniques should be relatively inexpensive, easy to operate, and require little or no sample preparation allowing them to be used in-line or at-line. Electronic noses, electronic tongues, and optical methods often based on vibrational spectroscopy have all demonstrated some potential to characterize complex food or beverage samples rather than relying solely on sensory analysis using human subjects (Bartlett et al., 1997; Hurst, 1999; Guadarrama et al., 2001; Deisingh et al., 2004; Gutiérrez et al., 2007; Röck et al., 2008). Thus, an automatic real-time aroma tracking system is needed to complement existing analytical systems (Sanaeifar et al., 2017).

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As reported by other authors, ENs are instruments designed to detect and identify chemical volatile compounds released by the food. As described before, an EN uses an array of semi-selective gas sensors which are subjected to the volatile components of food products to generate specific fingerprints (aroma responses) (Ampuero and Bosset, 2003; Luykx and Van Ruth, 2008; Wilson and Baietto, 2009; Sanaeifar et al., 2017). The fingerprints of conditions in the headspace of a food sample are utilized to construct an aroma dataset (Cynkar et al., 2010; Sanaeifar et al., 2017). Gas chromatography, mass spectrometry, and the combined technique gas chromatography–mass spectrometry allow both qualitative and quantitative analyses to be performed; accordingly, these techniques have been widely used to analyze the volatile compounds released by food. However, these methods require complex and costly sample pre-treatments and are not suitable for the online monitoring of volatile compounds (Gilar et al., 2003; Phan et al., 2012; Castro-Puyana et al., 2017; Kuuliala et al., 2018). Rapid developments in materials and electronic techniques have allowed for the creation of electronic nose systems based on electrochemical reactions. Such systems allow cheap online measurement to be made using small sample volumes and have therefore been used widely to measure volatile compounds released from food. However, electronic sensors have short useful lifetimes due to surface fouling and hysteresis and suffer from cross-sensitivity in their practical application (Loutfi et al., 2015; Verma and Yadava, 2015; Gu et al., 2017; Kodogiannis, 2017; Sanaeifar et al., 2017; Wojnowski et al., 2017; Kuuliala et al., 2018; Majchrzak et al., 2018). Table 8.1. summarizes the advantages and disadvantages of different types of electronic noses used by the food industry (Sanaeifar et al., 2017). Other sensors based on vibrational spectroscopy, infrared base sensors (near and mid infrared), have shown promise as rapid methods for the real-time monitoring of aroma and flavor in different food matrices. Consequently, infrared spectroscopy (IR) has become one of the most attractive and used methods of analysis in recent years, as it provides simultaneous, rapid, and non-destructive quantitation of the major chemical components of many agriculture-related products and plant materials (Su et al., 2017). Recently, new applications involving the determination of other minor compounds in TABLE 8.1  Advantages and Disadvantages of Commonly Used Electronic Noses Metal Oxide Advantages

Sensitive to a wide range of chemical compounds, fast recovery, low cost, and high reproducibility

Disadvantages

Sensors tend to drift, affected by humidity (sensor saturation)

Conducting Polymer Sensitive to a wide range of chemical compounds, fast recovery, diverse range of polymer coatings High sensor drift and susceptible to humidity and/or high moisture

Source: Adapted from Sanaeifar et al., 2017.

Electrochemical

Mass Spectrometry

Robust instrumentation

High sensitivity and stability, ideal for on-site analysis, quality control, enables quantitative and qualitative analysis High cost, large size instruments (however, some portable instrument become available)

Low sensitivity, no adequate for foods with high concentration of aromatic hydrocarbons in the matrix

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plant materials have also been reported (Kačuráková and Wilson, 2001; Cozzolino, 2014; Gordon et al., 2018; Roberts et al., 2018; Bureau et al., 2019). It has been reported that most volatile compounds released by foodstuffs have specific infrared absorption characteristics (Kačuráková and Wilson, 2001; Cozzolino, 2014; Gordon et al., 2018; Roberts et al., 2018; Bureau et al., 2019). Volatile compounds released by food can be analyzed qualitatively and quantitatively using these specific infrared absorption peaks. In recent decades this has resulted in multiple research groups studying infrared methods for detecting volatile compounds released by food during storage, maturity, and spoilage processes (Kačuráková and Wilson, 2001; Cozzolino, 2014; Gordon et al., 2018; Roberts et al., 2018; Bureau et al., 2019). The use of mid infrared spectroscopy (MIR) can also be performed using an openpath mode. Open-path MIR has been used extensively in environmental monitoring to quantify the components of gas clouds from distances of several meters to kilometers (La Spina et al., 2013; Akagi et al., 2014; Kira et al., 2016). The development of the focal plane detector technique has allowed open-path MIR to be used for imaging detection and to determine the spatial distributions of gas clouds directly. Hence, wide-area and remote detection of volatile components released from food is possible with the use of open-path MIR (Jiao et al., 2018). Colorimetric sensor arrays use chemical reactions between sensitive dyes and volatile compounds and have been used to monitor food spoilage. Although these methods allow volatile compounds to be visualized, the methods have problems associated with crosssensitivity and environmental pollution (Huang et al., 2014; Morsy et al., 2016; Sachdev et al., 2016; Schaude et al., 2017; Domínguez-Aragón et al., 2018).

8.3  DATA INTEGRATION AND ANALYSIS During the application of electronic noses and tongues, large datasets are generated. Therefore, in order to obtain the relevant information from the data generated by such instruments, different techniques based on multivariate data analysis (e.g., principal components analysis; partial least squares) and pattern recognition techniques (e.g., discriminant and cluster analysis) are used to detect and identify specific trends in the chemistry or chemical changes of the food under analysis. The application of these data analysis methods allows for the quantitative (e.g., calibration models) and qualitative analysis (e.g., cluster analysis, patterns) of the data generated. Specific details about multivariate data analysis methods and techniques, including pattern recognition methods, can be found elsewhere (Adams, 1995; Brereton, 2000, 2008; Esbensen, 2002; Gishen et al., 2005; Geladi and Kowalski, 1986; Geladi, 2003; Granato et al., 2014; Martens and Martens, 2001; Naes et al., 2002). Overall, the examples presented in this chapter highlight the need to combine chemometric and rapid analytical techniques to develop appropriate applications. Figure 8.1 shows the integration of sensors and multivariate data analysis methods to monitor and control the aroma of foods during processing and storage.

8.4  EXAMPLES AND APPLICATIONS IN FOOD SYSTEMS 8.4.1 Electronic Noses and Mass Spectrometry-Based Systems Andrés and collaborators (2002) reported on the use of a solid-phase microextraction (SPME) system coupled to a new direct-extraction device (DED) to monitor volatile

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FIGURE 8.1  Schematic representation of the use of electronic noses and chemometric

tools to control and monitor food processes. compounds during dry-cured ham ripening. The authors indicated that a DED allows for the utilization of the SPME fiber in the core of the sample with no damage to the fiber, enabling extraction of volatiles from the solid sample while avoiding sample handling (Andrés et al., 2002). According to the authors, major groups of volatile compounds extracted with SPME-DED agreed with the available scientific literature on the volatiles present in dry-cured ham, such as 3-methylbutanal or hexanal. In addition, observed changes in the profile of those volatile compounds throughout processing followed a typical pattern for the formation of volatile compounds (Andrés et al., 2002). Pionnier and collaborators (2004) monitored the kinetics of aroma compounds released during cheese consumption using an online atmospheric pressure ionization–mass spectrometry (API MS) system and an off-line solid-phase micro extraction-gas chromatography–mass spectrometry (SPME-GC-MS) instrument. The authors reported that ethyl hexanoate, heptan-2-one, and heptan-2-ol were the main volatile compounds. However, only the concentrations of ethyl hexanoate and heptan-2-one could be determined by the API-MS system (Pionnier et al., 2004). The combination of instrumental methods and sensory analysis is an excellent tool for the determination of the duration of the storage, the quality, and the origin of foods (Barié et al., 2015). Barié and collaborators (2015) reported on the use of polymer-coated surface acoustic wave (SAW) sensor arrays combined with SPME microextraction as a technique for a highly sensitive and selective organic gas detection system (Barié et al., 2015). These authors reported that discrimination between apple varieties, ripe and unripe pineapple, and finally the detection of off-flavor compounds was possible using the proposed system. The main advantages of the described system included its short analysis time (e.g., sample preparation, extraction, and measuring times) and low cost (Barié et al., 2015). Since the device is highly portable, a broad range of applications might be possible, including the online measurement of chemical and biochemical processes and detection of microorganism contamination (Barié et al., 2015). The rapid, objective monitoring of the ripening process of fermented food products is of great interest to the food industry. The application of an EN instrument to monitor this process was reported by Trihaas and Nielse (2005). In this study, headspace samples from two types of Danish blue cheese (traditional cheese and cheese from pasteurized milk) were analyzed (Trihaas and Nielsen, 2005). Data from the response of the chemical

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sensors were used to model the changes that occurred during shelf life (5, 8, 12, 20, and 33 weeks after brining) using multivariate analysis methods (Trihaas and Nielsen, 2005). The authors pre-processed the signal generated by the EN instrument using multiplicative scatter correction (MSC) (Trihaas and Nielsen, 2005). Differences, reflected in the cheese smell, were greater at early ripening stages, as determined by the EN system used (Trihaas and Nielsen, 2005). Rega and co-workers (2009) reported the use of dynamic SPME to monitor the release of volatile compounds generated during the baking process of cereal products (sponge cake model) by directly sampling its baking vapors. The steam generated during the process was analyzed using the dynamic SPME system, allowing for the monitoring of several volatile compounds with very different volatility and hydrophobicity properties (e.g., 5-hydroxymethylfurfural and 2-methyl-propanal) (Rega et al., 2009). The capability of a sensor array EN to discriminate between different milk samples by sensing their aroma was reported by Brambilla and collaborators (2010). The authors analyzed milk samples sourced from different production batches, brands, and processing systems (ultra-high temperature [UHT], partly skimmed, and commercially available). Principal component analysis (PCA) was used to put samples into different groups according to their odor profile. Moreover, the analysis of the olfactory fingerprints showed that 2 hours after the opening of the packaging, the flavor of anomalous samples evolved in a different way from that of the normal ones (Brambilla et al., 2010). Linear discriminant analysis (LDA) was also used to analyze the data generated by the EN system, where more than 98% of the samples were correctly classified (Brambilla et al., 2010). Volatile compounds in wines arise from many sources, such as from grapes during fermentation, yeast during fermentation, and/or during post-fermentation, storage, and processing (Callejón et al., 2012). Factors influencing the extraction of volatiles from grapes during fermentation have not been widely studied; an improved understanding of the processes and mechanisms involved in the formation or release of wine aroma compounds from grapes during fermentation could help winemakers to optimize or control wine composition and aroma (Callejón et al., 2012). These authors evaluated the effects of different skin contact times on changes in concentrations of volatile aroma compounds during the fermentation of Cabernet Sauvignon grapes using a SPME system coupled to GC-MS (Callejón et al., 2012). Results reported by these authors indicated that the duration and timing of the skin contact during fermentation showed a measurable effect on volatile composition (Callejón et al., 2012). Volatile compounds are important factors that affect the quality and flavor of dried medicinal and aromatic plant products such as mint (Mentha spicata L.) leaves (Kiani et al., 2018). Work by Kiani et al. (2018) evaluated the capability of an EN system as an alternative tool for monitoring the dynamic changes of the CVC of mint leaves during the hot air-drying process. The monitoring system includes a gas sensor array based on a metal oxide semiconductor (MOS) sensor system (Kiani et al., 2018). During the drying process, aroma parameters, humidity, and temperature of the outlet air, as well as sample weight, were acquired at given dryer settings. These authors reported that PCA analysis showed that the fingerprints of CVC for mint leaves are significantly different due to the drying conditions. In addition, partial least squares (PLS), multiple linear regression (MLR), and principal component regression (PCR) were applied to correlate the aroma parameters to the mint leaves’ moisture content as well as to determine the end-point of the drying process (Kiani et al., 2018). The reported models had high correlation coefficients in cross-validation (R>0.90) with an acceptable error margin (root mean square

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standard error of prediction, RMSEP=5.79). The overall results demonstrated the effectiveness and feasibility of monitoring mint leaf drying conditions using an electronic nose system (Kiani et al., 2018). Brazinha and collaborators (2010) experimentally demonstrated the use of mass spectrometry (MS) for the online, quantitative monitoring of organophilic pervaporation processes operated under variable operational parameters (different temperature parameters, condensation/downstream pressure) (Brazinha et al., 2010). The authors reported that, due to its high sensitivity, mass spectrometry is particularly suitable for the online monitoring of aromas in dilute streams under reduced pressure, which is similar to how natural aromas permeate (Brazinha et al., 2010). The authors also demonstrated how mass spectrometry is suitable for the online monitoring of solvent and co-solvent permeants (water and ethanol in the selected case study), enabling the technique for online determination of selectivity of the solutes of interest (Brazinha et al., 2010). Moreover, the MS system has proven to be a powerful tool for studying the fractionation of aromas, recovered by integrated pervaporation–condensation processes, allowing the online detection and monitoring of each solute vapor in the permeate stream. Additionally, the described technique allowed for accurate transient studies as it was capable of acquiring data points every 12–5 s in real time (Brazinha et al., 2010). Aprea and co-workers (2006) reported the use of PTR-MS to characterize olive oil headspace without concentration of the volatiles or pre-treatment of the samples. Comparison of extra virgin and defective (rancid) samples, as described by a panel of sensory judges, and the monitoring of thermo-oxidation processes were discussed (Aprea et al., 2006). Overall, PTR-MS was able to monitor rancidity in olive oil samples (Aprea et al., 2006). Coffees from different origins were roasted to different roast degrees and varying times using different temperature roasting profiles (Gloess et al., 2014). The formation of CVC during roasting was analyzed online using a PTR-ToF-MS instrument. These authors analyzed Coffea arabica from Colombia, Guatemala (Antigua La Ceiba), Ethiopia (Yirga Cheffe, Djimmah), and Coffea canephora var. robusta from Indonesia (Malangsari); the roasting profiles ranged from high temperature short time (HTST) to low temperature long time (LTLT) roasting (e.g., from medium to dark roast) (Gloess et al., 2014). The dynamic release of the online monitored CVC differed for the different coffees and showed a strong relation with the combination of time/temperature roasting profile (Gloess et al., 2014). The authors reported that for Guatemalan coffee samples, the formation of CVC started relatively early in the roasting process, while the CVC formation started much later in the case of Yirga Cheffe and Malangsari (Gloess et al., 2014). Differences were associated with the interactions between time and temperature of roasting, as well as the degree of roasting, and were influenced by both the intensity of volatiles and different chemical composition derived from the different coffee varieties and origins analyzed (Gloess et al., 2014). The hot water extraction process used to make an espresso coffee is affected by many factors, and the proper understanding of how these factors impact the profile of the final cup are important for the industry (Sanchez-Lopez et al., 2016). Sanchez-Lopez et al. (2016) investigated the effect of water temperature and pressure on the extraction kinetics of CVC in coffee using online monitoring of volatiles directly from the coffee flow using PTR-ToF-MS. The application of hierarchical cluster analysis (HCA) to the data allowed the grouping of the identified compounds into five families according to their time-intensity profiles (Sanchez-Lopez et al., 2016). The volatile compounds that were grouped into each family had similar physicochemical properties with polarity being

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determined as one of the main forces driving the CVC extraction kinetics; the effect of temperature on extractability was more pronounced for the less polar compounds (Sanchez-Lopez et al., 2016). It was observed by the authors that an increase in temperature produced a significant increase in the extraction of CVC, especially during the final stages of the extraction (Sachez Lopez et al., 2016). Gloess et al. (2014, 2018) used ion mobility spectrometry–mass spectrometry (IMS-MS) with corona discharge ionization for the online analysis of coffee roasting. The authors reported that this was the first time that the formation of CVC during coffee roasting was monitored not only in a positive but also in a negative ion mode, and not only with a MS system, but also with IMS (Gloess et al., 2018). The temporal evolution of more than 150 CVC was monitored during the roasting of Brazilian Coffea arabica. The use of IMS-MS allowed for the separation of isobaric and isomeric compounds. In a positive ion mode, isomers of alkyl pyrazines were found to exhibit distinct time-intensity profiles during roasting, providing a unique insight into the complex chemistry of this important class of aroma active compounds (Gloess et al., 2018). A negative ion mode gave access to species that were poorly detectable by other online methods, such as acids. In this study, the release of fatty acids during coffee roasting was investigated in detail. The author documents how the concentration of the fatty acids increases early in the roasting process followed by a decrease at the same time as other CVC start to be formed (Gloess et al., 2018). Wei and co-workers (2019) analyzed a wide variety of volatile constituents in various flaxseed oils (FSO) prepared from the roasting of the flaxseeds at different temperatures using SPME coupled with GC-MS. The authors identified more than 60 volatile compounds that were correlated with sensory analysis. The results indicated that all of the FSO samples had similar intensities for “oily” and “herbaceous” aroma descriptors, where “roasted” and “almond” aromas were statistically different (p < 0.05). The use of PCA showed that the first principal component (PC1) and the second principal component (PC2) explained 84% and 9%, respectively, of the variability associated with the volatile compounds present in the set of samples analyzed and prepared from different roasting conditions (Wei et al., 2019). Mouret and collaborators (2014) investigated the production of major volatile compounds associated with the predominant higher alcohols and esters formed during alcoholic wine fermentation and monitored it using an online GC system. The accuracy and frequency of the GC system measurements made it possible to calculate kinetic parameters, rates, and specific rates of production (Mouret et al., 2014). Results from this study indicated that esters (substantial proportions of which are lost in the off-gas, rather than of the total production (liquid content + losses) can lead to misinterpretation of the yeast metabolism (Mouret et al., 2014). The authors also highlighted that the specific production rate of individual higher alcohols reached their maximum values before the exhaustion of the corresponding precursor amino acids (Mouret et al., 2014). Isobutanol and isoamyl alcohol were formed from carbon metabolites and nitrogen metabolites and were consequently produced continuously throughout the fermentation process. On the other hand, propanol synthesis was strongly correlated with the presence of assimilable nitrogen, during both the growth and stationary phases (Mouret et al., 2014). Acetate ester concentrations correlated linearly with the concentrations of the corresponding higher alcohols, indicating that the availability of the precursors is the main limiting factor for producing these esters. The authors theorized that these results opened up the possibility for innovative approaches based on metabolic flux analysis taking such dynamics into account (Mouret et al., 2014).

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FIGURE 8.2  The use of chemometric (principal component analysis) to monitor changes during processing.

Xu and collaborators (2019) monitored changes in the volatile compounds of germinated chickpea, lentil, and yellow pea flour samples over the course of a 6-day germination using SPME-GC-MS/Olfactometry. The authors identified more than 100 volatile components involving 19 odor-active components utilizing GC-O. Both PCA and HCA methods revealed that lentil and yellow pea flour samples had similar aromatic profiles, while the decrease of bean flavor compounds along with the occurrence of unpleasant flavors was detected in chickpea flours upon germination (Xu et al., 2019). Six bean flavor markers, including hexanal, (E,E)-2,4-nonadienal, (E,E)-2,4-decadienal, 3-methyl-1-butanol, 1-hexanol, and 2-pentyl-furan, were employed to quantify bean flavor formation in the flours over the course of germination (Xu et al., 2019). The integration of sensors with multivariate data analysis or chemometrics to monitor a given process is illustrated in Figure 8.2. Changes during the process can be easily detected and/or monitored by the application of techniques such as PCA analysis. 8.4.2 Electronic Tongues The perception of aroma during oral processing is a complex interplay of many factors, partly related to the food matrix as well as to the oral processing conditions (Benjamin et al., 2012). The role of the tongue in transporting the bolus through the mouth by pressing it against the palate has been widely studied; however, the relationship between tongue pressures generated and CVC release is not clear (Benjamin et al., 2012). Pressure patterns during swallowing were found to be unique across subjects which may suggest personal flavor perception (Benjamin et al., 2012). These authors described an experiment for the release of volatile compounds in vitro during the process of eating, using a model mouth capable of reproducing actual human tongue pressure patterns with a computercontrolled artificial tongue driven by an actuator. In this experiment, tongue functionality was monitored by pressure and force sensors (Benjamin et al., 2012). The system was designed to incorporate oral features and conditions such as temperature, saliva flow, gas flow, and appropriate oral cavity volumes attached to an online CVC measurement system using proton transfer reaction–mass spectrometry (Benjamin et al., 2012). The authors concluded that the development of an innovative model mouth has a potential industrial application as a product development evaluation tool for food and pharmaceutical companies. Overall, the proposed system can provide fast and accurate feedback on

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the product’s flavor release and its physical properties under oral conditions (Benjamin et al., 2012). The use of electronic tongues to monitor food spoilage, enzymatic activity, and the production of toxic compounds derived from the storage of foods has been reviewed by Ghasemi-Varnamkhasti and collaborators (2018).

8.5 CONCLUSION The potential savings, in time and cost of analysis coupled with the environmentally friendly nature of these technologies, has positioned the so-called electronic nose and tongue systems as attractive techniques in the field of the quality analysis of foods (e.g., storage, process). Relatively inexpensive ENs such as a metal oxide sensor are commercially available and have been widely used by the food industry. However, volatile interference with the sensors determines the use of sample pre-treatment steps, which compromises the speed and the simplicity of the technique. However, a mass spectrometry-based electronic nose allows for the collection of large amounts of information from a sample in a very short time without the need for chromatographic separation or sample pre-processing, reducing the duration of the analysis. These technologies offer an exciting prospect of potentially providing the means for the development of small scale, cheap, portable hand-held instruments, which would be of great benefit to the whole food supply chain. The combination of sensors and chemometrics is a relatively new approach in the analysis of the aroma and taste of foods, and it can be used as a rapid technique for monitoring changes during processing and storage. However, data pre-treatment and care in the use of chemometric methods are required due to expected collinearity issues among variables, differences in signal and possible instrument drift and baseline shifts. Although these technologies are still relatively new, they will potentially become more readily available to smaller companies and businesses, as tools to detect, monitor, and prevent food spoilage and to monitor different steps during the production of foods. Despite multiple publications in the scientific literature regarding developments in ENs, there is a clear gap between the robust real-world application of this technology and the developments in both research and academia. This is a consequence of various roadblocks that still hinder the growth and uptake of these applications, such as the hesitancy of the industry to accept the integration of chemistry and mathematics (the benefits of chemometrics are often ignored by those who prefer to employ classical statistics), the lack of formal (academic) education in the use and application of instrumental methods as high throughput tools, or the implementation of a holistic approach to complex systems analysis.

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Chapter

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Hyphenated Electronic Nose Technique for Aroma Analysis of Foods and Beverages Adriana Marcia Graboski, Sandra Cristina Ballen, Juliana Steffens, and Clarice Steffens CONTENTS 9.1 Introduction 177 9.2 Electronic Nose 178 9.2.1 Sampling System 179 9.2.2 Detection System (Sensor Array) 180 9.2.2.1 Gas Sensor Response Mechanism 182 9.2.3 Signal Processing System and Standard Recognition Methods 183 9.3 Electronic Nose Application 183 9.4 Conclusion and Future Perspectives 186 Conflict of Interest 187 Acknowledgment 187 References 187

9.1 INTRODUCTION Human olfaction is still the main instrument for evaluating the aroma of many products, as well as being used for identifying the deterioration of a wide range of foods (GhasemiVarnamkhasti et al., 2018). Aroma is closely linked to the acceptance and quality of food. The flavor of a food is strongly influenced by its aroma. The chemical sensation caused by the taste of a food is due to the presence of the odors of small molecules that are sufficiently volatile so as to reach the sensory receptors during eating. There a layer of mucus that dissolves the molecules as soon as they reach these receptors; the molecules are perceived by the nose’s olfactory epithelium, which is then recognized by the brain as food characteristic (Firestein, 2001; Plutowska and Wardencki, 2007; Santonico et al., 2008). The aromas are used to characterize, improve, standardize, and/or reconstitute the aroma/flavor of the food, mask undesirable flavors and aromas, and improve sensory quality (Fani, 2015). Different aromas can be classified as either natural or synthetic. Natural flavors are obtained by physical, microbiological, or enzymatic methods from natural raw materials. They include essential oils, extracts (liquid and dry), balsams, and isolated natural

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flavoring substances. However, synthetic flavors are those obtained by chemical processes and are comprised of flavors identical to natural and artificial flavors. There are also mixtures of flavors, reaction/processing flavors, and smoke flavors (Agência Nacional de Vigilância Sanitária [ANVISA], 2007). Food flavors are the result of a balance between the concentrations of flavor compounds, which have different chemical natures, in the vapor phase. A distortion of the aroma profile may occur according to the modification of the food composition by favoring or hindering the release of certain volatile compounds (Seuvre et al., 2007). To evaluate the aroma quality in a food, sensory analysis, which uses human olfaction, is one of the most commonly used techniques. However, there are many limitations such as that the panel of testers is subjective and suffers incoherence and unpredictability due to individual variability, and decreased sensitivity due to prolonged exposure, fatigue, and variable mental states, and that it is time-consuming and limited to non-toxic compounds (Banerjee et al., 2012). Due to the deficiencies of sensory analysis methods, instrumental methods are used as a complement in the evaluation of foods and allow for detailed qualitative analysis (Wyllie, 2008). However, instrumental methods, such as gas chromatography/mass spectrometry (GC/MS), are also expensive and require trained people and considerable time for the analysis (Pizzoni et al., 2015). These methods sometimes do not provide accurate and reliable information (Ghasemi-Varnamkhasti et al., 2018). The development of faster and more efficient identification methods remains the focus of scientific research. In this sense, the electronic nose has attracted attention in many branches of industry for its potential applicability in aroma analysis, such as for quality and loss control, volatile release, and detection of aromas in food and beverages (Loutfi et al., 2015). The electronic nose has several advantages over traditional methods of gas analysis. Unlike other analytical instruments, it identifies the mixture of all the volatile compounds present in the sample, without identification of individual chemical species. It is considered a fast method, easy to operate, of small size, and it can perform the analysis in situ (Sun et al., 2018). It is also low cost, simple to handle, and has good portability. No reagents are needed for analysis and many applications are possible (Escuderos et al., 2013).

9.2  ELECTRONIC NOSE The first electronic nose model was developed by Persaud and Dodd (1982) around 1980. An electronic nose is an instrument that comprises an array of chemical sensors with partial specificity and an adequate pattern recognition system capable of distinguishing simple or complex odors (Gardner and Bartlett, 1994). It allows for an analysis of the composition of the gas mixture as a whole without the separation and identification of its specific components. It is an instrument that comprises a series of gas sensors (array) with different selectivities toward analytes (cross response) and an appropriate pattern recognition system. Intrinsically, each sensor exhibits a different selectivity and sensitivity with respect to particular components of the sample; however, they generate a chemical image characteristic of the gas mixture, called a “fingerprint.” The samples will be evaluated and classified (in terms of quality) based on these fingerprints or patterns (Gorji-Chakespari et al., 2017; Gębicki and Szulczyński, 2018). The operation of an electronic nose tries to mimic the biological process of human olfaction in aroma identification. In the electronic nose, the volatile compounds must be

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introduced through a sampling mechanism and transferred to the chamber containing the gas sensors. The human nose olfactory system is replaced by the sensors, which, when in contact with the aroma, interact with the sensitive layer. This layer undergoes reversible physical and/or chemical alterations, causing changes in the electrical properties, such as tension, for example, that will be transduced into electrical signals, pre-processed, and assessed by software. The results are later identified by a pattern recognition system. In the human nose, these electrical stimuli are transmitted to the brain which produces a recognition pattern in the memory. The brain can identify, classify, or perform a hedonic analysis of the sample (Deshmukh et al., 2015; Lisboa, Page, and Guy, 2009; Tudor Kalit et al., 2014; Santos, Lozano, and Aleixandre, 2017). A comparative illustration of the fundamental relations of the process of aroma recognition by a human and electronic nose is presented in Figure 9.1 (Kiani, Minaei, and Ghasemi-Varnamkhasti, 2016; Vagin and Winquist, 2015; Ramgir, 2013). The electronic nose (sensor array) mimics the behavior of the biological receptors in the human olfactory epithelium. It is used to mimic the behavior of the human brain in the task of recognizing and comparing odors or flavors. It can not only classify samples but accurately determine the concentration of substances when combined with a suitable multivariate calibration tool. Figure 9.2 shows an electronic nose system used for aroma detection. These systems generally consist of three main parts: (a) Sampling system in a chamber, (b) detection system with an array of sensors, and (c) data processing and pattern recognition algorithms in a computer (Kiani, Minaei, and Ghasemi-Varnamkhasti, 2016; Vagin and Winquist, 2015; Ramgir, 2013). 9.2.1 Sampling System Sample handling is an important step in a detection system using an electronic nose, where the quality of the analysis can be improved by using a suitable sampling system (Sanaeifar et al., 2017). The proper sampling of the volatile fraction and its transfer to

FIGURE 9.1  Comparison of the components involved in the detection and identification of aromas by human nose and electronic nose.

FIGURE 9.2  Electronic nose scheme for detection of aromas.

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the sensor chamber represents a significant challenge during the design of the analytical methodology (Majchrzak et al., 2018). The sampling chamber should be constructed of inert materials according to sample size, not be reactive with the volatile compounds, and not release odors. A heating or temperature control system must be used in order to increase the concentration of the volatile components in the top of the chamber and maintain the same conditions in all experiments (Sanaeifar et al., 2017). Sampling can be performed in a variety of ways through the use of headspace (static or dynamic) by using a diffusion method or sample enrichment (Cui et al., 2018; Estakhroyeh, Rashedi, and Mehran, 2017; Sanaeifar et al., 2017). Headspace is usually performed in a static or dynamic mode. In a static mode, the samples are incubated for a predetermined time. An aliquot of the top volatiles is collected using a syringe and then injected into the sensory chamber. The fact that only a relatively small fraction can be sampled is the main limitation of this method (Majchrzak et al., 2018). However, it has the advantage of avoiding the dilution of the volatiles in the free space, which increases the stability and sensitivity of the sensor and avoids disturbances within the sampling chamber (Wilson, 2012). In the dynamic mode, an airflow is used to load the volatile compounds into the sensory chamber (Ghasemi-Varnamkhasti et al., 2018), which is generally conducted using controlled air pressure or nitrogen gas pumps (Triyana et al., 2015). 9.2.2 Detection System (Sensor Array) The detection unit or multiple sensor array is an array of sensor elements capable of transducing chemical changes or interactions in measurable signals. This unit is considered the most important part of the electronic nose system (Kalantar-zadeh and Fry, 2008; Ramgir, 2013; Rudnitskaya, 2018). The most common types of sensors, the operation principles, and the advantages and disadvantages are shown in Table 9.1 (Wojnowski et al., 2017; Wardencki, Chmiel, and Dymerski, 2013; Deshmukh et al., 2015; Kiani, Minaei, and Ghasemi-Varnamkhasti, 2016; Tiggemann et al., 2016). Among the sensor types, metal oxide semiconductor (MOS) and conducting polymer (CP) are the most commonly used in electronic nose systems (Kiani, Minaei, and Ghasemi-Varnamkhasti, 2016). The principle of a sensor’s operation is based on the interaction of the sensitive layer with the volatile compounds of the sample being analyzed (Di Rosa et al., 2017). For this, the sensors are exposed to an inert gas (reference gas) for a baseline construction and to obtain a reference parameter, as can be seen in Figure 9.3. Later the volatile compounds generated by the headspace are transported through a tube to the chamber, in which they come in contact with the sensors. Then, again, an inert gas is generated to remove the volatile compounds adhered to the surface of the sensors and prepare them for a new measurement cycle (Nagle, Gutierrez-Osuna, and Schiffman, 1998). Gas sensors interact with the analyte to be detected by binding their molecules to the surface of the sensitive layer using one or more mechanisms, including absorption, adsorption, chemisorption, or chemical reactions. These generate physical and/or chemical changes, promoted by these processes, which are measured as an electrical signal (Arshak et al., 2004). An ideal gas sensor must have good reliability, robustness, high sensitivity, selectivity, and reversibility. In order to have a system with selective and reversible responses, a series of detection layers with different chemical properties must be used (James et al., 2005).

