Sensory Analysis for Food and Beverage Quality Control: A Practical Guide (Woodhead Publishing Series in Food Science, Technology and Nutrition)

Sensory analysis for food and beverage quality control

© Woodhead Publishing Limited, 2010

Related titles Chemical deterioration and physical instability of food and beverages (ISBN 978-1-84569-495-1) For a food product to be a success in the marketplace, it must be stable throughout its shelf-life. Changes due to food chemical deterioration and physical instability are not always recognised by food producers – who are more familiar with microbial spoilage – yet can be just as problematic. This book provides an authoritative review of key topics in this area. Chapters in Parts I and II focus on the chemical reactions and physical changes which negatively affect food quality. The remaining chapters outline the likely effects on different food products, for example baked goods, fruit and vegetables and beverages. Understanding and measuring the shelf-life of food (ISBN 978-1-85573-732-7) The shelf-life of a product is critical in determining both its quality and profitability. This book reviews the key factors in determining shelf-life and how it can be measured. Part I examines the factors affecting shelf-life and spoilage, including individual chapters on the major types of food spoilage, the role of moisture and temperature, spoilage yeasts, the Maillard reaction and the factors underlying lipid oxidation. Part II addresses the best ways of measuring the shelf-life of foods, with chapters on modelling food spoilage, measuring and modelling glass transition, detecting spoilage yeasts, measuring lipid oxidation, the design and validation of shelf-life tests and the use of accelerated shelf-life tests. Rapid and on-line instrumentation for food quality assurance (ISBN 978-1-85573-674-0) With its high volumes of production, the food industry has an urgent need for instrumentation that can be used on-line and which produces rapid measurements. As a result, there has been a wealth of research in this field, which is summarised in this important collection. The first part looks at techniques for the rapid detection of contaminants such as pesticides, veterinary drug residues and foreign bodies. The second part considers methods for identifying ingredients, including additives and measuring product quality. Details of these books and a complete list of Woodhead titles can be obtained by: • visiting our web site at www.woodheadpublishing.com • contacting Customer Services (e-mail: [email protected]; fax: +44 (0) 1223 893694; tel.: +44 (0) 1223 891358 ext. 130; address: Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK)

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Woodhead Publishing Series in Food Science, Technology and Nutrition: Number 191

Sensory analysis for food and beverage quality control A practical guide Edited by David Kilcast

Oxford

Cambridge

New Delhi

© Woodhead Publishing Limited, 2010

Published by Woodhead Publishing Limited, Abington Hall, Granta Park, Great Abington, Cambridge CB21 6AH, UK www.woodheadpublishing.com Woodhead Publishing India Private Limited, G-2, Vardaan House, 7/28 Ansari Road, Daryaganj New Delhi – 110002, India www.woodheadpublishingindia.com Published in North America by CRC Press LLC, 6000 Broken Sound Parkway, NW, Suite 300, Boca Raton, FL 33487, USA First published 2010, Woodhead Publishing Limited and CRC Press LLC © Woodhead Publishing Limited, 2010 The authors have asserted their moral rights. This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. Reasonable efforts have been made to publish reliable data and information, but the authors and the publishers cannot assume responsibility for the validity of all materials. Neither the authors nor the publishers, nor anyone else associated with this publication, shall be liable for any loss, damage or liability directly or indirectly caused or alleged to be caused by this book. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming and recording, or by any information storage or retrieval system, without permission in writing from Woodhead Publishing Limited. The consent of Woodhead Publishing Limited does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific permission must be obtained in writing from Woodhead Publishing Limited for such copying. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation, without intent to infringe. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Library of Congress Cataloging in Publication Data A catalog record for this book is available from the Library of Congress. Woodhead Publishing ISBN 978-1-84569-476-0 (book) Woodhead Publishing ISBN 978-1-84569-951-2 (e-book) CRC Press ISBN 978-1-4398-3142-7 CRC Press order number: N10199 The publishers’ policy is to use permanent paper from mills that operate a sustainable forestry policy, and which has been manufactured from pulp which is processed using acid-free and elemental chlorine-free practices. Furthermore, the publishers ensure that the text paper and cover board used have met acceptable environmental accreditation standards. Typeset by Toppan Best-set Premedia Limited, Hong Kong Printed by TJ International Limited, Padstow, Cornwall, UK

© Woodhead Publishing Limited, 2010

Contents

Contributor contact details......................................................................... xi Woodhead Publishing Series in Food Science, Technology and Nutrition ............................................................................................... xv Preface.......................................................................................................... xxiii

Part I 1

2

Designing a sensory quality control program ..........................

1

Designing a sensory quality control program................................. M. A. Everitt, ME Consultancy Ltd, UK 1.1 Introduction ............................................................................ 1.2 Company culture and commitment to quality ................... 1.3 Establishing a sensory quality control (QC) program ...... 1.4 Key elements of a sensory quality control (QC) program ................................................................................... 1.5 Overview of approaches used to define sensory targets... 1.6 External support and consultancy ....................................... 1.7 References ...............................................................................

3

Selection and management of staff for sensory quality control ..................................................................................... E. De Vos, Tate & Lyle Food and Industrial Ingredients, EMEA, France 2.1 Introduction ............................................................................ 2.2 Personnel required for sensory quality control ................. 2.3 Setting up a quality control (QC) panel .............................

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3 6 7 8 12 15 16

17

17 18 20

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Contents 2.4 2.5 2.6 2.7 2.8 2.9

3

Maintaining the quality control (QC) panel: performance, motivation and size ........................................ Possible issues ......................................................................... Case study: selection and management of staff for sensory quality control of cereal-based ingredients .......... Future trends .......................................................................... Sources of further information and advice ......................... References ...............................................................................

28 30 31 34 34 35

Proficiency testing of sensory panels ............................................... G. Hyldig, Technical University of Denmark, Denmark 3.1 Introduction ............................................................................ 3.2 Design and implementation of proficiency testing ............ 3.3 Panels ....................................................................................... 3.4 Analysis of data/validation of results .................................. 3.5 Panel performance ................................................................. 3.6 Glossary ................................................................................... 3.7 References and further reading ...........................................

37 38 43 44 45 46 46

Part II Methods for sensory quality control and analysis of results .......................................................................................

49

4

5

Sensory methods for quality control ............................................... L. L. Rogers, Consultant, UK 4.1 Introduction ............................................................................ 4.2 Descriptive specifications (DS) method.............................. 4.3 ‘In/out’ (or pass/fail) method................................................ 4.4 Difference from control (DFC) method ............................. 4.5 ‘A’ not ‘A’ method .................................................................. 4.6 Paired comparison methods (e.g. 2AFC, n-AFC, simple difference test) ........................................................... 4.7 Scaling method (including targeted scaling) ...................... 4.8 Ranking test ............................................................................ 4.9 Triangle test ............................................................................. 4.10 Quality scoring/grading/rating method ............................... 4.11 Magnitude estimation and duo–trio methods .................... 4.12 In-house and do-it-yourself (DIY) methods ...................... 4.13 References ............................................................................... Establishing product sensory specifications .................................... C. J. M. Beeren, Leatherhead Food Research, UK 5.1 Introduction ............................................................................ 5.2 Rationale using sensory specifications ................................ 5.3 Defining sensory specifications.............................................

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51 51 55 60 62 65 66 67 69 70 70 72 73 74 75 75 78 78

Contents 5.4 5.5 5.6 5.7 5.8 6

7

Reference samples ................................................................. Implementation of sensory specifications ........................... Maintenance and follow-up .................................................. Case study ............................................................................... References ...............................................................................

Combining instrumental and sensory methods in food quality control ..................................................................................... D. Kilcast, Consultant, Food and Beverage Sensory Quality, UK 6.1 Introduction: the perceptual basis of food quality ............ 6.2 The role of instrumental measurement ............................... 6.3 Sensory analysis of quality .................................................... 6.4 Instrumental measurement of quality factors .................... 6.5 Analysis and validation of instrumental measurements ... 6.6 Future trends .......................................................................... 6.7 Sources of further information ............................................. 6.8 References ...............................................................................

vii 83 84 93 94 96

97 97 98 99 101 105 113 115 115

Statistical approaches to sensory quality control ........................... C. Findlay, Compusense Inc., Canada and A. Hasted, QI Statistics, UK 7.1 Introduction ............................................................................ 7.2 Statistics defined ..................................................................... 7.3 Managing risk ......................................................................... 7.4 Knowing your product........................................................... 7.5 Methods of measurement and practical examples ............ 7.6 Practical considerations ......................................................... 7.7 Assessor proficiency and validation..................................... 7.8 Sensory instrumental correlations ....................................... 7.9 Product matching ................................................................... 7.10 Conclusions ............................................................................. 7.11 References and further reading ...........................................

118

Part III Sensory quality control in practice .........................................

141

8

Using sensory techniques for shelf-life assessment ....................... L. L. Rogers, Consultant, UK 8.1 Introduction ............................................................................ 8.2 What is shelf-life? ................................................................... 8.3 Setting or confirming shelf-life? ........................................... 8.4 The case study: Setting up shelf-life confirmation studies for an ambient product ............................................ 8.5 References and further reading ...........................................

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143 143 144 147 148 155

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10

11

12

Contents Sensory quality control for taint prevention .................................. D. Kilcast, Consultant, Food and Beverage Sensory Quality, UK 9.1 Introduction ............................................................................ 9.2 Chemistry of taint .................................................................. 9.3 Sources of taints ..................................................................... 9.4 Detection and analysis of taints ........................................... 9.5 Sensory testing procedures ................................................... 9.6 Diagnostic taint testing .......................................................... 9.7 Taint prevention ..................................................................... 9.8 The role of sensory quality control (QC) in taint prevention ...................................................................... 9.9 Ethical aspects ........................................................................ 9.10 Case studies ............................................................................. 9.11 Future trends .......................................................................... 9.12 Sources of further information ............................................. 9.13 References and further reading ........................................... Sensory quality definition of food ingredients ............................... A. Van Biesen, C. Petit and E. Vanzeveren, Puratos N.V., Belgium 10.1 Introduction ............................................................................ 10.2 Developing good quality ingredients in a consumer-oriented approach ................................................ 10.3 Case study 1: What’s your texture? ..................................... 10.4 Case study 2: A toast bread for Chinese consumers ......... 10.5 References ............................................................................... Sensory quality control in the chilled and frozen ready meal, soup and sauce sectors ....................................................................... M. Swainson and L. McWatt, University of Lincoln, UK 11.1 Introduction ............................................................................ 11.2 Sensory quality assurance (QA) in the recipe development process.............................................................. 11.3 Sensory quality assurance (QA) in the post-development product scale-up phase.......................... 11.4 Sensory quality assurance (QA) in the production process ................................................................. 11.5 Sensory quality assurance (QA) after product despatch .................................................................... 11.6 Conflicts of interest ................................................................ 11.7 Conclusions ............................................................................. 11.8 Acknowledgements ................................................................ 11.9 Sources of further information ............................................. Sensory quality control in the wine industry .................................. S. A. Langstaff, Applied Sensory, LLC, USA 12.1 Introduction ............................................................................ 12.2 Historical perspective ............................................................

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156 156 159 160 164 165 173 175 178 179 181 183 184 184 186 186 186 188 193 201 203 203 204 206 209 232 233 233 234 234 236 236 237

Contents 12.3 12.4 12.5 12.6 12.7 12.8 12.9 12.10 12.11 12.12 12.13 13

14

15

European standards of wine quality .................................... The concept of wine quality ................................................. Attempts to standardize wine quality evaluation .............. Wine and the development of sensory evaluation as a science ................................................................................... Factors affecting wine quality ............................................... Levels of wine quality............................................................ Approaches to determining wine quality............................ Current sensory quality control practices in winemaking ......................................................................... Future of sensory evaluation in the wine industry ............ Sources of further information ............................................. References ...............................................................................

ix 238 239 242 245 246 248 248 249 257 259 260

Sensory quality control of distilled beverages ................................ J. R. Piggott, University of Strathclyde, UK and S. Macleod, John Dewar and Sons Ltd, UK 13.1 Introduction ............................................................................ 13.2 Origins of sensory quality control of spirits ....................... 13.3 Procedures and precautions .................................................. 13.4 Current industry practices..................................................... 13.5 Taints and off-flavours ........................................................... 13.6 Sources of further information ............................................. 13.7 References ...............................................................................

262

Sensory quality control of fresh produce ........................................ E. Costell, I. Carbonell, A. Tárrega and S. Bayarri, Instituto de Agroquímica y Tecnología de Alimentos, CSIC, Spain 14.1 Introduction ............................................................................ 14.2 The role of sensory analysis in quality control of fruit and vegetables ........................................................................ 14.3 A case study: Influence of storage temperature on the sensory quality of apples ....................................................... 14.4 Acknowledgements ................................................................ 14.5 References ...............................................................................

276

Sensory quality management of fish ................................................ E. Martinsdóttir, Matís – Icelandic Food Research, Iceland 15.1 Introduction: quality indices for fish.................................... 15.2 Guidelines for sensory evaluation of fish ........................... 15.3 Sensory evaluation of fish ..................................................... 15.4 Developing a quality index ................................................... 15.5 Using quality indices in storage management and production planning ...............................................................

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276 277 280 290 290 293 293 295 296 303 305

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Contents 15.6 15.7 15.8 15.9

16

17

Keeping fish under different storage conditions ................ Future trends .......................................................................... Acknowledgements ................................................................ References ...............................................................................

306 307 310 310

Sensory quality control in foodservice ............................................ P. G. Creed, formerly of Bournemouth University, UK 16.1 Introduction ............................................................................ 16.2 Aspects of sensory analysis in foodservice ......................... 16.3 Formal methods applicable to foodservice ......................... 16.4 Informal methods applicable to foodservice ...................... 16.5 Sensory quality control in foodservice – a case study ...... 16.6 Future trends .......................................................................... 16.7 Sources of further information and advice ......................... 16.8 References ...............................................................................

316

Sensory quality control of consumer goods other than food ....... A. Giboreau, Institut Paul Bocuse, France 17.1 Introduction ............................................................................ 17.2 General recommendations .................................................... 17.3 The control of sensory quality of non-food products: cases ........................................................................ 17.4 Conclusion ............................................................................... 17.5 Future trends .......................................................................... 17.6 Sources of further information ............................................. 17.7 References ...............................................................................

337

Appendix: Going forward – Implementing a sensory quality control program .................................................................... M. A. Everitt, ME Consultancy Ltd, UK A.1 Piloting the program .............................................................. A.2 Refinement and consolidation .............................................. A.3 Quality assurance (QA) ........................................................ A.4 The effectiveness of a sensory quality control (QC) program ......................................................................... A.5 Maintaining the effectiveness of a sensory quality control/quality assurance (QC/QA) program .................... A.6 Continuous improvement...................................................... Index .............................................................................................................