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TABLE 9.1  Types of Sensors Most Used in Electronic Nose Systems, Operation Principles, and the Advantages and Disadvantages Sensor Type

Operation Principle

Advantages

Disadvantages

Conducting polymer (CP)

When the conductive layer comes in contact with the molecules of the analyte, the conductivity of the sensor changes

High susceptibility to changes in relative humidity Short lifetime (typically 9–18 months) due to oxidation of the polymer

Metal oxide semiconductor field-effect transistor (MOSFET)

The detection mechanism is based on the induction of polarization on the catalytically active surface caused by an intermediate reaction

Metal oxide semiconductor (MOS)

Show a change of resistance/tension on exposure to certain analytes caused by the reaction of volatile chemical compounds catalyzed by metal oxides Alteration in the mass of the piezoelectric sensor occurs during its exposure to odoriferous compounds (adsorption/ absorption of the compound), which causes a change in the resonance frequency Constructed from electrodes immersed in an electrolyte. The analyte molecules are reduced or oxidized causing a change in voltage or resistance

Operate at room temperature High stability Wide range of applications Excellent reproducibility High sensitivity High selectivity Short response time Easy synthesis Operate at 50–170°C, which reduces the impact of relative humidity on the output signal Small size High selectivity and sensitivity Good sensitivity to toxic and flammable substances Low response to humidity Good sensitivity, in the order of sub-ppm levels Good discrimination High response High recovery time

Piezoelectric Quartz crystal microbalance (QCM) Surface acoustic wave (SAW) sensors Bulk acoustic wave (BAW)

Electrochemical sensors

Operation under ambient conditions must be under constant control, which excludes its application in portable and commercial equipment Vulnerable to poisoning by volatile sulfur compounds and ethanol High power consumption

Fast response time Low cost Potential for high integration

Susceptibility to ambient temperature changes Complex fabrication process

Very low power consumption Operate at room temperature Not susceptible to humidity Robust

Low sensitivity

(Continued )

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TABLE 9.1  (Continued) Types of Sensors Most Used in Electronic Nose Systems, Operation Principle, and the Advantages and Disadvantages Sensor Type Optical sensors

Mass spectrometerbased electronic noses

Operation Principle

Advantages

Disadvantages

Measure the modulation of light properties or characteristics (changes in light absorption, polarization, fluorescence, wavelength, etc.) on gaseous analytes exposure Involves the introduction of volatile compounds in the ionization chamber of a mass spectrometer instrument without previous chromatographic separation

Low energy consumption High sensitivity Ability to identify individual compounds in mixtures Short response time Not susceptible to humidity

High cost Complex construction

Short response time High sensitivity Qualitative and quantitative analysis

High power consumption Complex construction High cost Big size

FIGURE 9.3  The typical response of the gas sensor when submitted to an analyte.

9.2.2.1 Gas Sensor Response Mechanism Gas sensor characterization/performance in an electronic nose system usually involves the study of parameters such as sensitivity, selectivity, response time, reversibility, reproducibility, and the limit of detection, among others (Liu et al., 2012b; Skoog et al., 2005; Dey, 2018). The following details the characterization of the response of the sensor.

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(a) Sensitivity is the ratio of the slope in the calibration curve and the standard deviation of the analytical signal at a given concentration. (b) Selectivity is the ability of the sensor to identify a specific gas in a gas mixture. (c) Response time is defined as the time spent by a sensor to generate a measurable stable output signal corresponding to 63.2% of its stable maximum value after the addition of the analyte. (d) Reversibility is the ability of the sensor to return to its original state (baseline) after analyte detection. (e) Reproducibility means the sensors must have a stable and reproducible signal over a period of time. (f) Limit of detection is the lowest concentration that can be distinguished with a certain level of confidence. An ideal sensor must have high sensitivity, selectivity, and stability; fast response and recovery times; and a low manufacturing cost (Dey, 2018). 9.2.3 Signal Processing System and Standard Recognition Methods In most electronic noses, the processing system consists of a computer with software for the acquisition and collection of the signals generated during volatile compounds detection (Majchrzak et al., 2018). Pre-processing techniques are applied to the data in order to reduce the amount of information being analyzed and to obtain the “olfactory pattern” of the samples and to extract the static parameters of the measurements (Rodriguez-Gamboa, AlbarracinEstrada, and Delgado-Trejos, 2011; Capelli et al., 2014). This procedure involves the extraction of certain significant characteristics of the sensors’ response curves in order to produce a set of numerical data that can be processed by the recognition system of the electronic nose (Capelli et al., 2014). Data analyses using algorithms are used to perform qualitative and quantitative classifications. Two classes of statistical methods are used most. Supervised methods are used to classify unknown characteristics of a dataset that present common properties based on training samples, including artificial neural network (ANN), linear discriminant analysis (LDA), and support vector machine (SVM) methods. Unsupervised methods such as principal component analysis (PCA) and cluster analysis (CA) separate input data into different clusters based on the similarity of sample characteristics (Cui et al., 2018; Majchrzak et al., 2018).

9.3  ELECTRONIC NOSE APPLICATION The electronic nose can be applied in many areas of industrial production and human activities, medical diagnostics, environmental monitoring, quality control of food products, or security systems. In the food industry, it is an important tool that can be used during food and beverage processing, determination of shelf life, freshness assessment, and authenticity or tamper assessment (Peris and Escuder-Gilabert, 2013). Some examples of the application of the hyphenated electronic nose technique in aroma analysis in foods and beverages are presented below. In food production processes that have chemical parameters that vary over time, it is of fundamental importance to comply with the regulations related to quality control

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FIGURE 9.4  Electronic nose system applied to detect and discriminate volatile com-

pounds in gummy candies aromatized with apple, strawberry, and grape. The gas sensor array is based on graphite interdigitated electrodes with conducting polymer layer. (Peris and Escuder-Gilabert, 2013). Problems with food authenticity assessment (adulteration) can cause a serious risk to health in some instances. In this way, the use of advanced sensor arrays have been reported as being used for authenticity/adulteration assessment in oils, juices, canned of fruits, milk, meat, tea, wine, whisky, and spices (Peris and Escuder-Gilabert, 2016). The aroma is used as a quality parameter and product conformity indicator, intimately related to the product’s acceptance by the consumer. For this purpose, Graboski et al. (2018a, 2018b) applied an electronic nose composed of an array of gas sensors with different polyaniline-based sensors for aroma volatile compound detection (apple, strawberry, and grape) in gummy candy. The authors observed that this system was able to discriminate between the aromas in the food matrix (gummy candies) using PCA (Figure 9.4). The control of the degree of maturity of fruit is very important due to its susceptibility to disease and fast deterioration. Electronic nose systems can be used for minimizing production losses and also preserving the unique features of each fruit. Manzoli et al. (2011) used a low-cost sensor array system for monitoring banana ripeness (Figure 9.5). The volatile compound released during banana ripening is ethylene; when the fruit is ripe and full-ripe it releases 2-hexenal, eugenol, and isopentanol. In the experiment, unripe bunches of bananas were used, and the maturation volatiles over the course of 5 days were evaluated. The criterion of ripeness was the color of the banana peel. The sensor array was able to produce a distinct pattern of signals that could be used to characterize the bananas’ degree of ripeness. To evaluate the preservation of the leaf aroma in the drying process of medicinal and aromatic plants, Kiani, Minaei, and Ghasemi-Varnamkhasti (2018) identified volatile compounds released by the foodstuff using an electronic nose. In the drying of mint leaves, in which aroma volatilization may alter the aromatic quality, the electronic nose system was capable of differentiating the mint aroma in different drying stages (fresh and dried mint) and of determining the time to terminate the drying process. In the beverage industry, for classification and quality-control purposes, different fruit aromas (strawberry, lemon, cherry, and melon) were evaluated using an electronic nose (Adak and Yumusak, 2016). By the use of neural networks, it was possible to train the nose separately with backpropagation, for 60% of the sample’s values for all four of the aroma types.

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FIGURE 9.5  Experimental setup of an electronic nose based on a gas sensor with polyaniline films applied to detect the volatiles emitted during banana ripening. Numbers indicate gas sensor (1), glass chamber (2), data acquisition (3), and computer (4).

Xiao et al. (2014) used an electronic nose to classify Chinese liquor (an alcoholic drink made from starter cultures) aroma mixtures with different origins and liquor flavor types. They identified 86 aromas, including acids, esters, alcohols, aldehydes, ketones, phenols, nitrous, and sulfuric compounds; and the electronic nose could differentiate between the liquor origins and flavor types. Liu et al. (2012a) evaluated 20 Chinese spirits which belonged to eight different flavors using a portable electronic nose. The authors found that a combination of an appropriate sensor array with a pattern recognition method could evaluate the quality and flavor of the samples. In a study by Gupta, Variyar, and Sharma (2015) using aroma data, in food irradiated by electromagnetic γ-rays or electron beams for improving safety and shelf life and reducing microbial contamination, the electronic nose was able to identify a food that was or not subjected to irradiation. First PCA and later LDA were used to classify grape and apple fruits in a chemometric approach. A 100% success rate was obtained for discriminating between irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, and 2.0 kGy). The quality of black tea aroma of multiple tea gardens spread across north and north-east India was evaluated by Tudu et al. (2009) using an electronic nose and a neural network for classification. It was possible to classify four tea gardens in north and north-east India using this system. Black tea quality is quite complex due to the presence of innumerable compounds. In this tea, a fermentation process can occur with biochemical reactions in green tea leaves. The transformation from a grassy to sweet floral aroma was identified by the authors using an electronic nose. Sharma et al. (2015) monitored the fermentation of black tea in real time using an electronic nose (an array of quartz crystal microbalance sensors coated with glucose, maltose, maltodextrin, β-cyclodextrin, d-glucose, and polyethylene glycols). The highest peak of the sensor represented the optimum fermentation point, proving that the electronic nose was able to identify the fermentation profile. The organoleptic requirements for evaluating honey odor included the analysis of dominant pollen in honey deposits, type, and origin. A classification of Polish honey types (acacia flower, linden flower, rape, buckwheat, and honeydew) was studied by Dymerski et al. (2014) using an electronic nose with chemometric data analysis. With PCA and

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cluster analysis, it was possible to discriminate between three types of honey (acacia flower, linden flower, and rape). Ampuero, Bogdanov, and Bosset (2004) employed an electronic nose for an investigation of honey matrices, and a good correlation was found between the data of the study and the classical method for the determination of the botanical origin of honey. Physiological changes from the minimal processing of vegetables can cause loss of taste and production of off-flavors in these products. Torri, Sinelli, and Limbo (2010) applied a commercial electronic nose to monitor the changes of volatile compounds in minimally processed pineapples during their storage at different temperatures. According to PCA, there was much more evident discrimination between the samples kept at higher temperatures than at lower temperatures. By CA there was a separation into two groups, “fresh pineapple” and “old pineapple,” making it possible to determine a stability time (maximum acceptability time for the loss of freshness) of 5.3 days at 5.3°C, 2.7 days at 8.6°C, and 1.2 days at 15.8°C. Therefore, this approach can be applied for evaluating loss of aroma quality, freshness, and influence on aroma and taste quality during storage. In the meat and fish industry, it is fundamental to have a fast and accurate detection system for microbial spoilage to avoid production losses. Food spoilage is associated with microbial volatiles, organoleptic changes, and the development of off-odors. These parameters can be used as indicators of bacterial presence and, subsequently, of meat quality. The commercial electronic nose was used for the detection of these microbial volatiles in beef food spoilage and was able to classify stored beef into two classes (“spoiled” and “unspoiled”) (Balasubramanian et al., 2004). It was also employed to monitor the spoilage of packaged beef fillet at different storage temperatures (0, 4, 8, 12, and 16°C). Food additives are added in products to enhance taste and preserve flavor or appearance during processing, packaging, and storage. But its presence in food products must be considered by consumers because in some cases it can represent health risks. Electronic nose application in citrus juices for monitoring food additives (benzoic acid and chitosan) was reported by Qiu and Wang (2017). The authors used a linear discriminant analysis for discrimination and classification and obtained 85.5 and 95.0% cross-validation for benzoic acid and chitosan, respectively, as observed in Figure 9.6. The addition, substitution, and removal of substances and the dilution of products are considered food adulterations. Controls on adulteration are required to avoid harm to human health. In this way, the hyphenated electronic nose can be applied for evaluating common fraudulent procedures (partial or complete substitution of an authentic ingredient), considering a specific odor (pattern recognition system) or authentic or traditional aroma of a product. For this purpose, many foods have been monitored such as milk, cheese, several oils (olive, sesame, palm olein, virgin coconut, flaxseed), meat and meat products, honey, juices, wine, vinegar, and alcoholic beverages (Gliszczyńska-Świgło and Chmielewski, 2017).

9.4  CONCLUSION AND FUTURE PERSPECTIVES The accuracy of an electronic nose’s performance may be limited by extrinsic factors such as humidity and temperature, or by intrinsic factors such as sensor drift or instrumentation errors. To classify and authenticate different analytes or properties in complex samples, a standard sample for evaluating the sensor’s response is required. To make this possible, different kinds of electrodes and an array of sensors can be used with a high sensitivity and even selectivity. The response obtained can be evaluated using a statistical

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FIGURE 9.6  Schematic representation of the electronic nose applied to citrus juice to discriminate benzoic acid and chitosan additives.

tool to discriminate between aroma samples. In future works, the use of statistical tools and more artificial intelligence in these devices are expected to discriminate between samples with very similar behavior.

CONFLICT OF INTEREST The authors declare that they do not have a conflict of interest.

ACKNOWLEDGMENT The authors acknowledge the National Council for Scientific and Technological Development, Coordination for the Improvement of Higher Education Personnel, Research Support Foundation of the State of Rio Grande, and Research and Projects Financing for the support received.

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Plutowska, B., and W. Wardencki. 2007. “Aromagrams—Aromatic profiles in the appreciation of food quality.” Food Chemistry 101 (2): 845–72. Qiu, S., and J. Wang. 2017. “The prediction of food additives in the fruit juice based on electronic nose with chemometrics.” Food Chemistry 230 (September). Elsevier: 208–14. Ramgir, N.S. 2013. “Electronic Nose based on nanomaterials: Issues, challenges, and prospects.” ISRN Nanomaterials 2013: 1–21. Rodriguez-Gamboa, J.C., E.S. Albarracin-Estrada, and E. Delgado-Trejos. 2011. “Quality control through electronic nose system.” In Modern Approaches to Quality Control, pp. 505–22. Janeza Trdine: Croatia. InTech. Rosa, A.R.D., F. Leone, F. Cheli, and V. Chiofalo. 2017. “Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment—A review.” Journal of Food Engineering 210 (October). Elsevier: 62–75. Rudnitskaya, A. 2018. “Calibration update and drift correction for electronic noses and tongues.” Frontiers in Chemistry 6 (September): 433. Sanaeifar, A., H. ZakiDizaji, A. Jafari, and M. de la Guardia. 2017. “Early detection of contamination and defect in foodstuffs by electronic nose: A review.” TrAC Trends in Analytical Chemistry 97 (December). Elsevier B.V.: 257–71. Santonico, M., P. Pittia, G. Pennazza, E. Martinelli, M. Bernabei, R. Paolesse, A. D’Amico, D. Compagnone, and C. Di Natale. 2008. “Study of the aroma of artificially flavoured custards by chemical sensor array fingerprinting.” Sensors and Actuators B: Chemical 133 (1): 345–51. Santos, J.P., J. Lozano, and M. Aleixandre. 2017. “Electronic noses applications in beer technology.” In Brewing Technology, pp. 177–200. InTech. Seuvre, A.M., E. Philippe, S. Rochard, and A. Voilley. 2007. “Kinetic study of the release of aroma compounds in different model food systems.” Food Research International 40 (4): 480–92. Sharma, P., A. Ghosh, B. Tudu, S. Sabhapondit, B.D. Baruah, P. Tamuly, N. Bhattacharyya, and R. Bandyopadhyay. 2015. “Monitoring the fermentation process of black tea using QCM sensor based electronic nose.” Sensors and Actuators B: Chemical 219 (November): 146–57. Skoog, D., D. West, J. Holler, and S. Crouch. 2005. Fundamentals of analytical chemistry. Analytical Chemistry 398: 27–8. Belmont: Thomson-Brooks/Cole. Sun, F., Z. Wu, Y. Chen, J. Li, S. He, and R. Bai. 2018. “Analysis of odors from thermally modified bamboo assessed by an electronic nose.” Building and Environment 144 (August): 386–91. Tiggemann, L., S. Ballen, C. Bocalon, A.M. Graboski, A. Manzoli, P.S. De Paula Herrmann, J. Steffens, E. Valduga, and C. Steffens. 2016. “Low-cost gas sensors with polyaniline film for aroma detection.” Journal of Food Engineering 180: 16–21. Torri, L., N. Sinelli, and S. Limbo. 2010. “Shelf life evaluation of fresh-cut pineapple by using an electronic nose.” Postharvest Biology and Technology 56 (3): 239–45. Triyana, K., M.T. Subekti, P. Aji, S.N. Hidayat, and A. Rohman. 2015. “Development of electronic nose with low-cost dynamic headspace for classifying vegetable oils and animal fats.” Applied Mechanics and Materials 771 (July): 50–4. Tudor Kalit, M., K. Marković, S. Kalit, N. Vahčić, and J. Havranek. 2014. “Application of electronic nose and electronic tongue in the dairy industry.” Mljekarstvo 64 (4). Hrvatska mljekarska udruga: 228–44.

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Tudu, B., A. Jana, A. Metla, D. Ghosh, N. Bhattacharyya, and R. Bandyopadhyay. 2009. “Electronic nose for black tea quality evaluation by an incremental RBF network.” Sensors and Actuators B: Chemical 138 (1): 90–95. Vagin, M.Y., and F. Winquist. 2015. “Electronic noses and tongues in food safety assurance.” In High Throughput Screening for Food Safety Assessment, pp. 265–83. Elsevier. Wardencki, W., T. Chmiel, and T. Dymerski. 2013. “Gas chromatography-olfactometry (GC-O), electronic noses (e-noses) and electronic tongues (e-tongues) for in vivo food flavour measurement.” In Instrumental Assessment of Food Sensory Quality, pp. 195–229. Elsevier. Wilson, A.D. 2012. “Advanced methods for teaching electronic-nose technologies to diagnosticians and clinical laboratory technicians.” Procedia—Social and Behavioral Sciences 46: 4544–54. Wojnowski, W., T. Majchrzak, T. Dymerski, J. Gębicki, and J. Namieśnik. 2017. “Electronic noses: Powerful tools in meat quality assessment.” Meat Science 131 (May): 119–31. Wyllie, S.G. 2008. “Flavour quality of fruit and vegetables: Are we on the brink of major advances?” In Fruit and Vegetable Flavour, pp. 3–10. Woodhead Publishing. Xiao, Z., D. Yu, Y. Niu, F. Chen, S. Song, J. Zhu, and G. Zhu. 2014. “Characterization of aroma compounds of Chinese famous liquors by gas chromatography–mass spectrometry and flash GC electronic-nose.” Journal of Chromatography B 945–6 (January): 92–100.

Chapter

10

Food Aroma Compounds by Capillary Electrophoresis Raffaella Colombo and Adele Papetti CONTENTS 10.1 Introduction 194 10.2 CE Instrumentation 194 10.2.1 System 194 10.2.2 Capillaries 194 10.2.3 Buffers 195 10.2.4 I njection and Separation 196 10.2.5 Detectors 196 10.3 Theoretical Principles 197 10.4 Separation Modes 197 10.4.1 Micellar Electrokinetic Chromatography (MEKC) 197 10.4.2 Chiral Capillary Electrophoresis (CCE) 198 10.4.3 Capillary Electrochromatography (CEC) 198 10.4.4 Non-Aqueous Capillary Electrophoresis (NACE) 198 10.5 Advantages and Limitations 199 10.6 Applications 199 10.6.1 Non-Enzymatic Reactions 199 10.6.1.1 Amines 200 10.6.1.2 Furanones 200 10.6.1.3 Phenols 200 10.6.1.4 Pyranones 201 10.6.1.5 Pyrazines 201 10.6.1.6 Pyridines 202 10.6.1.7  Thiols, Thioethers, and Di- and Trisulfides 202 10.6.1.8  Amino Acids as Precursors of Food Aroma Compounds 202 10.6.1.9  Other Substances as Precursors of Aroma Compounds 203 10.6.2 Enzymatic Reactions 203 10.6.2.1  Carbonyl Compounds 203 10.6.2.2 Terpenes 206 10.6.2.3  Other Substances as Precursors of Aroma Compounds 206 10.6.2.4  Phenols as Precursors of Food Aroma Compounds 206 References 210

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10.1 INTRODUCTION Capillary electrophoresis (CE) is a microscale technique, and its principle consists in the migration of ions/charged molecules in a buffer solution through an open, fused-silica capillary under an applied electric field. The separation is based on a molecule mass-tocharge ratio. From the early 1980s to date, CE has resulted in the application of different CE separation modes, the progression of coupling CE with sensitive detection systems, and the advances in microchip-CE technology. This improves the versatility of CE with important applications in food quality and safety, in food processing and stability, and also in foodomics (Pinero, Bauza, and Arce, 2011; Karlinsey, 2012; Acunha et al., 2016; Papetti and Colombo, in press). In particular, miniaturized CE systems (microchip-CE devices), which simultaneously allow sample preparation, separation, and quantification in a chip, and ensure rapid and sensitive methods for detecting fraud or contamination (Martín, Vilela, and Escarpa, 2012).

10.2  CE INSTRUMENTATION 10.2.1 System CE separations occur in a bare fused-silica capillary (30 to 100 cm long), with an inner diameter (i.d.) of 50–100 µm and an outer diameter (o.d.) of 150–360 μm. Both capillary ends are immersed in buffer (background electrolyte, BGE) reservoirs, together with platinum electrodes to keep the conductivity at the applied voltage (10–30 kV). Capillaries are covered with a copolymer (polyimide) with heat resistance and flexibility, not transparent to the UV light. Therefore, it is necessary to remove it to create a detection window, which is transparent to UV, providing online detection (Camilleri, 1997; Whatley, 2001). The voltage is applied to the total capillary length (L), but the analytes only cross the capillary until they reach the effective capillary length (l), which is the distance from the injection to the detection window. As a high electric field and the electrophoretic principle can cause Joule heating and zone broadening; the capillary temperature must be controlled with high-speed forced-air coolers or recirculating liquid coolant systems. Temperature also becomes an important parameter for maintaining a constant current and buffer viscosity and reproducible migration times (Weinberger, 2000; Whatley, 2001). Notwithstanding the presence of controlled temperature systems, the temperature inside the capillary can only be estimated, and the generated Joule heating increases proportionally over the electric field (applied voltage/capillary length) and the BGE conductivity and concentration (Whatley, 2001). Figure 10.1 illustrates a schematic CE system. 10.2.2 Capillaries CE uncoated capillary walls are made of silanol groups (Si–OH), which are weak acids (pK~7), and have a negative charge in alkaline conditions and buffers. To have a stable system, operative conditions must be setup in a pH range of 2–9 with buffers, responsible for pH control, and analysis reproducibility. The application of a voltage gives an electrical double layer (electric ζ potential) along the inner wall, in which buffer cations are attracted to the negative electrode, creating the so-called electroosmotic flow (EOF)

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FIGURE 10.1  Schematic representation of a CE system. (Reprinted with permission from Karlinsey, J. M. (2012). Electrophoresis. In Y. Picò (Ed.), Chemical Analysis of Food: Techniques and Applications (pp. 375–405). Waltham (MA): Academic Press Elsevier.)

(Slater, Tessier, and Kopecka, 2010). A new uncoated capillary must be conditioned with an appropriate volume of strong bases and buffers to create an EOF. EOF velocity depends on the applied electric field; it is directly proportional to buffer dielectric constant and ζ potential, and indirectly proportional to buffer viscosity. It is often necessary for suppressing EOF because of capillary–analyte interaction/absorption, which causes EOF mobility variation and irreproducibility. In this case, the capillary inner wall is chemically modified by covalent bonding (covalent coating) with neutral or hydrophilic substituents or by adding BGE polymeric modifiers (dynamic or adsorptive coating) (Horvath and Dolník, 2001; Whatley, 2001; Slater, Tessier, and Kopecka, 2010). A new capillary has to be pre-conditioned at high-pressure values (≥1 bar). The reagents for the conditioning procedure are different, based on uncoated or coated capillaries. An uncoated capillary is rinsed with 10 to 15 column volumes of strong bases, followed by the same column volumes of the BGE, to generate EOF. For a coated capillary, in which EOF must be suppressed, other solvents, such as ethanol or toluene, and appropriate coated agents are used. In addition, a conditioning inter-run is necessary to regenerate the capillary wall at the end of each analysis (Weinberger, 2000; Whatley, 2001; Slater, Tessier, and Kopecka, 2010). 10.2.3 Buffers The BGE modulates pH and silanol dissociation and drives the migration of analytes, which differ for migration times and mobilities. Buffers must be chosen based on a highsalt purity, high-buffer capacity (pH = pKa±1), low-absorbance, low-conductivity, and low-temperature coefficient. In the choice of a run buffer the parameters to take into account are pH value, ionic strength, types of salts (inorganic or organic), and operating temperature. Organic buffers have high ionic strength, which is correlated with a high

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buffer capacity; they exhibit low mobility/conductivity and less Joule heating, giving the possibility of using higher voltages with an increase in peak efficiency. On the contrary, organic buffers absorb UV–Vis light, and this causes an increase in baseline absorbance (Reijenga et al., 1996; Camilleri, 1997; Weinberger, 2000). The ionic force of BGE can be modulated in relation to the ionic force of the sample; in fact, a difference in the concentration/conductivity between a sample buffer and BGE allows for so-called “sample stacking,” a procedure able to increase peak efficiency and sensitivity (Camilleri, 1997; Whatley, 2001). 10.2.4 Injection and Separation The injection plug amount must be of 1–50 nL to optimize efficiency and resolution. The most used type of injection is by pressure or vacuum (hydrodynamic injection) by an onboard air pump, in which two parameters (pressure value and time) can be setup. The injected volume can be calculated with the Poiseuille equation; it is directly proportional to pressure, capillary inner diameter and pressure application time, and indirectly proportional to BGE viscosity and total capillary length (Whatley, 2001). Another type of injection consists of the use of low voltage values over short time periods (electrokinetic injection). It is rarely used because of a low reproducibility. After a sample injection, the application of voltage allows for the migration of ions. 10.2.5 Detectors CE detection occurs on-capillary with the advantage of having no void volumes and the disadvantage of a short path length (i.e., i.d.), which is responsible for the low concentration sensitivity of this technique. Absorbance detectors are the most used and can be UV detectors, which use only a portion of the available energy, or photodiode array detectors (PDA), which exploit the entire spectrum of UV light, with the advantage of estimating peak purity. For compounds, which have no or low UV absorption, such as inorganic ions or some acids, respectively, indirect UV detection can be a solution. In indirect UV, a molecule with a UV absorption and a mobility similar to the analyte, is added to BGE. When analytes migrate in the BGE, a displacement of the absorbing molecule occurs, and a decrease in absorbance is registered (Camilleri, 1997). Electrochemical (EC) detectors consist mainly of amperometric detectors (AD), in which current is measured. They are more sensitive in comparison with UV detection and are based on oxidation/reduction reactions between analytes and electrodes. This type of detection can be very useful in food analysis for compounds in trace levels (Karlinsey, 2012). Recently, the development of advanced EC detectors, such as CE contactless coupled detection (CE-CCD) and CE capacitively coupled contactless conductivity detection (CE‐C4D), has allowed for an increase in sensitivity, which is very useful in food analysis (Elbashir, Schmitz, and Aboul-Enein, 2017). Another detection system with increased sensitivity is represented by the laserinduced fluorescence (LIF) detector, which requires a process of derivatization (on- or off-capillary) for non-fluorescent molecules (Karlinsey, 2012). In the case of food aroma compounds, it is mainly used for amino acid precursors. Mass spectrometer (MS) detectors can add information on molecular weight and the mode MS/MS (MS2) also provides structural information. Coupling with CE improves

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velocity, resolving capacity, selectivity, and sensitivity. CE-MS is mostly applied to the analysis of traces of contaminants, residues, and biomarkers to ensure quality and authenticity in food samples (Ravelo-Pérez et al., 2009; Ibáñez et al., 2013).

10.3  THEORETICAL PRINCIPLES The electrophoretic mobility (µe) represents the velocity (v) of an ion/analyte through the capillary, in which an electric field (E) is applied (µe=v/E). EOF represents the velocity of the bulk flow (vEOF) in this electric field and it is expressed as a mobility (µEOF or µ0). In normal polarity and with injection at a node pole, cations and neutral species migrate with EOF and anions migrate against it. In reverse polarity, the inner wall assumes a positive charge, and the EOF moves in the opposite direction. The so-called apparent mobility (µapp) refers to the observed mobility of an analyte, which takes into account its effective mobility (µeff) and the EOF contribution (µapp= µEOF+µeff). The mobility is calculated based on migration time, voltage, and capillary length (µapp=l/tE; E=V/L; µapp=lL/Vt) (Camilleri, 1997). In ideal conditions (a small injection plug length and the absence of an interaction between the analyte and the capillary), only longitudinal diffusion is present, and as a consequence peak efficiency depends only on the solute molecular diffusion coefficient.