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316 317 322 326 329 329 330 331

337 339 342 349 349 350 350

353 353 353 354 354 356 357 358

Contributor contact details

(* = main contact) Editor and Chapters 6 and 9

Chapter 2

D. Kilcast Consultant, Food & Beverage Sensory Quality

E. De Vos Tate & Lyle Food and Industrial Ingredients, EMEA Parc Scientifique de la Haute Borne 2, Avenue de l’Horizon 59650 Villeneuve d’Ascq France

E-mail: [email protected]

Chapter 1 and Appendix M. A. Everitt ME Consultancy Ltd Sunrise Nurseries Gretton Fields Cheltenham GL54 5HJ UK

E-mail: [email protected]

Chapter 3

E-mail: [email protected]

G. Hyldig National Food Institute (DTU Food) Technical University of Denmark Søltofts Plads, Building 221 DK-2800 Kgs. Lyngby Denmark E-mail: [email protected]

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xii

Contributor contact details

Chapters 4 and 8

Chapter 11

L. L. Rogers Consultant UK

M. Swainson* and L. McWatt Department of Food Manufacture and Process Automation University of Lincoln Minerva House Park Road Holbeach PE12 7PT UK

E-mail: lauren.l.rogers@googlemail. com

Chapter 5 C. J. M. Beeren Sensory & Consumer Science Leatherhead Food Research Randalls Road Leatherhead KT22 7RY UK E-mail: CBeeren@leatherheadfood. com

Chapter 7

E-mail: [email protected]

Chapter 12 S. A. Langstaff Applied Sensory LLC 5055 Business Center Dr. #108–126 Fairfield CA 94534 USA E-mail: [email protected]

C. Findlay Compusense Inc. 679 Southgate Drive Guelph, ON Canada N1G 4S2

Chapter 13

E-mail: [email protected]

Chapter 10 A. Van Biesen*, C. Petit and E. Vanzeveren R&D Department Puratos N.V. Zone Maalbeek Industrialaan, 25 1702 Groot-Bijgaarden Belgium E-mail: [email protected]

J. R. Piggott* University of Strathclyde The Strathclyde Institute of Pharmacy and Biomedical Sciences 204 George Street Glasgow G1 1XW UK E-mail: [email protected] S. Macleod John Dewar and Sons Ltd 1700 London Road Glasgow G32 8XR UK E-mail: [email protected]

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Contributor contact details Chapter 14

Chapter 16

E. Costell*, I. Carbonell, A. Tárrega and S. Bayarri Instituto de Agroquímica y Tecnología de Alimentos CSIC PO Box 73 46100 Burjassot Valencia Spain

P. G. Creed 2 Haxen Cottages Allowenshay Hinton St George TA17 8TB UK E-mail: [email protected]

Chapter 17 E-mail: [email protected]

Chapter 15 E. Martinsdóttir Matís ohf Vinlandsleid 12 113 Reykjavik Iceland

A. Giboreau Institut Paul Bocuse Château du vivier BP 25 69130 Ecully France E-mail: agnes.giboreau@ institutpaulbocuse.com

E-mail: emilia.martinsdottir@matis. is

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Improving the health-promoting properties of fruit and vegetable products Edited by F. A. Tomás-Barberán and M. I. Gil Improving seafood products for the consumer Edited by T. Børresen In-pack processed foods: improving quality Edited by P. Richardson Handbook of water and energy management in food processing Edited by J. Klemeš, R. Smith and J-K Kim Environmentally compatible food packaging Edited by E. Chiellini Improving farmed fish quality and safety Edited by Ø. Lie Carbohydrate-active enzymes Edited by K-H Park Chilled foods: a comprehensive guide Third edition Edited by M. Brown Food for the ageing population Edited by M. M. Raats, C. P. G. M. de Groot and W. A. Van Staveren Improving the sensory and nutritional quality of fresh meat Edited by J. P. Kerry and D. A. Ledward Shellfish safety and quality Edited by S. E. Shumway and G. E. Rodrick Functional and speciality beverage technology Edited by P. Paquin Functional foods: principles and technology M. Guo Endocrine-disrupting chemicals in food Edited by I. Shaw Meals in science and practice: interdisciplinary research and business applications Edited by H. L. Meiselman Food constituents and oral health: current status and future prospects Edited by M. Wilson Handbook of hydrocolloids Second edition Edited by G. O. Phillips and P. A. Williams Food processing technology: principles and practice Third edition P. J. Fellows Science and technology of enrobed and filled chocolate, confectionery and bakery products Edited by G. Talbot Foodborne pathogens: hazards, risk analysis and control Second edition Edited by C. de W. Blackburn and P. J. McClure Designing functional foods: measuring and controlling food structure breakdown and absorption Edited by D. J. McClements and E. A. Decker New technologies in aquaculture: improving production efficiency, quality and environmental management Edited by G. Burnell and G. Allan More baking problems solved S. P. Cauvain and L. S. Young Soft drink and fruit juice problems solved P. Ashurst and R. Hargitt Biofilms in the food and beverage industries Edited by P. M. Fratamico, B. A. Annous and N. W. Gunther Dairy-derived ingredients: food and neutraceutical uses Edited by M. Corredig Handbook of waste management and co-product recovery in food processing Volume 2 Edited by K. W. Waldron Innovations in food labelling Edited by J. Albert Delivering performance in food supply chains Edited by C. Mena and G. Stevens Chemical deterioration and physical instability of food and beverages Edited by L. Skibsted, J. Risbo and M. Andersen Managing wine quality Volume 1: viticulture and wine quality Edited by A. Reynolds

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Improving the safety and quality of milk Volume 1: milk production and processing Edited by M. Griffiths 189 Improving the safety and quality of milk Volume 2: improving quality in milk products Edited by M. Griffiths 190 Cereal grains: assessing and managing quality Edited by C. Wrigley and I. Batey 191 Sensory analysis for food and beverage quality control: a practical guide Edited by D. Kilcast 192 Managing wine quality Volume 2: oenology and wine quality Edited by A. Reynolds 193 Winemaking problems solved Edited by C. Butzke 194 Environmental assessment and management in the food industry Edited by U. Sonesson, J. Berlin and F. Ziegler 195 Consumer-driven innovation in food and personal care products Edited by S. Jaeger and H. MacFie 196 Tracing pathogens in the food chain Edited by S. Brul, P.M. Fratamico and T.A. McMeekin 197 Case studies in novel food processing technologies Edited by C. Doona, K. Kustin and F. Feeherry 198 Freeze-drying of pharmaceutical and food products Tse-Chao Hua, Bao-Lin Liu and Hua Zhang 199 Oxidation in foods and beverages and antioxidant applications: Volume 1 understanding mechanisms of oxidation and antioxidant activity Eric A. Decker, Ryan J. Elias and D. Julian McClements 200 Oxidation in foods and beverages and antioxidant applications: Volume 2 management in different industry sectors Eric A. Decker, Ryan J. Elias and D. Julian McClements 201 Protective cultures, antimicrobial metabolites and bacteriophages of food and beverage biopreservation Edited by Christophe Lacroix

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Preface

The last 20–30 years have seen many significant developments in the use of sensory analysis in both the food and beverages industries, and also in many non-food industries. This growth has been driven by many varied factors, among which can be listed: the growing (and in some sectors, belated) recognition that quality perceptions can be measured by humans; acceptance by regulators that quality standards must incorporate a strong element of human perception; wider ranges of test procedures, together with a better understanding of their advantages and limitations; development of both statistical and non-statistical methods for data analysis; and an explosion of interest in perceptual mechanisms at physicochemical, psychological and neurological levels. As a consequence, researchers with any interest in perceived sensory quality can draw on an enormous range of techniques. This has given the companies in the manufacturing sector the power to add more relevant focus to development programs and higher probabilities of meeting consumer requirements. Paradoxically, however, the increased opportunities available in the use of sensory techniques has been paralleled by moves throughout industry to increase production automation and to reduce employee numbers correspondingly. This has left many companies with real difficulties in implementing valid sensory testing systems in circumstances when staff numbers and time availability are at a premium. This situation can generally be managed within the larger companies that are able to support the required sensory facilities and staff skills, and medium/small companies with a need for sensory evaluation as part of their R&D functions can frequently call on contract laboratories for their needs. Specific problems can often arise when companies incorporate sensory evaluation into their quality control (QC) operations, however. Suppliers

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both of ingredients and finished products will commonly find that, whilst their needs for sensory evaluation in R&D can be contracted out, it is much more difficult to outsource sensory QC testing, for which there is a need for regular and rapid examination of product quality, together with consequential action plans, and which can in most cases only be carried out on-site. Demands for routine sensory QC come from many directions, in particular from national regulatory bodies, through accreditation procedures and from customer demands higher up the production chain. Consequently, all companies face the need to have in place some form of in-house sensory QC testing, even when sensory panels for R&D are not seen as essential. For such companies, which can range up through all sizes, the main difficulty to be faced lies in translating the vast amount of information in the scientific literature on setting up, operating and interpreting sensory tests into practical sensory QC systems that can be managed effectively within their own business, and delivering their objectives. One unfortunate consequence of the barriers facing companies in implementing reliable sensory QC testing is the belief that instrumental measurements can be used to replace sensory panels. Whilst there are many circumstances in which instrumental measurements of quality parameters are invaluable in complementing sensory information, a position must be found for appropriate sensory procedures. Small businesses that are unable to operate sensory protocols that conform to best practice must have as a minimum some form of sensory testing in place, coupled with an understanding of the limitations inherent in such non-ideal procedures. One important difference between the use of sensory evaluation in R&D and in QC environments is that whereas a high level of commonality of testing is possible across R&D functions in different industry sectors, sensory procedures in QC functions are adapted to a large degree to fit the requirements and operating constraints of individual companies. This results in highly disparate sensory procedures becoming established across the industry, in spite of the increasing use of standardisation. Valuable lessons can therefore be learned from the experiences of sensory specialists in a wide range of product sectors, and this book aims to record such experiences. The experiences within the food and beverage sectors have in recent years been exported to a wide range of non-food industries, and such nonfood applications are included in this volume. No reliable sensory QC program can be successful without adequate planning, and the first section of this book deals with the key aspects in designing a sensory QC program, and including the selection and management of staff for sensory QC, and important recent developments in the proficiency testing of sensory panels. The second part of the book covers sensory methods and data analysis, and together with an overview of the available methods includes how the setting up of sensory specifications can be approached, and how instrumen-

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tal measurements can be used to complement sensory data. Issues associated with the statistical analysis of sensory data are covered in Chapter 7. In the third part of the book, the practical uses of sensory QC in a wide range of food and beverage applications are described, including specific applications to shelf-life assessment and to taint prevention. Finally, recent developments in the uses of sensory QC in non-food industries are described. Most companies have their own needs and constraints when setting up and operating sensory QC panels, and the aim of this book is to provide any company with some direction in establishing the basis for a reliable sensory QC program that meets the demands of a competitive business environment, and that will be able to adapt to changing future demands. David Kilcast

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1 Designing a sensory quality control program M. A. Everitt, ME Consultancy Ltd, UK

Abstract: This chapter provides an overview of some of the main areas that need due consideration when planning to establish a sensory quality control program. Particular attention is given to the importance of management commitment, sensory specifications and common failings, approaches used to set sensory targets, and training regime. Key words: sensory specifications, consumer focus, sensory training program, quality grading.

1.1 Introduction Gaining and maintaining a quality advantage have become primary competitive issues within the food and drink industry. As the range within sectors plus the overall standard of food available to consumers continues to increase so has the need for businesses to review their policies towards quality versus production volume. Defining product quality and setting the parameters by which it is measured and controlled has been and remains a major challenge with regard to the sensory element of the product specification. The approach taken by a company in the way it defines and subsequently measures quality can have a major influence on the degree to which a sensory program is incorporated into the overall quality monitoring system. Gavin (1984) discusses five classical approaches that can be used to define product quality: 1. Transcendent approach based on philosophy which states that quality is recognised only through experience and cannot be precisely defined. 2. Product-based approach founded on economics which views quality as a precise and measurable property relating to variation in the amount of a specified characteristic or ingredient.

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3. Manufacturing-based operations management approach which judges quality primarily by the level of conformance with a defined specification; any deviation is seen as a reduction in quality. 4. Value-based operations management approach which uses cost and price to define quality; a quality product would provide performance at an acceptable price or conformance at an acceptable cost. 5. User-based approach combines economics, marketing and operations management principles with the focus being on consumer satisfaction; a quality product would achieve greatest satisfaction for a specified group of consumers. A sensory quality control (QC) program will be most readily accepted and effectively incorporated into businesses that take the last approach. This approach also helps companies maintain a realistic balance between product quality, production volume and cost. A consumer-focused route is therefore advocated as most effective for setting up the sensory component of a QC program although other approaches based more on the use of internal business expertise are discussed.

1.1.1 Principle and objective of a sensory QC program The main aims of QC are to ensure: • • • • •

legal requirements are met; the product is safe and fit for use; there is compliance with nutritional guidelines and tolerances; the agreed/declared weight is provided; deviation from expected quality is kept within an acceptable tolerance.

The last area tends to be the most difficult to define and measure within a food or beverage program as the criteria typically used to judge ‘expected quality’ will primarily relate to a product’s sensory aspects (i.e. appearance, flavour and, texture) which are more prone to subjective interpretation due to personal perceptions and preferences. This can lead to the sensory part of a product quality specification being inadequately or in some instances inappropriately defined owing to insufficient appreciation of the sensory features that have most affect on the target consumers’ perception of quality.

1.1.2 Common failings in defining the sensory specification Visual characteristics Because visual characteristics are the most ‘tangible’ of all the sensory attributes these tend to receive the most attention with physical features (e.g. depth of a sponge layer, number of nuts on top of a Dundee cake)

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often taking precedence over the true sensory attributes such as depth and uniformity of colour, degree of clarity and brightness. Flavour, texture and mouth-feel characteristics Descriptions relating to these modalities are often written in vague, generalised terms (e.g. ‘typical flavour’, ‘freshness of flavour’, ‘standard texture’) which can lead to varied assumptions being made across assessors about the interpretation. Even when individual attributes are listed, issues can still arise, especially if the author of the specification is not familiar with sensory evaluation principles and the need for precise, unambiguous terminology. Terms can remain bundled and confusing (e.g. confectionery flavour, ripe flavour) or be too technical for general application (e.g. diacetyl instead of ‘buttery’, dimethyl sulphide instead of ‘cabbagey’, 2,4,6-trichloroanisole instead of ‘musty’). Relevance and priority of sensory attributes To aid initial focus when defining the specification, the sensory quality attributes can be considered as falling into two main groupings: those which relate to the key characteristics that affect consumers’ acceptance of a product and those which are of internal importance to a business in order for it to maintain cost-effective control of the production process and avoid unnecessary wastage of materials. The relevance of the attributes under the ‘internal’ listing does not ordinarily pose an issue as the association within a business of a defined term with a specific processing factor or raw material is usually readily understood and its need accepted. Visual defects and also off-flavour characteristics are typically most relevant in the ‘internal’ attribute list. Particular visual defects can, for example, indicate the need to reset peeling, cutting or slicing equipment which if left unchecked would lead to increased waste of raw material. The monitoring of specific off-flavours can aid suitable control of factors such as processing temperatures and flow rates, water conservation, fats and oil quality, etc. Failings are more common regarding specification of the characteristics that relate to consumer acceptance. Attributes that are of no particular importance are often specified whilst those that are key to consumers’ degree of liking can be given low priority or completely overlooked. To ensure that the most appropriate attributes are specified and avoid the risk of subjective influences, the key features that relate to a target market’s expectations of the sensory quality need to be understood by those responsible for setting and applying the specification. The most reliable way to achieve this is for the information gained from consumer and sensory research conducted during a products development or re-development to be communicated to the Quality and Production teams before routine production commences. Unfortunately a common failing is that this information is not transferred, even after the running of detailed and lengthy

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product development programs. This increases the risk of the sensory quality of routinely produced product deviating from its quality and hence eroding any quality advantage originally established at launch. Attribute weighting In addition to defining an appropriate set of sensory characteristics priorities can also be given to specific attributes to aid the most effective and efficient use of the specification. Again if a consumer-focused approach has been used to determine the main sensory criteria this will also enable weightings of importance to be designated more objectively for each and identify which are the most critical to control.

1.2

Company culture and commitment to quality

Without question companies that have established the most effective and successful sensory QC programs are those that have full management support from senior levels down, and view the control of sensory quality as an essential part of the overall QC system. These companies are commonly consumer-focused with a commitment to understanding customer/ consumer needs and achieving their total satisfaction, along with an aim for continuous improvement. The internal culture is usually one that encourages team working and ownership of quality across the workforce; it is not seen just as a function of the QC manager and operatives. Muñoz (2002) lists some of the main reasons that limit or prevent the development of a sound sensory QC program; the key issue running through relates to lack of support, interest and respect within an organisation, in particular from Plant, Quality and R&D management. 1.2.1 Winning management support As Lawless and Heymann (1998) state, ‘Without management support, especially from manufacturing, a QC program is bound to fail. In a typical case the program will amount to nothing more than “rubber stamping” of supervisory opinion thus supporting a management policy that maximises productivity at the expense of producing unacceptable products’. Production volume will remain a critical key performance indicator (KPI) for most if not all food manufacturing plants. However, quality systems that include standardised sensory assessment procedures are achieving greater acceptance as businesses increasingly strive to have a quality edge over their competitors. The opportunity therefore to promote the need for robust sensory QC programs has possibly never been greater. Selling the benefits It is important to show that developing a sensory component within a QC program will enhance it rather than detract. Management may be con-

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cerned that in addition to the initial cost involved in setting up a sensory program, it will also lead to constant interruption of the production process with a subsequent drop in volume. The following list provides some examples that can be used to help promote the benefits of having sound sensory measures and how they can aid quality control: • reduce sensory quality variability both within and between production shifts by ensuring operatives have a common understanding of the key attributes of the target product; • avoid the development of serious quality issues, for example, by early stage off-flavour and taint detection; • resolve problems quickly by providing precise descriptions about the sensory quality enabling a specific stage, setting or ingredient in the production process to be targeted; • increase production efficiency by showing that the time allocated to a stage in the process, e.g. pre-mixes, blending, maturation, can be shortened without being detrimental to the finished product quality; • aid risk versus benefit analysis for operations management decisions. Management support needs to be sustained and commitment needs to be actively demonstrated. Companies that maintain successful sensory QC programs not only have a firm belief in product quality but provide financial support through investment in staff and their training plus incentive and reward schemes.

1.3 Establishing a sensory quality control (QC) program Once commitment is gained for setting up a sensory QC program it can be followed by over-ambitious implementation strategies due primarily to lack of comprehension of the time required to address adequately the various stages that are needed, in particular the training of assessors and validation of their abilities. If insufficient time is given to implementation it can be to the detriment of the final quality of the system and subsequently the respect it receives within a business. The author recommends the most effective approach, and also most efficient in the longer term, is to select one product line together with one production site to initially pilot the system and get it fully established. Once the overall approach is agreed and all associated procedures are defined it becomes quicker and easier to roll the system out to other plants and for other products. It can also be beneficial initially to apply the system along with the development of a new product to: • help encourage the collaboration required between Product Development and Quality Assurance to ensure a sensory specification is consumer-focused;

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• overcome personal assumptions about the ‘ideal’ sensory quality which can be ingrained within a business especially for long-established product lines.

1.4 Key elements of a sensory quality control (QC) program Listed below are the main areas to address in preparation for implementation once management support has been gained and the product line agreed for initial roll-out: • • • • • • • • •

defined consumer-based sensory specification; program manager/coordinator; products for training purposes; training program; personnel to be trained; sensory assessment method; assessment area; data capture and dissemination; procedures and protocols.

1.4.1 Sensory specification As discussed earlier the specification needs to include the attributes that are important to consumers’ acceptance of the product plus those that provide information of internal importance. The specifications need to be precise and concise and cover only the key attributes so that time is not wasted assessing unnecessary characteristics either during panellist training or subsequent routine testing; typically the total number of attributes will be from five to nine. The attributes with their associated sensory targets need to be defined to enable appropriate training samples to be produced. Setting sensory targets is discussed further in Section 1.5. 1.4.2 Sensory program coordinator It is vital to have a coordinator of the system in order to maintain consistency in its application across the business. In most instances this individual will have a technical or quality control background rather than being a trained sensory professional. This should not pose an issue as long as they receive suitable sensory training prior to implementation and are given adequate authority to set up and subsequently audit the functioning of the program. The coordinator’s main responsibilities will be to: • organise and implement training programs; • work with Product Development and QC to define the sensory specifications;

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• ensure adequate supplies of training samples are maintained; • ensure all relevant documentation is maintained and updated as appropriate; • ensure the system continues to be applied in a sound and professional manner; • provide reports and reviews for management of results plus the overall performance of the system.

1.4.3 Training program This is a critical element in achieving the successful operation of the program. Without sensory training, assessors’ judgements will be based on their own preferences and therefore be unreliable and variable; and may be skewed away from the sensory requirements of consumers in the target market. Using a reference product to illustrate the target quality, and also varying degrees of deviation around it, builds objectivity and consistency of assessment. As part of the training program, it can be beneficial to start with a basic overview of sensory evaluation principles and measurement largely via practical illustrations, to help panellists better appreciate the need for the training. A short exercise at the end is also advisable to validate each panellist’s competency and highlight their weakest areas. For instance four to five products that illustrate target, just acceptable and sub-standard product could be presented. Depending on the level of ability desired a ‘pass’ level would be defined. The amount of training time required will depend largely on the detail of the specification, whether a general, integrated ‘go/no go’ or a more diagnostic evaluation is wanted and the competency level desired for panellists. Pecore and Kellen (2002) discuss a sensory evaluation program established in General Mills where ‘each location customizes their training to meet the product needs, but in general, all panellists receive a basic overview of sensory evaluation, train on all available reference samples, then practice for 3 months’. As a general guide, 8 hours training minimum (the length of individual sessions can be tailored to suit) is advised for the initial introduction and reference sample orientation, and a follow-on practice period over at least a month to consolidate learning is highly recommended. To use assessors’ data without allowing them a consolidation phase in the formal test context can be risky and jeopardise the quality of the resulting information and hence confidence in the system.

1.4.4 Training samples The purpose of the training samples is to illustrate the range of variation in the intensities of the target product and the extent to which they can vary before the deviation becomes unacceptable, i.e. show the quality ‘grades’

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that are to be designated. Off-flavours that could occur plus the range of possible defects also need to be demonstrated to develop panellists’ ability to detect and accurately describe and quantify them. Ideally the samples should be produced based on the information gained during product development to ensure consumer focus is retained. (Setting sensory targets is discussed further in Section 1.5.) A plentiful supply of the reference material, in particular the target standard, needs to be stored to aid re-calibration of panellists’ perceptions as well as for future training. If sufficient target product can be stored, it is advisable to have it available at the start of all evaluations to ensure assessor’s perceptions are freshly calibrated rather than letting them rely on memory. Optimal storage conditions should be applied to ensure minimum change over time. Frozen and ambient stable products (bottled, canned, dry packed, etc.) can usually be confidently held for 6 to 9 months in this way. Short shelf-life products pose more of a challenge and a written reference based on the descriptive profile plus the technical production specification set at launch may be the only option.

1.4.5 Selecting panellists Panellists involved in QC are typically recruited from the staff available at the manufacturing site/s. Recruitment is advised across all sections and shifts to encourage ownership of quality, commitment to the program and active problem-solving throughout the workforce. Guidance from manufacturing management, i.e. the factory manager, technical/quality manager and production director can prove valuable here by identifying the shift personnel who currently show most initiative towards quality control and problem solving. These people are most likely to become good ambassadors of the system and it is therefore beneficial to include them in the first round of training. By building an extended pool of trained panellists the assessments can be shared out, so helping reduce the risk of sensory adaptation and fatigue, and ensuring that an adequate number of panellists are available for each assessment occasion. Panellist screening and training are detailed further in Chapter 2.

1.4.6 Sensory assessment method Of all the aspects relating to sensory QC programs, comparison of the use of sensory methods is possibly the best documented. Muñoz et al. (1992) and Costell (2002) are but two publications that cover the topic in detail. Most popular methods tend be founded on a form of simplified descriptive analysis or a quality grading method. Various forms of grading (quality rating) methods have become very popular for online sensory control as they provide a quick, reliable (providing panellists have been fully trained

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against reference products) and practical approach to measuring deviations from the target quality. One of the most popular of all is the red, amber, green ‘RAG’ system operated globally by a number of multinational companies. The method comprises three grades into which finished product can be allocated: green = target quality, amber = borderline quality, red = unacceptable quality. The method is often applied in two ways: an integrated approach where the sensory characteristics are judged and graded in entirety and a diagnostic approach where the key attributes for each sensory modality are graded individually, with the final grade allocated based on the lowest performing feature. The former approach provides a rapid, basic check of overall sensory quality while the latter enables focused troubleshooting and detailed tracking of quality fluctuations over time.

1.4.7 Assessment area Some significant compromises are likely to have to be made in relation to the assessment area as owing to restrictions on panellists’ time away from their primary duties, it usually needs to be within close proximity of the production line; the existing QC laboratory being an obvious choice. Air and light quality, noise levels, distraction and limited space can be particular issues. Facilities need to be standardised and controlled as far as possible in line with recommended sensory practice. ISO 8589 (2007) provides general guidance for the design of test rooms. Important assessments should always be conducted in a defined area.

1.4.8 Data capture and dissemination An important consideration in the capture of the data is how quickly any resulting actions can be communicated back to the production line and associated management. Individual assessments will most commonly be recorded on a paper ballot and collated by a QC operative and typically only involve a few panellists, three to five for example. Prompt communication of the resulting actions should not pose a problem. The longer-term benefit of a sensory QC program which also helps promote its perceived value is gained through trend analysis. This allows the sensory features causing the most frequent deviations from target quality to be identified which in turn can be linked back to specific aspects of the production process. To facilitate the analysis and communication of this information, data from the individual assessments are best stored electronically. Most manufacturing sites will already have computerised systems to store data from other quality and technical measurements. The software can often be adapted to accommodate the sensory information as well; if not, it is advisable to plan to have a program developed. Excel software may be more than adequate. Findlay (2002) describes a computerised system for sensory QC that operates both at local and Internet level and that can also be inte-

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grated with other data systems such as a laboratory information management system (LIMS). 1.4.9 Procedures and protocols All associated procedures, protocols, assessment forms, etc., need to be standardised, with current versions maintained across the system in order to avoid bias and error from varied practices or out-of-date instructions being used. Key areas to address are: • sample collection, sampling point, e.g. directly off-line, after packing, after initial storage; • sample size; • preparation, cooking, serving procedures; • evaluation method; • assessment protocol: minimum portion, palate cleansing; • collating, recording, reporting results; • response procedures, hierarchy of actions dependent on result.

1.5 Overview of approaches used to define sensory targets Two approaches are advised which provide a sound route to achieving meaningful sensory specifications; a consumer-focused one, which advocates maximum use of consumer and sensory research information obtained during the product development process, and a producer-focused one, which utilises product knowledge from within a business. The former is advised as the most reliable and effective but it is not always possible due to time and budget constraints. 1.5.1 Consumer-targeted approach Figure 1.1 shows the key stages in the process of defining consumer-targeted sensory specifications. The development and refinement of consumer preference segmentation and preference mapping data analysis methods over the past two decades has enabled consumers’ widely varying individual preferences to be classified and measured in a more objective, reliable and consistent manner. As a descriptive sensory profiling technique typically supports the research, this approach provides the most comprehensive information about the key drivers of consumer acceptance along with the importance of each. The need for one or more target products to satisfy a market can be identified and information gathered to allow sensory targets to be set for each. By comparing the sensory profile of the most liked product (for the market in total or segmented groups) with ones for products that record a decrease in the degree of consumer liking, the acceptable range of deviation can be identified. This information provides the blueprint

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Consumer & sensory research information

Key preference drivers identified – positive and negative

Target product attributes defined with tolerance limits

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Sensory specification established

Fig. 1.1 Flow diagram illustrating the key stages in the process of defining consumer-targeted sensory specifications. Background image Copyright © 2007 Microsoft Corporation.

for defining the sensory grades and tolerance limits that will be set and also the reference samples to be used for training. The acceptance range can be set as tight or as loose as a business deems fit; either way with this approach it can be from a very informed perspective and appreciation of the business risks involved. Everitt (2009) details how to make effective use of preference mapping and cluster analysis data to define realistic sensory targets and establish practical consumer targeted sensory specifications. A sensory profile plot has the sensory panel mean intensity scores plotted on each attribute axis for each product. Intensity usually goes from low at the centre to the highest scores at the periphery as shown in Fig. 1.2. If the profile lines overlap or are in close proximity on a given attribute axis the difference between the scores is not statistically significant. Where the lines distinctly diverge, the differences are likely to be statistically significant (but always refer back to the associated ANOVA table from the original analysis); the larger the distance between the mean scores the greater the difference in the magnitude of intensity. Collaboration between departments Communication between Product Development, Manufacturing and Quality Control needs to be developed as an integral part of the development process to ensure that the sensory QC coordinator and supporting staff develop their knowledge of the desired sensory properties of the product as it develops in order to have a suitable specification ready for use from a product’s launch. This communication link is well established in some companies, particularly those where the sensory QC program is overseen and guided at a corporate level. For companies just starting or in the

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Sensory analysis for food and beverage quality control Greasiness 100 Smoothness

80

Depth of colour

60

Stickiness

Uniformity of colour

40 20 0

Firmness

Yellow colour

Caramelised

Baked odour Vanilla

Burnt Sweetness

729 Target 194 257 438

Fig. 1.2 Comparison of sensory profiles generated by a trained sensory panel, showing three products selected to represent the sensory quality range, i.e. products 729, 257, 438. Product 194 illustrates an unacceptable deviation for key attributes depth of colour, baked odour, caramelised and burnt.

early stages of implementing a system it would be advisable to aim to establish this link as a priority. Level of consumer information Product development projects do not all have the budget or even warrant the need for consumer research on the scale of preference mapping. However, quantitative consumer information, even that from the simplest of product guidance studies, can be useful in establishing the sensory criteria for the QC specification. Again it is advisable to develop a culture whereby the departments that commonly obtain this type of data, i.e. Research and Product Development and Marketing, automatically feed information through to Manufacturing and, in particular, Quality Systems.