10.4  SEPARATION MODES When only BGE is used, the technique is called capillary zone electrophoresis (CZE) or free-solution capillary electrophoresis (FSCE), and it can be applied only to charged analytes. The simple addition of different additives to BGE produces different CE-modes and separation principles. Here the main CE-modes applied in the analysis of food aroma compounds were selected. Table 10.1 shows the principles and parameters of CE-modes applied to food aroma compounds. 10.4.1 Micellar Electrokinetic Chromatography (MEKC) The presence of ionic or non-ionic surfactants (sodium dodecyl sulfate, dodecyltrimethylammonium bromide, Triton X-100), which are added to BGE in concentrations useful to constitute micelles, creates a sort of pseudophase, and for this reason the technique is TABLE 10.1  CE Separation Modes Mainly Used in the Analysis of Food Aroma Compounds CE Mode CZE MEKC CCE CEC NACE

Principle Mobility Hydrophobic/ionic interaction Diastereoisomer formation Mobility Hydrophobic/ionic interaction ion-pairing effects

Stationary Phase

BGE Additives

Type of Molecule

/ Surfactant Chiral selector /

/ / / Present

Charged Neutral/charged Chiral Neutral/charged

Organic solvent

/

Low soluble

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named “chromatohraphy.” In fact, they act as a pseudo-stationary phase with a hydrophobic core and a hydrophilic outer surface able to create hydrophobic and/or ionic interactions with the analytes. This technique also allows for the separation of neutral species contrary to CZE. The use of a basic buffer is recommended to minimize interactions between the surfactant and the capillary wall (Terabe, 2009). The MEKC separation mode can also be applied in chiral conditions (see Section 10.4.2), allowing enatiomeric separation, such as for studying the stability of catechins in tea infusions (Mirasoli et al., 2014). 10.4.2 Chiral Capillary Electrophoresis (CCE) CCE allows for the performance of chiral separations, adding neutral or charged chiral selectors to the BGE without the stationary phases of chiral liquid chromatography (LC). In the food field, it is mainly used for the separation of racemic amino acids, as potential precursors of food aroma compounds (Belitz, Grosch, and Schieberle, 2009; Acunha et al., 2016). Chiral selectors have different structures (cyclodextrins (CDs), crown ethers, proteins, and macrocyclic antibiotics). In CDs, which are cyclic oligosaccharides, and crown ethers, the presence of a cavity favors the formation of a hydrophobic or hydrophilic complexation, respectively (Rizzi, 2001; Tsioupi, Stefan-Vanstaden, and Kapnissi-Christodoulou, 2013). CDs and their derivatives are the most widely used selectors because of high BGE solubility, no or low absorption in the UV range, and high versatility. It is also important to setup the selector concentration. This parameter influences the effective mobilities and as a consequence the method’s selectivity (Rizzi, 2001; Tsioupi, Stefan-Vanstaden, and Kapnissi-Christodoulou, 2013). 10.4.3 Capillary Electrochromatography (CEC) CEC is a hybrid technique of CE and LC; in fact, the capillary contains a stationary phase and consequently two principles, such as partition and mobility, drive the separation. This technique finds application in the analysis of aromatic compounds (Svec, 2004). The development of new coating polymers or materials has considerably increased CEC specificity and versatility (Iacob, Bodoki, and Oprean, 2014; Tarongoy, Haddad, and Quirino, 2018). 10.4.4 Non-Aqueous Capillary Electrophoresis (NACE) NACE is a CE mode in which organic solvents, such as methanol, ethanol, and acetonitrile, are added to the BGE. It is useful for analytes with very low solubility in aqueous solutions. This addition improves CZE selectivity. NACE can be used as an alternative to MEKC (Pinero, Bauza, and Arce, 2011; Kenndler, 2014). Other CE modes, such as capillary gel electrophoresis (CGE) or capillary isotachophoresis (cITP) and capillary isoelectric focusing (cIEF), not treated here, are specific for DNA or peptides and proteins, respectively. In these CE modes, running buffers are replaced by other solutions (gel, particular buffers, or ampolytes) (Pinero, Bauza, and Arce, 2011; Karlinsey, 2012; Acunha et al., 2016; Papetti and Colombo, in press). Table 10.1 illustrates different CE modes with principles, characteristics, and applications.

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10.5  ADVANTAGES AND LIMITATIONS On-capillary detection contributes to an increase in peak efficiency, which is also ensured by the geometry of the capillary. In fact, this induces the creation of a uniform flow, contrary to the laminar flow of LC. Analysis time in CE is short (10–20 min); the small volumes (injection and capillary) allow for the low consumption of the sample and buffer, and the free-solution system gives the benefit of environmental compatibility. In addition, the high surface area to volume ratio minimizes the Joule heating effect and partially resolves the problem of reproducibility. The problem of errors in quantitative analysis, due to the different migration velocities of the solutes, can be resolved. In fact, the overestimation of the peak area of solutes with low mobility and the underestimation of the peak area for those with high mobility are normalized considering the peak area divided by the migration time (area normalization) (Camilleri, 1997). The possibility of modulating many parameters, such as capillary type and length, buffer (pH, type, and ionic force), injection type and values, voltage, temperature, conditioning type and time, and the possibility of applying different separation modes, provides a great potential for a wide range of applications (ions, drugs, proteins, natural products, and foodrelated complex molecules from amino acids to lipids, carbohydrates, DNAs, and vitamins) (Reijenga et al., 1996; Pinero, Bauza, and Arce, 2011; Papetti and Colombo, in press). Poor sensitivity remains the only limit, which depends on the small light path (~30 µm with an internal diameter of 50 µm), but it can be enhanced, not only by using appropriate detectors, but also by using capillaries with an extended path length or offline or online preconcentration procedures (Camilleri, 1997; Whatley, 2001; Breadmore et al., 2017). Table 10.2 illustrates the specific advantages and limitations of each CE-mode treated here.

10.6 APPLICATIONS 10.6.1 Non-Enzymatic Reactions These reactions, such as lipid peroxidation, the Maillard reaction (for example caramelization), and amino acid degradation, if occurring at room temperature, influence aroma compounds only during long storage. On the contrary, heat treatment increases kinetics and aroma diversity in the case of roasting and frying. This type of reaction can be the source of a great number of volatile compounds, but they are usually in small TABLE 10.2  Pros and Cons of Different CE Modes CE Mode CZE MEKC CCE CEC NACE

Pros Simplicity Neutral molecules Improved analyte solubility Efficiency Low consumption of chiral selectors Efficiency Easy coupling with MS detectors Improved analyte solubility Efficiency Selectivity

Cons Limited to charged molecules Interaction of the analyte with surfactant Setup of separative conditions Setup of BGE composition high voltages Variations in pKa values and mobility

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concentrations, so only a few generated compounds will be aroma active. The parameters which mainly influence non-enzymatic reactions consist of precursor type and quantity, temperature, and time (Belitz, Grosch, and Schieberle, 2009). 10.6.1.1 Amines Amines are Strecker-products, and their formation is pH-dependent (Belitz, Grosch, and Schieberle, 2009). Among them, 2-phenylethylamine is a flavoring agent used as a food additive, and a lot of its derivatives represent synthetic adulterants of dietary supplements (Pawar and Grundel, 2017). He et al. (2016) demonstrated the application of a CZE-UV method for monitoring biogenic amines, 2-phenylethylamine included, in biological samples, and this method could also be applied in food aroma analysis. 10.6.1.2 Furanones Among furanones, which are secondary products of the Maillard reaction, 4-hydroxy2,5-dimethyl-3-(2H)-furanone, also known as strawberry furanone or furaneol, are responsible for a caramel-like odor present in many food products, mainly in fruit (pineapple, strawberry), boiled beef, and medium roasted coffee. It is a racemic compound, and CCE can be a powerful technique. A CCE method was set up to monitor the racemization rate in different pH storage conditions (Raab et al., 2003). Also, 3-hydroxy-4,5-dimethyl-2-(5H)-furanone, named Sotolon, is a racemic furanone; its enantiomers do not differ in aroma quality (caramel), and it is typical of coffee and sherry. Taga et al. (2012) analyzed this molecule using CZE-UV, without considering enatiomers, but with the aim of monitoring Sotolon as a food additive. Without any sample preparation, in comparison with LC and gas chromatography (GC) techniques, it was possible to quantify ppm levels. 10.6.1.3 Phenols These compounds can be derived from phenolic acids and lignin in the presence of heat or microorganisms. They are present in food and food products, such as meat, coffee, milk, beer, whiskey, cooked apple, and asparagus. They give a different aroma quality, mainly smoky, depending on the type of phenol. For example, p-cresol, which is responsible for the smoky aroma of coffee, milk, and roasted peanuts, can be analyzed using CEC. Notwithstanding, the focus of this work was not to analyze aroma compounds but to determine contaminated samples, this method can rapidly and efficiently obtain the soil content of phenols, p-cresol included, after a sample preconcentration step with supercritical fluid extraction (Fung and Long, 2001). Among phenols in food, 4-ethylphenol, with a woody aroma, can be detected by CZE. This study aimed to determine different bisphenols and 4-ethylphenol as contaminants in plastic water bottles. A solid phase extraction procedure prior to analysis and an AD also allowed for the quantification of traces (Browne et al., 2013). Vanillin with a vanilla aroma characterizes vanilla, butter, rum, coffee, cooked asparagus, and can be analyzed using MEKC-UV. The setup method with an optimization of buffer concentration, pH, and modifier has been useful for rapidly detecting and quantifying (5–500µg/mL) four flavor compounds (vanillin, ethylvanillin 2-methoxyphenol, and 2-ethoxyphenol) in cocoa drinks (Ohashi et al., 2007). Also CZE methods without a sample pre-treatment (Panossian et al., 2001; Lahouidak et al., 2018) or with simple online preconcentration steps (Heller et al., 2011) rapidly allowed for the quantification of aldehydes, vanillin included, in brandy, whiskey, and wine. This determination is very important for recognizing aged or counterfeit products (these products contain only vanillin and no other aldehydes) (Panossian et al., 2001). Lahouidak et al. (2018) recently set up a method able to simultaneously quantify natural, synthetic, and prohibited flavoring compounds. It allowed

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FIGURE 10.2  Comparison among CE profiles of a natural vanilla extract (A) and

non-natural vanilla products (B–D) to determine vanilla flavors for food authenticity (COUM, coumarin; EVA, ethyl vanillin; VAN, vanillin; PHB, p-hydroxybenzaldehyde; VANA, vanillic acid; PHBA, p-hydroxybenzoic acid). (Reprinted with permission from Lahouidak, S., Salghi, R., Zougagh, M., and Ríos, A. (2018). Capillary electrophoresis method for the discrimination between natural and artificial vanilla flavor for controlling food frauds. Electrophoresis, 39, 1628–33. doi: 10.1002/elps.201700480.) for the rapid gain of a complete fingerprint of vanilla markers, discriminating between vanillin and artificial vanilla compounds (vanillic acids, ethyl vanillin), and adulterants, such as coumarin, as illustrated in Figure 10.2. For the rapid determination of vanilla-related flavors in food fraud, a CE microchip coupled with electrochemical detectors can also be applied (Avila et al., 2007). A chiral MEKC-PDA method was applied to the analysis of catechins in tea infusions obtained using tea leaves stored in different conditions (Mirasoli et al., 2014). In this work, BGE was added with sodium dodecyl sulfate and (2-hydroxypropyl)-β-cyclodextrin as a chiral selector, in order to identify (+)-catechin, epicatechin, epigallocatechin, epicatechin gallate, epigallocatechingallate, and caffeine. In aged samples, catechin content decreased or increased in relation to the storage time. 10.6.1.4 Pyranones Maltol, with its vanilla-related flavor, represents a product obtained from carbohydrates, which increases the sweet taste in food; it is found mainly in roasted coffee, heated butter, and biscuits. Its synthetic products, which are sweeter than maltol, are used extensively in food aromatization (Belitz, Grosch, and Schieberle, 2009). Avila et al. (2007) set up a CE-microchip device with an ED for food authenticity, discriminating simultaneously between maltol and ethyl maltol, together with vanillin, ethyl vanillin, and vanillic alcohol. 10.6.1.5 Pyrazines These compounds are both synthesized and degraded by a few types of bacteria and fungi and formed during the heating of food. They contribute to odors, characterizing

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roasted food, such as coffee, beef products, bread, corn, and fried potatoes, and for this reason they are often used as flavoring additives in the modern-day food industry (Müller and Rappert, 2010; Migita et al., 2017). In the literature there are no CE-methods applied to volatile pyrazines, but some CE-modes, which have been setup in different food matrices to analyze other aromatic eterocyclic compounds, such as for example triazines, can be adapted for this purpose (Elbashir and Aboul-Enein, 2015). CZE (Arribas et al., 2011) and MEKC (Fang et al., 2014) are the most powerful techniques; in addition, CEC (Chang et al., 2006) and NACE (Carabias-Martínez et al., 2006) can also be potential tools. 10.6.1.6 Pyridines Pyridine derivatives are the source of a typical roasted/cracker-like odor, typical of bread, cooked meat, and a particular type of rice (Mirasoli et al., 2014; Wei et al., 2017). Their amount increases in a temperature range of 50–75°C over time (1 hour). Pyridines, such as 2-acetylpyridine, have a low UV absorbance, but with a preconcentration step CZE could also be a potential technique of analysis. Hattori et al. (2017) setup a method in which CZE was used to analyze pyridines, after a preconcentration step, obtained through transient isotachophoresis. 10.6.1.7  Thiols, Thioethers, and Di- and Trisulfides These compounds can be generated following heat or long storage times, starting mainly from amino acids (Cys and Met) or monosaccharides. They are responsible for a roasted, toasted, and meat-like odor, but also a sulfurous and cabbage-like aroma. Among them, 2-methyl-3-furanthiol, which characterizes the typical meat odor of fermented soy sauce (Meng et al., 2017) and wine (Tominaga and Dubourdieu, 2006), represents an interesting sulfur compound, nowadays analyzed by GC; however, CE-MS could also be a potential technique for quantifying it, because of its very low odor threshold. 10.6.1.8  Amino Acids as Precursors of Food Aroma Compounds In some foods, as for example grape, wine, and vinegar, amino acids represent important precursors of aroma compounds, such as alcohols, aldehydes, esters, and ketonic acids, which are responsible for important organoleptic properties (taste, aroma, and color). In general, it is not particularly easy to analyze amino acids with proper sensitivity, but the use of LIF and electrochemical detectors can overcome this disadvantage (Callejón, Troncoso, and Morales, 2010). Amino acids, such as glycine, alanine (Ala), valine (Val), leucine (Leu), isoleucine (Ile), and phenylalanine (Phe), are precursors of carbonyl compounds and can undergo degradation, giving origin to Strecker aldehydes, responsible for a malty aroma. A Strecker reaction also involves methionine (Met), Val, Leu, Ile, and Phe, which become precursors of amines, giving rise to a malty/fishy odor (Belitz, Grosch, and Schieberle, 2009). In general, amino acids and racemic amino acids in food are an important marker of authenticity, quality, and origin and can be analyzed by CZE, MEKC, and CCE to monitor the formation of aroma substances (Karlinsey, 2012; Viglio et al., 2012; Tsioupi, Stefan-Vanstaden, and Kapnissi-Christodoulou, 2013; Poinsot et al., 2018). In particular, Val, the precursor of methylpropanal and Leu and Ile, precursors of 3-methylbutanal and 2-methylbutanal, respectively, can be rapidly quantified using a setup CCE method with indirect UV detection (Qiu et al., 2017). Cysteine (Cys) and Met are precursors of sulfurous compounds (Belitz, Grosch, and Schieberle, 2009) and total Cys or Met content can be rapidly monitored by CZE-UV

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with uncoated capillaries (Kubalczyk and Bald, 2009) or innovative coated capillaries (Vitali et al., 2014). Ala under heating can also be a precursor of odorous pyrazines (Belitz, Grosch, and Schieberle, 2009). 10.6.1.9  Other Substances as Precursors of Aroma Compounds Thiamine, which is a precursor of sulfurous compounds (i.e., 2-methyl-3-furanthiol), is a water-soluble vitamin, and a potential candidate for a technique in a free solution, such as CZE (Belitz, Grosch, and Schieberle, 2009). It can be analyzed using CZE-UV (Vitali et al., 2014) and CZE-ESI-MS for increasing method selectivity and sensitivity (Trenerry, 2001; Maráková et al., 2014). Among other precursors of sulfurous molecules (thiols, thioethers, di-, and trisulfides), monosaccharides can also be analyzed by CE. Sugars, such as ribose, but also rhamnose and glucose, in the presence of Cys and under a heating process, are the source of 3-mercapto-2-pentanone, 2-methyl-3-furanthiol, and 2-furfurylthiol with a characteristic meat aroma (Belitz, Grosch, and Schieberle, 2009). Saccharides (ribose and glucose) are also food additives, mainly present in juices, soft and high-energy drinks, and wines; they can be easily and rapidly detected and quantified by CE-UV (Rovio, YliKauhaluoma, and Sirén, 2007) or by CE-C4D, improving CE-sensitivity (Tůma et al., 2011; Vochyánová et al., 2012). Recently, a CE-MS was set up to rapidly analyze the profile of monosaccharides (ribose, rhamnose, and glucose included) as a signal in coffee samples adulterated with soybean and corn (Daniel et al., 2018). 10.6.2 Enzymatic Reactions Enzymes can be indirectly involved in food aroma formation and enhancement; in fact, after tissue disruption, they could release precursor compounds (e.g., ortho-quinone structures from phenolic compounds or amino acids from proteins or sugar from polysaccharides) which are then converted in aroma compounds by further non-enzymatic reactions (Belitz, Grosch, and Schieberle, 2009). 10.6.2.1  Carbonyl Compounds Ketones and aldehydes have a sensory relevance in foodstuff as they can both improve food quality by generating a pleasant flavor and odor, and also indicate food deterioration, bacterial fermentation, and off-flavors. Therefore, their detection attracts a growing relevance and CE with UV detection has emerged in the last few years due to its main known advantages. A derivatization step is always needed to avoid losses of the carbonyl compounds. Among aldehydes, cis and trans 2-hexenal, possessing the odor of freshly cut grass and leaves, have been determined together with acetaldehyde in different yogurts, juices, and yogurt–juice mixtures using a fully automated approach based on pervaporation coupled online with MEKC using a flow injection manifold and the replenishment system of a CE apparatus (Ruiz-Jiménez and Luque de Castro, 2006). Figure 10.3 illustrates this combined approach. The integration of a pervaporation step in an analytical process other than separation represents a good alternative to headspace static or dynamic systems (Bryce, Izquierdo, and Luque de Castro, 1997). Furthermore, this approach is time-saving as the removal of analytes from the matrix, their preconcentration, and derivatization take place in the acceptor chamber of the evaporator and require about 10 min, and the separation of the analytes in CE can be simultaneous with the next sample pervaporation.

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FIGURE 10.3  Setup of a pervaporation and CE interface to determine volatile aldeydes in slurry samples, as yogurt and juice (AS, airstream; WS, water stream; SS, sample stream; PP, peristaltic pump; DR, derivatisation reagent; SV, selection valve; IV, injection valve; PH, pre-heater; M, membrane; PU, pervaporation unit; WB, water bath;. W, waste). (Reprinted with permission from Ruiz-Jiménez, J., and Luque de Castro, M. D. (2006). Online pervaporation-capillary electrophoresis for the determination of volatile analytes in food slurries. Journal of Chromatography A, 1128, 251–8. doi:10.1016/j. chroma.2006.06.031.)

The validated CZE-PDA method improves sensitivity by 10 times. Acetaldehyde can be considered as an indicator of off-flavors and is classified as toxic by the International Agency for Research on Cancer (1985). A novel home-made miniaturized CZE-AD system was proposed for its fast detection in wine and waterlogged samples by D. Zhang et al. (2011). 2-thiobarbituric acid was selected as a derivatization agent as it is an electro-attractive species that easily originates adducts, thus facilitating the determination of acetaldehyde. This derivatization agent was also used for the detection of other aldehydes, such as hexanal, 2,3-butanedione (diacetyl), and methyglyoxal, in different food matrices, that is, olive and sunflower seed oils, white, red and glutinous rice wines, and water-soaked products (sea cucumber, jelly fish, and tendons of beef) using CZE-AD. The home-built three-electrode electrochemical cell which was used consisted of a 300 mm diameter carbon disk working electrode, a platinum auxiliary electrode, and a saturated calomel electrode as the reference electrode in combination with a BAS LC-3D amperometric detector. The developed method was simple, accurate, and useful for the analysis of aldehydes in many different food matrices. Figure 10.4 shows the detection of acetaldehyde and formaldehyde through the formation of adducts with 2-thiobarbituric acid (J.-B. Zhang et al., 2011). Another derivatization agent, 4-hydrazinobenzoic acid (the primary terminal amine in this hydrazine reacts with the carbonyl group forming an imide), was recently used in the development of a CZE-PDA method for the analysis of different wine samples. A gas-diffusion microextraction method was applied, thus obtaining a pre-concentrated and cleaned-up (only volatile and semi-volatile compounds are extracted) sample. This methodology showed good results regarding linearity and precision (de Lima et al., 2018). Benzhydrazide, a water-soluble substance, was used as a derivatizing agent for the determination of aldehydes in yogurt and vinegar using MEKC-PDA. The ­derativization reaction is fast, simple, not strongly pH- and temperature-dependent, giving rise to only one derivative for each aldehyde which is stable up to 3 hours (Donegatti, Moreira Gonçalves, and Pereira, 2017). Methylgyoxal was also quantified, even at low concentrations, in tap water and beer samples using an optimized CZE-PDA method; o-phenylendiamine was

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FIGURE 10.4  CZE-ED profiles of the standard mixture (A), Chinese white liquor (B), and white wine (C). 1, acetaldehyde-2-thiobarbituric acid adduct; 2, formaldehyde2-thiobarbituric acid adduct; 3, 2-thiobarbituric acid. (Adapted with permission from Zhang, D., Zhang, J., Li, M., Li, W., Aimaiti, G., Tuersun, G., Ye, J., and Chu, Q. (2011). A novel miniaturized electrophoretic method for determining formaldehyde and acetaldehyde in food using 2-thiobarbituric acid derivatization. Food Chemistry, 129, 206–12. doi:10.1016/j.foodchem.2011.04.025.)

used as a derivatizing agent and a C18 SPE as a purification step prior to the analysis (do Rosário et al., 2005). An environmentally friendly miniature CZE-AD method developed for the trace quantification of four aliphatic aldehydes present in water samples, as disinfection by-products, also provided a good separation of glyoxal and methylglyoxal after derivatization with 2-thiobarbituric acid. This new method was based on thee hollow-fiber liquid-phase microextraction of the analytes (Li et al., 2015).

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10.6.2.2 Terpenes Terpenes are generally analyzed using gas chromatography. In the last two decades, only a CEC-PDA method has been developed for the detection and quantification of 11 aromatic and terpenic compounds in extracts of the spices from Piper nigrum. A C18 sorbent was the best sorbent for packing the fused-silica capillary and a mixture of 50 mM ammonium acetate solution (pH 6.0) and ACN, 10:90, v/v, the best mobile phase for the separation of all the tested analytes (Musenga et al., 2007). 10.6.2.3  Other Substances as Precursors of Aroma Compounds Allium genus vegetables are rich in S-alk(en)yl-l-cysteine-sulfoxides, the precursor of the typical odor and flavor of these vegetables. Methyl-cysteine sulfoxide (methiin) and 2-propenyl-cysteine sulfoxide (alliin) have a special flavor which is called kakumi. As both of these compounds have no specific UV absorbance, an electrolyte for indirect detection was selected, and the developed CE-UV method did not require any derivatization (but only vegetable boiling, extraction, dilution, and filtration); it was simple and required less than 25 min for each analysis (Horie and Yamashita, 2006). Another method also applicable to the analysis of isoalliin, propiin (S-propylcysteineS-oxide), ethiin (S-ethylcysteine-S-oxide), and butiin (S-butylcysteine-S-oxide) was setup by Kubec and Dadáková (2008). The methanolic extracts of Allium and Brassica vegetables were derivatized with fluorenylmethyl chloroformate, and subsequently separated by MECK-UV. Simplicity, sensitivity, high specificity, and very low running costs, make this method suitable for the routine analysis of large numbers of samples. Glucosinolates (alkyl-N-hydroximine esters with a β-dthioglucopyranoside group linked to the hydroximine carbon in Z configuration to the sulfate group) are precursors of isothiocyanates (ITCs), generated by the action of enzyme myrosinase; ITCs are responsible for the pungent sensory characteristics of Brassicaceae vegetable food, and they are easily recognized. These compounds are not easily detectable in UV but can still be detected after derivatization, a process not compatible with myrosinase measurements. Therefore, Bellostas and co-workers developed a micellar electrokinetic capillary chromatography for monitoring the myrosinase catalyzed hydrolysis of 2-hydroxy substituted glucosinolates (progoitrin, glucosibarin, glucobarbarin, glucotropaeolin, and gluconasturtiin), and the simultaneous formation of the corresponding degradation products which are not ITCs at all (oxazolidine-2-thiones and nitriles) (Bellostas, Sørensen, and Sørensen, 2006). Recently, a fast, robust, and simple MECK method was developed and validated for the simultaneous quantification of glucosinolates and ITCs by Gonda et al. (2016). The low detectability of ITCs in UV was overcome using in-vial derivatizing with mercaptoacetic acid, without inhibiting the enzyme activity. Different Brussel sprouts, horseradish, watercress, and radish samples were successfully analyzed, and sinigrin, gluonasturtiin, and allyl isothiocyanate quantified. This method can be used not only for glucosinolate determination, but also for myrosinase activity measurement, and isothiocyanate release estimation. 10.6.2.4  Phenols as Precursors of Food Aroma Compounds 2,4,6-trichloroanisole, usually produced by naturally occurring airborne fungi and bacteria (Aspergillus sp., Penicillium sp., Actinomycetes, Botrytis cinerea, and Streptomyces), is the main component responsible for cork taint in wines; 2,4,6-trichlorophenol is its

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TABLE 10.3  Food Aroma Compounds Detected by CE Techniques Aroma Description

Type of Food

Furaneol

Strawberrylike Pineapplelike Caramel

CCE-PDA (Raab et al., 2003)

Sotolon

Caramel

Fruit Beer Coffee (medium roasted) Boiled beef White bread Emmental cheese Coffee Sherry

p-Cresol

Smoky

Coffee Sherry Milk Roasted peanuts Asparagus

CEC-PDA (Fung and Long, 2001)

4-Ethylphenol

Woody

Coffee Milk Tomatoes, Roasted peanuts Soya sauce

CZE-AD (Browne et al., 2013)

Food Aroma Compound

CE Mode

CZE-PDA (Taga et al., 2012)

(Continued )

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TABLE 10.3  (Continued) Food Aroma Compounds Detected by CE Techniques Food Aroma Compound

Aroma Description

Type of Food

Vanillin

Vanilla

Vanilla Coffee Rum Whiskey Asparagus Cooked butter

MEKC-PDA (Ohashi et al., 2007) CZE-PDA (Panossian et al., 2001; Heller et al., 2011; Lahouidak et al., 2018) CE-microchip-ED (Avila et al., 2007)

Catechins *–H = (–)-Catechin –OH = (–)-Gallocatechin

Bitter

Tea

Chiral MEKC-PDA (Mirasoli et al., 2014)

CE Mode

**–H=Epicatechin –OH=Epigallocatechin

***–H=Epicatechin gallate –OH=Epigallocatechin gallate

(Continued )

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TABLE 10.3  (Continued) Food Aroma Compounds Detected by CE Techniques Food Aroma Compound

Aroma Description

Type of Food

Maltol

Caramel

Roasted coffee Biscuit Cooked butter Chocolate Beer

CE-microchip-ED (Avila et al., 2007)

2-Acetylpyridine

Roasted

White bread

CZE-PDA (Hattori et al., 2017)

2-Hexenal

Freshly cut grass/ leaves Fruity

Yogurt Juice Wine Sherry

MEKC-PDA (RuizJiménez and Luque de Castro, 2006) CZE-ED/AD (D. Zhang et al., 2011; J.-B. Zhang et al., 2011)

Methylglioxal

Cheesy

Wine Beer Biscuit Bread

CZE-PDA (do Rosário et al., 2005)

Terpenic compounds

Woody

Fruit Vegetables Jam Wine

CEC-PDA (Musenga et al., 2007)

Acetaldehyde

CE Mode

precursor and it has been detected in wines using a headspace single drop microextraction automatically inline coupled with CE. This extraction becomes a very promising sample pre-treatment technique when a single drop hanging at the inlet tip of a capillary for CE is used as the acceptor phase; in fact, high enrichment factors were obtained in a short time. Furthermore, a headspace single drop microextraction performed before

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the sample injection and a large volume sample stack using an electroosmotic flow pump after the sample injection leads to the achievement of enrichment factors of several thousand-fold, with LODs in the nanomolar range for chlorophenols in wine (Park et al., 2012). Table 10.3 illustrates a summary of published CE applications in food aroma compounds.

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Müller, R. and Rappert, S. (2010). Pyrazines: Occurrence, formation and biodegradation. Applied Microbiology and Biotechnology, 85, 1315–20. doi:10.1007/ s00253-009-2362-4. Musenga, A., Mandrioli, R., Ferranti, A., D’Orazio, G., Fanali, S., and Raggi, M.A. (2007). Analysis of aromatic and terpenic constituents of pepper extracts by capillary electrochromatography. Journal of Separation Science, 30, 612–9. doi:10.1002/ jssc.200600456. Ohashi, M., Omae, H., Hashida, M., Sowa, Y., and Imai, S. (2007). Determination of vanillin and related flavor compounds in cocoa drink by capillary electrophoresis. Journal of Chromatography A, 1138, 262–7. doi:10.1016/j.chroma. 2006.10.031. Panossian, A., Mamikonyan, G., Torosyan, M., Gabrielyan, E., and Mkhitaryan, S. (2001). Analysis of aromatic aldehydes in brandy and wine by high-performance capillary electrophoresis. Analitycal Chemistry, 73, 4379–83. doi:10.1021/ ac0014818. Papetti, A. and Colombo, R. (in press). High performance Capillary Electrophoresis for food evaluation. In X. Wang and J. Zhong (Eds.), Evaluation Technologies for Food Quality. Academic Press Elsevier. Park, S.T., Kim, J., Choi, K. Lee, H.R., and Chung, D.S. (2012). Headspace-single drop microextraction with a commercial capillary electrophoresis instrument. Electrophoresis, 33, 2961–8. doi:10.1002/elps.201200317. Pawar, R.S. and Grundel, E. (2017). Overview of regulation of dietary supplements in the USA and issues of adulteration with phenethylamines (PEAs). Drug Testing and Analysis, 9, 500–17. doi:10.1002/dta.1980. Pinero, M.-Y., Bauza, R., and Arce, L. (2011). Thirty years of capillary electrophoresis in food analysis laboratories: Potential applications. Electrophoresis, 32, 1379–93. doi:10.1002/elps.201000541. Poinsot, V., Ong-Meang, V., Ric, A., Gavard, P., Perquis, L., and Couderc, F. (2018). Recent advances in amino acid analysis by capillary electromigration methods. Electrophoresis, 39, 190–208. doi:10.1002/elps.201700270. Qiu, J., Wang, J, Xu, Z., Liu, H., and Ren, J. (2017). Quantitation of underivatized branched-chain amino acids in sport nutritional supplements by capillary electrophoresis with direct or indirect UV absorbance detection. PLOs One, 12, e0179892. doi:10.1371/journal.pone.0179892. Raab, T., Hauck, T., Knecht, A., Schmitt, U., Holzgrabe, U., and Schwab, W. (2003). Tautomerism of 4-hydroxy-2,5-dimethyl-3(2H)-furanone: Evidence for its enantioselective biosynthesis. Chirality, 15, 573–8. doi:10.1002/chir.10247. Ravelo-Pérez, L.M., Asensio-Ramos, M., Hernández-Borges, J., and RodríguezDelgado, M.A. (2009). Recent food safety and food quality applications of CE-MS. Electrophoresis, 30, 1624–46. doi:10.1002/elps.200800670. Reijenga, T.P.E.M., Verheggen, J.H.P.A., and Martens, F.M. Everaerts. (1996). Buffer capacity, ionic strength and heat dissipation in capillary electrophoresis. Journal of Chromatography A, 744, 147–53. doi:10.1016/0021-9673(96)00273-7. Rizzi, A. (2001). Fundamental aspects of chiral separations by capillary electrophoresis. Electrophoresis, 22, 3079–106. doi:1​0.100​2 /152​2-268​3(200​109)2​2:153.​0.CO;​2-F. Rovio, S., Yli-Kauhaluoma, J., and Sirén, H. (2007). Determination of neutral carbohydrates by CZE with direct UV detection. Electrophoresis, 28, 3129–35. doi:10.1002/ elps.200600783.