1.5.2 Producer-focused approach Some businesses primarily use an internally focused approach to set the target parameters of the sensory quality of their products. A crossfunctional team will be convened from Product Development, Marketing, Manufacturing and Quality Systems, for example. This team will initially grade a set of samples into a range of references for calibration purposes. The samples will either have been specifically formulated to illustrate

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specific variations in sensory quality or have been sampled from production over different shifts and time points. In some instances the customer of the product will take the lead in defining the calibration range. The selected set will represent target quality plus acceptable and unacceptable deviations. These samples will then be used to train an assessment panel to recognise the defined quality grades. King et al. (2002) describe a sensory quality system developed on this basis that has proved to work very effectively at a global level. Although with this approach the target quality can be controlled very well to the defined limits, it may be skewed away from what the consumer ideally desires. A customer, for example from within Food Service, may use a combination of key criteria quite differently and set different priorities from those of consumers when judging the sensory quality. Consumer research on the calibration set is therefore advised at some point to check that the specified quality adequately meets with the expectations of the final user. Sales data and consumer complaint information If the production of a product is well established then sales and also consumer complaint data may be useful in helping ensure that the typical production quality satisfies consumers. Sales volume figures will indicate how well the product is being received in the market and show market share compared with key competitors. The comparison of product quality with that of competitors can be useful to help highlight which characteristics might benefit from modification. Tracking trends in consumer complaint data to identify which features of a product cause the most issues has limited use in the main for monitoring sensory quality, especially in relation to flavour and texture. Sensoryrelated complaints most commonly refer to visual factors and in particular defects as consumers can most readily identify and describe them. Flavour and texture descriptions tend to be vague and can often be misleading about the cause of the issue. The use of this type of data is recommended only as a secondary guide for checking the sensory quality as it is very much a retrospective approach so by the time any significant issues have been highlighted, product reputation may already have been harmed in the market.

1.6 External support and consultancy Not all business can invest in a dedicated sensory professional and therefore make use of external sensory science expertise. The advantages can be to help speed up the implementation process in particular for: • developing a training program; • planning the implementation stages;

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• auditing current practice; • training the sensory coordinator plus pool of assessors; • defining suitable methods plus procedures. Expert advice can help ensure that all important criteria needed for the reliable functioning of a system are highlighted, the most effective methods are utilised and a suitable standard of training is provided. Continued support from a sensory professional is also recommended to provide ongoing guidance and help with future training and maintaining the robust functioning of the system as required. The availability of an external expert may be restricted due to other professional duties, therefore contact well ahead of a proposed implementation schedule is advised to agree responsibilities and ensure the required commitment can be provided.

1.7 References costell, e. (2002) A comparison of sensory methods in quality control. Food Quality and Preference, 13, 6, 341–353. everitt, m.a. (2009) Consumer targeted sensory quality. In Global Issues in Food Science and Technology (G. Barbosa-Canovas, A. Mortimer, D. Lineback, W. Spiess, K. Buckle and P. Coionna, Ed.), Vol. 8, 117–128. Academic Press. findlay, c. (2002) Computers and the internet in sensory quality control. Food Quality and Preference, 13, 6, 323–428. gavin, d.a. (1984) What does product quality really mean? MIT Sloane Management Review. 15 October, 25–35. international organization for standardization (2007) Sensory analysis: general guidance for the design of test rooms. ISO 8589 Edition 2. king, s., gillette, m., titman, d., adams, j. and ridgely, m. (2002) The Sensory Quality System: a global quality control solution. In Food Quality and Preference (H.J.H. MacFie and H.L. Meiselman. Ed. A.M. Muñoz. Guest Ed.) Vol 13, 6, 385–395, Elsevier. lawless, h.t. and heymann, h. (1998) Sensory Evaluation of Food: principles and practices, Vol. 16, 548–577. Chapman & Hall. muñoz, a.m. (2002) Sensory evaluation in quality control: an overview, new developments and future opportunities. Food Quality and Preference, 13, 6, 329–339. muñoz, a.m., civille, g.v. and carr, b.t. (1992) Sensory Evaluation in Quality Control. Van Nostrand Reinhold. pecore, s. and kellen, l. (2002) A consumer-focused QC/sensory program in the food industry. Food Quality and Preference, 13, 6, 369–374.

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2 Selection and management of staff for sensory quality control E. De Vos, Tate & Lyle Food and Industrial Ingredients, EMEA, France

Abstract: This chapter will discuss the ‘human’ factor behind sensory quality control. It will start by indicating the different people needed for sensory quality control and their particular profiles and tasks. The steps to be taken for starting a panel will be detailed from recruitment, to screening and training, to final selection of suitable panellists. Health and ethics aspects of sensory activities will be considered. Also, the ways to maintain panel performance, motivation and panel size will be discussed, as these topics are key in ensuring reliable and consistent panel results. A number of issues that may occur when setting up and running a panel will be touched on briefly, and a case study on setting up panels for quality control of cereal-based ingredients will give an example of how panel implementation and maintenance are addressed at Tate & Lyle. The chapter concludes with some comments on likely future trends and an overview of possible sources of additional advice. Key words: sensory quality control panel set-up, sensory personnel, sensory panel performance monitoring, sensory panel motivation, cereal-based ingredients, sensory quality control.

2.1 Introduction Product quality and consistency have traditionally been monitored by analytical techniques in areas such as microbiology or chemistry. These techniques, however, usually do not give adequate information towards the organoleptic quality and the appreciation of this organoleptic quality by the end user or consumer, as they cannot measure human perception. For example, it is possible that a small amount of a chemical that causes taints at a very small dose remains unspotted via chemical analysis while it does give rise to an off-note. Also, a product may conform to specifications for, for example, sugar and salt, but if each of these components was present in levels just below the maximum specification level, synergy and interaction

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between them may result in a product that has a too strongly perceived intensity of sweetness, saltiness or both. Analytical techniques alone are not enough to ensure a full product quality picture, and should therefore be complemented by sensory analysis using people as judges. As Simpson (2003) states, ‘the human “instrument” displays a response that in some cases is more sensitive, and in all cases more complex, than any chemical instrument yet developed’. Originally, often one single expert taster or grader decided on the organoleptic quality of a product. However, these people were not infallible, as their evaluation was often psychologically biased towards their own preferences or skewed for physiological reasons such as anosmia. Currently, only a few specialised branches of industry, such as tea, coffee and wine production, use this method, whereas elsewhere alternative sensory techniques have been established that try to minimise bias and improve reliability by using a group of people.

2.2 Personnel required for sensory quality control Conducting a sound sensory quality control (QC) program not only involves tasters, but also people for setting up and maintaining the program and for ensuring that results are fed back to the responsible quality and manufacturing staff in a timely, consistent and accurate manner, so that the necessary preventive or corrective actions can be taken. The number and background of people involved in sensory analysis and their method of working will differ between larger companies with multiple affiliates and a smaller company consisting of only one or two separately operating units. Larger companies with multiple affiliates typically have a sensory department linked to corporate R&D, consisting of one or more scientists dedicated to sensory analysis and/or consumer research and one or more panels. When implementing a QC program across the different affiliates of the company, the methodology is often developed by the corporate team in close communication with the affiliate. Corporate sensory people set up and train the local panels or at least assist in these tasks. Afterwards, the affiliates take the responsibility for gearing sensory tests, maintaining the panel and feeding back the sensory quality results while staying in contact with the corporate sensory department so that when issues occur, the corporate sensory department can be called upon for assistance. Smaller companies typically do not have a dedicated sensory department. In many cases a member of the QC department takes up the responsibility for sensory method development, panel set-up and panel maintenance as an additional task in his or her job description. A number of authors have reported on the particularities of implementing a sensory panel in small processing operations or in multiplant organisations; amongst them are

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Carlton (1985), Ford (1991), King et al. (2002), Mastrian (1985) and Stouffer (1985). Whether in a smaller or larger company, a number of key people are required for establishing a sensory QC program. These typically include a sensory responsible, preferably a sensory professional, one or more technicians and suitable panellists. Additionally, other resources such as specialists in statistics or external consultants in sensory might be called upon ad hoc. The International Organization for Standardization provides guidance on sensory staff responsibilities (ISO, 2006a) and recruitment and training of panel leaders (ISO, 2006b), and is a good source for further reading on this topic. 2.2.1 Sensory responsible The first task of any sensory responsible starting a sensory QC program is to sell the program to the organisation (Rutenbeck, 1985), in order to demonstrate the goals, the functions and the added value of the program and to generate interest, as well as to obtain the necessary management support and commitment and sufficient budgets. The tasks of a sensory responsible, often referred to as ‘panel leader’ or ‘panel supervisor’, are setting up the required sensory facilities and providing the necessary equipment or tools, recruitment and training of panellists, and maintaining panel performance, panel size and motivation after implementation of the panel. This person is responsible for setting up the quality control program, selecting the most suitable test methodology and sample presentation, analysing and interpreting the results, deciding on how much information is disclosed to panellists, and disseminating results to the involved departments. It is important that a person who takes up the sensory responsible role has a certain profile, as not everyone is suited to performing this task. A sensory panel leader or panel supervisor preferably has a food science background and sensory science knowledge and expertise. Strong organisation and motivation skills are key. A panel leader should have an interest in people and should be able to take a leading role without biasing the panellists’ opinions. A good panel leader needs basic statistical knowledge and skills, is able to translate problems into sensory tests and is able to make correct interpretations of sensory results. Good communication and cooperation with management and other departments – for sensory QC mainly the production plant and the quality department – are key in order to trigger the right actions. 2.2.2 Support staff The assistant, often a lab technician, needs to make sure the samples are prepared and presented to the panellists according to the right procedure and is present in the panel room for the duration of the test. This person also manages adequate cleaning and maintenance of the panel room and

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materials. Taking inventory of supplies such as cups, mineral water for rinsing, crackers for cleansing the palate and tissues is another important task, as this will allow for on-time and organised sensory testing. The technician is also responsible for record-keeping. The assistant, as well as the panel leader, need to be precise and accurate in performing their duties. The technicians should receive proper training on the methodologies that are used in sensory testing from the sensory responsible. Clearly, if an assistant is not available, the panel leader will perform the tasks of the assistant.

2.2.3 Panellists As for all other quality control measurements, the ‘instrument’ in sensory evaluation is critical. The ‘instrument’ in this case is the panel, made up from a number of panellists who are responsible for the day-to-day evaluation of raw materials and/or finished products. These people should be aware of the basic principles of good sensory practice and should be screened and trained in order to guarantee that they are suitable for the job at hand. Particular attention should be paid to their ability to recognise the presence of off-flavours or taints. Section 2.3 step by step illustrates the different phases to undertake when setting up a panel.

2.2.4 Other resources As sensory data acquisition often is automated via a software package, it is wise to involve the information services and information technology (IS/ IT) department or a computer specialist in the loop, to avoid issues that might arise with systems and networks. Also, an in-house or external statistician can come in handy in case specific questions regarding test set-up or data analysis need to be answered. External consultants in the sensory field may be called upon to help with initiating, implementing and validating the QC program, in case of need.

2.3 Setting up a quality control (QC) panel Setting up a QC panel implies a number of consecutive stages: pre-screening and recruitment, screening, training, final selection and validation/follow-up. The first stages aim at obtaining the required sensitivities and precision, and the last stage is needed to control and maintain the panel’s efficiency. Before starting any sensory QC program, management must provide full support for all stages of panel set-up, maintenance and activities of the panel. Hence, the first task of any panel leader starting a sensory QC program is – as mentioned earlier – to sell the program to the organisation.

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Just as the quality of data obtained from instruments to a large extent depends on the instrument’s good functioning and correct calibration, similarly sensory data will depend greatly on the functioning of the panel which in turn depends on adequate recruitment, screening, training and selection. Controlled methods for panel recruitment, screening and training will allow biases that otherwise would influence panel results to be minimised. Obviously, panellists’ personal likes and dislikes should be put aside when participating in sensory QC panel work.

2.3.1 Pre-screening and recruitment Important questions arise when recruiting people to form a sensory panel (ISO, 1993): where should one look for the people, how many people should be selected and how shall they be selected? The health and ethics aspects of panel recruitment – think about allergy problems and problems with food avoidance by ethnic and religious groups – should obviously be considered during the process. Where to look for potential panellists Candidates for panels in general can be recruited within the company or externally from the neighbouring region, or both. There are advantages and disadvantages to employing external panellists for QC sensory. For quality control, answers are needed rapidly and at times linked to the production scheme in order to get quick guidance on product release. Although external panellists are employed solely for a sensory purpose and have no other commitments, the facts that they are not on the spot and are available only for fixed times during the week may become a drawback since test planning will be less flexible. The other disadvantage of using external panellists is the increased cost as they need to receive compensation and the follow-up of paperwork requires time and money (ISO, 1993; Lyon, 2002). On the other hand, the ‘real’ cost of running such a panel is relatively low since panellists are paid as casual staff, without overhead loading, and company staff are not diverted from their main roles (Kilcast, 1992). Another disadvantage of recruiting externally is the fact that people might leave at short notice, and that confidentiality of the results might be an issue. Since sensory QC typically requires people who know the products under evaluation thoroughly, internal panellists seem more suitable for QC purposes. The major disadvantage of using internal assessors is that their ability to participate in sensory sessions depends on their main job role, and their agenda. Time away from the main job may be a concern. Lawless and Heymann (1997) indicate that panel participation can, however, be a welcome break for workers, can enhance their sense of participation in corporate quality programs, can expand their job skills and their view of manufacturing, and does not necessarily result in a loss of productivity.