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Chapter

11

Proton-Transfer-Reaction– Mass Spectrometry Iuliia Khomenko and Brian Farneti CONTENTS 11.1 Importance of Direct Injection Mass Spectrometry Analysis of Food VOCs 217 11.2 PTR-MS Technology 219 11.2.1 Drift Tube 221 11.2.2 Switchable Reagent Ion 222 11.2.2.1 Mass Analyzers 223 11.2.2.2 Quadrupole Mass Spectrometer (QMS) 223 11.2.2.3 Time-of-Flight–Mass Spectrometer (ToF-MS) 223 11.2.2.4 Data Analysis 224 11.3 PTR-MS Innovation 226 11.3.1 Toward Increasing of Sensibility 226 11.3.2 Toward High Throughputness 227 11.3.3 Toward Compound Identification 229 11.4 Application of PTR-MS Analysis in Food Science 230 11.4.1 Screening VOC Fingerprinting Analysis 230 11.4.2 Real-Time VOC Evolution Analysis 233 11.5 Challenges and Future Perspectives 233 References 234

11.1  IMPORTANCE OF DIRECT INJECTION MASS SPECTROMETRY ANALYSIS OF FOOD VOCS The current mission of the agro-food industry is to guarantee food safety and, at the same time, improve perceived food quality and fulfill consumer expectations. To address these issues, a broad and objective quality detection system for food products is needed. One of the chief quality traits for the agro-food industry is the development of volatile organic compounds (VOCs), associated with the shelf life and taste quality of food products. However, the so-called “phenotyping bottleneck,” caused by the absence of high-throughput and non-invasive methodologies, impedes an effective evaluation and prediction of food VOCs (Furbank and Tester, 2011). The recent advancement of high-throughput and non-invasive screening technologies stimulated the development of phenomics as a multidisciplinary study, which links biophysics, biochemistry, and several “omic” analytical techniques, such as transcriptomics, proteomics, and metabolomics. The latter focuses on the analysis of

217

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metabolites produced by the regulatory processes of the cell as a response of biological systems to genetic or environmental changes (Fiehn, 2002). VOCs belong to secondary metabolites. The ability to synthesize specific VOCs has been selected through the course of evolution in different organisms for different purposes (Pichersky and Gang, 2000). VOCs present in fruit, vegetables, meat, dairy products, and other foodstuffs are responsible for their aroma, flavor, and taste. All these factors contribute to the consumer’s preferences and perception of the food. The rapid development of mass spectrometry (MS) has made possible the high-resolution characterization of several metabolites from complex matrices in a single measurement, with a considerable impact on the field of VOC analysis (Herrero et al., 2012). Cevallos-cevallos et al. (2009) classified metabolomic analyses into targeted or untargeted. Targeted analyses focus on the identification and quantification of a determined array of metabolites. On the contrary, untargeted metabolomics put a spotlight on the detection of as many groups of metabolites as possible without the need to precisely quantify them. Ibáñez et al. (2013) distinguished two untargeted metabolomic analytical approaches: (i) “metabolic profiling” refers to analysis of a class of metabolites (chemically related metabolites or associated with a particular pathway); (ii) “metabolic fingerprinting” refers to analysis of the total set of metabolites for rapid classification without identifying individual metabolites. The complexity of the aroma footprint depends on the nonlinear simultaneous interaction of a mixture of numerous molecules. Their fast and non-invasive detection can be used for food quality control and for the monitoring of fundamental and industrial processes (Biasioli et al., 2011a). Cumeras (2017) defined volatilomics as a part of metabolomics, which focuses on the totality of VOCs produced by living organisms. According to this, the overall study of aroma compounds in food can be considered as food volatilomics. Thus, the term “volatilome” is determined as the entire VOC collection of a sample. Recently this definition has been mentioned in human (Amann et al., 2014), plant (Bicchi and Maffei, 2012), fruit (Farneti et al., 2017a, 2017b), bacteria (Casaburi et al., 2015), and fungal (Li et al., 2016) studies. The extreme complexity and large variation of VOC concentrations in food samples are challenging for any existing analytical technology (Ibáñez et al., 2013). The rapid development of mass spectrometry and its application in metabolomics have had a very significant impact in the field of VOC analysis (Herrero et al., 2012). Recently, the progress of MS techniques has largely focused on instrumental improvements to obtain a higher mass resolution, mass accuracy, sensitivity, and enhanced reproducibility (Ibáñez et al., 2013). According to the techniques and strategies used to transport VOCs to the instrumentation for further analysis, MS techniques can be divided into gas chromatographic and direct injection. Direct injection mass spectrometric (DI-MS) techniques (Biasioli et al., 2011b) have opened up new prospects for food aroma analysis by decreasing the time needed for sample preparation and analysis. Different DI-MS techniques have witnessed a flurry of developments providing the possibility for rapid, direct, real-time, and high-throughput volatilome analysis (Huang et al., 2011). These approaches do not require a chromatographic step prior to MS detection, allowing for the direct analysis of samples (Ibáñez et al., 2013). DI-MS techniques differ in sample preparation, sampling, inlet architecture, ionization processes, and detection (Biasioli et al., 2011b). The most prominent examples are electronic noses, atmospheric-pressure chemical ionization (APCI), direct analysis in real-time–mass spectrometry (DART-MS), ion mobility spectrometry-mass–spectrometry (IMS-MS), selected ion-flow-tube–mass spectrometry (SIFT-MS), and proton-transfer-reaction–mass spectrometry (PTR-MS). In the absence of chromatographic separation, compound identification is only possible up to its chemical formula and depends on ion chemistry and the resolution of the mass spectrometer (Nozie et al., 2015). Proton-transfer-reaction–mass spectrometry coupled with a time-of-flight

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mass analyzer (PTR-ToF-MS) gives the possibility of detecting and quantifying VOCs in a direct, simultaneous, and real-time approach at very low levels (pptv) with both high mass and time resolution (Jordan et al., 2009a). A breakthrough in high-throughput in this field took place when PTR-ToF-MS was connected for the first time to a multipurpose GC automatic sampler (Yener et al., 2014). However, one of the major challenges of DI-MS techniques, and particularly of PTR-MS, is the lack of separation of chemical isomers. For this reason, a comparison with GC analysis is often mandatory. Nevertheless, there is a growing interest in developing methods for improving the specificity of DI-MS methods without losing sensitivity and time resolution.

11.2  PTR-MS TECHNOLOGY Proton-transfer-reaction–mass spectrometry is a highly sensitive direct injection technique, developed in the early 1990s for analysis of volatile organic traces in ambient air (Lindinger et al., 1998). PTR-MS belongs to a chemical ionization technique, which has been used since the 1960s (Ellis and Mayhew, 2014). This technique became successful due to its much softer ionization than those available at that time. This type of ionization allows for the reduction of fragmentation and the observation of the molecular ion of the compound of interest. The chemical ionization is achieved through various reactions such as a simple charge transfer (Equation 11.1) or more complicated chemical reactions (Equations 11.2–11.4).

X+ + M → M+ + X



XH + + M → MH + + X

Proton transfer (11.2)



X + + MH → M + + HX

Hydride transfer (11.3)



X + + M + Z → MX + + Z

Adduct formation (11.4)

Charge transfer (11.1)

Nowadays, protonated water (H3O+) is the most used reagent ion in PTR-MS. Protontransfer-reactions prevail when protonated water (H3O+) is used as a reagent ion. These reactions are exothermic and happen when the proton affinity of the compounds exceeds the proton affinity of the reagent ion. This makes PTR-MS selective to the organic compounds presented in the air at trace concentrations and blind to inorganic gases such as oxygen, nitrogen, and carbon dioxide. Table 11.1 demonstrates the proton affinity of compounds presented in food matrices (Hunter et al., 2010). However, propanol and higher alcohols show an increasing tendency to undergo the hydride transfer reaction on the proton transfer reaction. Some groups of compounds such as C3 and higher alcohols trend to fragment, losing their hydroxyl group. This type of reaction is called hydride transfer. The typical PTR-MS instrument presented by Lindinger et al. (1998) is composed of an ion source, a drift tube, and an ion detection system (Figure 11.1). This type of ion source generates ions with a purity of over 99.5%. An ion source usually consists of a hollow cathode discharge source where water vapor or other gases are injected. The electron impact ionization of H 2O produces H 2O+ and the bulk of fragment ions, such as H+, H 2+, OH+, and O+, are transformed into H3O+ due to the fast reactions

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TABLE 11.1  Proton Affinity of Some VOCs Substance Oxygen Nitrogen Carbon dioxide Water Ammonia Methanol Ethanol Acetaldehyde Propanal Acetone Ethyl acetate Acetic acid Methanethiol

Molecule O2 N2 CO2 H 2O NH3 CH3OH C2H5OH C2H4O C3H6O C3H6O C2H4O2 C2H4O2 CH4S

Proton Affinity (kJ/mol) 421 494 541 691 854 754 776 769 786 812 836 784 773

Source: Adapted from Hunter et al., 2010.

FIGURE 11.1  Schematic drawing of a PTR-MS coupled with a quadrupole mass

spectrometer. (Equations 11.5–11.16). A lower pressure in the ion source than in the drift tube can provoke the formation of some impurities such as O2+ and NO+ in very small amounts. Their traces can also be produced in the ion source itself.

e – + H 2O → H 2O + 2e – (11.5)



e – + H 2O → H 2+ + O + 2e – (11.6)

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e – + H 2O → H+ + OH + 2e – (11.7)



e – + H 2O → O+ + 2H + 2e – (11.8)



e – + H 2O → OH+ + H + 2e – (11.9)



O+ + H 2O → H 2O+ + O (11.10)



H+ + H 2O → H 2O+ + H (11.11)



H 2+ + H 2O → H3O+ + H (11.12)



H 2+ + H 2O → H 2O+ + H 2 (11.13)



OH + + H 2O → H3O+ + O (11.14)



OH + + H 2O → H 2O+ + OH (11.15)



H 2O+ + H 2O → H3O+ + OH (11.16)

11.2.1 Drift Tube The drift tube is a crucial part of the PTR-MS instrument where the chemical ionization happens. Ions from the ion source are extracted into the drift tube because of an electric field. There they react with VOCs which are directly injected in the drift tube with the minimum flow around 40 sccm. The drift tube is filled with air as a buffer gas to dilute both reagents. Since the drift tube is a chemical reactor, it is possible to calculate the concentration of reagents according to the principles of chemical kinetics (Equation 11.17; Lindinger et al., 1998). 1  VOC.H  1 ⋅ ⋅ 109 (11.17) C ( ppbv ) = ⋅ H3O+  N kt +



Where C is the concentration of VOC in the analyte gas (ppbv), k is the reaction rate constant (cm3/s), t is the ion travel time in the drift tube, [VOC.H+] and [H3O+] are signals of these molecules measured in counts per second (cps). In order to obtain the precise VOC quantification, without any additional calibration with specific gases, the reactions should occur under well-defined and controlled conditions, such as the ion travel time t and the gas density in the drift tube N. In additon to the parameters above, the voltages applied to the drift tube, the temperature, and the inside pressure play an important role in chemical reactions. Another important drift tube parameter is the reduced electric field E/N, where E is an electric field across the drift tube, which is made by the voltage applied to the drift tube, and N is the gas density which is regulated by the drift tube pressure, which is obtained through continuous pumping. High values of E/N correspond to the higher collision energy, which increases the ion fragmentation. Lower E/N provokes the cluster formation of a primary ion such as H3O+(H2O)n. in the case of protonated water (Ellis and Mayhew, 2014).

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11.2.2 Switchable Reagent Ion Proton-transfer-reactions only allow for the detection of those organic compounds whose proton affinity is higher than H 2O. For improving compound detection, different reagent ions should be applied. Jordan et al. (2009a) presented the combination of PTR-MS with a switchable reagent-ion (SRI) technology. A modified design of the drift tube allows for the easy and fast switching between H3O+, O2+, and NO+ ions. For some particular applications, Kr+ can be used as well (Agarwal et al., 2014). Recently some studies have shown the possibility of measuring with NH4+ as a primary ion (Zhu et al., 2018; Hansel et al., 2018). The formation of O2+ or NO+ ions in the ion source happens according to the following reactions (Equations 11.17–11.23):

e – + O2 → O2+ + 2e – (11.17)



e – + O2 → O+ + O + 2e – (11.18)



e – + N 2 → N 2+ + 2e – (11.19)



e – + N 2 → N + + N + 2e – (11.20) N 2+ + O2 → O2+ + N 2 ,



k  kc (11.21)



O+ + N 2 → NO+ + N (11.22)



N + + O2 → NO+ + O (11.23)

In the case of O2+ and NO+ modes, different reaction pathways occur instead of proton-transfer-reactions. Reactions of charge transfer characterize O2+ as a source of ions (Equations 11.24–11.25). O2+ + VOC → VOC + + O2



(

O2+ + VOC → VOC +



(VOC )

+ *

)

*

+ O2

Non-dissociative (11.24)

(F − fragment; N − natural fragment)

(11.25)

+

→F +N

When NO+ is used, the target volatile may undergo charge transfer, hydride (H –) or hydroxyl (OH –) ion abstraction or both, according to the conditions of the drift tube. The reactions of NO+ are given in Equations 11.26–11.27. In these equations, X represents the abstracted ion (H – or OH –) and B the stabilizing buffer gas.

NO+ + VOC → [VOC − X]+ + XNO (11.26)



NO+ + VOC + B → VOC.NO+ + B (11.27)

NO+ as a primary ion separates isomeric aldehydes and ketones because the former appear at their parent mass and the latter as H-subtracted masses (Ellis and Mayhew, 2014).

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Kr+ possesses higher ionization energies than common air constituents and is responsible for non-dissociative and dissociative charge transfer useful for the ionization of some inorganic compounds presented in the air, such as CO, CO2 , CH4, NOx, and SO2. Thus, this reagent ion has found its main application in environmental studies (Sulzer et al., 2012). 11.2.2.1 Mass Analyzers The third main part of a PTR-MS instrument, after the ion source and the drift tube, is the ion detection system or mass analyzer as it is shown in Figure 11.1. It is used for ion separation according to their m/z, which is the mass number of an ion m, divided by its charge number, z. Each type of mass analyzer is characterized by several important parameters, such as mass resolution, sensitivity, transmission, and dynamic range. The first can be explained as the ability to distinguish the signal from ions with a small difference in m/z values. Not all ions which enter a mass spectrometer will reach the detector. The transmission is caused by a variety of factors and depends on the m/z value, which is why it is important to take it into account during VOC quantification. The dynamic range is the ratio of the highest and the lowest detectable ion signal. Usually, the region with a linear response to the analyte concentration is considered. The noise level influences a lot on the recognition of small ion signals. Nowadays, the main mass analyzers coupled to PTR are the quadrupole, the time-of-flight, and their hybrid quadrupole time-of-flight. 11.2.2.2 Quadrupole Mass Spectrometer (QMS) First, PTR-MS instruments were equipped with quadrupole mass spectrometers (Ellis and Mayhew, 2014). A typical QMS consists of four parallel metal rods equally positioned from the center in the vacuum chamber. The voltage applied along rods accelerates ions in the system. Each opposing rod is connected electrically, and a voltage with a different frequency is applied between the different pairs of rods which provoke ion oscillations in the xy direction. Ions with a specific m/z are in resonance with this oscillation and pass through the quadrupole until they reach the detector. Ions with other m/z are lost. The advantages of a QMS system include operation in a low vacuum (10–2 to 10–3 Pa) and compact size. However, a QMS is characterized by a low mass resolution (m/Δm) such as up to the nominal mass. For this reason, the separation of isobaric compounds is not possible with such a mass analyzer. Moreover, QMS has a bell-shape transmission curve, which means that it is less sensitive to the big molecules rather than to small ones. 11.2.2.3 Time-of-Flight–Mass Spectrometer (ToF-MS) The time-of-flight–mass spectrometer (ToF-MS) uses the principle of the free flight of ions in a vacuum system. A bunch of ions are accelerated using a repeller and detected by a detector (Figure 11.2, Ellis and Mayhew, 2014). In order to prolong the ion path, ToF analyzers are equipped with a reflector array or simply a reflectron which reverses the motion of the ions thus doubling the length of ToF analyzer. The reflectron also groups ions of the same m/z traveling slower or faster due to the reflectron voltages. The kinetic energy (Ek) of an ion accelerated by a constant voltage depends on its mass and speed (Equation 11.28).

Ek = zeV =

mv 2 (11.28) 2

where z is the charge number, e is the elementary charge, V is the acceleration voltage, m is the mass of the ion, and v is the velocity of the ion. This is why the time-of-flight of an ion can be described by the formula in Equation 11.29.

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FIGURE 11.2  Schematic of a time-of-flight–mass spectrometer.



tflight =

L = v

m 1 L (11.29) v 2eV

Time-of-flight is proportional to the square root of m/z which means that for ions with a specific m/z it will take specific time to reach the detector and thus it is possible to recalculate the m/z value according to its exact ion time-of-flight. Among the advantages of using a ToF analyzer are the high mass resolution (up to 15,000), very low detection limit (pptv levels), good transmission, and short time for whole spectrum acquisition (a second or even less). This type of mass analyzer is usually big in size, which can considerably increase the size of a whole instrument. In 2004, for the first time, Blake et al. reported the potential of coupling ToF to a PTR ion source and published data about their first prototype of such a PTR-ToF-MS device. Jordan et al. (2009a) introduced the first commercial version of a PTR-ToF-MS instrument developed by Ionicon (Figure 11.3; Innsbruck, Austria). 11.2.2.4 Data Analysis PTR-MS instruments produce a lot of information which should be extracted and analyzed in an appropriate way. In the case of a PTR-QMS system, data extraction is very easy since the software stores the values of signal and mass peak concentration directly in an ASCII file. PTR-ToF-MS data are stored in very complex datasets and require sophisticated analysis with multiple steps. A typical PTR-ToF-MS mass spectrum, with an upper mass-to-charge

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FIGURE 11.3  Schematic drawing of the PTR-ToF-MS instrument. (From Jordan et al.,

2009a.) (m/z) threshold of 400, consists of around 350,000 data bins. Typical integrated acquisition speed is 1 spectrum/second. The concentration of a specific mass peak depends on the intensity of the peak and primary ion and on some drift tube parameters, such as temperature and voltage. For this reason, the analysis of such datasets needs specific software for a proper elaboration. Several data processing and analysis software products offer their own approaches for data elaboration such as PTR-MS Viewer (Ionicon, Innsbruck, Austria), PTR-ToF Data Analyzer (Müller et al., 2013), University of Innsbruck, Innsbruck, Austria), PTRwid (Holzinger, 2015; Utrecht University, Utrecht, the Netherlands), TOFOffice (Edmund Mach Foundation, San Michele all’Adige, Italy), and also several others which are been developed for the individual needs of each laboratory equipped with PTR-ToF-MS. Cappellin et al. (2011) highlighted the three main “pillars” of a data analysis methodology: spectra analysis, multivariate analysis and data mining, and analytical information (Figure 11.4). External mass-scale calibration is performed before data acquisition for spectra alignment and for facilitating the online monitoring of ongoing experiment. For this reason, external calibration is done on reference mass peaks of known compounds which are constantly present during all measurement steps, such as primary ions or other peaks belonging to impurities of the instrument (H3O+, O2+, NO+). However, in this case, the calibration precisely corrects only a part of a spectrum with low mass range. This problem was overcome by adding a constant reference mass peak at high m/z with a constant intensity and without any interference with a sample signal. PerMaSCal (Ionicon Analytik GmbH, Innsbruck, Austria) is a commercially available permeation device. Permeation rate and respectively peak intensity are strongly temperature dependent. To eliminate any distortion of the VOC sample, PerMaSCal is connected directly to the drift tube where the constant stream of the calibration compound diffuses into the sample gas. 1,3-Diiodobenzene (C6H4I 2) was selected as a permeation unit because of its low toxicity, simple isotope pattern, ionization with different primary ions, and low probability of overlaps with common

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FIGURE 11.4  Main steps during PTR-ToF-MS data analysis. (From Cappellin et al., 2011.)

VOCs. It was observed as m/z 330.848 and m/z 203.946 (fragment, iodine abstraction) in the H3O+ mode and as m/z 329.840 in both O2+ and NO+ modes. The main steps of data pre-processing presented in all software are (i) dead time correction, (ii) mass-scale calibration, (iii) reference peak determination, (iv) peak extraction, and (v) calculation of concentration in ppbv.

11.3  PTR-MS INNOVATION 11.3.1 Toward Increasing of Sensibility The performances of mass spectrometers and detectors are critical for the detection sensitivity of PTR-MS (Blake et al., 2009). However, the small exit aperture at the end of the drift tube limits the sensibility of the instrument. This aperture is necessary for maintaining a sufficiently low pressure in the mass spectrometer while sustaining a pressure in the drift tube, which is several orders of magnitude higher. One of the possible solutions could be the radio frequency (RF) ion funnel, developed by Shaffer et al. (1997). This ion funnel uses a series of electrodes with progressively reducing aperture sizes. Different modifications have been made to the ion funnel design since its initial introduction taking into consideration the ion motion through trajectory simulations. In 2012, Barber et al. presented the combination of the RF ion funnel with PTR-MS. The drift tube of a PTR-MS instrument was modified to allow the operation in both modes: as a conventional drift tube and as an ion funnel. In 2012, Barber et al. and Sulzer et al. published the implementations of their minor improvements in a PTR-ToF-MS setup, such as a modified ion source, drift tube, transfer lens system, and so on. In 2014, Sulzer et al. introduced a new prototype of a PTR-ToF-MS instrument with a quadrupole ion guide instead of a transfer lens system. This innovation improves the efficiency of ion transfer from the drift tube to the mass spectrometer. In combination

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with an elevated drift tube pressure of 3.8 mbar this instrument boosts the sensitivity by over two orders of magnitude compared to the first commercial device released in 2009 (Jordan et al., 2009a). A more radical redesign of the reactor was implemented in the PTR3 instrument, which operates at a higher pressure (50−80 mbar), uses a tripole to enhance the ion kinetic energy, and relies on a large sampling flow to transport ions down the reactor, thereby allowing for much longer reaction times and more efficient product ion formation (Krechmer et al., 2018). In 2018, Krechmer et al. presented a new chemical ionization source called Vocus, which consists of a discharged reagent-ion source and a focusing ion-molecule reactor (FIMR) and is coupled to PTR-ToF-MS instrument developed by TOFWERK (Thun, Switzerland). A FIMR consists of a glass tube with a resistive coating, mounted inside a radio frequency quadrupole (Figure 11.5). The axial electric field is used to enhance ion collision energies and limit cluster ion formation. The RF field focuses ions on the central axis of the reactor and improves the detection efficiency of product ions. Krechmer et al. (2018) concluded that trajectory calculations and calibrations showed that it increased the detection efficiency by about an order of magnitude. Nowadays, only H3O+ is used as a reagent ion, but it is possible to operate the reaction chamber with other ions. The sensitivity of the instrument does not depend on ambient humidity. 11.3.2 Toward High Throughputness Since PTR-MS belongs to the class of DI-MS techniques, its main capability is to sample and analyze gas samples continuously. For this reason, PTR-MS, from the very beginning, was mostly applied in the ambient and environmental studies of air pollutants. Later it was successfully applied for the headspace VOC analysis of different food products. There are two types of headspace measurements: static and dynamic. During static headspace sampling, VOCs are taken once with a syringe and introduced into an inlet using low flows without any carrier gas. One of the drawbacks of this method is a pressure drop inside the vial. During dynamic headspace analysis higher flows pass through a vial requiring a carrier gas, such as zero air or nitrogen, in order to avoid vacuum creation (Romano et al., 2015; Figure 11.6). From the very beginning, all the measurements were performed manually, including sample incubation at higher temperatures for aroma release stimulation. In this case, the analysis was possible only in the presence of

FIGURE 11.5  Design of Vocus consisting of a discharge reagent-ion source and a focus-

ing ion-molecule reactor (FIMR). (From Krechmer et al., 2018.)

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FIGURE 11.6  Schematic representation of the setup for direct VOC measurements.

FIGURE 11.7  Schematic representation of a multipurpose sampler MPS for GC coupled with PTR-ToF-MS.

personnel who manually measured the samples. This procedure increases the time of the experiment and the possibility of human factor errors. Direct injection of VOCs into an instrument allows the analysis of complex aroma blends in a very short time; thus, manual headspace measurements become a bottleneck for experiments. In order to increase the measurement throughputness, automatic samplers were coupled to PTR-MS instruments. The pioneering work of Makhoul et al. (2014) was done by connecting a multipurpose sampler (MPS) for GC (Gerstel, Germany) to PTR-ToF-MS (Ionicon, Innsbruck, Austria) through a MPS purge tool and a headspace adapter using a conventional syringe-based system (Figure 11.7; Makhoul et al., 2014). Tray temperature and experiment time sequences are fully controlled by the MPS. The maximum number of vials can be increased drastically according to the number of trays connected to the MPS and the programming sequence of the MPS. The purge tool, together with the headspace adapter, enables the exchange of the gas phase into the headspace above the sample with a control flushing gas. It can be used both for sample preparation and measurement. Moreover, it can be used for prevention of oxidation processes in such products as wine, olive oil, or butter. In this case, a sample should be purged with nitrogen for the elimination of oxygen from a vial headspace. Such autosamplers give the possibility of re-measuring the same vials in order to follow the product’s evolution over time, which is very important in fermentation or aging studies. Another example of the automatization of PTR-MS measurements is an autosampler for PTR-MS made by Ionicon (Innsbruck, Austria), which is suitable for static and dynamic headspace analysis up to 270 vials. It is equipped with a heated injection cell

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with a direct connection to a PTR-MS inlet (http​://ww​w.ion​icon.​com/p​roduc​t/acc​essor​ ies/a​utosa​mpler​). The sample size limits the usage of an autosampler. For instance, the 20 mL vial permits the measurement of only small berries, leaves, coffee beans, their powder or puree, liquid foods like oils, milk, juices, and parts of solid products like chocolate, meat, fish, and so on. If the sample is too big to enter the vial, which can be used for an autosampler, or its size cannot be diminished, it should be measured manually (e.g., aroma profiles of intact apples or strawberries). The monitoring of online processes which cannot be reduced to a 20 mL vial scale should be monitored manually. However, if these processes are not very fast and the experimental setup allows several of them to run simultaneously, it is possible to increase the throughput of such measurements analyzing one sample after another in a continuous way. It is possible to organize using the automation of the Multivalve Port (Ionicon, Innsbruck, Austria) or the set of switchable valves. 11.3.3 Toward Compound Identification PTR-MS, coupled to a ToF analyzer, provides the separation and identification of isobaric compounds with precision up to the fourth digit after a comma. However, isomer compounds still cannot be distinguished in this way. The first trials to distinguish structural isomeric compounds were made using PTR+SRI-MS technology and switching different reagent ions to distinguish between different classes of compounds, that is, ketones and aldehydes (Jordan et al., 2009b). This approach works fine with the simple matrices of several compounds. Food samples consist of a blend of various VOCs, and their separation according to different reagent ions becomes very complicated. In this case, these data represent a food sample fingerprint and can be used in the data analysis for sample discrimination or characterization (Yener et al., 2015). The best solution for isomeric compound identification is the application of a fast-chromatographic separation. In order not to lose the high analytical throughput of the methodology, which is the chief feature of the PTR-MS technique, the gas chromatographic separation should be as fast as possible. Recently a fastGC add-on (Ionicon, Innsbruck, Austria) was implemented as an additional inlet system which consisted of four valves and the flow controller (FC N2) (Figure 11.8, Romano et al., 2014). FastGC uses nitrogen as a carrier gas. Figure 11.8 visualizes all valves in their NO (normally open) state which corresponds to a fastGC disable mode. FastGC requires higher inlet flows in order to fill the sampling loop. There are several parameters which have a great influence on fastGC analysis, such as a thermal ramp, sampling time, and carrier gas flow. The polarity of the chromatographic column should be chosen according to analytical needs and the matrix features. Several applications of a fastGC add-on coupled to PTR-ToF-MS have been reported by the scientific community. Materić et al. (2015) showed the potential of this setup for separating monoterpenes from the standard mixture and biological material using an 80-second run. Romano et al. (2014) applied a fastGC add-on to wine analysis not only for isomer separation but also for the removal of the ethanol effect without the need to drastically change the ionization conditions of the experiment. Pallozzi et al. (2016) studied the implementation of the setup discussed above to detect VOC emissions from plants; in particular, the assessment of the potential interferences generated on the isoprene signal by other biological VOCs, naturally emitted by some species or induced by abiotic and biotic stresses.

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FIGURE 11.8  Schematic drawing of a fastGC add-on. (From Romano et al., 2014.)

11.4  APPLICATION OF PTR-MS ANALYSIS IN FOOD SCIENCE The instrumental characteristics of PTR-MS technology make it an ideal tool for rapid, non-invasive, solvent-free classification of food products according to quality standards and origin. In the last two decades, PTR-MS has been progressively applied more in food science, as revealed by the increasing number of peer-reviewed publications (Figure 11.9), including studies about totally different matrices, from lactic acid fermented products to fruit and vegetables. 11.4.1 Screening VOC Fingerprinting Analysis PTR-MS is particularly suited to developing reliable food VOC fingerprints because it provides handier analytical information (concentration estimation and reduced fragmentation) in comparison with the application of MS-e-noses based on electron impact ionization (Biasioli et al., 2011b). PTR-MS-based e-noses, equipped with multipurpose autosamplers, provide a rich, informative, and high-throughput fingerprint. The most efficient way to exploit this spectrometric fingerprint is through unsupervised multivariate methods for data compression and visualization, and through the setting of classification or calibration models by supervised multivariate methods and data mining. In addition, the association of these VOC fingerprints with sensory analysis by human testers provides confirmatory information on the use of PTR-MS for food discriminatory analysis such as the classification of food samples based on geographical location or on production processes.

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FIGURE 11.9  Peer-reviewed publications (till the end of 2018) about the use of PTR-MS technology on food product analysis.

One of the first studies exploiting rapid PTR-MS VOC fingerprinting for food classification, regarded the discrimination of red orange juices stabilized by different treatments (Biasioli et al., 2003). Likewise, Gasperi et al. (2009) explored the effects of supercritical CO2 treatments and N2O pasteurization in apple juice VOCs by PTR-MS. Tsevdou et al. (2013) used PTR-ToF-MS to investigate the effects of thermal or high hydrostatic pressure treatment on the flavor development of yogurt in the absence or presence of a transglutaminase protein.