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How many candidates should be recruited When setting up a panel it is generally recommended to recruit 1.5 to 3 times as many potential panellists as finally needed (Cross et al., 1978; ISO, 1993; Lawless and Heyman, 1997; Lyon et al., 1992) because the final number of panellists available for a test will most likely be brought down owing to insufficient sensitivity to quality defects or absence of panellists because of illness, abandon, lack of motivation or job migration. Recruiting enough potential panellists from the start increases the chances of ending up with a sufficiently large panel size. Unfortunately, not all companies have the luxury of starting with a sufficiently big pool of potential panellists. In reality, cost, resource and time constraints often limit the number of panellists to the available employees in the production plant or the quality department. When only a few people are available, it remains important to get an idea of the capabilities of these people and to screen and train them, even if it will be more difficult to turn down potential candidates at the end of the process. As multiple methodologies for sensory QC exist (cf. Chapter 4), the final panel size that is needed will be dictated by the applied methodology. The amount of training that panellists receive, the reproducibility of panellist results and the variability of the products under test will have an influence on the required number of panellists as well. It is clear that the panel should contain enough members to cater for and minimise variability in results. When resources are limited, it is important to thoroughly train the panellists and allow them to gain a lot of experience: smaller but highly trained and very sensitive panels usually lead to more reliable results than panels with more members that are less trained and less sensitive. How to screen and recruit Before recruiting candidates, the panel leader should first explain the aim of setting up a sensory panel and the merits it will bring to both the company and the panellist, what is going to happen during the screening and training process and during the actual evaluation sessions afterwards and what is expected from the potential panellists in terms of commitment and time requirements. When designing and executing sensory tests – including screening and training sessions – the IFST guidelines for ‘Ethical and Professional Practices for the Sensory Analysis of Foods’ (IFST, 2010) should be given full consideration. This document mentions the following general principles: • The scope of permitted tests using human subjects should be defined in a written Organisation Ethical Policy that will depend on the nature of each individual organisation, but should typically comprise an internal mechanism to define and monitor ethical procedures together with expert input from external sources where appropriate.

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• Procedures should be carried out in such a way as to reduce any risk to the health of the participants by excluding individuals at risk (e.g. allergic candidates), to ensure that products under test are microbiologically safe and to provide an ingredient list where required. For QC sensory testing in particular, any information that might be relevant to posssible unidentified hazards should be explained. • Participants should be volunteers who should be able to withdraw from the testing at any time, without having to give reasons. When novel foods or ingredients, foods containing ingredients that are not approved in the country in which the test is carried out, or foods produced by novel processes are to be evaluated, a safety risk assessment has to be made and informed consent must be given by the panellists before starting any sensory tests. Reference can be made to the guidelines published by, for example, the Advisory Committee on Novel Foods and Processes (ACNFP, 1992). Once potential candidates are aware of what will be expected from them, a questionnaire or a personal interview will provide the panel leader with extra background information. The following aspects of the candidates are worth investigating (ASTM, 1981; ISO, 1993): • Availability: candidates should not only be regularly available to attend training sessions – as the learning curve is steepest during this time – but also during routine tasting sessions. It is wise to exclude candidates who travel frequently or have continual heavy workloads, temporary employees or students. • Health: candidates should be in good health, and should not suffer from any disabilities which may affect their senses, or from any allergies or illnesses, and should not take medication which might impair their sensory capacities and thus affect the reliability of their judgements. It may be useful to know whether candidates have dental prostheses, since they can have an influence in certain types of evaluation involving texture or flavour. Colds or temporary conditions (for instance, pregnancy) should not be a reason for eliminating a candidate. • Motivation, willingness and interest: panellists who are volunteering to participate and are interested in sensory analysis and the products to be investigated are to be preferred over panellists who are forced to participate and are not interested as the former are more likely to become better assessors. According to Howard (1972), ‘interest is linked to the increase of a panellist’s capacity while following the different stages’. • Character and personality: persons with extreme strong or weak characters often are not ideal candidates (Costell, 1983) as they either want to dominate the group or do not speak their minds. Candidates must show interest and motivation, must be punctual in attending sessions, reliable and honest and have a positive attitude to the use of sensory analysis.

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• Dislikes and attitudes to foods: strong dislikes for certain foods or beverages that are under investigation need to be determined, as well as other reasons for not consuming certain products because of cultural, ethnic, health or other reasons. Panellists should be willing to taste and smell all test products as part of their learning experience. • Knowledge and aptitude: panellists require certain physical and intellectual abilities, in particular the capacity to concentrate and remain unaffected by external influences. Certainly for quality tests, a detailed knowledge of the product under test is beneficial. • Ability to communicate: panellists should have the ability to express themselves clearly so as to be able to communicate the sensations they perceive. Other factors such as educational background, experience in sensory analysis or smoking habits may also be recorded, but exclusion of candidates should not be based on these. When gathering information on panellists, the panel leader has to make sure that recording of data is in accordance with the provisions of relevant data protection legislation of the country concerned (IFST, 2010). In France for example, the ‘Commission Nationale de l’Informatique et des Libertés’ (French Data Protection Authority) has issued an Act (CNIL, 2009) relating to the protection of individuals with regards to the processing of personal data. The information provided by panellists and kept on file should be treated as strictly confidential. Panellists should have the option to change the provided information at any time. The information gathered via the questionnaire or the personal interview will allow for selection of the candidates who are deemed suitable to proceed to the screening phase. 2.3.2 Screening Screening should not be considered as part of the training stage, but rather as a tool to eliminate candidates who cannot detect large differences in attributes. ASTM (1981) defines screening as ‘the assessment of candidate potential and a precursor to training’. Basically, screening will allow potential sensory impairments to be checked and will give preliminary information on the candidate’s capabilities of recognising and describing basic odours, tastes and certain taints before starting the training. Even if a candidate has prior sensory knowledge, he or she should be screened to avoid including persons that are not able to discriminate between or are not sufficiently sensitive to flavours of interest. There is no specific screening method: the screening tests and the screening standards to be used will depend on the senses that will be required, and the food products and properties that have to be assessed. It is therefore recommended that the same product(s) are used during the screening as the one(s) that will finally be evaluated (Sidel et al., 1981). Also, some

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scaling exercises (Meilgaard et al., 2006) might be included to assess the concept of ‘proportion’ to the potential candidates. Potential candidates should be screened on colour vision (Ishihara, 1971) and the incidence of ageusia or aneusmia (ISO, 1991). They should, depending on the requirements of the test situation, be subjected to matching tests, which will determine whether candidates are able to match test samples at well above threshold levels to standard samples, triangle tests for detection of a stimulus, ranking tests for discrimination between levels of intensity of a stimulus and/or tests to determine their descriptive ability, as described in ISO standard 8586-1 (1993). Based on the results of the screening tests, candidates with abnormal threshold levels should be excluded from the training, but candidates who show an ability to discriminate and who are also consistent and reproducible in their discrimination should be included. As ability and sensitivity increase with training, screening criteria should not be excessively harsh and should take into account candidates’ potential rather than their current performance. Candidates with high success rates at the screening stage are to be expected to be more useful than others, but those who show better results with repetition are likely to respond well to training.

2.3.3 Training Having passed the screening stage, suitable candidates proceed to the training stage. The aim of training is threefold (ASTM, 1981; ISO, 1993): 1. To provide assessors with rudimentary knowledge of procedures used in sensory analysis in order to familiarise the individuals with the test procedures. 2. To improve the individual’s ability to detect, recognise and describe sensory stimuli. 3. To train assessors to use this expertise and to improve the individual’s sensitivity to and memory for test attributes, so that they may become proficient in the use of such methods with particular products and that sensory judgements will be precise and consistent. Familiarisation with sensory test procedures Training should include an introduction to sensory analysis, the senses and how to use the senses. Candidates should be introduced to a number of general codes of conduct regulations, in order to ensure good sensory practice: • • • • •

be punctual; do not eat, drink, smoke 30 minutes before the session; do not use (strong) perfume, aftershave or lipstick; keep silent in the panel room; be objective and disregard personal likes and dislikes.

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Additionally, panellists should be instructed to take into account the correct order of product evaluation, starting with colour/appearance, afterwards odour, texture, flavour (aroma and taste) and finally aftertaste (ISO, 1993). Indications should be given on the sample size that should be considered, whether swallowing is mandatory or spitting is allowed, whether to use, for example, crackers or just plain water for rinsing, the interval between sips/ bites, etc. (ASTM, 1981). Candidates should be made familiar with the methodology and format of the test, the forms to fill out or the computerised data acquisition system. Once assessors fully understand the sensory tests they have to perform, and are familiar with the specific knowledge required to perform the tests correctly, they can proceed to further training. Basic training: improving the ability to detect, recognise and describe sensory stimuli Whatever the methodology used for QC sensory testing, thorough training using the adequate reference and training standards must make sure that the variability in sensory response inherent in any group of people is reduced. This lengthy program of training is essential in ensuring that attributes causing quality defects are captured on time and that at the same time individual (over)sensitivities to certain attributes do not bias results so that defective products are not released for sale. It is obvious that the main aim of training is to familiarise potential panellists with standards, deviations from the standards and the limits of acceptable variation. Therefore, potential panellists should, again depending on the requirements of the test situation, be subjected to different training sessions. Training in detection and recognition of tastes and odours by means of various methods, for example matching, recognition, paired comparison, triangle and duo–trio tests, demonstrates tastes or odours at high and low concentrations and trains candidates in recognising and describing them correctly (ISO, 1991, 1993, 2006c). Training in the use of scales introduces candidates to the concepts of rating, classification, interval and ratio scales by initially ranking a series of single-odour, single-taste and single-textural stimuli with respect to the intensity of a particular characteristic. The various rating procedures are then used to attach meaningful magnitudes to the samples (ISO, 1993). Although profiling is less used for QC purposes because of its lengthy procedure to obtain results, an introduction to the development and the use of descriptors can be of interest to make candidates aware of the idea of profiling. Panellists are presented with a series of simple products and are asked to develop vocabularies for describing their sensory characteristics, in particular terms which allow samples to be differentiated (ISO, 1985, 1993). It is important to include sufficient time for exercises during the basic training sessions, in order to allow the candidates to gradually build up

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experience. During the basic training stage, the panel leader carries out a follow-up evaluation to assess the interest and dedication of each panellist and double-checks their sensory capacities and correct application of the sensory methodology. Assessors should be encouraged and motivated by immediate feedback of results after each session. Extended training: building expertise After basic training, assessors should undergo a period of extended product training to familiarise themselves with the type of products that will be tested on a routine basis once the panel has been established, the relevant sensory parameters for the products at hand and the acceptable range of variation of these parameters and products. The type of training sessions will depend on the particular QC test situation, and may, for example, comprise difference assessments in which samples similar to those that will eventually be evaluated are presented to the candidates, who evaluate them using one of the different assessment procedures (ISO, 1993). In order to carry out effective quality control, panellists must have a depth of knowledge which can be gained only by long periods of exposure to the product range and defects which are likely to occur. Lyon et al. (1992) indicate that panellists must be able to make allowances for normal withinbatch variation, or batch-to-batch variation. Knowledge of normal product variation within a batch or between batches comes with experience over time. Repeated exposure to the products and the defects or off-taints that may occur will help build this experience. Once panellists have been tasting products for a while and show good repeatability, noteworthy acuity, or particular aptitude regarding specific attributes (e.g. a taint) or classes of materials, they can be taken to a higher level by improving their memory for sensory attributes, learning to keep clear and logical notes, gaining background knowledge on the range of products from lectures, books, trade press and technical contacts, gaining knowledge of technical aspects such as raw materials, production and distribution of the products concerned, increasing their communication skills with other experts and with non-experts and increasing their self-discipline (ISO, 2008). Additional training sessions that focus on these topics can help in further building the knowledge base and experience.

2.3.4 Selection Once the training stage is completed, it is time to select the most suitable candidates to form a panel based on the results that they have obtained during repeated training exercises. Selected candidates should perform consistently, should be able to differentiate the samples that are presented in difference assessments and should be able to rank the samples that are presented in ranking tests. The ability to detect adulterated samples at

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decreasing concentrations can also be used as a criterion for selection. Candidates who perform these tasks less well than others should be rejected (ISO, 1993). Data obtained from rating and scoring exercises should be analysed by ANOVA to examine the individual results of each assessor. Assessors who have a high residual standard deviation, indicating inconsistency, or for whom the variation among the samples is not significant, indicating poor discrimination, should be considered for rejection (ISO, 1993). No additional specific selection procedure is advocated, amongst those already outlined, for qualitative descriptive analysis. Focus here should be on development and use of descriptors (profiles) and descriptive assessment (ISO, 1993). Final selection of members of the panel will be made according to panellists’ availability, sensitivity, reproducibility and their capability to provide valid sensory data. Failing to identify and eliminate non-discriminators before a test will increase the likelihood of missing real product differences (Sidel et al., 1981). The final number of panellists will be defined by the QC methodology that is applied. In any case, all suitable panellists should become members of the ‘pool’ from which to draw panellists in the future. In the event that after training only a limited number of suitable panellists remain, it is recommended not to include people who have less than satisfactory training results just to achieve a predetermined panel size (Cross et al., 1978).

2.4 Maintaining the quality control (QC) panel: performance, motivation and size 2.4.1 Monitoring panel performance After implementation of the panel and having started routine testing, it remains critical to monitor panellist performance regularly, as one needs to certify that panellists are able to repeatedly achieve accurate, precise and reproducible results (ISO, 1993; Muñoz et al., 1992). The monitoring methodology will depend on criteria derived from the particular method that is used for QC testing, but should be relative to the panel as a whole and to the panellist’s past performance. The monitoring program should make use of blind controls or blind deviating samples that are included in routine tests. Unacceptable variation or disagreement in the results of these samples should trigger retraining for individual panellists or the panel as a whole. Performance records should be kept and periodically reviewed. When panellists are retrained or training is given to potential new panellists, data should only be included in test results when they are in sufficient agreement with data from previously qualified panellists. Repeated review sessions and practice at recognising each parameter and the change in intensity of each parameter caused by spiking higher

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levels of certain compounds in the sample is important to develop and maintain the skills required. Training and monitoring should be a continuing process that is repeated regularly during the normal operation of the panel. The best way to maintain the high competence level of the group is to keep on testing regularly. Frequent participation is necessary for motivation and to guarantee performance (ISO, 2005): panellists can indeed improve on their performance with repeated trials. When similar panels are used across different plants, in different countries, one has to ensure that for the same sample the (same) QC method gives the same result, even though the individual panellists are different. A nice tool to check for alignment is a round robin test, which is typically managed by the corporate sensory department. On a regular basis, typically monthly or bi-monthly, a blind sample is sent out to all participating panels, after which evaluation is performed locally and results are sent back to the corporate sensory department. Statistical tests are then performed on the received data and conclusions are drawn on the performance of the different panels. The conclusions are fed back to the participants, to ensure proper remedial actions, if required.

2.4.2 Panel motivation Even when good sensory practice is assured, panellists can start losing their motivation. They may become bored when they frequently participate in routine evaluation sessions. Lack of feedback on their results and lack of support or recognition from management are other factors that can decrease a panellist’s drive. In order to keep the panel performing adequately, it is important to maintain interest and motivation. Panellists should have faith in the group and should like to come to sessions. A ‘motivation maintenance’ program can include a number of activities: • Give information on why a particular test is needed and performed. In order not to bias panellists with details on the product samples, it is important to supply information on a test or a series of tests only after all related tests have been finished. • Inform the panel regularly on individual performance and the performance of the panel as a whole. Positive feedback will give a boost to the panellist’s self-confidence and motivation. Negative feedback, when given in a tactful, constructive manner, will help to make panellists aware of their weak points and can trigger individual retraining actions. • Give a small snack after sessions that include samples with a bad taste. • Reward the panellists who come to panel sessions regularly and/or perform well. Although money can be an incentive, most companies offer other incentives such as personalised ‘panel’ gift items such as mugs, umbrellas, travel clocks or pens, tickets for the movies, chocolate

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goodie bags or gift vouchers, or organised special activities in or outside the company, for example, special panel Christmas lunches or wine tasting sessions. Distributing raffle tickets for a prize draw can be a nice incentive to encourage attendance at panel sessions. Mentioning panel achievements in the company newsletter can stress the importance of the panel to management and the rest of the business. Recognising noteworthy individual panellist contributions in terms of attendance or correct identification of off-notes or blind control samples is a means of applauding the good work of panellists. • The sensory responsible, the plant, the QC and the panellist’s management should appreciate and acknowledge the efforts that panellists undertake in order to be able to attend sensory sessions. Panellists should be reminded regularly that their contribution is key in guaranteeing the panel’s good functioning and subsequent problem solving and support achievements. Obviously, communication of achievements to the rest of the business is important too. • Provided a larger pool of trained panellists is available, rotating the panel regularly can improve motivation and fight boredom. 2.4.3 Panel size It is important to maintain enough panellists in the panel over time, whatever the recommended panel size as dictated by the applied QC methodology. The sensory responsible should make sure that a sufficiently high number of panellists is always available in the pool to ensure adequate numbers during the actual evaluations. There are many reasons why panellists may quit the panel and abandon their sensory task: illness, people leaving the company, time constraints, working day(s) off-site and shifts. If the normal panel size cannot be guaranteed over time, new recruitment and training should be scheduled to fill the gap of those who have left and to ensure that the panel can keep on performing as it is meant to.

2.5 Possible issues A QC sensory panel undoubtedly has its added value. However, it is clear that setting up and maintaining a QC panel requires substantial efforts, both from sensory staff and panellists. Panel motivation and maintenance of panel performance and panel size may pose some challenges, but a couple of other issues may make routine running of sensory panels even more cumbersome. With internal panellists, the time away from the person’s main job might be a concern as time constraints in the manufacturing environment may not always allow people to leave their workplace to participate in sensory sessions.

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When the plant operates in shifts, even more complexity is added as availability of panellists is in this case typically limited during certain times of day or night, or during weekends. When the available shift workers are thoroughly trained and have a long-lasting experience in tasting the product(s) at hand, this may still lead to reliable results; however it remains advisable to use multiple panellists instead of only one or two. When standardising sensory QC procedures and coordinating sensory activities across multiple plants in different geographical locations, issues related to cross-cultural differences might occur. Language and effective communication can be an issue (Cardello, 1993) as there may be subtle differences in wording or definitions, and difficulties in expressing oneself or in understanding what is meant in another language. As Carlton (1985) indicated, local customs may also need attention: it is sometimes critical to do some groundwork to become familiar with the country’s culture before the sensory work begins. Carlton (1985) stresses for example that knowing the ranking rules and where people fall within the rankings is important, that power and authority within the group can affect interaction as well, that gender-effect may undermine the credibility of a female trainer in a male-dominated social structure and that dressing a bit more casually might promote group interaction and individual participation. Another issue that may arise is the fact that, strictly speaking, consumer studies are needed to establish the range of acceptable products or the cut-off points (Muñoz et al., 1992). This obviously adds an extra cost to the sensory quality program.

2.6 Case study: selection and management of staff for sensory quality control of cereal-based ingredients As an ingredient supplier, Tate & Lyle performs sensory quality control on ingredient batches before releasing them to customers. Once the appropriate sensory method has been established at the Global R&D Sensory level, the method is implemented across the different production plants. For most ingredients, a degree-of-difference (DoD) from control method with a defined cut-off point for rejection is used. The panel set-up process in each production plant involves the Global R&D Sensory responsible and the local panel leader, typically someone working in the local QC department who is taking up this additional task. Potential panel candidates are recruited internally, from the local plant, depending on their availability, interest and basic sensory acuity. As soon as the pool of candidates is established, training starts. Initial training is given by the Global R&D Sensory responsible so as to train both the ‘trainer’, i.e. the (local) panel leader, and the panellists. The training program starts with an introduction of the method to the panel leader and the potential panellists and is followed by multiple

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sessions that have the aim of training both qualitatively and quantitatively, using spiked training standards, spiked test samples and good as well as deviating production samples.

2.6.1 Qualitative training The qualitative training sessions show panellists the ‘types’ of (off-)notes that can occur in a specific ingredient category. Panellists receive a bland reference sample and spiked training standards with the (off-)notes in a concentration at the upper range of the DoD scale. Although this high concentration will not be likely to occur under normal production circumstances, it is used for familiarising the panellists with what is meant by a specific (off-)note and will help in identifying the (off-)note when it occurs in lower concentrations afterwards. After having memorised the different training standards, they are put aside, and two or more coded unknown samples are provided for the panellists to identify within the (off-)notes of the previously received set. When the right (off-)note type is mentioned, the panellist is awarded a score of 100% for that sample.

2.6.2 Quantitative training The quantitative training sessions show panellists the ‘intensity’ of (off-) notes that can occur in a specific ingredient category. Per (off-)note, panellists receive a bland reference sample and spiked training standards with the given (off-)note in concentrations at the lower, middle and upper range of the DoD scale. After having memorised the different concentration levels for that (off-)note, the training standards are put aside and a test sample with unknown concentration is provided for the panellists to identify within the concentrations of the previously received set. When the right (off-)note concentration is mentioned, the panellist is awarded a score of 100% for that off-note. The higher the deviation from the right concentration, the more points are deducted (e.g. when using a 0 to 10 scale, each deviation from the right concentration will result in a decrease of 10% in score). The above procedure is repeated using blind samples at various appropriate concentrations for each (off-)note.

2.6.3 Qualitative/quantitative training The set-up of the qualitative/quantitative training sessions is similar to the set-up of the quantitative training sessions, with the difference that the (off-)note in the range of concentrations is now unidentified. The unknown sample now must be identified both in terms of its type (qualitative) and its concentration (quantitative). The panellist is now awarded an average score between the qualitative part and the quantitative part.

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2.6.4 Spiked samples Whereas the previous sessions were performed on specific training sheets, the next sessions start making use of the routine evaluation sheet to familiarise the panellists with the normal evaluation procedure and form. The ‘spiked’ sessions consolidate the learnings from the previous training sessions. Panellists now receive a bland reference and three unknown samples, of different type and concentration, which they have to define and score on the form using the DoD scale. Again, panellists are awarded an average of the qualitative and quantitative scores.

2.6.5 Production samples The ‘production samples’ sessions are similar to the ‘spiked samples’ sessions, but the unknown samples are now not artificially spiked as before, but are real samples provided by the production plant. Again, panellists receive a bland reference and three unknown samples, for which they have to fill out the form using the DoD scale. The panellists’ result now is not a percentage, but relates to a benchmark score from earlier tests by, for example, Global R&D Sensory or a production plant.

2.6.6 Training results After having passed the full training program, panellists get a final score, which is an average over the scores of the different sessions. In order to become a member of the routine panel, a minimum score of 70% is required. Panellists scoring lower than this 70% are – at least temporarily – kept in the pool, but need additional training and re-testing to check if they are found to be fit for the panel after having gained more experience. In the meantime, their routine evaluation results are not taken into account in the final panel result for a given sample. During each of the training sessions, the result of the panel as a whole is checked also. Obviously this result should be in line with what is expected, but can deviate because of individual panellists’ scoring deviations.

2.6.7 Panel maintenance and follow-up All subsequent re-training and monitoring in the weeks/months following the initial training is the responsibility of the local panel leader and may involve Global R&D Sensory when needed. A yearly round robin scheme is maintained by Global R&D Sensory as a tool to follow-up the results of the panels in the different production units and to determine whether or not corrective re-training actions need to be taken. Therefore, one production plant sends out a sample to all the other plants in the scheme every 2 months. All panels evaluate the sample and the results are sent back to Global R&D Sensory, after which statistical

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techniques are used to analyse panel and panellist reliability. Feedback is reported to the involved panels, after which corrective actions can be taken depending on the need.

2.7 Future trends People involved in product quality may be inclined to replace their regular ‘human’ panels by devices such as ‘electronic noses’ or ‘electronic tongues’. Properties of these devices should however be considered before deciding on this action. An electronic nose is a device that – after sufficient training with several training samples, both conforming to and deviating from the reference standard – allows differentiating samples on the basis of volatile components and to group similar samples (Gardner and Bartlett, 1999). An electronic tongue is a device with a similar working principle as an electronic nose, but is different in that the differentiation between the samples is done on the basis of water solubility instead of volatility of components (Legin et al., 1997). The major drawback of electronic nose and electronic tongue devices is that, although they are able to group similar samples together on the basis of differences in odour (volatile compounds) or taste (water-soluble compounds), they do not pinpoint what the cause of the difference is. This makes it difficult to address issues in the production process and to take remedial actions. This drawback could be countered by adding a mass spectrometer to the system, as this allows compounds to be separated and defined based on their mass. Electronic nose or tongue devices can have their place in quality control provided that their limitations are well known. However, they will never be able to replace a human panel, as they are not as sensitive as the human palate and are not able to evaluate complex matrices in the same way as humans.

2.8 Sources of further information and advice Most information in this chapter is based on the guidelines for panel set-up as defined by ISO (1991, 1993, 2006c, 2008), but has been adapted for use in a QC environment. ASTM standard 758 (1981) is an additional source of further, general panel set-up information. Specific methods to initiate a sensory QC/QA program and the issues related to this have been well documented by, for example, Beckley and Kroll (1996), Costell (1992), Jellinek (1985), King et al. (2002), Lawless and Heyman (1997), Lyon et al. (1992), Muñoz et al. (1992) and Yantis (1992).

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Additionally, Campden and Chorleywood Food Research Association (CFRA) have published guidelines that are principally aimed at descriptive panels, but do include some useful information that can be adapted towards QC panels: Guidelines for the Selection and Training of Assessors for Descriptive Sensory Analysis (Lyon, 2002), Guidelines for the Motivation of Sensory Panels Within the Workplace (Kapparis et al., 2008) and Practical Guidelines for Monitoring On-going Job Performance of Sensory Descriptive Panellists (Pfeiffer et al., 2008).

2.9 References acnfp (1992), ‘Guidelines on the Conduct of Taste Trials Involving Novel Foods or Foods Produced by Novel Processes’, Advisory Committee on Novel Foods and Processes, available from http://www.acnfp.gov.uk/acnfppapers/inforelatass/ guidetastehuman/guidetaste. astm (1981), Guidelines for the Selection and Training of Sensory Panel Members, American Society for Testing and Materials, Special Technical Publication 758. beckley j p and kroll d r (1996), ‘Searching for sensory research excellence’, Food Technol., 50(2), 61–63. cardello a v (1993), ‘Cross-cultural sensory testing: a changing tide?’, Cereal Foods World, 38(9), 699–701. carlton d k (1985), ‘Plant sensory evaluation within a multiplant organization’, Food Technol., 39(11), 130–133, 142. cnil (2009), Act no. 78-17 of 6 January 1978 on Data Processing, Data Files and Individual Liberties, Amended by the Act of 6 August 2004 relating to the protection of individuals with regard to the processing of personal data and by the Act of 12 May 2009 relating to the simplification and clarification of law and lightening of procedures, Commission Nationale de l’Informatique et des Libertés, available from http://www.cnil.fr/fileadmin/documents/en/Act78-17VA.pdf costell e (1983), ‘El equipo de catadores como instrumento de analisis’, Rev. Agroquim. Tecnol. Aliment., 23(1), 1–10. costell e (1992), ‘Sensory analysis applied to quality control of citrus fruits’, Rev. Esp. Cienc. Tecnol. Aliment., 32(3), 269–281. cross h r, moen r and stanfield m s (1978), ‘Training and testing of judges for sensory analysis of meat quality’, Food Technol., 32(7), 48–54. ford a l (1991), ‘Sensory testing in a small company’, Food Australia, 43(6), 237. gardner j w and bartlett p n (1999), Electronic Noses. Principles and Applications, Oxford University Press. howard a (1972), ‘Taste panel technique. I. Reproducibility, reliability and validity’, Food Res. Quart., 32, 80. ifst (2010), ‘Ethical and Professional Practices for the Sensory Analysis of Foods’, The Institute for Food Science & Technology. Available from http://www.ifst.org/ documents/misc/practicesforsensoryanalysis1.pdf ishihara s (1971), Tests for Colour Blindness, Kanahara Shuppan Co.Ltd., TokyoKyoto, Japan. iso (1985), Sensory Analysis – Methodology – Flavour profile methods, ISO 6564:1985(E), International Organization for Standardization. iso (1991), Sensory Analysis – Methodology – Method of investigating sensitivity of taste, ISO 3972:1991(E), International Organization for Standardization. iso (1993), Sensory Analysis – General guidance for the selection, training and monitoring of assessors – Part 1: Selected assessors, ISO 8586-1:1993(E), International Organization for Standardization.