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Assessments of food quality using PTR-MS also include the rapid detection of food spoilage and aging. Aprea et al. (2006) characterized the VOC headspace of several virgin olive oil samples based on their oxidative alteration. Spoiled olive oil samples were characterized by a significantly increased content of aldehydes such as heptanal, octanal, and nonanal. Moreover, Aprea et al. (2008) monitored the oxidative alterations of olive oils online during thermal treatments, following the time-evolution of several aldehydes. Heenan et al. (2009) used PTR-MS to compare VOC and sensory characteristics of bread, with the aim of understanding how bread freshness is perceived by the consumer. Partial last squares regression modeling of VOCs and sensory characteristics confirmed the possibility of using PTR-MS as a tool to predict bread sensory quality and freshness. Makhoul et al. (2014) showed, using a PTR-ToF-MS, that the impact of yeast strains on VOC production during the bread-making process exceeds the impact of the flour. Positive results of the multivariate modeling of the link between sensory evaluations by a trained panel and PTR-MS measurement were obtained in discrimination analyses of different cheese types (Biasioli et al., 2006), butter and butter oil (van Ruth et al., 2007), tomato (Muilwijk et al., 2015), and chocolate (Deuscher et al., 2019). Another important application of PTR-MS analysis is the investigation of the geographical origin of food. In combination with appropriate statistical techniques, PTR-MS has indeed been shown to be able to provide geographical identification for several food products, including coffee (Yener et al., 2014, 2015), tea (Yener et al., 2016), chocolate (Acierno et al., 2016), butter (Maçatelli et al., 2009), olive oil (Araghipour et al., 2008), truffles ( Vita et al., 2015), unifloral honey (Kuś and van Ruth, 2015), sea roe (Phillips et al., 2010), saffron (Masi et al., 2016), Dutch cumin cheese (Galle et al., 2011), dry-cured ham (Sánchez del Pulgar et al., 2011), and wine (Campbell-Sills et al., 2016). PTR-MS application was recently demonstrated as a powerful phenotyping tool for aroma assessment in both genetic and quality-related studies of fruit and vegetables (F&V). PTR-MS was indeed successfully applied to discriminate between aroma variability in tomato (Farneti et al., 2012, 2013), apple (Farneti et al., 2014, 2015, 2017a, 2017b), blueberry (Farneti et al., 2018), raspberry (Aprea et al., 2009), and pepper (Taiti et al., 2015). Another important aspect of the F&V production chain is the optimization of quality upon delivery to the consumer; in this, VOCs should be considered as a central trait for determining the “from farm to fork” strategies. The end of the “flavor life” often precedes the end of shelf life as determined by visual and textural features, mainly due to changes in aroma compound concentration and off-flavors development. Aroma and flavor are essential for determining the quality of alcohol beverages. It can help to distinguish raw material variety, geographical origin, fermentation process, technological regimen, aging, spoilage phenomena, and adulteration. However, when a molecule is present at too high concentration, such as ethanol in the case alcoholic beverages, the depletion of the parent ion (H3O+) and the formation of undesired ions make the ionization process more complicated and hinder the application of PTR-MS in enological studies. Different approaches have been proposed for overcoming these issues. Lindinger, the father of PTR-MS, proposed the use of ethanol as a carrier gas to completely deplete hydronium and use protonated ethanol as parent ion. This method was implemented by Boscaini et al. (2004). However, not only protonated ethanol is formed but also a large number of charged clusters which make spectra difficult to decipher. Spitaler et al. (2007) applied a simple dilution of the headspace with the obvious reduction of sensitivity. Recently Campbell-Sills et al. (2016) demonstrated that dilution with argon directly in the PTR-MS drift tube destroys ethanol and ethanol/water clusters and cleans the spectra with a reduced loss of sensitivity and allows the discrimination of wines of different

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origins. This approach was successfully applied for studying the fermentation of beer wort (Richter et al., 2018). Romano et al. (2014) tried a fastGC add-on which, without increasing the minimum analysis time of PTR-MS (about 3 min), allowed for the separation of ethanol and the use of the standard PTR-MS parameters. 11.4.2 Real-Time VOC Evolution Analysis The flavor quality of a product is not a stable trait, but it can rapidly and drastically change over time. The use of PTR-MS as a process analytical technology (PAT) allows real-time monitoring of VOCs during processing. PTR-MS can be used as an analytical fingerprinting tool for monitoring food quality during the processing phase. PTR-ToF-MS has been proposed as a non-invasive technology for the quality control of milk lactic acid fermentation (Soukoulis et al., 2010). This work, which represents the first application of PTR-ToF-MS for studying a dynamic biochemical process, has demonstrated the outstanding ability of the instrument to monitor and identify VOCs which are either formed or depleted during the fermentation process. The successful application of PTR-MS in the field of lactic acid fermentation helped to continue the investigation of aroma formation during yogurt and kefir preparation (Benozzi et al., 2015; Yépez et al., 2019). Similarly, Capozzi et al. (2016) characterized, for the first time, the dynamic of VOCs associated with aromatic bakery yeasts (Saccharomyces cerevisiae) proposing several biomarkers helpful for optimizing the aromatic performances of commercial yeast preparation. In 2017, Khomenko et al. showed the possibility of monitoring the aroma evolution of wine yeast strains grown aerobically on a solid substrate for 11 days. PTR-MS analysis was also successfully applied for monitoring coffee VOC dynamics during production steps, mostly during roasting and extraction. The complexity of the aromas released by coffee beans during roasting was thoroughly investigated using PTR-MS by comparing different coffee origins and varieties and also different roasting conditions (Mateus et al., 2007; Gloess et al., 2014; Wieland et al., 2012). For the final coffee quality, extraction of ground coffee with hot water is equally important to the flavor profile induced by the roasting process. The extraction technique and conditions used for coffee preparation strongly influence the flavor profile in the cup, and it is often the only parameter that can be influenced by the consumer at home. Sánchez-López et al. (2014) measured the VOC kinetics produced during coffee extraction by using a PTR-ToF-MS coupled with a dilution lancet connected to a commercial espresso coffee machine. The process of bread baking and toasting was recently studied by Pico et al. (2018). The authors monitored the aroma development of wheat and gluten-free bread in an online mode, which gave them the possibility of evaluating the generation of different tentatively identified compounds of lipid oxidation, Maillard reaction, and other processes. Recently Pedrotti et al. (2018) applied the “aromatic” point of view to the aging processes of anhydrous milk fat stored in two different types of packaging.

11.5  CHALLENGES AND FUTURE PERSPECTIVES PTR-MS technology provides several advantages to VOC analysis in food science and technology. Its key feature is the possibility of detecting and quantifying VOCs in a direct, continuous, and real-time way at very low levels (pptv), with both high mass and

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time resolution. The high time resolution allows real-time monitoring of fast food processes such as the formation of volatile compounds during thermal processes (i.e., cooking, baking, or roasting), or during food fermentation. On the other hand, compound identification is still a weak aspect of this technology. Fragmentation, complex peak structure, and/or the presence of isomeric compounds may still make this challenge impractical, especially in complex matrices. The ionization based on proton transfer provides a soft ionization where most mass peaks appear on their parent ions; however, some residual fragmentation is not always avoidable and negligible. Right now, the cutting-edge research on PTR-MS technology focuses on the improvement of compound identification, instrument sensibility, and analytic throughputness. Overall, it has to be noted that the aforementioned characteristics of PTR-MS technology do not suggest PTR-MS as an alternative to the gas chromatographic method but as a complementary tool for the study of volatile compounds and a valuable technique when speed, sensitivity, and online measurements are required. After more than two decades of its history, PTR-MS found its application not only in scientific research but also in industrial projects where the high stability, reproducibility, automation, and throughputness are required. Moreover, with the rise in awareness of anthropogenic pollution, the absence of any dangerous solvents or reagents needed for sample preparation and analysis gives PTR-MS an additional value as a green chemistry technique.

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Tsevdou, M., Soukoulis, C., Cappellin, L., Gasperi, F., Taoukis, P. S., and Biasioli, F., 2013. Monitoring the effect of high pressure and transglutaminase treatment of milk on the evolution of flavour compounds during lactic acid fermentation using PTR-ToF-MS. Food Chemistry, 138 (4), 2159–67. van Ruth, S. M., Koot, A., Akkermans, W., Araghipour, N., Rozijn, M., Baltussen, M., Wisthaler, A., Märk, T. D., and Frankhuizen, R., 2007. Butter and butter oil classification by PTR-MS. European Food Research and Technology, 227, 307. https​:// do​i.org​/10.1​0 07/s​0 0217​- 007-​0724-​7. Vita, F., Taiti, C., Pompeiano, A., Bazihizina, N., Lucarotti, V., Mancuso, S., and Alpi, A., 2015. Volatile organic compounds in truffle (Tuber magnatum Pico): Comparison of samples from different regions of Italy and from different seasons. Science Reports, 5, 12629. doi:10.1038/srep12629. Wieland, F., Gloess, A. N., Keller, M., Wetzel, A., Schenker, S., and Yeretzian, C., 2012. Online monitoring of coffee roasting by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS): Towards a real-time process control for a consistent roast profile. Analytical and Bioanalytical Chemistry, 402, 2531–43. Yener, S., Romano, A., Cappellin, L., Granitto, P. M., Aprea, E., Navarini, L., Märk, T. D., Gasperi, F., and Biasioli, F., 2015. Tracing coffee origin by direct injection headspace analysis with PTR/SRI-MS. Food Research International, 69, 235–43. Yener, S., Romano, A., Cappellin, L., Märk, T. D., Sánchez, J., and Gasperi, F., 2014. PTR-ToF-MS characterisation of roasted coffees (C. arabica ) from different geographic origins. Journal of Mass Spectrometry, 929–35. doi:10.1002/jms.3455. Yener, S., Sánchez-Lopez, J. A., Granitto, P. M., Cappellin, L., Märk, T. D., Zimmermann, R., Bonn, G. K., Yeretzian, C., and Biasioli, F., 2016. Rapid and direct volatile compound profiling of black and green teas (Camellia sinensis) from different countries with PTR-ToF-MS. Talanta, 152, 45–53. Yépez, A., Russo, P., Spano, G., Khomenko, I., Biasioli, F., Capozzi, V., and Aznar, R., 2019. In situ riboflavin fortification of different kefir-like cereal-based beverages using selected Andean LAB strains. Food Microbiology, 77, 61–8. Zhu, L., Mikoviny, T., Morken, A. K., Tan, W., and Wisthaler, A., 2018. A compact and easy-to-use mass spectrometer for online monitoring of amines in the flue gas of a post-combustion carbon capture plant. International Journal of Greenhouse Gas Control, 78, 349–53.

Chapter

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Stable Isotope Dilution Assay Hans-Georg Schmarr CONTENTS 12.1 12.2 12.3 12.4

Definitions and Historical Development 241 Principle of an Isotope Dilution Assay 242 Requirements for Labeled Standards and Some Considerations for Their Use 243 Optimization Strategies for SIDA-Based Applications 248 12.4.1 Non-MS SIDA Applications 248 12.4.2 H/C MDGC with SIDA-Based Quantification 249 12.5 Conclusion and Summary 252 References 254

12.1  DEFINITIONS AND HISTORICAL DEVELOPMENT Quantitative analysis has various calibration approaches to instrumental methods. One of these is the internal standard method that utilizes the addition (spiking) of a substance (the internal standard) in a constant amount to all samples, blanks, and calibration standards. Calibration then involves plotting the ratio of the analyte signal to the internal standard signal as a function of the analyte concentration of the standard. This ratio for the samples is then used to obtain their analyte concentrations from a calibration curve (Skoog, Holler, and Crouch, 2016). Spiking of the samples is done before further sample preparation, extraction, and analysis. According to the IUPAC definition, in chromatography, this internal standard is a compound added to a sample in a known concentration to facilitate the qualitative identification and/or quantitative determination of the sample components (McNaught and Wilkinson, 1997). It is generally acknowledged that such an internal standard should be as near as possible to the analyte, without existing as such within the sample matrix. The best available standard is a labeled analyte that does not exist naturally. The practice of the quantification of organic compounds utilizing the addition of a labeled internal standard is called an isotope dilution assay (IDA) and was first reported in 1940 for the analysis of fatty acids and amino acids in complex mixtures by Rittenberg and Foster (Rittenberg and Foster, 1940). Furthermore, it was also recognized as a promising tool for chemical analysis in other areas, for example, following the decay of radioactive nuclides, with sensitivity potentialities exceeding standard analytical chemistry procedures (Inghram, 1954). Later, Sweeley et al. described the beneficial use of mass spectrometric (MS) detection for the determination of unresolved gas chromatographic effluents, here in particular the isotopic derivatives (isotopologues or also named isotopomers) of glucose and deuterated glucose (Sweeley et al., 1966). The

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principle of IDA was also used in the field of pharmacological chemistry (Sadee, Segal, and Finn, 1973). The first application for quantification of flavorful food components was described by Cobb in 1969, using radioactively labeled flavor compounds (Cobb, 1969). In 1976, a theoretical basis for isotope dilution was established by Pickup and McPherson (1976), and it was proposed as a reference method with high accuracy and precision (Björkhem et al., 1976; Cohen et al., 1980). An important early application from the field of aroma research was published in 1987 by Schieberle and Grosch (1987), who quantified acetylpyrazine, 2-methyl-3-ethylpyrazine, and 2-acetyl-l-pyrroline in wheat bread crust with stable isotopes, thus naming it the “stable isotope dilution assay” (SIDA). In the same year, Harris et al. published an early application of the trace-level (ng/L) analysis of potent 2-methoxy-3-alkylpyrazines in wine (Harris et al., 1987). Since then, in the 1990s, numerous analytical methods with a SIDA-based quantification for a great number of potent aroma compounds were developed (Guth and Grosch, 1990, 1993; Sen and Grosch, 1991; Sen et al., 1991; Blank, Schieberle, and Grosch, 1993; Cerny and Grosch, 1993; Grosch, 1993; Allen, Lacey, and Boyd, 1994; Semmelroch et al., 1995; Kerscher and Grosch, 1998; Wagner and Grosch, 1998; Kotseridis et al., 1999a,b; Kotseridis, Baumes, and Skouroumounis, 1999; Lin et al., 1999; Pfnuer et al., 1999), and thereafter. Some of the earlier applications had been summarized together with other developments in methods for the analysis of flavor compounds and their precursors (Schieberle, 1995). A perspective paper dealing with important aspects for the quantification of the sensory-active constituents of foods was published in 2012 (Schieberle and Molyneux, 2012). Noteworthy reviews from the application of SIDA in the field of flavor analysis were contributed by Mosandl (1992), Schieberle (1995), Allen and Lacey (1997), Blank et al. (1998), Milo and Blank (1998), and Werkhoff et al. (2002). A basic description of how to apply a SIDA-based quantification has been summarized by Milo (2005). Calibration issues in SIDA analyses with MS detection, particularly for a situation when overlapping ions exist in the fragmentation pattern of the labeled standard and unlabeled analyte, were discussed in various publications (Colby and McCaman, 1979; Sabot et al., 1988; Sabot and Pinatel, 1993; Sabot, 1994; Fay et al., 2000; Fay, Metairon, and Baumgartner, 2001; Yang et al., 2006). Also worth mentioning are recent reviews targeting application fields beyond flavor analysis (Rychlik and Asam, 2008, 2009; Rychlik, 2011). Today, SIDA can be considered as the accepted state-of-the-art method for the most accurate and reliable quantitative determination of volatile compounds in the flavor research field and elsewhere.

12.2  PRINCIPLE OF AN ISOTOPE DILUTION ASSAY Most of natural elements show a natural distribution of (stable) isotopes; for example, carbon consists of 98.9% 12C, 1.1% 13C, and traces of radioactive 14C, or hydrogen that consists of 99,9885% 1H, 0,0115% 2H (deuterium; D), besides trace amounts of the labile 3H (tritium). With appropriate synthetic approaches, organic compounds (analytes) can be labeled up to 100% with a minor (stable) isotope in a certain position, for example, 2 H, 13C, or 15 N. The synthesized compound exhibits a higher molecular weight than the analyte, but almost identical physicochemical properties. Therefore, such “isotopomers” or “isotopologues” are ideal internal standards for quantitative analysis. Methods based on the use of labeled internal standards with stable isotopes are called “stable isotope dilution assays.” The term “dilution” refers to the fact that the analyte is “diluted” with its isotopomer, the internal standard (Figure 12.1). In a SIDA approach, losses of the

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FIGURE 12.1  Origin of the term “dilution” in stable isotope dilution assays: addition of a standard with a different isotopic distribution—the original isotopic distribution of the analyte has been “diluted.” (Reprinted with permission from Rychlik and Asam, 2008.)

analyte caused by analytical procedures such as, for example, extraction, distillation, or degradation are compensated, if the labeled isotopomer is added to the sample prior to the workup procedure and conditions for reliable equilibration are provided (Figure 12.2). This way, time-consuming recovery and spiking experiments, necessary with structurally different internal standards, can be minimized. The quantitation can simply be done by monitoring the target compound (or a fragment or molecular ion of the analyte) and the corresponding signal for the isotopomeric internal standard. The concentration of the analyte and its isotopomer is determined by GC or GC-MS. In the literature, MS is often used as a detector, mainly due to its ability to discriminate between the labeled and unlabeled compound, allowing quantification via the peak areas of selective mass traces. With MS detection, the expression “stable isotope dilution–mass spectrometry” (SID-MS) may also be found in the literature, whereas “non-MS SIDA” can be found along with other detectors.

12.3  REQUIREMENTS FOR LABELED STANDARDS AND SOME CONSIDERATIONS FOR THEIR USE Since the isotopically labeled analogue of the analyte is used as a standard, some considerations of the labeled standards are necessary. Labeling with deuterium (D) is preferably used because the introduction of this isotope is relatively cheap when compared with other isotopes. In order to be considered chemically stable, labeling with (stable) isotopes should be performed in a non-exchangeable position. Under certain circumstances, 13C-labeling is strongly recommended, particularly as protons in the α-position to carbonyl functions are known to enolize and may exchange with protons from the sample. A possible deuterium/protium (D/H) exchange of such a labeled standard during the analytical procedure then has to be ruled out, for example, by testing its stability

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FIGURE 12.2  The ratio of isotopologic analyte and standard remains stable until the

final mass spectroscopic analysis. For a structurally different internal standard, however, the ratio between standard and analyte can alter during sample preparation. (Reprinted with permission from Rychlik and Asam, 2008.) under conditions used for isolating the aroma components, but also throughout the instrumental analytical process. Examples for critical analytes with exchangeable protons within the structure that require labeling with 13C were shown, for example, for 4-hydroxy-2,5-dimethyl-3(2H)-furanone (Furaneol®), as illustrated in Figure 12.3 as c-Ia (Blank et al., 1997) and c-Ib (Blank et al., 1997). On the other hand, its ethyl analogue (homofuraneol) can only be deuterated in a well-defined position, as shown for 2(or 5)-[2,2,2,-2H3]ethyl-4-hydroxy-5(or 2)-methyl-(2H)-furanone (d-II) (Preininger and Grosch, 1994; Blank et al., 1997). The additional CH 2 within the ethyl group serves as a barrier that prevents a D/H-exchange. Other examples requiring 13C-labeling are, for example, 3-hydroxy-4,5-dimethyl-2(5H)furanone (sotolone) c-III (Blank et al., 1996), or 2,3-butandione (diacetyl) c-IV (Schieberle and Hofmann, 1997). The homologous

FIGURE 12.3  Preferred position and type of labeling of compounds containing an

α,β-dicarbonyl moiety (▪ indicates the labeling position with (Reprinted with permission from Milo and Blank, 1998.)

13

C, ● with deuterium).

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5-ethyl-3-hydroxy-4-methyl-2(5H)-furanone (V) (Blank, Schieberle, and Grosch, 1993), and 2.3-pentanedione (VI) (Milo and Grosch, 1993) can be deuterated as long as a CH 2 group is located between the enolizing structure and the deuterated moiety. An interesting observation of a D/H-exchange phenomenon was recently described for the analysis of 2-aminoacetophenone (2-AAP) in wine, a compound associated with the so-called atypical aging off-flavor (ATA). The method used 1-(2-​amino​pheny​l)-2,​ 2,2-t​rideu​terio​-etha​none (2-AAP-d3) as an internal standard (Figure 12.4), and headspace solid-phase microextraction (HS-SPME) for sample preparation (Schmarr, Keiser, and Krautwald, 2016). In this work, calibration was done with multipoint calibration curves, also including repetitions over time. The repetition of the calibration curves obtained when samples were measured in a time series with standards, then kept at a basic pH for longer periods of time, showed different (increasing) slopes when area ratios were plotted as 2-AAP/2AAP-d3. Obviously, 2-AAP-d3 underwent a proton exchange in alkali solution as could only be verified by analyzing 2-AAP-d3 at pH 9 versus 3.5 (Figure 12.5). In this case, non-deuterated 2-AAP was built over time (Figure 12.5A). No proton exchange was observed at a native wine pH of 3.5 (Figure 12.5B). These findings of the D/H-exchange problem were in accordance with previous work in which 2-AAP was analyzed in milk powder (Preininger and Ullrich, 2001). In this latter work, the isotopic standard had actually been prepared via a proton exchange under highly alkaline conditions (pH 14). Although 2-AAP-d3 seemed to be stable in an aqueous matrix, quantitation in milk powder via SIDA, was not possible due to deuterium exchange. These authors stated that a deuterated 2-AAP standard must therefore be labeled in non-CH acidic position for use in milk powder. Schmarr et al. discussed the differences observed between their work on wine aroma and the previous study on milk powder, both using 2-AAP-d3 as an internal standard, with respect to the kinetics for the proton exchange on the one hand and the partitioning and extraction between the aqueous and the headspace phases on the other. They argue that as long as the extraction step is considerably faster than the D/H exchange, a problem with SIDA-based quantification might not be detected. However, as HS-SPME usually is a longer process (extraction times are often in the range of about an hour), this may become critical, and a potential D/H exchange should thus be considered. Another interesting aspect discussed in this work is the situation with autosamplers that are often used in routine analyses today. Here, the analysis of multiple samples might further complicate the story. If samples already spiked with an isotopic standard remain on the sampler tray waiting for subsequent processing, then excessive proton

FIGURE 12.4  2-AAP and its isotopologue used as an internal standard for SIDA-based

quantification. (Reprinted with permission from Schmarr, Keiser, and Krautwald, 2016.)

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FIGURE 12.5  Stability of area ratios (2-AAP/2-AAP-d3) obtained for buffered model

wines spiked with a fixed amount of 2-AAP-d3. (A) Increasing area ratios over time are a sign for proton exchange at pH 9. (B) Stable area ratios at a native wine pH (here pH 3.5) allow the use of 2-AAP-d3 as an internal standard for SIDA-based quantification. (Reprinted with permission from Schmarr, Keiser, and Krautwald, 2016.) exchange in the aqueous medium has to be expected, as was the case with 2-AAP-d3. Within the time frame necessary for multipoint calibration runs, but also for a longer series of samples resting on the sample tray, a back exchange of the deuterium atoms occurs. Alternatively, in order to prevent (or minimize) the risk of a D/H exchange, the internal standard would have to be added just before the explicit analysis of each sample. Today, this might be realized when modern sampler technology is used. New sampler generations (e.g., from CTC Analytics AG, Zwingen, Switzerland) can be equipped with a tool exchange station, allowing a timed exchange of an injection tool (e.g., a microliter syringe for spiking versus a SPME fiber tool for extraction). Such equipment then allows the timed standard addition and allows finally a switching back to the SPME fiber tool for further processing of the sample. This way, prolonged periods of potential D/H-back-exchange can be minimized, ruling out the risk of a false SIDA quantification (Schmarr, Keiser, and Krautwald, 2016). Detection method should also be well-thought-out when considering the labeling strategy beforehand. With common MS detection in SIDA applications, in order to minimize interferences with masses originating from the analyte, the labeling should

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increase the molecular weight of the standard by at least two units, preferably three units, in order to minimize interferences with the natural isotope distribution of the analyte (Fay et al., 2000; Milo, 2005). Depending on the fragmentation of the molecule and the ionization method applied (electron impact (EI) or chemical ionization (CI) with different reactant gases), it is also required that the labeled ion species is accessible, and in trace-level analyses also of adequate abundance. Some aspects that exploit the potential of mass spectrometry in this respect have been outlined in detail by Blank et al. using trans-4,5-epoxy-(E)-2-decenal as an example (Blank et al., 1998; Lin et al., 1999). Care should also be given to the proper selection of specific masses, as particularly in trace-level analysis with complex matrices, the low-molecular weight and (sometimes high) fragmentation of flavor compounds (with common EI) complicate reliable quantification. Often, only low-molecular weight fragment ions of minor selectivity remain and compete with ubiquitous fragment ions from the matrix. An example of such a problematic situation was recently shown for α-ionone in wine that was impeded by co-eluting compounds, although detection was performed with a generally considered selective (and thus “safe”) detection method using MS/MS with a triple quadrupole mass spectrometer (Langen, Wegmann-Herr, and Schmarr, 2016). Due to their scarce presence in nature, labeled fragment ions usually do not exhibit such a problem and result in far better signal-to-noise ratios for their peaks, but still, the analyte as such must be detected and quantified without skepticism. With the availability of accurate mass detection (highResMS), some of the problems discussed above might be circumvented, but up to now the high instrument costs prevent their common dissemination, and applications are usually outside the field of flavor analysis. Still, a recent application for the quantification of 19 aldehydes in human serum with HS-SPME-GC-highResMS might serve as an inspiring example. This application used SIDA-based quantification with 13C-labeled standards (Silva et al., 2018). In the case of SIDA applications that do not involve mass spectrometric detection (non-MS SIDA), the required isotope effect necessary for a chromatographic resolution that allows individual integration of both the standard and analyte depends on the number of labeled atoms (particularly deuterium atoms as outlined below) incorporated, but also on the position within the structure of the molecule. The inverse isotope effect (the deuterated (heavier) isotopologues are eluted earlier than the nondeuterated (lighter) ones) in gas–liquid partitioning chromatography (GLC) is caused by van der Waals (particularly London) dispersion forces. Apolar locations are favorable in this respect. With deuterated compounds, the fundamental difference in bond lengths of C–H and of C–D is that the latter is shorter by about 0.005Å. Therefore, molar van der Waals volumes are lower, resulting in lower London dispersion interactions and an inverse isotope effect. Compared to 13C-isotopes, the reduction in the van der Waals volume is more pronounced, and the isotope effect thus stronger with deuterated compounds. This fundamental understanding of the isotope effect, particularly that it is higher with deuterated standards than with 13C isotopic compounds, has to be considered when labeled standards for non-MS SIDA applications have to be chosen. A detailed discussion on the nature of the isotope effect, examples of its dependence on the substitution pattern, and the influence of chromatographic conditions, particularly the contribution of the chemical nature of the stationary phase, has been published recently and might inspire further studies (Schmarr et al., 2012b). The experience gained in this latter study provided the basis for the optimization of multidimensional GC applications using labeled internal standards, as will be discussed in the next chapter.

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12.4  OPTIMIZATION STRATEGIES FOR SIDA-BASED APPLICATIONS 12.4.1 Non-MS SIDA Applications As already mentioned, besides the almost identical behavior of isotopic labeled standards, a chromatographic separation of labeled and non-labeled compounds can be achieved and was demonstrated in the 1960s, at the beginning of the field of GC (Bruner and Cartoni, 1963, 1965; Bruner, Cartoni, and Possanzini, 1969). The nature of the so-called isotope effect, the elution order of the labeled and non-labeled analyte, was reviewed by Matucha et al. (1991) and discussed in detail with particular emphasis on novel ionic liquid stationary phases in a later work (Schmarr et al., 2012b). Based on this knowledge, today, optimization of the chromatographic separation and proper selection of the degree and structural variation of the deuteration allows for fine-tuning of the isotopic separation, and consequently an individual integration of the internal standard and analyte. This is the prerequisite for non-MS based SIDA applications, as has been presented in recent flavor analysis work using electron capture detection (ECD) for haloanisoles (Schmarr et al., 2012a; Slabizki and Schmarr, 2013), pulsed flame photometric detection (PFPD) in a sulfur mode for sulfur compounds (Koschinski et al., 2010; Schmarr et al., 2010; Ullrich, Neef, and Schmarr, 2018), or a thermionic detector in a nitrogen-selective mode (NPD) (Schmarr et al., 2010). A low-cost, robust, selective, and sensitive non-MS detection, as with the PFPD in the examples shown in Figure 12.6, favors the development of quantitative non-MS SIDA-based methods and was recently demonstrated for a routine HS-SPME analysis of low-molecular weight volatile sulfur compounds in wine (Ullrich, Neef, and Schmarr, 2018). Another application that benefits from the high selectivity and sensitivity of the ECD is the analysis and SIDA-based quantification of off-flavor haloanisoles in wine and cork soaks with limits of detection (LODs) in the sub-ng/L range (Schmarr et al., 2012a; Slabizki and Schmarr, 2013). Based on an automated headspace solid-phase microextraction (HS-SPME), the latter method only needs marginal sample preparation and achieved LODs for the most relevant cork off-flavor compounds, such as 2,4,6-trichloroanisole (TCA), 2,3,4,6-tetrachloroanisole (TeCA), and 2,4,6-tribromoanisole (TBA) well below their sensory threshold values. It is noteworthy that for the complex matrix situations (here wine), reliable trace-level quantification had only been achieved after applying heart-cutting multidimensional gas chromatography (H/C MDGC). Such trace-level non-MS SIDA quantification was possible because the necessary chromatographic resolution of the internal standard and the target analyte peaks were obtained with highly deuterated [2H 5]-isotopologues and well-chosen chromatographic conditions (Figure 12.7). Recently, an interesting isotopic separation of the highly volatile compound acetaldehyde and its deuterated isotopologue acetaldehyde-2,2,2-d3 was achieved in a temperature-programmed run on a porous layer open tubular (PLOT) capillary column coated with particles of divinyl-benzene ethylene glycol/dimethylacrylate (Rt ® -UBOND). In a preliminary study, static headspace extraction and gas chromatographic separation (HS-GC-FID) of acetaldehyde from aqueous solutions was shown as an application. For detection, the method used a flame ionization detector (FID) that represents a robust and low-cost alternative to MS detection. Good linearity was obtained in a calibration range from 0.4 to 40 mg/L, with peak integration benefitting from the inverse isotope effect encountered on the specific porous polymer (Figure 12.8 and

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FIGURE 12.6  Separation of thiols and their [2H10]-isotopologues on a polyethylene gly-

col stationary phase with PFPD detection (S-mode) and enantioseparation of 3MH and [2H10]-3MH and their isotopologues on AlphaDex 120 (*). Isothermal oven temperatures were at 120°C; (a) 3MH; polyethylene glycol; (b) 3MHA; polyethylene glycol, 90°C; (c) 4MMP; polyethylene glycol, 140°C and 100°C; and (d): AlphaDex 120, respectively. Hydrogen was used as carrier gas at 4 mL min-1; besides AlphaDex 120, which was at 75 kPa constant inlet pressure. Fused silica column dimensions were 30 m × 0.32 mm i.d. (0.25 mm i.d. for AlphaDex 120). All axis captions as for 3MH (a). (Results presented earlier in Schmarr et al., 2010.) 12.9). Furthermore, separation of methanol and deuterated methanol (d3) could also be achieved under the same chromatographic conditions (Figure 12.10) (Schmarr, Wacker, and Mathes, 2017). Although non-MS SIDA applications were shown to be promising alternatives to those with MS detection, their occurrence in the literature is still scarce. Maybe the examples presented here will inspire future method development in this direction. Common arguments against the omnipresent detection with MS in the scientific literature are particularly instrument cost, maintenance costs, and the required operator training level. 12.4.2 H/C MDGC with SIDA-Based Quantification Fundamental knowledge of the isotope effect might also be utilized for optimization strategies in H/C MDGC applications, when cut windows should be minimized in order

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FIGURE 12.7  HS-SPME-MDGC-ECD 2D chromatograms of (A) TCA-d5/TCA and

(B) TBA-d5/TBA standards. The indicated shoulder visible at the peak of TCA-d5 indicates co-elution, problematic for quantification on a 35% diphenylpolysiloxane stationary phase column. Only minor co-elution (shoulder) of a system background compound (lowest trace); chromatogram (B) with TBA on TG-1301MS. Integration of TBA is not hampered for investigated calibration ranges (overlayed traces). (Reprinted with permission from Slabizki and Schmarr, 2013.)

to avoid co-transfers of potentially interfering matrix compounds from a 1D to the 2D separation column (Schmarr, Slabizki, and Legrum, 2013) (Figure 12.11). An example of this optimization strategy favoring narrow-cut windows was presented with the trace-level analyses of 3-alkyl-2-methoxypyrazines (3-iso-propyl-2-methoxypyrazine (IPMP), sec-butyl-2-methoxypyrazine (SBMP), and 3-iso-butyl-2-methoxypyrazine

FIGURE 12.8  Separation of deuterated (d3) and non-deuterated (nd) acetaldehyde after HS-GC-FID analysis on a 30 m × 0.32 mm i.d. fused silica capillary, coated with 10 µm of Rt-U-Bond. (Reprinted with permission from Schmarr, Wacker, and Mathes, 2017.)

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FIGURE 12.9  Calibration curve shows good linearity for acetaldehyde in the calibrated

range from 0.4 to 40 mg/L. (Reprinted with permission from Schmarr, Wacker, and Mathes, 2017.) (IBMP) in a complex matrix (Schmarr, Slabizki, and Legrum, 2013). In addition to SIDAbased quantification with deuterated internal standards, enantioseparation of the chiral SBMP occurred on an enantioselective 2D separation column. The authors discussed in detail the benefit of their minimization strategy in order to reduce the transfer of possibly co-eluting matrix compounds. A crucial point in their application was to choose a

FIGURE 12.10  Separation of deuterated (d4 or rather d3 after proton exchange in aqueous solution) and non-deuterated methanol after HS-GC-FID analysis on a 30 m × 0.32 mm i.d. fused silica capillary, coated with 10 µm of Rt-U-Bond. (Reprinted with permission from Schmarr, Wacker, and Mathes, 2017.)