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iso (2005), Sensory Analysis – Methodology – General guidance, ISO 6658:2005(E), International Organization for Standardization. iso (2006a), Sensory Analysis – General guidance for the staff of a sensory evaluation laboratory – Part 1: Staff responsibilities, ISO 13300-1:2006, International Organization for Standardization. iso (2006b), Sensory Analysis – General guidance for the staff of a sensory evaluation laboratory – Part 2: Recruitment and training of panel leaders, ISO 13300-2:2006, International Organization for Standardization. iso (2006c), Sensory Analysis – Methodology – Initiation and training of assessors in the detection and recognition of odours, ISO 5496:2006(E), International Organization for Standardization. iso (2008), Sensory Analysis – General guidance for the selection, training and monitoring of assessors – Part 2: Expert sensory assessors, ISO 8586-2:2008(E), International Organization for Standardization. jellinek g (1985), Sensory Evaluation of Food, Chichester, Ellis Horwood Ltd. kapparis e, pfeiffer j c and gilbert c g (2008), Guidelines for the Motivation of Sensory Panels Within the Workplace, Guideline No. 57, Campden & Chorleywood Food Research Association Group. kilcast d (1992), ‘New developments in sensory analysis’, International Food Ingredients, 2, 2–8. king s, gillette m, titman d, adams j and ridgely m (2002), ‘The Sensory Quality System: a global quality control solution’, Food Qual. Pref., 13, 385–395. lawless h t and heymann h (1997), ‘Sensory evaluation in quality control’, in Lawless H T and Heymann H, Sensory Evaluation of Food – Principles and practices, New York, Chapman & Hall, 548–584. legin a, rudnitskaya a, vlasov y, di natale c, davide f and d’amico a (1997), ‘Tasting of beverages using an electronic tongue’, Sensors Actuators B: Chem., 44, 291–296. lyon d h (2002), Guidelines for the Selection and Training of Assessors for Descriptive Sensory Analysis, Guideline No.37, Campden & Chorleywood Food Research Association Group. lyon d h, francombe m a, hasdell t a and lawson k (1992), Guidelines for Sensory Analysis in Food Product Development and Quality Control, London, Chapman & Hall, 47–57, 82. mastrian l k (1985), ‘The sensory evaluation program within a small processing operation’, Food Technol., 39(11), 127–129. meilgaard m c, civille g v and carr b t (2006), Sensory Evaluation Techniques, 4th Ed., Boca Raton, FL, CRC Press, 163–165. muñoz a m, civille g v and carr b t (1992), Sensory Evaluation in Quality Control, New York, Van Nostrand Reinhold, 28–40, 84–107, 126–139, 154–167, 179–198, 225–226. pfeiffer j c, kapparis e and gilbert c g (2008), Practical Guidelines for Monitoring On-going Job Performance of Sensory Descriptive Panellists, Guideline No. 58, Campden & Chorleywood Food Research Association Group. rutenbeck s k (1985), ‘Initiating an in-plant quality control/sensory evaluation plan’, Food Technol., 39(11), 124–126. sidel j l, stone h and bloomquist j (1981), ‘Use and misuse of sensory evaluation in research and quality control’, J. Dairy Sci., 64, 2296–2302. simpson w j (2003), ‘Sensory quality management and its role in brewery operations’, Scandinavian Brewers’ Review, 60(5), 18–23. stouffer j c (1985), ‘Coordinating sensory evaluation in a multiplant operation’, Food Technol., 39(11), 134–135. yantis j e (1992), The Role of Sensory Analysis in Quality Control, American Society for Testing and Materials, Manual Series: MNL 14.

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3 Proficiency testing of sensory panels G. Hyldig, Technical University of Denmark, Denmark

Abstract: When designing a proficiency test for sensory panels, it is important to consider the type of sensory panel and the material that is selected as proficiency test item. Most sensory panels work with more than one method and often with both descriptive and discriminative tests. To illustrate the importance of choosing the methods and proficiency test item, some trials are described in the chapter. Univariate as well as multivariate statistical methods can be used for the data analysis. The results from proficiency testing provide a tool that enables laboratories to evaluate and demonstrate the reliability of the data produced. Key words: proficiency testing, proficiency test item, sensory panels, sensory analysis.

3.1 Introduction What is proficiency testing and why is it important for sensory panels? A proficiency test can be used to validate laboratory performance. The ISO definition of laboratory proficiency testing is ‘determination of laboratory testing performance by means of interlaboratory comparisons’. It is a comparison of a laboratory’s reported result for the analyte in question with the best estimate of the ‘true’ value of the analyte and hence not a validation of the analytical method. There is no difference in procedure to produce results for a single commodity, internal quality control and in research projects; it must be reliable, and the result may not depend on which laboratory has performed the analysis. Many laboratories conducting chemical or bacteriological analysis participate in proficiency tests on a regular basis and have done for many years. In work with sensory panels, numerous efforts have been made to validate the assessor’s performance, which is of course essential, but it is not enough to ensure that the panel performance is reliable: therefore it is necessary for sensory panels to participate in proficiency tests. For laboratories that have been accredited for a specific analytical method, the accreditation bodies demand that the laboratories participate

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in proficiency testing at regular intervals. However, it is not only laboratories with an accreditation that can benefit from participating in a proficiency test. Big companies with several laboratories in different locations can also use proficiency testing to validate the performance of their laboratories and to prove that the results are independent of geography. Proficiency testing can be very useful in large research projects with participation of laboratories working with the same analysis often situated in different countries. There are general standards for proficiency testing such as ASTM E130195 ‘Standard guide for proficiency testing by interlaboratory comparisons’; EA-4/09 (2003) ‘Accreditation for sensory testing laboratories’; EN ISO/ IEC 17025 (2005) ‘General requirements for the competence of testing and calibration laboratories’; ILAC-G13:2000 ‘Guidelines for the requirements for the competence of providers of proficiency testing schemes’; ISO 17025 (1999) ‘General requirements for the competence of calibration and testing laboratories’; ISO Guide 35:2006 ‘Reference materials – General and statistical principles for certification’; ISO/IEC 17025:2005 ‘General requirements for the competence of calibration and testing laboratories’; ISO/IEC Guide 43-1 (1997) ‘Proficiency testing by interlaboratory comparisons – Part 1: Development and operation of proficiency testing schemes’; and ISO/IEC Guide 43-2 (1997) ‘Proficiency testing by interlaboratory comparisons – Part 2: Selection and use of proficiency testing schemes by laboratory accreditation bodies’. The standards for the proficiency testing of chemical methods can be useful when setting up proficiency test for sensory methods. Organizations such as the ISO (International Organization for Standardization/Organisation internationale de normalisation), NMKL (Nordisk metodikkomite for levnedsmidler/Nordic Committee on Food Analysis), EA (European Accreditation), ASTM (American Society for Testing and Materials), ILAC (International Laboratory Accreditation Cooperation) and FAPAS (part of the Food and Environment Research Agency, an agency of the UK Government Department for Environment, Food and Rural Affairs) have made standards and guidelines for proficiency testing, validation and many of these are used by the different national accreditation bodies. In the reference list there are additional guidelines and standards. For objective sensory analysis of food, there are several challenges regarding the methods and materials that are chosen for the proficiency test, especially if the samples are going to be sent over long distances. In this chapter the challenges for a proficiency test of objective sensory panels will be outlined and some suggestions for the proficiency test will be made.

3.2 Design and implementation of proficiency testing It is important to consider the type of sensory panel and the material that is selected as a proficiency test item when designing a proficiency test for

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sensory panels. A sensory panel may not be trained or be accredited for it to evaluate all kinds of food material; such a specialised panel could be a sensory panel where the assessors are tested, selected and trained to have a high performance to evaluate wine (ISO 5496, 1992, ISO 6658, 1985, ISO 8586-1, 1993, ISO 8586-2, 1994, ISO 8589, 1988, ISO/CD 13300 – 1, Part 1 and Part 2, 2002, NMKL Procedure No. 6, 1998). It can be extremely difficult for such a panel to evaluate, for example, vegetables. This must be taken into consideration, otherwise it would be similar to using a pH electrode specially designed for measuring pH in cheese for measuring pH in wine. Another matter is how to choose the material for a proficiency test item. Sizeable amounts of material are needed for sensory evaluation, and it has to be stored and transported in a way that keeps the sensory quality constant over time. If the test is international and performed on food from the marketplace as the proficiency test item, it must be taken into consideration that the same product can be modified for each market in a way that the product differs in sensory quality from country to country, implying that the sensory characteristic is also different. Another consideration in international proficiency testing is the vocabulary used. It is vital that the definition of the different attributes is exactly the same in all languages. Terminological consistency is key here. The coordinators are responsible for organising all activities involved in the proficiency test from designing and setting up the proficiency test to the final report. In addition to this, confidentiality is something that has to be taken into consideration. The outcome of the proficiency test can be critical for some laboratories. Normally the identities of the participating sensory panels are kept confidential and only known to a minimum of persons involved in the coordination group. It can also be practical to get an agreement about the rights to use the results from the proficiency test. An overview of the different steps in designing a proficiency test is outlined in Table 3.1. The first two steps comprise the selection of a method and material for the proficiency test item. Sections 3.2.1 and 3.2.2 discuss the selection processes in more detail. Step 3 is a guideline for preparing the proficiency test item and the execution of the test. The guideline must be written in clear and simple language and contain a detailed description of the preparation such as sample size, heat treatment, serving temperature, coding, serving material (e.g. coloured glass, white porcelain bowl) and which order to serve the item for the assessors, number of replicates, which palate cleaner to use between samples, how to collect the results, the time frame of the session, numbers of samples and breaks in a session. If necessary, there can also be a guideline for setting up the software program for executing the sensory test. Guidelines for training the sensory panels are made in step 4. Before the proficiency test, the sensory panel has to be trained in the vocabulary and the scale. The training will depend on the method and the proficiency test

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Table 3.1 The different steps in a proficiency test Step

Description

Step 1 Step 2 Step 3

Selection of method Selection and test of material for the proficiency test items Guidelines for preparation of the proficiency test items and the execution of the test Training material and guidelines for training Coding and consignment of the proficiency test items to the participating laboratories Proficiency test Collection of data from the proficiency test round Analysis of data and report of the results

Step 4 Step 5 Step 6 Step 7 Step 8

item. This can be compared with the calibrations that are necessary before many chemical analyses. In the guidelines, there must be a description of all the attributes, how to use the scale and the use of the training samples. Step 5 relates to coding and consignment of the proficiency test items. If the laboratory that is preparing and sending the proficiency test items is also participating in the proficiency testing, it can code and send the samples to another laboratory; this laboratory then makes a new coding and the proficiency test items can be sent to all the participating laboratories. Step 6, the proficiency test, includes all the participating laboratories conduct the proficiency test according to the guidelines. Each laboratory makes a report including all details about the progress of the proficiency test and all the results. The coordinator collects all the reports in step 7 and checks that everything has been carried out according to the guidelines. Step 8 is data analysis and a report of the conclusion of the performance of the participating laboratories (see also Section 3.4).

3.2.1 Methods Most sensory panels work with more than one method and often with both descriptive tests and discriminative tests (ISO 3972, 1991, ISO 4121, 2003, ISO 6564, 1985, ISO 8587, 1988). It can be useful to choose a simple test such as basic taste of liquids or a ranking test. Here, levels of different concentrations can be tested, so determining at which concentration levels the sensory panel is reliable and robust. On the other hand, if the sensory panel most often works with sensory profiling tests, it is necessary also to use these kinds of test in a proficiency test. When using a profiling test, it can be discussed if the panel should develop its own vocabulary, or if the vocabulary should be defined before the test. If the vocabulary is defined before the test, then all sensory panels must use the same set of attributes (ISO 5492, 1992). The first can be an advantage for the sensory panels, but

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will complicate the collection of data and the data analysis. Scales can also give the same problem; if, however, continuous scales are used by all, the problem is smaller compared to some using category scale and some line scale. Keeping the method simple and using standard methods is key here.

3.2.2 Material The material that is chosen must be manageable for it to be sent to the different participating laboratories without delay or damage; also preparation for the sensory evaluation must be kept simple. At each step in the preparation, thawing, mixing, diluting, heat treatment, etc., there are opportunities to introduce bias and then the samples prepared in one laboratory will not have exactly the same sensory characteristics as for the same sample prepared in another laboratory. In that respect, the guidelines for preparation of the samples must be clear and easy to understand. If real food items are chosen, it must be checked that the batch is from the same production day. If the participating laboratories are asked to buy the item themselves, it must be checked that content and sensory quality are exactly the same for all participating laboratories. The easiest way to overcome this problem is to decide that one laboratory is responsible for buying or producing the materials and sending it to all the participating laboratories. The proficiency test item must be assessed for sufficient homogeneity and stability. It must be ensured that the proficiency test item is unchanged during storage or transport. If it is essential to keep the temperature constant or within a fixed interval during transport, it can be necessary to make a dummy shipping before the real proficiency test item is sent to the participating laboratories. Assessment of homogeneity will depend on the material. It can be liquid or solid, but it also has to be taken into consideration that the material may be mixed or diluted before the proficiency test. One way of assessing the homogeneity and stability of the proficiency test item is to prepare a package with samples of material and make a trial shipment, ensuring the distance and transport are realistic. When the samples arrive, it can be ascertained whether the sensory quality is the same as when it was sent. Since the sensory profile of many food materials will change over time even at a constant temperature, the timescale for the proficiency test in the different laboratories must be very tight, and the ideal solution would be that all the sensory panels do the proficiency test on the same day. The amount of material must be large enough to ensure that all assessors will get the right amount of sample material. There must also be material for training of the sensory panel. If the proficiency item is within the range of food material that the sensory panel normally evaluates, the panel needs one training session before the proficiency test and in other cases two. The guidelines for the training must include an account of the training samples and a detailed description of the definition of the sensory attributes.

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The test material can be in a concentrated form to make it easier to send/ transport. In that case the water for diluting must be specified; a solution can be to use bottle water from a specified spring. Water is not sensoryneutral and therefore it must be standardised as to the type/brand of bottled water to be used in the proficiency test. Some examples of design for proficiency testing are now given. In proficiency tests described by Tomic et al. (2009), the proficiency item was a type of candy, wine gums, produced specially for the proficiency test by a large candy company. The formulas for the wine gums were designed to give five different samples with variation (low, high and medium) in sugar and acid content. Sample 1 had high sugar and low acid, sample 2 had high sugar and high acid, sample 3 had medium sugar and low acid, sample 4 had low sugar and low acid, and sample 5 had low sugar, and high acid. All wine gums had the same content of raspberry flavour and intensity of red colour. The methods were sensory profiling with nine attributes: sugar coat, transparency, acidic flavour, raspberry flavour, sweet taste, biting (strength used in the first bite), hardness, elasticity and stickiness (to the teeth in the mouth). The scale was a 15 cm unstructured line scale with anchor points. Tap water was used to clean the palate between each sample. Each of the five samples was evaluated in three replicates, resulting in a total of 15 samples to be tested by each panel. The results were collected in a spreadsheet according to the guidelines, sent together with the samples. In this proficiency test 26 sensory panels participated, 10 from Norway, 1 from the United Kingdom, 2 from Sweden and 13 from Denmark. The panels were industrial sensory panels that usually perform quality control and panels from research institutes. Another example of proficiency test item and set-up is from a proficiency test of sensory profile panels by McEwan et al. (2002). Here the proficiency test items were six commercially available red wines deemed to have notable and distinguishable characteristics, selected by a large professional wine importer in Sweden. The samples, 750 ml bottles, were wrapped in aluminium foil before coding to keep the samples anonymous. Twelve sensory panels participated and they were asked to generate their own vocabulary (up to 30 attributes), but were required to cover odour, taste, mouthfeel, aftertaste and the four basic tastes as defined by ISO. The sensory panels used different kind of scale, most of them a continuous scale from 0 to 100, but one panel used a category scale from 0 to 9. McEwan et al. (2002) found that wine was a very difficult proficiency test item to work with, the main reasons being that if the panels were inexperienced in profiling wine, they needed more training than just one day, which is the normal time for training in connection with a proficiency test. McEwan et al. (2003) also did a proficiency test where the method was a ranking test. Here the proficiency test item was apple juice with five mixtures of sugars added (glucose and fructose), diluted with bottled natural mineral water. Each mixture comprised 50 ml of apple juice, 50 ml of water

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and 6.5 g of one of the five sugar mixtures. In the proficiency test, 14 sensory panels participated from eight European countries (the UK, Ireland, Spain, Italy, Denmark, Norway, Sweden and Finland). The assessors were asked to rank the apple juice samples according to perceived sweetness intensity. The ranking method followed the ISO standard, but with the exception that the panels were allowed to use their normal procedure: either 1 = most and 5 = least or 1 = least and 5 = most. Before data were analysed, the rank order was converted to follow the ISO standard. A ring trial with hard cheese was described by Hunter and McEwan (1998). Twelve different varieties of hard cheese were selected and the criteria for selection were: bovine origin; internationally traded; having different sensory characteristics; and being available to the project. The method used was sensory profiling. Seven sensory laboratories from Denmark, France, Germany, Italy, Norway, the UK and Switzerland participated in two ring trials. In the first ring trial, laboratories used their normal methodology for quantitative descriptive profiling. After the first ring trial the participating laboratories agreed on one vocabulary, which was communicated to all partners in English, with a definition for every attribute. The vocabulary consisted of the following attributes: • for odours: animal, strength, acid, fruity, creamy, yoghurt, ammonia and hay/grass; • for tastes: salt, acid, bitter, strength, ammonia, animal, creamy, sweet, fruity, toasted and pungent; • for texture: rubbery/elastic, crumbly, grainy, hardness, melting and coating/adhesive. This vocabulary was then independently translated into the language of the sensory panel by the sensory scientist at each laboratory. In the second ring trial in this study, the laboratories in Denmark, Norway and the UK required only a short period of training (2 to 5 h) on the new vocabulary which was not very different from the vocabulary they used in the first ring trial. In contrast, the laboratories in France, Germany, Italy and Switzerland required 5 to 10 h of training since they were asked to make large changes to their normal procedures. These examples illustrate the importance of choosing methods and the proficiency test item. When sensory panels are going to use new methods, materials or attributes it is necessary to give the laboratories more time for training the sensory panels.