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FIGURE 12.11  Strategy to optimize H/C MDGC with SIDA-based quantification. (a) Isotopic separation due to a pronounced (normal) isotope effect in 1D requires a widecut window. The window size depends on peak widths and the resolution (R s) of the isotopic standard and the analyte (*). (b) Narrow-cut windows with a 1D separation that show no or only a marginal isotope effect. Mandatory widths for cut windows to transfer the analyte (R-H n) or the labeled (here deuterated) internal standard (R-D n) are indicated with arrows in black, whereas arrows in light gray indicate optional transfer periods to ensure a complete transfer.

suitable chemical composition for the 1D column stationary phase. A medium polar ionic liquid phase (SLB-IL60) resulted in an insignificant 1D separation between labeled and unlabeled compounds. Critical aspects of such narrow-cut windows with a partial transfer versus a complete transfer were discussed, particularly with respect to consequences for the resulting quantitative data as well as for the calculated enantiomeric composition. Some of these results will also be presented hereafter. First, the increase in selectivity with a narrow- (18 s) versus a wide- (42 s) cut window was obvious and is demonstrated in Figure 12.12. With a transfer period of 42 s, a major co-eluting peak was seen at the 2D retention of SBMP, that was not transferred with a transfer period of only 18 s (Figure 12.12b), the latter allowing undisturbed quantification on the corresponding quantifier fragment ion traces (Figure 12.12c). On the other hand, an excessively narrow-cut window in 1D has to be avoided as often an intended 1D co-elution is not perfect, and partial transfer of the labeled and unlabeled compound then results in erroneous data when using SIDA-based quantification (Figure 12.13). Otherwise, in enantio-MDGC with an achiral stationary phase in 1D and an enantioselective 2D separation, a partial transfer of the 1D peak does not influence the enantiomeric composition as the achiral 1D separation cannot separate the enantiomers (Figure 12.13c).

12.5  CONCLUSION AND SUMMARY Isotopic dilution is probably the most precise and accurate method available today to quantify flavor compounds, particularly if the internal standard can be homogeneously distributed within the sample matrix. However, some considerations are necessary beforehand, concerning the type and chemical nature of the labeling with respect to the stability of the labeled compound in the sample matrix, but also with respect to the method used for detection. With common MS detection, an overlap of standard and

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FIGURE 12.12  H/C enantio-MDGC with SIDA-based quantification of MPs in galba-

num oil. (Copyright by Springer Nature, reproduced with permission.) (a) 1D Separation on SLB-IL60 as pre-column; (a*) cut windows. (b) Separation on the enantioselective 2D column (Lipodex G ®), upper trace with 42 s cut windows (each) or 18 s (lower trace); absolute scaling intensities given for estimation of real peak size. (c) Quantification on narrow, 18 s cut windows via quantifier ions: m/z 137 (140), 138 (141), and 124 (127) for IPMP (d3 -IPMP), SBMP (d3 -SBMP), and IBMP (d3-IBMP), respectively. (Reprinted with permission from Schmarr, Slabizki, and Legrum, 2013.) analyte ions should be avoided; otherwise calibration curves have to take the theoretical and measured isotopic enrichment into account. It is also evident that labeled ions should be selective and of reasonable abundance, with some applications then demanding other ionization techniques than standard electron impact ionization. In non-MS SIDA applications, labeling strategy and chromatographic optimization have to be optimized to guarantee a good chromatographic separation of standard and analyte, allowing their individual signal integration. Since H/C MDGC is a common method for quantifying flavor compounds in complex matrices and/or in trace-level situations, optimization strategies outlined here allow for the minimization of cut windows, and thus the risk of transferring potentially co-eluting compounds from 1D to 2D.

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FIGURE 12.13  Consequences of complete or partial transfer in MDGC analysis. (Copyright by Springer Nature, reproduced with permission.) (a) Co-eluting isotopic MPs in 1D allow narrow-cut windows, but complete or partial transfer conditions (as indicated by the arrows) must be considered. (b) Influence on peak area ratios of unlabeled/ labeled MP (n=2). (c) Influence on peak area ratios for (S)/(R)-SBMP (n=2). (Reprinted with permission from Schmarr, Slabizki, and Legrum, 2013.)

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Schmarr, H.-G., C. Sept, U. Fischer, and S. Koschinski. 2010. Stable isotpe dilution analysis with element-specific rather than mass spectrometric detection. Poster Presentation, 34th International Symposium on Capillary Chromatography (ISCC) and the 7th GC×GC Syposium, May 30–June 4, 2010, Riva del Garda, Italy. Schmarr, H.-G., M. Wacker, and M. Mathes. 2017. Isotopic separation of acetaldehyde and methanol from their deuterated isotopologues on a porous layer open tubular column allows quantification by stable isotope dilution without mass spectrometric detection. Journal of Chromatography A 1481:111–5. Schmarr, H.-G., P. Slabizki, and C. Legrum. 2013. Optimization in multidimensional gas chromatography applying quantitative analysis via a stable isotope dilution assay. Analytical and Bioanalytical Chemistry 405 (20):6589–93. Schmarr, H.-G., P. Slabizki, S. Müntnich, C. Metzger, and E. Gracia-Moreno. 2012b. Ionic liquids as novel stationary phases in gas liquid chromatography: Inverse or normal isotope effect? Journal of Chromatography A 1270 (0):310–7. Semmelroch, P., G. Laskawy, I. Blank, and W. Grosch. 1995. Determination of potent odorants in roasted coffee by stable isotope dilution assays. Flavour and Fragrance Journal 10 (1):1–7. Sen, A., G. Laskawy, P. Schieberle, and W. Grosch. 1991. Quantitative determination of β-damascenone in foods using a stable isotope dilution assay. Journal of Agricultural and Food Chemistry 39 (4):757–9. Sen, A. and W. Grosch. 1991. Synthesis of six deuterated sulfur-containing odorants for use as internal standards in quantification assays. Zeitschrift für LebensmittelUntersuchung und-Forschung 192 (6):541–7. Silva, L. K., G. A. Hile, K. M. Capella, M. F. Espenship, M. M. Smith, V. R. De Jesús, and B. C. Blount. 2018. Quantification of 19 aldehydes in human serum by headspace SPME/GC/High-resolution mass spectrometry. Environmental Science & Technology 52 (18):10571–9. Skoog, D. A., F. J. Holler, and S. R. Crouch. 2016. Principles of Instrumental Analysis, 7th ed. Boston, MA: Cengage Learning. Slabizki, P. and H.-G. Schmarr. 2013. Analysis of corky off-flavour compounds at ultra trace level with multidimensional gas chromatography-electron capture detection. Journal of Chromatography A 1271 (1):181–4. Sweeley, C. C., W. H. Elliott, I. Fries, and R. Ryhage. 1966. Mass spectrometric determination of unresolved components in gas chromatographic effluents. Analytical Chemistry 38 (11):1549–53. Ullrich, S., S. K. Neef, and H.-G. Schmarr. 2018. Headspace solid-phase microextraction and gas chromatographic analysis of low-molecular-weight sulfur volatiles with pulsed flame photometric detection and quantification by a stable isotope dilution assay. Journal of Separation Science 41 (4):899–909. Wagner, R. K. and W. Grosch. 1998. Key odorants of French fries. Journal of the American Oil Chemists’ Society 75 (10):1385–92. Werkhoff, P., S. Brennecke, W. Bretschneider, and H.-J. Bertram. 2002. Modern methods for isolating and quantifying volatile flavor and fragrance compounds. In Food Science and Technology, ed. R. Marsili, pp. 139–204. Boca Raton, FL: CRC Press. Yang, Z.-Y., E. Y. Zeng, J.-Z. Wang, and B.-X. Mai. 2006. A numerical scheme to diagnose interferences in gas chromatography–mass spectrometry quantitation of coeluting isotopically labeled and unlabeled counterparts with partially overlapping ion profiles. Journal of Chromatography A 1116 (1):265–71.

Section

III

Principles of Processing, Evolution, and Modification

Chapter

13

Food Processing, Cooking, and Aging: A Practical Case Study Emmanuel Bertrand CONTENTS 13.1 Introduction 261 13.2 Elaboration of Processed Cheese 262 13.3 Process Making for Processed Cheese 266 13.4 Reactions Occurring during the Cooking of Processed Cheese 268 13.4.1 Lipid Degradation 270 13.4.2 The Maillard Reaction 272 13.4.3 Caramelization Reactions 272 13.5 Identification of Flavor Modifications during Processing 274 13.6 Conclusion 277 References 277

13.1 INTRODUCTION The conservation of foodstuffs has always been a top priority concern for human beings. One of the oldest examples of processed food is the discovery of fermented kefir, a dairy product, in China more than 4000 years ago (Yang et al., 2014). How to ensure the permanent supply of perishable goods that are produced in agreement with the seasons? How to guarantee safe (with respect to the microbiological risk of spoilage) and well-balanced food (with respect to energy and nutritional needs)? These are two questions that scientists are still trying to answer with the combination of our modern industrial tools and knowledge but in a context of high demographic pressure and possible resource scarcity in the future. In the past, the discovery of fire to cook food might have been one of the explanations for the successful evolution of Homo sapiens (Gibbons, 2007). Traditional preservation processes such as fermentation (yogurt, cheese, wine, and beer making as examples), drying (meat, fish, fruits, and vegetables) and sugar addition (jams and marmalades) have enabled the preservation of many food products with different consequences: an acidification of the environment and the installation of a microbiologically safe and edible microflora (cheese and yogurt), a reduction of water activity (aw), and thus a reduction of the associated spoilage reactions (Figure 13.1). However, in parallel, it is observed that the organoleptic properties of the processed products are drastically modified by texture changes and the apparition of newly formed compounds that may be either desirable or in some cases rejected by consumers. Modern preservation techniques using hot- and cold-thermal technologies and alternative technologies (such as high pressure or ionization

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FIGURE 13.1  Water activity (aw) and spoilage reaction rates. (Adapted from Labuza

and Dugan, 1971; Filey and Given, 1986, reproduced by Bertrand et al., 2018, with permission.) technologies) have helped to overcome some of these aspects by trying to keep foodstuffs in a condition that is as close as possible to a native and fresh state. More recently, the development of standardization and industrialization, the development of marketing techniques (and product segmentation), and the analyses of consumer preferences have led to the development of ultra-processed food. In this case, additives are added for technological (to facilitate the production processes) or marketing (colorings and aromas to increase consumer palatability and trigger product purchase or re-purchase) purposes rather than nutritional or hedonistic reasons (Monteiro, 2009). In this chapter, we will focus on the development of the aromatic properties of products during their manufacture and storage through the example of processed cheese. This example is of interest as processed cheese comes from the second processing of milk. Its manufacture involves the mixing, heating, and texturing of dairy (cheese, butter, and milk powders) and non-dairy products (emulsifiers, citric acid, and sodium chloride). The result is a homogeneous product, generally spreadable and with a long shelf life, often over 6 months. Some of the raw materials used have already been previously processed, either by microbiological (Cheddar cheese) or thermal (milk powder) processes, which increases the potential for the origin of the newly formed molecules. During its manufacture and storage, lipid oxidation, caramelization, and Maillard reactions form odorous compounds, some of which are potentially undesirable for the flavor of the product.

13.2  ELABORATION OF PROCESSED CHEESE Figure 13.2 represents all the ingredients and additives authorized for the production of processed cheese. Among them, several are optional and provide a specific texture or aroma to the product. Six ingredients (solid line) are essential: a cheese matrix, milk powder, milk fat, sodium chloride, water, and emulsifiers (Caric, 2000; Commission du

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FIGURE 13.2  Authorized ingredients for the production of processed cheese (full line:

basic ingredients; dotted line: optional ingredients). (According to Caric, 2000 and Commission du Codex Alimentarius, 1995.) Codex Alimentarius, 1995). Because of its success, the cheese matrix is now made specifically to be processed and is no longer considered as an efficient way to remanufacture cheeses with visual defects (cracks or holes). Cheddar, Gouda, Emmental, and Mozzarella are generally used alone or in a mixture. For fresh processed cheeses, the cheese matrix used is generally a freshly prepared curd without any ripening. The criteria considered for the selection of the cheese matrix to be processed are the contribution to the flavor, the contribution to the texture, nutritional, economic, and market considerations. The ripening time of the cheese matrix is one of the most important parameters. It determines both the aromatic profile and the content of certain simple sugars. The microbiological reactions that occur during the ripening of the cheese matrix are the origin of several volatile compounds with interesting flavoring properties, such as diacetyl, or methylated aldehydes. Figure 13.3 presents the major biochemical origins of these volatile compounds, derived from lactose, lipid, and protein catabolism (McSweeney, 2004; Marilley and Casey, 2004). Some of these volatile compounds can also be obtained using the Maillard reaction such as, for example, 2,3-butanedione, 2-methylpropanal, and more generally Strecker aldehydes, 2,5-dimethylpyrazine, and tetramethylpyrazine. Other compounds can also be obtained by lipid degradation reactions such as butanoic acid or methylated ketones. Figure 13.4 represents the catabolism of the amino acid leucine that leads to the formation of 3-methylbutanal. It is also obtained during Strecker degradation when leucine is involved in the reaction. Brickley et al. (2007) obtained confocal microscopic photographs of three processed cheeses produced under the same manufacturing conditions from Cheddar cheeses ripened for 7 (A), 28 (B), and 168 (C) days, respectively. A reduction in the average diameter of the fat globules from 120 μm for processed cheese made from 7-day aged Cheddar to about 2 μm for processed cheese made from 168-day aged Cheddar was observed. The ripening of the Cheddar cheese matrix resulted in an increase in small peptides and free amino acids that tend to stabilize the lipid–water interface better than proteins. Bley et al. (1985a,b) measured the intensity of non-enzymatic browning using the color index (ΔE) resulting from the color difference between samples manufactured from Cheddar cheeses with different manufacturing and ripening conditions, and a reference sample according to the classical formula ΔE=½ (ΔL 2+Δa2+Δb2).

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FIGURE 13.3  Microbiological reactions occurring during the ripening of the cheese

matrix. (Modified from Marilley and Casey, 2004.) Where ΔL is the color difference between the product and the reference according to luminance L (white to black), Δa according to the a-axis (red to green), and Δb according to the b-axis (yellow to blue). The thermal scales applied during the production of processed cheese correspond to those of batch systems: 80°C for a few minutes. This study makes it possible to relate several factors, such as ripening time, lactose content, galactose content, salt to relative humidity ratio, and the cooling rate of the processed cheese, with the intensity of the observed non-enzymatic browning. Galactose causes a browning more intense than glucose and much moreso than lactose. It is also observed that a shortened cooling time limits the phenomenon of non-enzymatic browning. The effect of Cheddar cheese ripening results in a much lower browning if the starters used are able to degrade galactose. Therefore, this example highlights the possibility of limiting and controlling the Maillard reaction occurring during the production of processed cheese by controlling the process parameters and the ingredients used in the formulation. The amino acid composition of cow milk can be found in the Fox and McSweeney (1998) or Farrell et al. (2004) studies. The technological processes used make it possible to obtain a range of milk powders with a broad range of compositions and functional properties. An extensive description of the technological processes used to obtain these different milk powders can be found in the Dairy Processing Handbook (Bylund, 1995). Given the differences in the composition of α-lactalbumin and β-lactoglobulin, a careful selection of the mixture of milk powders can modulate the ratio between the different amino acids. In addition to this, the production of powders from whey generally requires significant mechanical and thermal treatments that lead to possible denaturation of the three-dimensional structure of serum proteins. Amino groups from asparagine, glutamine, lysine, and arginine will be thus more easily accessible and more responsive to the Maillard reaction. If this reaction is to be minimized, it will be necessary to select “low heat” or even

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FIGURE 13.4  Leucine catabolism. (Modified from Marilley and Casey, 2004.)

“extra low heat” powders. The intensity of the heat treatment undergone to obtain the powder is equivalent to 70°C for 15 seconds or less (Bylund, 1995). Sithole et al. (2005) studied non-enzymatic browning occurring during the storage of whey powders obtained from three different producers. The very different browning rates obtained emphasize the major importance of the process. However, results obtained by sensory analysis suggest that the lifetime of these powders is limited by the degradation of their functional properties rather than by taste, and odors that are not significantly altered during 19 months of storage at room temperature (Thomas et al., 2004). The fat composition of cow milk is presented in work by Jensen (2002). Fatty acids are 97% esterified to triglycerides, and to diglycerides and monoglycerides. The free fatty acid content in milk is less than 0.5%. Only unsaturated fatty acids are sensitive to oxidation. Linoleic and linolenic acids are the most sensitive due to their high degree of unsaturation (Jensen, 2002). Phospholipids also contain polyunsaturated fatty acids. In addition, due to their good emulsifying properties, they are found at the interface of the emulsion, in a position vulnerable to oxidation. The milk fat used may be butter or anhydrous milk fat. Anhydrous milk fat is obtained by melting and centrifuging the butter and leads to a product containing more than 99% fat. To date, butter has more than 230 identified volatile compounds. However, only a small number of them are considered key odorant molecules for the typical flavor of butter. These are essentially 2,3-butanedione with a characteristic buttery smell, butyric acid with a rancid smell, and δ-decalactone with a peachy smell (Mallia et al., 2008). Emulsifying agents have a major technological role in the production of processed cheese. They are generally composed of a mixture of citrates, orthophosphates, pyrophosphates, and polyphosphates (Caric, 2000). Their role is to supplement the emulsifying capacities of proteins and work schematically as follows: (i) sequestering calcium bound to caseins, (ii) improving the solubility and dispersion of proteins, (iii) regulating the pH of the medium, and (iv) improving the hydration of caseins. Polyphosphates also have the advantage of bacteriostatic properties. Figure 13.5 shows the principle of

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FIGURE 13.5  Chelation of calcium by polyphosphates during cheese making. (Adapted from Caric, 2000.)

operation of these emulsifying agents. The calcium contained in the calcium–paracaseinate complex is removed by the ion exchange properties of phosphates. This increases the solubility of caseins in water and therefore their emulsifying properties. When the higher temperatures are reached, bonds between peptides are broken, and the anions of emulsifying agents can bind to proteins. This makes the proteins more hydrophilic, and proteins are able to bind water molecules. This results in an increase in the viscosity of the mixture, known as the creaming phenomenon (Panouille et al., 2005).

13.3  PROCESS MAKING FOR PROCESSED CHEESE The production of processed cheese involves many unit operations, as represented in Figure 13.6. All these steps can be reduced to five essential unit operations: (i) the mixing of raw materials, (ii) cooking and temperature control (ensuring the necessary sterilizing force for the right microbiological preservation of the product), (iii) creaming (to obtain the desired texture), (iv) cooling, and (v) packaging steps. Thermal operations are particularly important. Indeed, it is during these stages that major physicochemical modifications regarding oxidation reactions and non-enzymatic browning occur. They are carried out within a framework of discontinuous or continuous processes. The batch manufacturing process generally uses a “cutter” type device. Such a system is represented schematically in Figure 13.7. One of the first patents for such a device was registered in the United States by Kraft in 1916. It is a large tank of variable volume, sealed tightly by a cover and equipped with a knife system allowing the mixing of different raw materials. This type of system ensures all unit operations in the same device. Improvements to the current cutters (direct or indirect steam injection, partial vacuum) allow temperatures to quickly increase to around 120 to 130°C. The exact thermal settings to be applied can be calculated using the parameters of Bigelow or Weibull, once the nature and quantity of the microorganisms have been determined (van Boekel, 2002; Vanasselt and Zwietering, 2006). For example, Bacillus cereus spores can sometimes be naturally present in an

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FIGURE 13.6  Diagram of the production of processed cheese. (Adapted from Caric, 2000.)

initial processed cheese matrix (Caric, 2000). The duration of these thermal treatments should be kept as short as possible in order to not generate sensory defects related to the Maillard reaction occurring at these temperatures and pH conditions. In this case, however, the product has not reached the texture desired at the end of the cooking step. A second heat treatment step at moderate temperatures ranging from 80 to 90°C for about 5 to 30 minutes is then carried out under partial agitation. During this stage, the combined action of the melting salts sequestering calcium and temperature will allow the desired texture to be obtained thanks to the creaming phenomenon. The product is then ready to be packed in different formats and cooled. All of the previously presented unit operations are adapted for industrial production. A number of patents have been filed to guarantee each step of the industrial operations. The main processing steps remain very similar to those of batch production. The raw materials are first prepared and melted. Storage tanks may be necessary in order to ensure the continuity of production. These tanks must be maintained at temperatures above 80°C to avoid any gel formation on the processed cheese. The residence time inside these tanks is variable but can last 1 or 2 hours. In a process proposed by Eyles et al. (1996), the thermal treatment is carried out at an ultra-high temperature (UHT) using direct steam injection and flash cooling. In this case, the proportion of vapor incorporated into the cheese matrix is then released during the cooling step. The product is then subjected to shear stress to adjust its viscosity. It is finally packaged into single portions (Weber, 2000; Weber and Didiot, 2009), and overpackaged into boxes. Depending on the case, it can be cooled quickly at the portion stage by passing through a cooling tunnel or being cooled more slowly in a cold room. Figure 13.8 presents the effects of time and temperature on the kinetics of the destruction of microorganisms and physicochemical changes in the milk. To ensure both a sufficient sterilizing force (for microbiological safety) and minimal physicochemical damage to

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FIGURE 13.7  Schematic representation of a cutter.

the sensory and nutritional properties of the product, UHT treatment (and direct steam injection particularly) has proven to be the most efficient of all the thermal processes compared in Figure 13.8. The thermal performances obtained for three milk sterilization processes are compared in Figure 13.9. Treatment by direct steam injection ensures the shortest residence time at a high temperature compared to the other techniques. Storage is carried out under refrigerated conditions in Europe, North America, and Japan, or under nonrefrigerated conditions in the case of emerging countries. It is believed that the same reaction pathways occur; lipid oxidation and the Maillard reaction but at lower reaction rates than during thermal processing. These reactions are associated with a number of physicochemical reactions (Schär and Bosset, 2002).

13.4  REACTIONS OCCURRING DURING THE COOKING OF PROCESSED CHEESE During the manufacturing and storage stages, processed cheese undergoes chemical, biochemical, and physicochemical modifications. Schär and Bosset (2002), propose eight

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FIGURE 13.8  Destruction of milk microorganisms and temperature-induced changes. (Adapted from Bylund, 1995 and Fox and McSweeney, 1998.)

categories of physicochemical alterations that may occur during the manufacture and storage of processed cheese: light-induced reactions, non-enzymatic browning, interactions with the packaging, polyphosphate hydrolysis during storage, changes in the ionic balance, formation of crystals, water loss leading to variations in water activity, and reactions induced by heat-resistant enzymes. Some of these reactions lead to texture changes during the storage of processed cheese, while others have a more significant role in the evolution of flavor during production or storage. From a reactive point of view, the latter corresponds to the degradation of lipids, Maillard, and caramelization reactions. The

FIGURE 13.9  Typical thermal scales for direct and indirect steam injection and steriliza-

tion in milk bottles used to obtain the same sterilizing force (F), F=40. (Adapted from Bylund, 1995 and Richardson, 2001.)

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loss of water vapor, in addition to texture changes, can also influence reaction kinetics. In the case of well-managed processed cheese production, with microbiologically stable portions, reactions of microbiological origin do not occur. Therefore, we will not provide much detail on the reactions occurring during ripening. 13.4.1 Lipid Degradation Lipid degradation generally occurs in two different ways: (i) The hydrolytic pathway corresponds to the hydrolysis of triglycerides to free fatty acids. The reaction is generally favored in an acidic or basic environment, with increased heat and humidity. It can be induced in the manufacturing process, by vigorous agitation, sudden heating, or cooling as well as by the homogenization processes which can be responsible for damage to the milk fat globule membrane. Free fatty acids are more sensitive to oxidations than their corresponding triglycerides. (ii) The oxidative pathway of fat can be divided into three steps; initiation, propagation, and termination. Figure 13.10 summarizes all the reaction mechanisms leading to lipid oxidation. Initiation occurs in three different ways and results in the formation of free radicals. They can be induced by auto-oxidation, light-induced oxidation, or enzymatic oxidation. During auto-oxidation, unsaturated lipids LH lose one hydrogen atom to form a lipid radical L° in the presence of an initiator in Reaction 13.1. The initiation rate increases with the number of insaturations present in the lipids.

LH + In → L° + InH (13.1)

Light-induced oxidation involves the formation of hydroperoxides in a direct reaction with an oxygen singleton 1O2 without any radical lipid formation (Reaction 13.2). 1O2 is formed by the activation of oxygen on a sensitizer such as riboflavin (vitamin B2) or

FIGURE 13.10  Mechanisms of lipid oxidation. (Adapted from Eskin and Przybylski, 2001.)

LH: unsaturated lipid; L°: Lipid radical; LOO°: peroxide radical; LOOH: hydroperoxide.

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chlorophyll (Mortensen et al., 2004). In milk, riboflavin is present at levels of 1 to 3 mg per liter (Causeret and Cirot, 1959). Light and ultraviolet rays can be involved in the initiation step by activating the riboflavin. A superoxide radical 1O2 is then formed. This radical is 1450 times more reactive than 3O2 . It will react with an unsaturated lipid to form hydroperoxides. The hydroperoxides produced in this pathway form cyclic compounds more easily than those induced in the auto-oxidation pathway. 3



sensitizer + 3O2 ® 1sensitizer + 1O2 ° 1

O2 ° + LH ® LOOH

(13.2)

Enzymatic oxidation is very important in the case of ripened cheeses. Oxidative enzymes act most frequently on free unsaturated fatty acids. They catalyze the formation of intermediate hydroperoxides similar to those formed by the non-enzymatic route. However, the isomers produced are region- and stereospecific. Iron is one of the components of the lipoxygenases. Propagation takes place in two steps corresponding to the formation of a hydroperoxide (Reaction 13.3) and its degradation into free radicals (Reaction 13.4). The lipid radical L° reacts with oxygen to form a free peroxide radical LOO°. This reaction is favored in the presence of light. The free peroxide radical reacts with another unsaturated lipid L’H to form a hydroperoxide LOOH and a new lipid radical L’° which will continue the propagation:

L° + O2 → LOO° LOO° + L’H → LOOH + L’°

(13.3)

This unstable hydroperoxide LOOH will then degrade into new free radicals: LOOH → LOO° + H°

LOOH → LO° + OH°

(13.4)

2LOOH → LO° + H 2O + LOO° The free peroxide radicals react with each other to form non-radical products (Reaction 13.5). This termination reaction is favored in an environment where oxygen partial pressure is low. The termination reaction can also take place through the action of antioxidants AH capable of generating free radicals that are stabilized by resonance and which will not participate in the reaction.

LOO° + L’OO° → LOOL’ + O2 L° + AH → LH + A°

(13.5)

The hydroperoxides formed during the lipid oxidation reaction have no odor nor flavor. On the other hand, the compounds resulting from their degradation, presented in Figure 13.10, such as aliphatic aldehydes, are responsible for the flavors resulting from oxidation. Many different volatile compounds are obtained, such as ketones, methylated-ketones, aliphatic aldehydes, alcohols, furan compounds, and lactones. Pentanal and hexanal are two of the most formed products. However, these are generic compounds that do not specifically originate from a single substrate. Figure 13.11 schematically represents the degradation kinetics of fatty acids and the appearance of

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FIGURE 13.11  Schematic representation of the lipid oxidation kinetics. (Adapted from Labuza and Dugan, 1971 and Kamal-Eldin et al., 2003.)

lipid oxidation resulting compounds. Many factors increase the lipid oxidation rate. One such is a higher content in free fatty acids, as they are more easily accessible than the ones esterified by glycerol. Similarly, long-chain and polyunsaturated fatty acids are more sensitive to oxidation. Transition metals such as iron, copper, or aluminum can be accidentally or temporarily introduced by contact with the processing material and promote oxidation. On the other hand, phenols and citric acid are known as sequestering agents of metals and can be used to reduce this undesirable oxidation. Concerning the processing parameters, a higher temperature or a higher oxygen partial pressure both increases the rate of the oxidation and reduces the induction period. Finally, it has been established that the oxidation is minimal for water activities between 0.3 and 0.5. 13.4.2 The Maillard Reaction The nucleophilic addition of a free amine function with a reducing sugar was discovered in 1912 by the French chemist and doctor Louis-Camille Maillard. This reaction is part of the non-enzymatic browning reactions with caramelization due to the formation of polymers called melanoïdins that cause a characteristic brown color at the advanced stages of this reaction (Maillard, 1913). This is a very important reaction for the food industry as it explains a large part of the sensory properties, aroma, and taste of cooked products. However, it was not before 1953 that Hodge proposed a complete reaction scheme, which is still in use today. A full description of the reaction can be found in Bertrand et al. (2018) or in Chapter 14 of this book. 13.4.3 Caramelization Reactions The caramelization reaction is described by Kroh (1994) as the succession of six reactions leading to the degradation of reducing sugars without the intervention of nitrogen

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FIGURE 13.12  Sugar degradation during caramelization. (Adapted after de Bruijn, 1986.)

compounds. The absence of nitrogen compounds is the main difference between caramelization and the Maillard reaction. It is characterized by the formation of a brown color and aroma compounds typical of caramel. Figure 13.12 shows these reactions. The first step consists of a reaction of 1,2-enolization of the reducing sugar called Lobry de Bruyn–Alberda van Ekenstein’s rearrangement, according to the names of the two researchers who highlighted it in 1885. This reversible step is followed by a second nonreversible enolization and then the β-elimination of a water molecule. The fourth step is a dicarbonyl cleavage, which will lead to the formation of carboxylic acids (formic and acetic acid). The last two steps are retro-aldolization and condensation-aldolization. This leads to the formation of osuloses, which are highly reactive dicarbonyl compounds, and to the release of hydrogen ions, causing a decrease in pH over time up to values in the range of 4 to 5. Osuloses then lead to the formation of compounds that are typical for the color and flavor of caramel: three oxygenated heterocyclic compounds, 5-hydroxymethylfurfural, hydroxydimethylfuranone, and hydroxyacetylfurane are formed during the caramelization of glucose (Coca et al., 2004). The distinction between the caramelization reaction and the Maillard reaction is extremely delicate when considering the presence of amino groups in the reaction medium. However, the formation of acid, as shown in Figure 13.12, seems to be a relevant indicator of the caramelization pathway in such a situation. The reaction favors high temperatures, generally above 120 or even 150°C, depending on the sugars involved, and extreme pH conditions, that is, strongly basic (pH 9) or acidic (pH 3) (Kroh, 1994). In a model reaction medium consisting of an equimolar mixture of glucose and glycine buffered at pH 6.8, Martins and van Boekel (2005) showed that temperatures of treatment above 120°C favor caramelization pathways in relation to the Maillard reaction path. These thermal scales are higher than those normally encountered during the manufacture of processed cheeses. In addition, the pH of processed cheese, between 4 and 7, is not favorable to the caramelization reaction during the manufacturing process.

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13.5  IDENTIFICATION OF FLAVOR MODIFICATIONS DURING PROCESSING Bertrand et al. (2011) studied the evolution of the composition of odorous molecules during the cooking of a model processed cheese matrix: 346 volatile compounds were identified. Many were not specific (branched alkanes, alkenes, terpenes) to particular reactions. Only 81 structures were significantly influenced by the thermal treatments applied to the model cheese matrix (p ribose > fructose > glucose) (Ashoor and Zent, 1984). A conservative estimate of possible reaction combinations of the first step of the Maillard reaction demonstrates the possibility of diverse aroma, flavor, and color products. Considering each of the amino acids with a free amino group plus the side groups of lysine and arginine and limiting the reducing sugars to eight of the more common simple carbohydrates, there are over 576 Amadori or Heyns potential products at this point alone. The path each intermediate may take through the rest of the reaction pathway, as well as possibilities for the intermediates of the condensation product to react with other substrates, highlights the diversity of this reaction. Following the condensation reaction and loss of water forming an intermediate imine, there are two potential paths: a reversible cyclization generating a glycosylamine or the formation of a Schiff base (Figure 14.2). The reaction generating the Schiff base is reversible, especially so in acidic conditions, where the initial products are easily regenerated. However, the N-glycosylamine rearrangement into the Amadori or Heyns compound is irreversible, as the precursor Schiff base can be converted into a cyclic hemiaminal which easily mutarotates, whereas the formation of a furanose (Amadori) or ketose (Heyns) hemiacetal has a similar mutarotation to carbohydrates where the equilibria lies in the cyclic form. The Schiff base will form an enaminol (enol form) and, depending on the initial sugar and pH, will have one of three possibilities. At a higher pH, a loss of amine generates a deoxydicarboyl compound. At a lower pH, a 1–2 enolization results in the generation of the keto Amadori product, while oxidation and hydrolytic loss of the amine results in a glucosone cyclic product. Under heat, each of these intermediates can form into flavor/ aroma products via the next set of advanced Maillard reactions.

FIGURE 14.2  Formation of Amadori and Heyns compounds in an early Maillard reaction.

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14.2.2 Intermediate Stage Depending on the initial condensation and the various pathways already presented, there are several different fates for the Amadori, Heyns, and other intermediates formed thus far, in what is classically called the intermediate stage (Figure 14.1). An example of aroma and flavor compound generation through deoxyosone dehydration includes the generation of furanones, isomaltol, and maltol in meats (Eskin and Shahidi, 2013). Although this is perhaps more accurately described as a pool of possible reactants, intermediates, and products, which we will discuss later, Amadori and Heyns products will degrade into one of several possible pathways depending on pH and temperature. Further decomposition and dehydration of the three waters of Amadori products in acidic conditions will lead to the formation of a furan ring containing hydroxymethlfurfural and furfural. Glycine has been reported to increase the formation of this set of products, presumably as this increases the reaction from the Amadori product over an alternative degradation of glucose without utilizing the Maillard reaction (Pastoriza et al., 2018). Also, under acidic conditions, sugar dehydration results in the production of deoxyosone osloses 1-, 2-, 3, or 4-deoxysones. These can then cyclize into the maltols, and furanones described earlier, and can in subsequent reactions with ammonia and hydrogen sulfide produce a number of meaty and other savory flavors. An additional pathway of both Heyns and Amadori compounds involves dehydration under more neutral or basic conditions resulting in the loss of two water molecules generating the antioxidant-reduced enediol. The acid-catalyzed fragmentation and loss of amine generates the dicarbonyl deoxyosones. Further dehydration creates dehydroreductones. Both forms of reductones serve as a fork in the degradation pathways, where in basic conditions they undergo a retro-aldolization into acetone compounds, diacetyle, glyoxal, pyruvaldehyde, glycolaldehyde, and glyceraldehyde, each with their own potential as a flavorant/odorant, or they can further react with food compounds on their own or in reactions with amino acids (Strecker reaction). The presence of the carbonyl group stabilizes the enediol form as an a-oxo-enediol with a strong acidic tendency leading to a strongly reducing potential for the compounds. As such, both the reductones and the dehydroreductones play a key part in browning in subsequent reactions. A third pathway involves the fission or fragmentation of Amadori/Heyns degradation compounds (Figure 14.1). The reaction is initiated by an oxidative fission or reversal of the aldol condensation reaction. Through rearrangement, reduction, and saccharinic rearrangement (migration of alcohol transforming from a dihydroxyl enol to a carboxyl compound), the cleavage reaction forms small decarbonylated aldehyde, alcohol, and acetic compounds, including acetic acid, formaldehyde, diacetyl, glyoxal, acetal and acetaldehyde and 2,3 butanedione (Weenen and Apeldoorn, 1996; Nursten, 2005). An important pathway that involves the loss of free amino acids and for the most part not peptides or proteins, as observed in the initial Maillard condensation reaction, is the oxidative degradation of amino acids in the presence of α-dicarbonyls formed by the Amadori/Heyns compounds (reductones and fission products). Deoxyosones, diacetyl, pyruvaldehyde, hydroxyacetone, glyoxyal, and other decomposition products are potential Strecker reactants. The diversity of potential degradation dicarbonyl compounds and free amino acids combine to make this reaction a major contributor to the flavor profile of food and beverages. Because this reaction proceeds at a greater rate with free amino acids, the pathway is more prevalent in foods high in free amino acids or protein hydrolysates. The reaction involving cysteines and ribose is responsible for many of the aroma and flavor of cooked meats. Interestingly, the reactive α-carbonyl of glyoxal can react with the lesser reactive arginine side chain

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FIGURE 14.3  The Strecker reaction.

(in the absence of competing reactant cysteine) preferred over lysine. While the Strecker degradation reaction imparts a key effect on the flavor profiles of roasted foods including meats and cocoa and coffee beans, both the higher- and lower-molecular weight products of the reaction play a contributory role. It is the further reactions (with a second amino acid or condensation of various intermediates) that produce the compounds involved in the flavor of heated foods. These aroma and flavor products from the Strecker degradation (Figure 14.3) are often organized by the products: pyrroles, oxazoles and oxazolines, and thiazole derivatives. Heterocyclic nitrogen-containing compounds such as pyrazine have been reported to be important in the flavor of many heated and toasted foods, from meats, broths, and vegetables, most with a low odor threshold. Pyrroles were first identified in roasted coffee beans and are a key component of the heterocyclic compounds formed during non-enzymatic browning. Ribose and b-hydroxy amino acids are precursors for these compounds, which are further processed into furans and fururyl-substituted pyrroles. When heated in the presence of serine, threonine, and various monosaccharides, the important flavors of heat-treated cereal and popcorn were identified. Some of the compounds bring about a caramel-like aroma of cooked meats. The Strecker degradation production of several oxazoles further provides complex green and vegetable-like notes in cooked meats. Cysteine and methylglyoxyl Strecker degradation products are examples of thiazole heterocyclic compounds. The nucleophilic attack of the sulfur of cysteine at the carbon of an imine intermediate formed by the reaction between ammonia and an aldehyde creates a range of similar cyclic molecules. These low odor threshold volatiles give sulfurous, onion-like aromas to meats, potato chips, and roasted peanuts. 14.2.3 Late Reactions While many intermediates provide both volatile and soluble components of flavor and aroma, the reactions generate brown-colored compounds as well as flavors. This final

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step is a polymerization of many of the substances into both high and low-molecular weight melanoids. Much of these are condensation reactions potentially between all of the intermediates presented thus far. Polymerization of aminated products including pyrrols, furaldehyes, and others create a brown poorly defined anionic compound. If the backbone of the browning pigment is built from a carbohydrate backbone, such as those found in dark liquids, the products are known as melanosaccharides. The browning reactions found on crusts of bread and baked goods often have protein at the core and are then called melanoproteins. The constitution of melanoids, in general, is dependent on the amount of aldehyde and amines involved in the polymerization. Often the condensation products are polymers of hetercyclic compounds. Proteins, via linkage with arginine or lysine side groups, act as a scaffold for carbohydrate-involved melanoidins. Thermal processing of meats shows high levels of carbohydrate-induced browning in this fashion. Despite a growing role in health as both an antioxidant and other biological effects, very little has been learned about the structure of these compounds. Most of our understanding comes from using model systems, taking a reductionist approach mixing various intermediates, and investigating the final products.

14.3  POOL THEORY The difficulty in determining the intermediates and the final melanoid polymers stems from the diversity, condition (pH, water availability, temperature), and concentration of potential reactants in the processed foods. Thus, a particularly interesting and descriptive theory first described by Yaylayan in 1997, is that a cascade of reactions does not follow the paths described (Figure 14.4). Rather the products are propagated by chemical pools generated from various precursors. The pools result from three defined pools of precursor parents: sugars, amino acids, and Amadori/Heyns products. In addition to the interaction between pool constituents, each parental compound can fragment as they interact in the “pool.” Depending on the condition, the initial pool will direct the final product

FIGURE 14.4  Pool theory of the Maillard reaction.

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into polymers, dimers, heterocycles ore “other compounds.” As the reactions propagate, each pool can easily interact and within or between pools, generate new Maillard reaction products. While making it difficult to predict these products, it is a more applicable description vs. the pathway model.

14.4  ADVANCED GLYCATION END PRODUCTS For some time, there have been concerns about the loss of nutritional value of foods or even potential toxicity due to the formation of Maillard products. The loss of lysine as a nutritional source in processed foods and a decrease in protein function, solubility, and digestibility after the reaction with Maillard products are examples of the role of these reactants in food processing. In addition to changes in the nutritional state of foods due to the Maillard reaction, there are biological health concerns with some of the side reactions between Maillard intermediates and proteins or lipids. Advanced glycation end products (AGEs) are the result of side reactions from the Amadori product and α-dicarbonyl compound reaction with proteins or lipids. In general, these products are formed by the reactions of reducing sugars or the degradation of biological macromolecules (carbohydrates, lipids, proteins) or ascorbic acid. The results are that proteins or lipids that have become modified (glycated) with carbohydrates in a non-ATP dependent process. Foods especially high in AGE products include those sterilized, subjected to ultra-high-temperature processing for pasteurization or roasting. Once absorbed, these compounds have been implicated as factors in a number of degenerative and aging-related diseases. Focusing on protein–AGE formation, the end products can result in either a protein– AGE adduct (mono or polysubstituted) or proteins crosslinked with AGE (protein–AGE– protein). In addition to late/advanced stage reactants, Amadori products also degrade into reactive carbonyls, which in turn react with amino groups forming AGE compounds. Most of the detected AGE products are modifications of primarily lysine or arginine side groups with a limited set of reactions known involving cysteine side groups (Lund and Ray, 2017). Production of AGE products via the Hodge pathway involving the degradation of Amadori or Hayns intermediates can happen in a single step producing a carboxylated methyllysine or other AGE products depending on the reducing sugar (Figure 14.5). Formation of reactive dicarbonyls and their condensation with amino groups of proteins via the Namiki pathway results in another suite of AGE products from glyoxal, methyl-glyoxal, and 2-deoxy-glucose intermediates. AGE products are based on a multitude of reactants with highly diverse and heterogeneous structures (Figure 14.6). One of the earliest AGE crosslinked proteins and highly prevalent in tissues and cooking is carboxymethl-lysine (CML) formed by the degradation of Amadori products or the addition of a reactive glyoxal to a protein’s lysine residue. Reactions initiated with ribose will result in pentosidine, a crosslinked AGE formed between an arginine and a lysine. The reaction is a ribose-Amadori product involving ascorbic acid via the Hodges pathway. The reactive dicarbonyl also can generate a number of AGE products. Glyoxal-lysine dimer (GOLD) is an imidazolium ion formed from the cyclic dimerization of two lysine side groups and glyoxal. A similar AGE product is produced when the crosslinked proteins start instead with methyglyoxyl and two lysine residues (MOLD). Similarly, dimers of lysine and arginine are formed with either glyoxal or methyglyoxal. The reaction between arginine and cysteine with glucose results in 8-hydroxy-5-methyldihydrothiazolo (3,2 alpha) pyridinium-3-carboxylate, also named Maillard reaction product X (MRX). MRX is found in both prepared food and

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FIGURE 14.5  Maillard production of AGE compounds.

in vivo on long-lived proteins and may be an important participant in the progression of diabetes. The MRX compound is likely formed from glucose with proteins and has been identified after cysteine and arginine mixtures were incubated with glucose. Foods high in fat and protein show the highest levels of AGE products after processing or cooking. Foods cooked in high fat and protein content including those prepared

FIGURE 14.6  Select AGE reactants and crosslinkers+.

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with mayonnaise, olive oils, or almonds all show significant AGE products (Nguyen, 2006). Common mono- and disaccharide glucose displays the slowest rate while fructose, ribose, and glucose-6-phosphate generate AGE crosslinked proteins at the fastest rate (Pasupulati et al., 2016). Foods high in AGE products at first were thought to be poorly absorbed, and the biomedical impact of AGE compounds focused on endogenously produced glycans. However, measurement of the health impact of a diet high in AGE products showed increased tissue AGE content, and ingesting foods high in AGE products increased the risk of heart, kidney, and other diseases including diabetes in mice (Gkogkolou and Bohm, 2012). Interestingly in diabetic animals, clearance of AGE products was reduced, indicating a longer transit time concomitant with increased risk of renal-vascular injury (Nguyun, 2006). Fortunately, a number of approaches have been formulated to create effective inhibitors of AGE compounds. Trapping the reactive alpha-dicarbonyls using epicatechin from green tea reduced off-flavors and AGE products in processed milk. A number of other phenolic flavonoids are under investigation for their trapping and radical scavenging ability in addition to other approaches limiting high heat processing times (Lund and Ray, 2017).

REFERENCES Ashoor, S.H. and J.B. Zent. 1984. Maillard browning of common amino acids and sugars. J. Food Sci. 49(4): 1206–7. Bertrand, E., P.E. Boustany, C.B. Faulds, and J.L. Berdague. 2018. The Maillard reaction in food: An introduction. In Reference Module in Food Science, pp. e1–10. Elsevier. Eskin, M.A. and F. Shahidi. 2013. Biochemistry of Foods, 3rd Ed. Elsevier. Gkogkolou, P. and M. Bohm. 2012. Advanced glycation end products. Key players in skin aging? Dermatoendocrinology 4(3): 259–70. Hellwig, M. and T. Henle. 2014. Baking, ageing, diabetes: A short history of the Maillard reaction. Angew. Chem. Int. Ed. 53(39): 10316–29. Hodge, L. and C.E. Rist. 1953. The amadori rearrangement under new conditions and its significance for non-enzymatic browning reactions. J. Am. Chem. Soc. 75(2): 316–22. Horn, M.J., H. Lichtenstein, and M. Womack. 1968. Availability of amino acids. A methionine-fructose compound and its availability to microorganisms and rats. J. Agric. Food Chem. 16(5): 741–5. Kim, J.S. and Y.S. Lee. 2009. Study of Maillard reaction products derived from aqueous model systems with different peptide chain lengths. Food Chem. 116(4): 846–53. Lund, M.N. and C.A. Ray. 2017. Control of Maillard reactions in foods: Strategies and chemical mechanisms. J. Agric. Food Chem. 65(23): 4537–52. Lu, L.C., C. Hao, R. Payne, and C.T. Ho. 2006. Effects of water content on volatile generation and peptide hydrolysis in Maillard reaction of glycine, diglycine and triglycine. J. Agric. Food Chem. 53: 6443–7. Maillard, L.C. 1911. Condensation des acides amines en presence de la glycerine: Cycloglycyglycine et polypeptides. C. R. Hebd. Seances Acad. Sci. 153: 1078–80. Maillard, L.-C. 1916. Syntheses des matieres humiquesa par action des acides amines sur les sucres reducteurs. Ann. Chim. 5(0): 258–316. Nguyen, C.V. 2006. Toxicity of the AGEs generated from the Maillard reaction: On the relationship of food-AGEs and biological-AGEs. Mol. Nutr. Food Res. 50: 1140–9.

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Nursten, H. 2005. The Maillard Reaction Chemistry, Biochemistry and Implications. Royal Society of Chemistry, Cambridge. Pastoriza, S., J. Quesada, and J.A. Rufian-Henares. 2018. Lactose and oligosaccharides: Maillard reaction. In Reference Module in Food Science, pp. e1–19. Elsevier. Pasupulati, A.K., P.S. Chitra, and G.B. Reddy. 2016. Advanced glycation end products mediated cellular and molecular events in the pathology of diabetic nephropathy. BioMol. Concepts 7(5–6): 293–309. Provost, J.J., K.L. Colabroy, B.S. Kelly, and M.A. Wallert. 2016. The Science of Cooking: Understanding the Biology and Chemistry Behind Food and Cooking, 1st Ed. John Wiley & Sons. Weenen, H. and W. Apeldoorn. 1996. Carbohydrate cleavage in the Maillard reaction. In Flavour Science: Recent Developments, A. J. Taylor and D. S. Mottram (eds.), Vol. 197, pp. 211–6. Royal Society of Chemistry, Cambridge. Yaylayan, V. 1997. Classification of the Maillard reaction: A conceptual approach. Trends Food Sci. Technol. 8(1): 13–8.

Chapter

15

Production of Food Aroma Compounds (Microbial and Enzymatic Methodologies) Lorena de Oliveira Felipe, Bruno Nicolau Paulino, Adones Sales, Gustavo Molina, and Juliano Lemos Bicas CONTENTS 15.1 Introduction 293 15.2 Microbial-Derived Aroma Compounds 294 15.2.1 Lactic Fermentation 295 15.2.2 Alcoholic Fermentation 297 15.2.3 Microbial Production of Aroma Additives (Bioaromas) 298 15.3 Enzyme-Derived Aroma Compounds 299 15.3.1 Enzymatic Aroma Generation during Processing 300 15.3.2 Enzyme Production of Aroma Additives (Bioaromas) 301 15.4 Final Remarks 302 References 302

15.1 INTRODUCTION Aromas are part of our daily lives, as well as the history of humanity. They were useful as a survival tool in prehistory, and nowadays they affect us on a physical, psychological, and social level (Classen et al., 2002). The presence of aroma in foods, for example, is an important characteristic related to quality that contributes to memories that influence our preferences, mood, and even buying decisions (Schab and Cain, 1991; Beyts et al., 2017; Schoumacker et al., 2017; Wijk et al., 2018). The general perception during food consumption is formed by a set of physical and chemical sensations perceived by the senses. Texture, fluidity, or crispness, for example, are perceived by touch and hearing (Szczesniak, 2002). The palate identifies the taste (salty, sweet, bitter, sour, and umami), while smell is responsible for the perception of the aroma. The result of these interactions is called flavor (Auvray and Spence, 2008). As part of the flavor perception, aroma is the result of volatile compounds in foods. This sensation may result from one single compound, but it usually comes from the interaction between mixtures of volatiles and is also influenced by the nonvolatile constituents (Taylor and Linforth, 2003). From more than 7000 volatile compounds already identified in food, only 5% have an impact on food aroma. These key food odorants (KFOs) have been organized into generalists, intermediaries, and individualists, based on their distribution in food products (Dunkel et 293

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al., 2014). Aroma compounds, usually found in very low concentrations (i.e., in the ppm range or even lower), may originally be present in the food (i.e., volatiles from raw materials) or produced during food processing or storage. During processing, these compounds may be formed by enzymatic and non-enzymatic reactions or by fermentation processes (Longo and Sanromán, 2006). Moreover, aroma compounds may be added to foodstuffs (i.e., used as additives) to develop the sensory characteristics of these products (Chambers and Koppel, 2013). In this case, aroma additives might be isolated from nature, synthesized by chemical means, or produced biotechnologically (Gupta et al., 2015). Food preference, purchase, and consumption are influenced by aromas, which encourages the use of these compounds as additives in the food industry. In 2016, the global aroma market reached $4.08 billion, and it is expected to grow at a compound annual growth rate (CAGR) of 6.8% to reach $6.48 billion by 2023 (Statistics: MRC, 2017). Part of this market includes the so-called “biotech aromas” or “bioaromas,” which are biotechnologically produced using microorganisms (or other cells) or enzymes, either native or genetically modified, the process of which is summarized in Figure 15.1. The interest in such an approach to aroma production is mostly driven by technical, environmental, and commercial concerns, for example, milder reaction conditions, no need for toxic or polluting catalysts, and the higher economic value of the resulting products (Felipe et al., 2017). This chapter will explore key examples of aroma compounds formed during processing through the action of microorganisms (fermentation) or enzymes as well as the production of aroma additives via these two routes.

15.2  MICROBIAL-DERIVED AROMA COMPOUNDS Fermentation processes yield aroma compounds which are associated with the singular and well known sensorial perception attributed to fermented products, such as beers, cheeses, and wines. In addition, the biosynthesis of volatile compounds by microorganisms is the basis for the biotechnological production of the so-called bioaromas. Recognized as natural compounds, these food additives have gained attention in the food industry due to the increased interest in food attributes such as freshness, healthfulness, and naturalness (Román et al., 2017). Therefore, in the following paragraphs, the main aroma compounds produced during microbial fermentation will be discussed, with a focus on lactic and alcoholic fermentation, as well as some exemplification related to the bioaromas produced by microorganisms.

FIGURE 15.1  Stages of the biotechnological production of aroma.

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15.2.1 Lactic Fermentation Lactic fermentation allows for the production of different compounds that deliver the singular aromatic identity of some classic products, such as butter, cheese, and yogurt (Tamime, 2002). Lactic acid bacteria (LAB), especially Lactobacillus sp.; Leuconostoc sp., and Streptococcus sp., are involved in such a process. LAB are classified as homofermentative or heterofermentative (Gänzle, 2015) depending on the metabolic route used to produce energy. In homolactic fermentation, LAB such as Streptococcus sp. use the Embden–Meyerhof–Parnas pathway to convert glucose into lactic acid (C3H6O3) as a sole product (2 mols per glucose), besides yielding a “fresh” flavor (Martinez et al., 2013). On the other hand, in heterolatic fermentation, other LAB, such as Leuconostoc, use the Pentose–Phosphate pathway to convert glucose to produce, ethanol (C2H5OH) or acetic acid (C2H3O2), as well as lactic acid (1 mol per glucose) and carbon dioxide (CO2). Additionally, during heterolatic fermentation, the citrate is concomitantly metabolized with carbohydrates forming aroma compounds: besides the acetaldehyde, C4 carbonyl molecules, such as diacetyl (C4H6O2), acetoin (C4H8O2), and butanediol (C4H10O2), are also obtained (Figure 15.2) (Gänzle, 2015). Also produced from the citrate pathway, acetaldehyde may result from threonine by the action of threonine aldolase (Zouraria et al., 1992; Chen et al., 2017).

FIGURE 15.2  Formation of volatile compounds during lactic acid fermentation. (From

Lindsay, 2007.)

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Acetaldehyde (ethanol) is recognized as a character impact compound, associated with the fresh flavor in yogurt. In such products, milk fermentation is carried out by a mixed culture of Streptococcus thermophilus and Lactobacillus delbrueckii ssp. bulgaricus (Zouraria et al., 1992). At the end of the process, more than 90 different volatile compounds may be identified in this dairy product, such as acetone, diacetyl, ethanol, and acetaldehyde, the latter volatile being reported as the major compound. In yogurt, the odor threshold of acetaldehyde is in the range of 0.0079–0.039 mg kg−1. However, the ideal concentration for consumer acceptability should be between 14 and 20 mg kg−1 (Chen et al., 2017). Diacetyl (2,3-butanedione) is a key aroma compound in butter flavor. The aroma and taste threshold values for this compound are 5 ppb (recognition) and 50 ppm, respectively (Burdock, 2002). A mixed culture of Lactococcus lactis and Lactococcus diacetilactis is used to ferment milk cream to produce mature or acid butter. The former strain produces lactic acid; while the latter, which shows a pronounced ability to metabolize citrate, is responsible for diacetyl production. Proteolysis and lipolysis are considered key processes in ripened cheese production, such as blue cheeses (e.g., Bleu d’Auvergne, Cabrales, Danablu, Gorgonzola, Roquefort, and Stilton), which are characterized by the presence of methyl ketones. Briefly, the fermentation process for blue cheese production can be divided into two parts: primary fermentation, associated with the activity of LAB, and secondary fermentation (ripening) carried out by fungi, mainly Penicillium roqueforti (Martin and Cotton, 2016). The main metabolic pathway for methyl ketone production involves the action of lipases for liberating free fatty acids (FFA), which are further oxidized (β-oxidation), followed by the decarboxylation of the resulting β-keto acids. Later, some of these methyl ketones may be reduced to its correspondent alcohols (McSweeney and Sousa, 2000) (Figure 15.3). Some of the non-converted FFA, such as butanoic (C4H8O2) and caproic acid (C6H12O2), are also important for the aroma of blue cheese (McSweeney and Sousa, 2000; Ardö, 2011). Other cheese aromas formed by enzyme action will be discussed in Section 15.3.1. Many studies have discussed the main parameters that affect the aroma composition of dry fermented sausages, showing the role of microorganisms such as bacteria, yeasts and molds, and endogenous enzymes in the different pathways involved in flavor development, including carbohydrate metabolism, and the degradation of free amino acids and fatty acids (Flores et al., 2015; Flores and Olivares, 2015). The LAB present in fermented meat products are related to carbohydrate fermentation, which is used to produce lactic

FIGURE 15.3  Formation of aroma compounds (short-chain fatty acids and methyl

ketones) in blue cheese.

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acid and volatile compounds associated with the aroma of these products, including acetaldehyde, diacetyl, as well as short-chain alcohols and organic acids (Ravyts et al., 2012). In this context, lipases and proteinases are considered as the key enzymes associated with the production by microorganisms from starter cultures of the typical aroma in fermented meat products (Molly et al., 1997; Cocolin et al., 2006; Franciosa et al., 2018). Section 15.3.1 also presents other aroma compounds formed by enzyme action in meat. 15.2.2 Alcoholic Fermentation The main commercially relevant alcoholic fermentations employ yeasts of the Saccharomyces genus, particularly S. cerevisiae, which produces carbon dioxide (CO2) and ethanol (C2H6O) as final products of glucose metabolism. In addition to contributing to the alcoholic aroma note associated to fermented beverages, ethanol is also regarded as a carrier agent of other aroma active compounds, such as “fusel alcohol” (higher alcohols), organic acids, and esters, which are simultaneously produced during the fermentation process. Together, these odor-active compounds are essential for establishing the unique aroma of the final product (Hazelwood et al., 2008). Regarding higher alcohol production, these compounds result from amino acid metabolism via the Ehrlich pathway. In this case, the amino acids (either from the substrate or produced de novo by the microorganism) are first transformed into the corresponding α-ketoacids via transamination, followed by its decarboxylation to the corresponding aldehyde, which undergoes reduction reactions to yield higher alcohols (Pires et al., 2014). Thus, it is expected that the quantity and quality of higher alcohols obtained will be correlated to the concentration of the amino acids originally present or supplemented in the fermentative substrate. These higher alcohols contribute to different notes in the final product, such as floral, fruity, herbaceous, pungent, and rose notes (Hazelwood et al., 2008; Pires et al., 2014). Thereafter, the alcohols initially formed (e.g., ethanol and higher alcohols) can be esterified to organic acids from yeast metabolism via enzymatic condensation. This results in the production of esters, which are volatiles associated with fruity notes. For instance, different esters can be identified (Table 15.1) and most of them are reported as important odor-active compounds in beer, mainly due to their low threshold values (Hazelwood et al., 2008; Pires et al., 2014). On the other hand, the diacetyl production is a big concern to the beer industry, since this product results in buttery notes (off-flavor) in the final product (Krogerus and Gibson, 2013).

TABLE 15.1  Main Aroma Esters in Beer Compound

Chemical Formula

Ethyl acetate 1 Ethyl hexanoate 1 Ethyl octanoate 1 Isoamyl acetate 1 Isobutyl acetate 2 2-phenylethyl acetate 1

C4H8O2 C8H16O2 C10H20O2 C7H14O2 C6H12O2 C10H12O2

Aroma Description “Fruity,” “solvent-like” 3 “Apple-like” 4 “Apple” 4 “Banana” 3 “Fruity” 3 “Honey,” “roses,” “flowery” 3

Sources: Adapted from 1 Cordente et al., 2012; 2 Pires et al., 2014. Note: 3 Acetate ester; 4 medium-chain fatty acid (MCFA) ethyl ester.

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TABLE 15.2  Some Examples of Secondary Aroma Compounds in Wine Chemical Group Alcohols Aldehydes

Ketone

Key Examples 1-Hexanol (C6H14O) 1-Octen-3-ol (C8H16O) Acetaldehyde (C2H4O) Hexanal (C6H12O) Octanal (C8H16O) β-Damascenone (C13H18O)

Aroma Description Grass 1 Mushroom 2 “Green” aroma 3 “Citrus” fruit 3 “Baked apple” 1

Sources: Adapted from 1 Escudero et al., 2007; 2 Genovese et al., 2007; 3 Culleré et al., 2011.

In terms of wine, its aroma profile results from the complex combination of primary, secondary, and tertiary aromas. The first refers to volatiles (mostly terpenes) that are naturally present in different grape varieties and, in general, are associated with floral notes (Styger et al., 2011). Fermentation and ripening are responsible for secondary and tertiary aroma production, respectively. In general, the secondary aromas (see some examples in Table 15.2) are produced in an analogue way, as explained at the beginning of this section. Besides alcoholic fermentation, malolactic fermentation also takes place in winemaking. This fermentation reduces wine acidity by converting malic acid into lactic acid using LAB. In such a process, butter notes can be introduced as a result of lactic fermentation (Betteridge et al., 2015), as already cited in Section 15.2.1. Moreover, other volatiles may be formed by the action of microorganisms. Englezos et al. (2018a), for instance, evaluated the volatile profile of red wine produced using mixed fermentations carried out with Starmerella bacillaris and S. cerevisiae using four red grape varieties and two different inoculation protocols. The results suggested a higher activity or expression of β-glycosidase enzymes by the non-Saccharomyces yeast as observed by the release of monoterpenes (e.g., citronellol, geraniol, linalool, and C13 -norisoprenoid β-damascenone) from the glycoside precursors present in grapes. The enzymatic formation of these compounds is very important due to their significant contribution to the fruity and floral aroma of wine (Pons et al., 2017) (some of the examples of enzyme aroma formation in wine will be presented in Section 15.3.1). The result of different combinations of these compounds results in a huge variety of wines, which are distinguished by their unique “bouquet” (Cappello et al., 2017). 15.2.3 Microbial Production of Aroma Additives (Bioaromas) As mentioned in Section 15.1, aroma compounds may be recovered from nature, produced by chemical synthesis, or by biotechnological means. The compounds extracted directly from natural matrices are subjected to seasonality effects, besides usually being associated with low yields and productivities (very long production times). In contrast, chemical synthesis is associated with high yields and low costs, but the by-products eventually formed in the reactions may negatively affect the sensorial perception of the final product. Moreover, most chemical processes are regarded as non-environmentally friendly processes. Aroma compounds obtained by biotechnological means, on the other hand, may be labeled as “natural” and are produced in environmentally friendly

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TABLE 15.3  Some Aroma Compounds Commercially Produced by Biotechnological Means Compound

Route

Company

Farnesene Nootkatone Patchouli Vanillin

De novo synthesis Biotransformation De novo synthesis Biotransformation De novo synthesis De novo synthesis

Amyris Evolva, Oxford Biotrans, Isobionics Amyris, Firmenich Solvay IFF, Evolva Evolva

α-Santalol

Source: Adapted from Sales et al., 2018.

conditions, besides being independent of seasonal and climatic conditions. The biggest challenge of this process is to overcome the low yields that are usually achieved (Bicas et  al., 2016; Felipe et al., 2017). The aroma compounds produced by microorganisms can be done in two different ways: by de novo synthesis, where the target molecule(s) is(are) synthesized from simple substrates following complete metabolic routes; or by a biotransformation process, where a substrate is converted to a homologous molecule in one (biotransformation) or a few (bioconversion) catalytic steps. Considering the specificity usually attributed to biological reactions, it is even possible to obtain enantiomerically pure compounds via such bioprocesses (Bicas et al., 2016, 2009). Another important motivation for producing “biotech” aromas is related to their market value: although usually cheaper than their counterparts extracted from nature, the bioaromas are more valued than their synthetic analogue (Felipe et al., 2017). Consequently, different companies currently have industrial-scale production processes for the “biotec” aromas, such as the examples shown in Table 15.3 (Sales et al., 2018). In this context, it is worth mentioning the growing importance of synthetic biology for the large-scale microbial production of bioaromas (Meadows et al., 2016). In this sense, scientists are currently mimicking nature to obtain aroma compounds of interest to the consumer: by manipulating microorganisms in order to reproduce the metabolic routes found in plants, the genetically modified strain starts producing the target molecules originally present in the chosen fruit or vegetable. Moreover, genetic modifications may also be used to overcome some of the challenges associated with large-scale production processes. Strategies such as the reduction of substrate toxicity and the increase in yields have been considered. Thus, some genetically modified microorganisms (e.g., Schizosaccharomyces pombe, Saccharomyces cerevisiae, and Escherichia coli) are already being employed on an industrial scale for the commercial production of important flavors and fragrances materials (e.g., vanillin, patchouli, and ambroxide, respectively) (Felipe et al., 2017).

15.3  ENZYME-DERIVED AROMA COMPOUNDS Besides being employed in the food and beverage industries for a wide range of applications (Singh and Kumar, 2019), enzymes such as lipases, proteases, peroxidases, lipoxygenases, amine oxidase, among others, are related with aroma (and off-flavor) production in food products. Enzymatic aroma production can take place in situ during the processing and storage of foods, that is, through the action of enzymes endogenously present, added, or produced in the food, or in vitro, where the synthesized products are further

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used as food additives (Imafidon and Spanier, 1994; Furuya et al., 2017; Sales et al., 2018). These two topics will be discussed in the next section. 15.3.1 Enzymatic Aroma Generation during Processing Enzyme-modified cheeses (EMC) are food products in the form of pastes or powders containing concentrated cheese flavors resulting from the action of several enzymes, such as peptidases and lipase, on mild cheese or fresh cheese curd (Kilcawley et al., 1998; Azarnia et al., 2010; Miri and Najafi, 2011). The action of peptidases is associated with the production of free amino acids, which are important for taste and are the precursors of aroma compounds (Nuñez, 2016). Moreover, the liberation of free fatty acids from triacylglycerols through the action of lipolytic enzymes is also essential not only to their direct impact on aroma (short-chain fatty acids) but also for being precursors of different volatile organic compounds (Collins et al., 2003). After oxidation reactions, for instance, free fatty acids can be converted into β-ketoacids, which are transformed into methyl ketone when decarboxylated, in a similar sequence of reactions already cited in Section 15.2.1 for blue cheese (Figure 15.3). The catabolism of free fatty acids can also produce secondary alcohols, lactones, and acids with different contributions to the aroma of cheeses (McSweeney and Sousa, 2000; Collins et al., 2003). Moreover, γ- and δ-hydroxylated free fatty acids produced by the action of lipoxygenase and hydratase enzymes are precursors of lactones (Dirinck and DeWinne, 1999; Singh et al., 2003). Other important precursors of aroma compounds in EMC, such as α-keto acids, may also be formed from amino acids by transamination reactions mediated by aminotransferase (Bertuzzi et al., 2018). These compounds can be converted into aroma compounds, including branchedchain and aromatic aldehydes, and methanethiol (Ganesan and Weimer, 2017). Some aldehydes, such as 2-methylpropanal, 2-methylbutanal, and 3-methylbutanal, are formed by the transamination of valine, isoleucine, and leucine, respectively, while the formation of acetoin, diacetyl, or 2,3-butanediol is related to the oxaloacetate formed after the transamination of aspartic acid (Singh et al., 2003; Ardö, 2006; Le Bars and Yvon, 2008). In meat products, the production of aroma compounds is related to several reactions during processing, including the enzymatic degradation (proteolysis, glycolysis, and lipolysis) of some constituents, which also has a positive impact on the production of nonvolatile compounds associated with meat flavor (Toldrá and Flores, 2000; Khan et al., 2015). Moreover, endogenous enzymes act during the postmortem aging of meat to tenderize it as well as to generate flavor precursors (free amino acids), which further react with other degradation products and yield volatile compounds related with meat aroma (Khan et al., 2015). The application of glycosidases for wine aroma enhancement has also been considered. Recent examples include the application of extracellular glycosidase from yeast for wine aroma enhancement (Hu et al., 2016; Ma et al., 2017; Sun et al., 2018), including the use of extracellular β-glucosidase from Issatchenkia terricola to significantly increase the amount of monoterpenes and norisoprenoids during the fermentation (González-Pombo et al., 2011). Moreover, the action of β-lyases produced by yeast during winemaking may also transform nonvolatile precursors, such as cysteine and glutathione conjugates, on volatile thiols, such as 4-mercapto-4-methyl-2-pentanone (4MMP), 3-mercapto-1-hexanol (3MH), and acetate 3-mercaptohexyl acetate (3MHA), which contribute positively to fruit notes in wine aroma (Murat et al., 2001; Englezos et al., 2018b).

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15.3.2 Enzyme Production of Aroma Additives (Bioaromas) The application of isolated enzymes in aroma synthesis is a well-studied process and several compounds can be produced using this approach. The use of lipases to generate different flavor esters (e.g., esters of glycerol, aliphatic alcohols, and terpene alcohols), usually associated with fruity notes, is the classic example. Besides catalyzing hydrolysis reactions, which are key reactions for the formation of cheese aroma (see Sections 15.2.1 and 15.3.1), lipases may also catalyze esterification and interesterification reactions under certain circumstances (e.g., low water activity), leading to ester production (Akacha and Gargouri, 2015). The enzymatic synthesis of butyl stearate and ethyl stearate from stearic acid and the respective alcohols (n-butanol and ethanol, for instance, has been reported using Novozym 435 (lipase) as a catalyst (Pereira et al., 2018). The production of ethyl caprylate from caprylic acid and ethanol using free or immobilized lipase and cyclo-octane as the solvent is another recent example of such a process (Patel et al., 2015). During recent years, new approaches have been considered for developing ester flavor production using lipases. These include the adoption of immobilization techniques (physical adsorption, entrapment or microencapsulation, and covalent binding) to stabilize the enzyme; the use of unconventional solvents (supercritical CO2) in the reaction media; and the application of microwave irradiation techniques (Yadav and Thorat, 2012; Lisboa et al., 2018), such as the examples given in the following paragraphs. The production of n-butyl propionate through the esterification of propionic acid with n-butanol, for instance, was reported using different immobilized enzymes (Lypozyme RM IM, Lypozyme TLIM, and Novozym 435) under microwave irradiation to reduce the reaction time (Bhavsar and Yadav, 2018a). Similarly, this enzyme also showed the best performance in the synthesis of ethyl valerate from valeric acid and ethanol as precursors, exhibiting a synergistic effect when microwave irradiation was applied (Bhavsar and Yadav, 2018b). The microwave-assisted synthesis of isoamyl acetate was also described using two commercial lipases and different acyl donors, such as acetic acid, acetic anhydride, and ethyl acetate, as well as the microwave energy levels (Zare et al., 2019). Moreover, the use of supercritical CO2 as a solvent is another approach being considered for lipase-catalyzed ester aroma production, such as isoamyl acetate, eugenyl acetate, isobutyl acetate, isoamyl propionate, isopropyl propionate, isoamyl laureate, geranyl acetate, and others (Santos et al., 2016, 2017; Dias et al., 2018a,b). Another use of lipases is for kinetic resolution of racemic mixtures of different aroma compounds (Todea et al., 2018). Considering that different enantiomers exhibit a singular odor, the application of enzymes for the kinetic resolution of racemic mixtures stands out as an interesting strategy for obtaining enantiomerically pure aroma compounds for the food, pharmaceutical, and cosmetic industries (Jadhav et al., 2016), such as the examples presented next. 2-Phenylethanol presents a rose-like odor, while 1-phenylethanol is reported as the predominant aroma compound found in tea flowers (Hua and Xu, 2011; Zhou et al., 2018). The enzymatic resolution of racemic 1-phenylethyl acetate to (S)-1-phenylethanol (enantiomeric excess of 97% and conversion of 28.5%) has been achieved using a novel marine microbial GDSL lipase, which exhibited opposite stereo-selectivity than other common lipases in both transesterification reactions and hydrolysis reactions (Deng et al., 2016). The application of partially purified cutinase from Fusarium ICT SAC1 was also described in the kinetic resolution of (R,S)-1-phenylethanol using microwave irradiation to increase conversion efficiency and enantioselectivity (Kamble et al., 2017). Although the use of different enzymes is well established for aroma production, further studies are needed, especially focusing on

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the production of new flavor compounds of industrial interest and on discovering new enzymes that are more adapted to the processing conditions.

15.4  FINAL REMARKS Aroma compounds have historically been produced in food by empirical processing, such as in fermented dairy and meat products. Only in recent decades could scientists achieve greater understanding of the mechanisms behind the origins of these compounds. Some of the main examples of aromas formed in these traditional fermentations were presented in this chapter. This chapter also presented key examples of aroma compounds produced by microorganisms or enzymes which are already a commercial reality, some of them based on synthetic biology. Therefore, it is expected that genetically engineered microorganisms or enzymes will still be increasingly employed to develop a better aroma profile in traditional fermented products as well as to produce new aroma compounds by biotransformations or de novo synthesis.

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Chapter

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Novel and Emerging Technologies (Benefits and Limitations) Mohammad Hassan Kamani, Hanieh Amani, Amir Yeganehshakib, and Amin Mousavi Khaneghah CONTENTS 16.1 Introduction 307 16.2 Novel Thermal Processing Techniques (Definition, Principles, and Applications) 308 16.2.1 Ohmic Heating 309 16.2.1.1 Advantages and Disadvantages of Ohmic Heating 309 16.2.2 Microwave Heating 310 16.2.2.1 Advantages and Disadvantages of Microwave Heating 313 16.2.3 Infrared (IR) Heating 314 16.2.3.1 Advantages and Disadvantages of IR Heating 315 16.2.4 Radio Frequency (RF) Heating 315 16.2.4.1 Advantages and Disadvantages of RF Heating 316 16.3 Novel Non-Thermal Technologies (Definition, Principles, and Applications) 317 16.3.1 Pulsed Electric Field (PEF) 318 16.3.1.1 Advantages and Disadvantages of PEF 320 16.3.2 High-Pressure (HP) Technology 320 16.3.2.1 Advantages and Disadvantages of HP 324 16.3.3 Ultrasound (US) Technology 324 16.3.3.1 Advantages and Disadvantages of US 327 References 327

16.1 INTRODUCTION The processing of food in order to preserve perishable foods from spoilage goes back to prehistoric times. The need to improve food quality gradually increased over time, and more technologies were developed and introduced to the food industry (Lund, 2003). The primary goal of all these technologies is to make desirable changes to the food matrix. These changes involve various reactions such as microbial/enzyme inactivation, protein coagulation, starch swelling, textural softening, and the formation of aroma components. However, each technology has its own limitations and may also cause some undesirable changes concerning nutritional and sensory properties (Fellows, 2009; Sun, 2014). Sensorial characteristics are well known for their importance to consumer acceptability (Sun, 2014). Among sensory parameters, aroma plays a distinct role since its profile 307

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determines the flavor of the food that we consume (Parker et al., 2014). The aroma may directly or indirectly be affected by processing methods. This effect may be positive or negative, depending on the type of product or the technique used (Caminiti et al., 2011). This issue has recently encouraged food researchers to devote efforts to implementing new techniques to minimize the adverse effect of processing (Czechowska et al., 2005; Sun, 2014). In this respect, various novel methods have been introduced in recent years which are generally classified into thermal and non-thermal. The latter has gained more attention due to its higher performance and lower adverse effects on sensory and nutritional qualities (Pereira and Vicente, 2010; Caminiti et al., 2011; Surowsky et al., 2014). This chapter aims to provide an overview of the principles, applications, and summarized impacts of each method on the aroma and flavor of food products. In addition, in order to give better insight into the merits of each method, the benefits and limitations are provided.

16.2  NOVEL THERMAL PROCESSING TECHNIQUES (DEFINITION, PRINCIPLES, AND APPLICATIONS) Thermal processing is the most commonly used technological method, which provides the required microbiological safety for food products. This technique relies on the generation of heat outside the food and its transfer inside by convection and conduction mechanisms. The conventional examples of this method are baking, roasting, steaming, blanching, boiling, pasteurization, sterilization, extrusion cooking, and different kinds of drying techniques (e.g., sun, osmotic, oven, drum, or spray drying) (Richardson, 2001; Rawson et al., 2011). These methods have some limitations that hinder their application to a wide range of foods. For instance, they might not be able to adequately transfer heat to the center of semi-solid or solid foods due to the low thermal diffusivities of the materials. In addition, lengthy exposures to high-temperature heat may lead to a quality degradation of the food matrix (Jiao et al., 2018). The methods mentioned above might also be associated with heat-induced reactions involving changes of flavor, aromatic compounds, and final sensorial quality of the food (Richardson, 2001). From a flavor point of view, some of these major reactions are (a) thermal degradation of lipids; (b) dephosphorylation of proteins; (c) hydrolysis of peptide bonds; (d) Maillard reactions; and (e) interaction of lipid oxidation and Maillard reaction products (Cadwallader and Singh, 2009; He et al., 2013). Some of these reactions/ impacts are desirable in the processing of foods like bread, cereal, coffee, nuts, malt, and cooked meat due to generation of the specific desired flavor. However, the same impact, in particular under severe conditions, is considered to be undesirable concerning organoleptic attributes for foods like fruit juices, milk, and dairy products (Richardson, 2001; Perez and Rouseff, 2008; Cadwallader and Singh, 2009). Table 16.1 shows examples of the negative impacts of thermal pasteurization on the flavor/aroma quality of some food products. To tackle this issue, interest has been growing over the past decade to design thermal techniques that minimize adverse effects and ensure the retention of sensory and nutritional quality. The prime examples of these technologies include, but are not restricted to, ohmic, microwave, infrared, and radiofrequency heating. They may be used alone or combined with increased efficiency. The basic idea residing behind all of these processing methods is the mode of heat transfer in a food matrix, which is discussed in detail in each respective section of this chapter (Rawson et al., 2011; Jiao et al., 2018). These new

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TABLE 16.1  The Negative Effect of Conventional Thermal Methods on the Flavor/Aroma of Processed Foods Product Reduced-calorie carrot juice Mixed fruit juices (orange/apple blend) Fruit smoothies (apple, orange, strawberry, and banana) Longan juice Milk

Passion fruit juice

Processing Conditions Pasteurization (65°C for 30 min) Mild pasteurization (70°C for 0.5–1.5 min) Pasteurization (85°C for 7 min) Pasteurization (100°C for 1 min) Thermal processing, in particular, at high temperature (UHT) Pasteurization (75°C for 1 min)

Effect on Flavor/ Aroma

Reference

Unacceptable cooked flavor Reduced odor score to marginal

(Sinchaipanit et al., 2013) (Bukvicki et al., 2014)

Development of a cooked-fruit flavor

(Hurtado et al., 2015)

The loss in flavor compound Generation of various cooked, sulfurous and cabbage-like off-flavor Damaged aroma, flavor and color

(Zhang et al., 2010) (Cadwallader and Singh, 2009; Eskin and Shahidi, 2012) (Sandi et al., 2003)

technologies allow the production of high-quality food products with improved heating and energy efficiency (Pereira and Vicente, 2010; Kaur and Singh, 2016). 16.2.1 Ohmic Heating Ohmic heating takes its name from Ohm’s law, which describes the relationship between current, voltage, and resistance (Icier and Ilicali, 2005). It is also known as electrical resistance heating, Joule heating, electro-heating, direct electrical resistance heating, and electro-conductive heating (Rawson et al., 2011; Cullen et al., 2012). This technology is basically a thermal–electrical method, which is based on the passage of an electrical current through a food product that has an electrical resistance (Figure 16.1). In other words, the electrical energy is converted to heat energy within the food (Icier and Ilicali, 2005; Cullen et al., 2012). It is an instant heating method and has been suggested to be more uniform than other electro-heating techniques (Sarang et al., 2008; Rawson et al., 2011). This technology can mainly be used for liquids and multiphase liquid–solid mixtures, particularly for media that are difficult to process using conventional heat exchangers (Cullen et al., 2012). In addition, its usage for solid foods like whole turkey meat, beef, and ham has also been documented (Pongviratchai and Park, 2007; Zell et al., 2010, 2009, 2012). Thus far, a large number of potential food applications have been introduced for ohmic heating, including evaporation, extraction, sterilization, pasteurization, fermentation, blanching, and dehydration (Knirsch et al., 2010). However, only a few studies evaluated the effect of this technology on food aroma, flavor, and odor, which are represented in Table 16.2. 16.2.1.1 Advantages and Disadvantages of Ohmic Heating Ohmic heating has significant advantages over conventional thermal methods. These advantages include (a) shorter treatment time with minimal heat damage and nutrient

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FIGURE 16.1  Basic scheme of ohmic heating system. (From Cappato et al., 2017.)

loss; (b) high temperature with high‌ uniformity; (c) possibility of use for foods with low thermal conductivity; (d) suitability for continuous processing without heat transfer surfaces; (e) better energy efficiency with low maintenance costs; (f) easier precise control of temperature compared to conventional heating methods; (g) no residual heat transfer after shut off of the current; (h) higher quality and fresher tasting; (i) reduced fouling; and (j) its being an environmentally friendly system (Pereira and Vicente, 2010; Sakr and Liu, 2014; Wen, 2014). On the contrary to the mentioned advantages, some disadvantages have also been reported, which are (a) higher cost of installation and operating system as compared to conventional methods; (b) insufficient efficacy for the foods containing fat globules, which act as a non-conductive substance; (c) requested adjustment according to the conductivity of the liquid product; (d) narrow frequency band; and (e) complex coupling between temperature and electrical field distribution (Sakr and Liu, 2014; Kaur and Singh, 2016). 16.2.2 Microwave Heating Microwave heating is another thermal processing method that has gained considerable attention in the food industry in recent years (Vadivambal and Jayas, 2010). Microwaves are electromagnetic waves within a frequency band of 300 MHz to 300 GHz (Regier et al., 2017). When microwaves impinge on a dielectric food, part of the energy is reflected, part is transmitted, and part is absorbed by the material, where it is dissipated as heat. Heating is due to an increase in molecular kinetic energy and associated molecular friction (Meda et al., 2017). In other words, this technology is based on the transformation of alternating electromagnetic field energy into thermal energy by affecting the polar molecules of the food material (Venkatesh and Raghavan, 2004; Vadivambal and Jayas, 2010). It has the potential to deliver heat instantly throughout the product due to volumetric heat generation (Vadivambal and Jayas, 2010). Volumetric heat generation means that materials can absorb microwave energy internally and convert it into heat. Thus far, many food applications have been suggested for this useful method including cooking, baking, pre-heating, dehydration/drying, thawing, sterilization, enzyme inactivation, pasteurization, sterilization, blanching, tempering, and extraction of bioactive compounds (Venkatesh and Raghavan, 2004; Puligundla et al., 2013; Orsat et al., 2017).

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TABLE 16.2  The Effect of Novel Thermal Technologies (Ohmic, Microwave, Infrared, and Radio Frequency Heating) on the Flavor/Aroma of Processed Foods Technology

Processing Conditions

Product

Ohmic heating

70°C/10 min/ 17.5–20 V

Ohmic heating

80°C/50 Hz/10 and 33 V cm−1

Ohmic heating

120°C/>20 min/50 Hz/8 kV

Orange juice

Ohmic heating

HTST (6 min) to a target temperature of 95°C with a total residence time of 8 min; LTLT (5 min ohmic heating to 70°C, 8 min holding)/0–250 V, 50 Hz Microwave–hot air drying (40–70°C/40 W)

Cooked ham

Microwave heating

Juice blend (pumpkin, carrot, orange, celery, grapefruit) Concentrated juice (orange and pineapple)

Garlic cloves

Microwave heating

Microwave roasting (730 W/10 min)

Cumin seed

Microwave heating Microwave heating Microwave heating

Pasteurization of milk (80–92°C/15 s/532 W) Sterilization (915 MHz/60 kW) Blanching (128°C/11 min/5 kW, 915 MHz)

Milk Skim milk Peanut

Effect on Aroma, Flavor, and Odor

References

No negative influence on flavor

(Dima et al., 2015)

No significant effect on the flavor quality of juices Improved aroma volatile concentrations Lesser flavor and aroma scores as compared to the conventional method

(Tumpanuvatr and Jittanit, 2012)

Higher retention of the volatile components responsible for flavor strength Better retention of characteristic flavor compounds like total aldehydes in microwaveroasted samples No adverse effect on flavor Improved flavor quality The occurrence of off-flavors (stale/ floral and ashy off‐flavors) observed

(Leizerson and Shimoni, 2005) (Zell et al., 2012)

(Sharma and Prasad, 2001)

(Behera et al., 2004)

(Valero et al., 2000) (Clare et al., 2005) (Schirack et al., 2006)

(Continued )

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TABLE 16.2  (Continued) The Effect of Novel Thermal Technologies (Ohmic, Microwave, Infrared, and Radio Frequency Heating) on the Flavor/Aroma of Processed Foods Technology

Processing Conditions

Microwave heating

Drying in a three-step process: (a) heating for 6 min (Leaf temperature 91°C), followed by 1 h cooling; (b) heating for 4 min leaf temperature 106°C), followed by 30 min cooling; and (c) heated for 2 min (leaf temperature 115°C)/700 W Roasting (160 to 220°C/6 kW/1.1 to 1.3 μm wavelength of radiation)

IR

IR

IR

Processing of leaves by Far‐Infrared (FIR) 250°C/15 to 25 min and additional 300°C for 5 min/6.1 W/cm 2/  4.14 µm Roasting (130– 150°C/5000 W/m2)

Product

Higher sweet aroma; Promoted content of total catechins and theaflavins; helpful to maintenance polyphenols and volatile compounds

(Qu et al., 2019)

Sesame seed

Good flavor at 160 to 200°C/ Detection of burned flavor above 200°C Enhanced aroma of leaves in all samples

(Kumar et al., 2009)

No negative influence on flavor Prevent the formation of particular flavors and aromas Removal of spicy flavor (pungent) by inactivation of the myrosinase enzyme Significant change in volatile flavor profiles No significant adverse effect on flavor quality Slightly uncooked flavor observed

(Yang et al., 2010)

Green tea leaves

Almond

Thawing by Far-IR (170–230°C for 30–60 min)

Frozen chestnut

RF

Heating (112°C/13.5 MHz)

White mustard

RF

Pre-heating for pest control (48–52°C/0–20 min/12 kW) Sterilization (up to 135°C/27 MHz 6/kW)

Orange

Macaroni and cheese

Post-drying (8–9 min/27.12 MHz/2 kW)

Partially baked cookies

RF

References

Black tea

IR

RF

Effect on Aroma, Flavor, and Odor

(Park et al., 2009)

(Hee, 2006)

(Ildikó et al., 2006)

(Birla et al., 2005) (Wang et al., 2003) (Koray Palazoğlu et al., 2012) (Continued )

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TABLE 16.2  (Continued) The Effect of Novel Thermal Technologies (Ohmic, Microwave, Infrared, and Radio Frequency Heating) on the Flavor/Aroma of Processed Foods Technology

Processing Conditions

RF

Sterilization (6 kW, 27.12 MHz/20 min)

RF

Cooking (73°C for 40 min)

RF

Pasteurization (75– 85°C/10 min/27.12 MHz /600 W)

Product Vacuumpackaged Caixin (Brassica campestris L.) Leg and shoulder hams

Vacuum-packed ham

Effect on Aroma, Flavor, and Odor

References

No significant effect on the flavor quality

(Liu et al., 2015)

No significant change in the flavor quality but distinguished overall acceptability observed No significant change in the odor

(Zhang et al., 2006)

(Orsat et al., 2004)

In conventional methods, energy is transferred due to thermal gradients, but microwave heating is the conversion transfer of electromagnetic energy to thermal energy through direct interaction of the incident radiation with the molecules of the target material. The difference in the way energy is delivered can result in many potential advantages (Venkatesh and Raghavan, 2004). For instance, in the microwave, heat is generated throughout the material and leads to a faster heating rate as compared to conventional cooking. This may result in the prevention of severe damage to the quality attributes, such as color, flavor, and nutrients of the product (Vadivambal and Jayas, 2010). In addition, microwaves penetrate a food product and do not act at the surface level. This conversion of energy into heat throughout the product is more efficient and results in a better final quality in processed food (Contreras et al., 2017). The output of various studies regarding the quality improvement of flavor/aroma is presented in Table 16.2. Due to these major advantages, microwave technology has become very popular for both the industrial and domestic sectors. 16.2.2.1 Advantages and Disadvantages of Microwave Heating Apart from the rapidness of the microwave method, there are other quantitative and qualitative advantages for this heating method, which are (a) shorter start-up time; (b) greater energy efficiency; (c) precise equipment control; (d) space saving; (e) ease of operation; (f) lower maintenance; (g) lower noise levels; (h) improved nutritional quality of processed food; and (i) better retention of taste and color qualities (Richardson, 2001; Vadivambal and Jayas, 2010; Contreras et al., 2017; Meda et al., 2017; Yolacaner et al., 2017). Moreover, microwave heating is very advantageous when it is used for the extraction of bioactive compounds. Reduced nutrient losses and extraction time, improved extraction yield, rapid and volumetric heating of the absorbing medium, low solvent consumption, and higher selectivity of target molecules are the examples of benefits of this method when it is used for extraction purposes (Orsat et al., 2017). Although microwave technology has considerable advantages for the processing of food, serious drawbacks have been observed. A major disadvantage is non-uniform

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temperature distribution, which leads to the occurrence of cold and hot spots in the heated food. This may result in poor quality in the end product (Vadivambal and Jayas, 2010; Dorantes-Alvarez et al., 2017). This issue may also lead to the incomplete destruction of microbes, which may cause a microbial safety issue in the cold spots (Vadivambal and Jayas, 2010). In addition, non-uniform microwave heating may cause tough and leathery crumbs of bakery products due to the occurrence of different patterns of starch transformation (Yolacaner et al., 2017). Another weakness of microwave technology is the short baking time when used for bakery products. Bakery products generally need a long time to complete their physicochemical reactions, while microwave heating has a short overall period, and therefore the final quality of microwave-baked products is not desirable. Rapidly generated gas and steam, insufficient gelatinization, and changes in gluten structure have been reported for microwave-baked bread as well (Yolacaner et al., 2017). Another issue with microwave heating is the large number of factors that affect the microwave heat transfer behavior such as the thickness, the geometry, and the dielectric properties of the food (Vadivambal and Jayas, 2010). The high penetration power of microwave energy affects heat transfer behavior and may cause overheating of products leading to scorching, depending on the nature and geometry of the material, dielectric properties, and oven design (Ekezie et al., 2017). Flavors generated as a result of browning reactions do not exist in microwave-baked products. During microwave baking, individual flavor components are subjected to losses through distillation, flavor binding by starches and proteins, and chemical degradation. Many of the nutty, brown, and caramel-type aromas observed in conventional cakes were lacking in microwave-baked cakes. Also, during the microwave baking of cakes, undesirable flavors such as flour or egg-like flavors develop. In order to overcome these issues in bakery products, different solutions, including the use of flavoring agents and the combination of microwave heating with other methods like hot air, infrared heating, or steam have been proposed (Yolacaner et al., 2017). 16.2.3 Infrared (IR) Heating Infrared radiation is a type of energy in the form of an electromagnetic spectrum, which arises from the movement of electrons in the atoms and molecules inside the exposed food matrix (Cullen et al., 2012; Rastogi, 2012). IR radiation can fall into three regions depending on its wavelength. These regions are (a) the near-infrared (NIR; 0.78–1.4 µm); (b) middle-infrared (MIR; 1.4–3 µm); and (c) far-infrared (FIR; 3–1,000 µm) (Krishnamurthy et al., 2008; Riadh et al., 2015). IR heating has been found to be more effective when compared to a conventional heating method, and therefore numerous processing applications have been suggested for this alternative method. The primary applications of IR include blanching (e.g., carrots and leaves), thawing (e.g., frozen tuna or frozen potato purée), drying (e.g., pasta, vegetables, fish, and fruit), heating (e.g., flour), frying (e.g., meat), roasting (e.g., cereals, coffee, cocoa, chestnuts, hazelnuts, and sesame seeds), and baking (e.g., pizza, biscuits, and bread) (Richardson, 2001; Hebbar et al., 2004; Cullen et al., 2012; Rastogi, 2012). IR can also be used for pasteurization and sterilization by inactivating pathogens in foods as well as packaging materials (Richardson, 2001; Krishnamurthy et al., 2008). It can inactivate microorganisms by damaging intracellular components such as DNA, RNA, ribosomes, cell envelopes, and/or proteins in the cell (Cullen et al., 2012).

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16.2.3.1 Advantages and Disadvantages of IR Heating IR radiation is generally considered an ideal source of energy for the heating processes of foods due to its basic characteristics such as high heat transfer capacity, direct heat penetration into the food product, fast regulation response, and better possibilities for process control (Richardson, 2001; Cullen et al., 2012; Riadh et al., 2015). Food heated using an IR treatment can be rapidly cooled, since the infrared radiation mainly heats a thin layer of the surface, and consequently provides a less negative change in the quality of the final product (Cullen et al., 2012). In recent years, IR technology has gained significant superiority over traditional thermal methods owing to its remarkable benefits. Some of these major advantages are listed in Table 16.3 (Krishnamurthy et al., 2008; Cullen et al., 2012; Riadh et al., 2015). Moreover, IR energy in the form of FIR is easily absorbed by the main components of foods, that is, organic materials and water. This greatly helps in efficiently pasteurizing food materials since water readily absorbs radiation in the IR region and this results in rapid temperature increase (Cullen et al., 2012). Using IR technology for dehydration purposes is also very beneficial. IR heating allows for a high rate of water evaporation without quality losses such as surface hardening, deformation/ shrinkage, and better quality characteristics concerning the loss of ascorbic acid, color, and aroma of dried foods (Pan et al., 2008; Riadh et al., 2015). Although IR provides many benefits for food processing, it has a few disadvantages. The major drawback of IR technology is its low penetration power. Therefore, it cannot deeply penetrate and contribute to the heating of high-volume samples. Due to this limitation, IR heating is mostly used where a surface-heating application is required (Cullen et al., 2012). The other limitations of the IR method are provided in Table 16.3 (Krishnamurthy et al., 2008). 16.2.4 Radio Frequency (RF) Heating Among all electro-technologies, radio frequency is considered a unique technique for food processing (Piyasena et al., 2003; Jiao et al., 2018). RF heating (also called capacitative dielectric heating) involves the transfer of electromagnetic energy directly into the product, which results in frictional interaction between molecules (Piyasena et al., 2003). TABLE 16.3  The Major Advantages and Disadvantages of Infrared Heating Technology Advantages Reduced heating time Uniform heating Equipment compactness The absence of solute migration in food material The lesser quality loss during processing Preservation of vitamins Higher energy efficiency and saving space Intermittent energy source Cleaner operational environment Lower operational costs and versatility

Disadvantages Fracturing the food materials due to prolonged exposure Low penetration power

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In other words, in this technique, heat is generated within the food materials by molecular friction using a high frequency alternating electrical energy (Awuah et al., 2014). The principles of RF heating are very similar to microwave heating. However, the main difference between these two is the wavelength. The wavelength of RF is designated to be 22 to 360 times as great as microwave frequencies, which allows RF energy to penetrate dielectric materials more deeply than microwaves (Wang et al., 2003). For this reason, RF heating is known as a high-frequency dielectric heating system (Piyasena et al., 2003). The RF band of the electromagnetic spectrum covers a broad range of high frequencies, typically either in the kHz range (3 kHz < f ≤ 1 MHz) or MHz range (1 MHz