3.3 Panels If the proficiency test is designed for sensory panels evaluating only one kind of food material, as for example wine, then the obvious choice for the

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proficiency test item is wine. On the other hand, if the panels participating in the proficiency test are panels evaluating either different kinds of food or only one kind of food, then the proficiency test item must be chosen in such a way that the test will validate the sensory panels analytical performances and not focus on how good the panel is at including new matrices.

3.4 Analysis of data/validation of results First of all, the coordinator must go through all reports from the participating laboratories to see if the guidelines have been followed and look for errors: for example, the panel might have turned around the scale or used the scale incorrectly. The next step is to collect all the results in one spreadsheet and check for errors in the raw data, for example 102 instead of 10.2. In proficiency tests for chemical laboratories the analysis of performance is often expressed in the standardised form of a z-score. A z-score relates the error in a result to the designated standard deviation of the results for the analysis in question. In sensory analysis, the panels not only assess one analyte, but in sensory profiling the intensity of many different attributes or the panel make a ranking order, and therefore the use of a z-score is not very suitable for assessing the panel performance. There will normally be some variation among assessors in one panel and there will likewise be variation between different sensory panels. For welltrained sensory panels, there can be differences between the assessors in how they are using the scale and also for the different attributes even if there is a definition for each attribute. If sensory profiling is used in a proficiency test this can give problems in cases where the performance of the sensory panels only is validated by the value of intensity for each attribute. It is necessary to look at the pattern to see how well the panel is discriminating between the samples. Therefore the sensory distances between the material used for the proficiency test item is very important. If the differences are too big, the task will be too easy for the panels. Conversely, if the differences are too small it will create an even bigger problem, especially if some of the sensory panels also use different scales. In this respect it is difficult to talk about a true value and it would be more useful to have an expected profile for the proficiency test item. The ranking between the different attributes in the different samples must be the same for all the sensory panels participating in the proficiency test. Given that these sensory profiles cannot be known in advance, unless previous data are available, the expected profile can be derived as a consensus from all the data provided in the proficiency test round. However, there must be a sufficient number of panels to have confidence in such a consensus. Furthermore, one panel cannot be allowed to distort the consensus. Moreover, in a proficiency test

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round, the panels could possibly all be good or bad. Therefore, the consensus approach is easy, but it does not always offer the best solution. One way to overcome this is to make the samples by mixing different kinds of ingredient, such as sugars with known sweetness, rancid oil, salt or different spices in different concentrations. Of course one should be aware of how the mixtures influence the intensity of each others. Univariate as well as multivariate statistical methods can be used in the data analysis. The univariate methods focus on the differences for each attribute while the multivariate methods look at differences at a more general level, such as a pattern, taking into account correlations between the attributes. A sensory panel only rarely assesses one attribute and therefore the multivariate methods are much more suitable for comparing the performances of sensory panels. The data analysis for the proficiency test described by Tomic et al. (2009) where 26 sensory panels participated was done with PanelCheck (see more details in Section 3.2.2.). PanelCheck software is open source and may be downloaded, distributed and used for free. The program is designed to make assessor validation, but it can be used for panel validation as well. Of course other statistical programs can be used. In the study, data analyses were carried out first at a global level, based on data from all 26 panels where each panel was treated as if it was an ‘individual’ assessor. This means that the performance of panels was visualised by the different plots. As a result from this process Tomic et al. identified 3 of the 26 panels that needed further analysis at a more detailed level. The methods they used were mixed model ANOVA, Tucker-1 plot, Manhattan plot, one-way ANOVA based F plot, MSE plot, p*MSE plot, profile plot and line plot. They argued that the reason for using multiple plots and their methods was that each of the plots contains unique information on panel and assessor performance. Plots from such an analysis with performance information can also be used by panel leaders as feedback to improve panel performance and performance of individual assessors.

3.5 Panel performance The results from proficiency testing are one of several tools that enable laboratories to evaluate and demonstrate the reliability of the data produced. In addition to validation and accreditation, proficiency testing is an important demand of the European Union and is increasingly important in laboratory accreditation. Accreditation bodies will also ask laboratories for more detailed information of the panel performance. To give a full picture of the panel performance one has to take a longer-term view and not look at one test only. Owing to the special problems with true values in sensory analyses it is recommended that the sensory panel participate in proficiency tests that use different kinds of sensory methods.

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When setting up the significant levels or the levels of acceptable performance, they must be chosen for each proficiency test item. They should reflect the panel performance and not how adaptable the panel is to new products/materials. McEwan et al. (2003) show how too tough criteria can cause only the top expert panels to perform well in the proficiency test they took part in. As a supplement in the validation of the panel performance, the panel leader can use reference materials that are within the sensory range of the sensory panel’s normal working area. Such references can be used in the daily routine of the sensory panel.

3.6 Glossary Coordinator

The person responsible for coordinating all activities involved in the operation of a proficiency test. Proficiency test The determination of laboratory testing performance by means of interlaboratory comparison. Proficiency test item The material used in the proficiency test. Proficiency test round A single complete sequence of circulation of proficiency test items, to all participating laboratories, in a proficiency test scheme. Proficiency testing scheme The system for objectively checking laboratory results by means of an external agency. Reference material Material or a substance with one or more properties sufficiently homogeneous and well established to be used for calibration and/or training. True value The actual concentration of the analyte and for sensory analysis the intensity of the attributes in the matrix. Ring trial A single complete sequence of circulation of proficiency test items to all participants in a proficiency test scheme.

3.7 References and further reading astm E1301-95 Standard guide for proficiency testing by interlaboratory comparisons. astm STP758 Guidelines for the selection and training of sensory panel members. ea-4/09 (2003) Accreditation for sensory testing laboratories. en iso/iec 17025 General requirements for the competence of testing and calibration laboratories (2005).

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hunter, e.a. and mcewan, j.a. (1998). Evaluation of an international ring trial for sensory profiling of hard cheese. Food Quality and Preference, Vol. 9 (5), 343–354. ilac-g13:2000 Guidelines for the requirements for the competence of providers of proficiency testing schemes. iso 17025 (1999) General requirements for the competence of calibration and testing laboratories. iso 3534 (1993) Statistics – Vocabulary and symbols. Part 1: Probability and general statistical terms. iso 3972 (1991) Sensory analysis – Method of investigating sensitivity of taste. iso 4121 (2003) Sensory analysis – Guidelines for the use of quantitative response scales. iso 5492 (1992) Sensory analysis – Vocabulary. iso 5496 (1992) Sensory analysis – Initiation and training of assessors in the detection and recognition of odours. iso 5725-1 (1994) Accuracy trueness and precision of measurement methods and results. Part 1: General principles and definitions. iso 6564 (1985) Sensory analysis – Flavour Profile Methods. iso 6658 (1985) Sensory analysis – General guidance. iso 8586-1 (1993) Sensory analysis – General guidance for selection, training and monitoring of assessors – Part 1: Selected assessors. iso 8586-2 (1994) Sensory analysis – General guidance for selection, training and monitoring of assessors – Part 2: Experts. iso 8587 (1988) Sensory analysis – Ranking. iso 8589 (1988) Sensory analysis – General guidance for the design of test rooms. iso guide 30 (1993) Terms and definitions used in connection with reference materials. In: International vocabulary for Basic and General Terms in Metrology, 2nd Edition, ISO, Geneva. iso guide 34 (2000) Reference materials – General requirements for the competence of reference material producers. iso guide 35 (2006) Reference materials – General and statistical principles for certification. iso/cd 13300 – 1 (2002) Sensory analysis – General guidance for the staff of a sensory evaluation laboratory – Part 1: Staff responsibilities. iso/cd 13300 – 1 (2002) Sensory analysis – General guidance for the staff of a sensory evaluation laboratory – Part 2: Recruitment and training of panel leaders. iso/iec 17025 (2005) General requirements for the competence of calibration and testing laboratories. iso/iec guide 43-1 (1997) Proficiency testing by interlaboratory comparisons – Part 1: Development and operation of proficiency testing schemes. iso/iec guide 43-2 (1997) Proficiency testing by interlaboratory comparisons – Part 2: Selection and use of proficiency testing schemes by laboratory accreditation bodies. mcewan j.a., hunter, e.a., van gemert, l.j. and lea, p. (2002) Proficiency testing for sensory profile panels: measuring panel performance. Food Quality and Preference, Vol. 13, 181–190. mcewan j.a., heiniö, r.-l., hunter, e.a. and lea, p. (2003) Proficiency testing for sensory ranking panels: measuring panel performance. Food Quality and Preference, Vol. 14, 247–256. nmkl procedure 6 (1998) General guidelines for quality assurance of sensory laboratories. nmkl procedure 14 (2004) SENSVAL: Guidelines for internal control in sensory analysis laboratories. nmkl procedure 16 (2005) Sensory quality control.

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nmkl procedure 20 (2007) Evaluation of results from qualitative methods. panelcheck software. www.matforsk.no/web/sampro.nsf/webTemaPE/ PanelCheck!OpenDocument thompson, m. and wood, r (1993) The international harmonised protocol for the proficiency testing of (chemical) analytical laboratories. Pure and Applied Chemistry, Vol. 65, 2123–2144. tomic, o., luciano, g., nilsen, a.n., hyldig, g., lorensen, k. and næs, t (2009) Analysing sensory panel performance in a proficiency test using the PanelCheck software. Accepted by European Food Research and Technology, Vol. 230, No. 3, 497–511.

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4 Sensory methods for quality control L. L. Rogers, Consultant, UK

Abstract: The chapter includes a reminder about the importance of choosing the right method, agreeing how the results will be used and the impact of choosing the wrong test for the objective. An introduction to the use of action standards is included. The chapter gives an overview of a large number of tests, giving advice on which are the best and most popular in the world of quality measurements. Each test section has: an introduction to the test including popularity, advantages and disadvantages, example uses; samples, including what format and how many, how much is required, balanced designs; panellists, number and level of training; example test set-up, for example, the ballot paper/test sheet; and references for further reading and information. Key words: sensory science, action standards, quality methods.

4.1 Introduction This chapter is concerned with the sensory methods available for quality control in the food and beverage industry. Several methods are described and discussed, including their popularity and applicability to quality control situations. Where methods are highly aligned to quality control, further detail is given in each method section: • introduction (including popularity, advantages and disadvantages, example uses); • samples (quantity, type, balanced designs); • panellists (number and level of training); • example test set-up (e.g. questionnaire/ballot paper/test sheet); • references for additional information. Many industries are still not using sensory science to its full capabilities in quality control. There seems to be a continued reliance on experts to judge sensory quality and in some industries, where they have moved away from experts, they have moved to the use of small numbers of panellists

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making subjective rather than objective measurements. In the future it would be beneficial if the food industry paid more attention to the sensory attributes of their products and how the changes in these attributes can affect consumer liking. This change would see industries using recommended objective sensory methods, linked to statistical process control and agreed action standards. Consumer-led quality methods mean that the identification of the sensory characteristics most important to the consumers’ view of ‘quality’ is the way forward. Consumers will not continue to buy a product if it does not meet their expectations – and they may not complain: in today’s busy environment they are more likely never to buy the product again. These consumer-led quality methods could also be linked to instrumental measurements, to aid production facilities in making excellent products time and time again. It can be daunting when starting out in your sensory science career (and even later!) to see the huge number of sensory methods available. How can the right method be selected to meet the test objective? The objective of the test and the manner in which the results will be used are key to the selection of the correct test method. There may also be constraints due to facilities which mean that some methods will not always be available. Obviously this needs to be taken into account when deciding which method to use. One of the most important aspects to consider before selecting which method to use is to be clear of the objective of the sensory study. This will involve finding out different pieces of information to determine exactly why the sensory test is required so that the test can be designed to meet the objective. For example there is little point conducting a full sensory profile if the client only wishes to know if there is a texture change because of the introduction of a new ingredient supplier for a thickening agent. Another important consideration before deciding which method to use is how the results will be collected, analysed and subsequently used. A useful technique is the use of action standards as these can be incredibly helpful in designing the test. An action standard (AS) defines the aim of the overall experiment but also states the action (or next steps) to be taken dependent upon the results. In the example above, where a new supplier was under discussion for the thickening agent, the action standard might have read: AS1: ‘If the sensory test confirms that there is a textural difference between the new supplier and our existing supplier for product X, we will not proceed with the new supplier.’

The objective is clear: the client wishes to know if there is a difference in texture, but also will be rejecting the new supplier if there is a difference in texture. In this case a simple difference test could have been selected to determine if there was a texture difference. The action standard below might have resulted in a totally different sensory approach:

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AS2: ‘If the sensory test confirms that there is a textural difference between the new supplier and our existing supplier for product X, we will need to understand what the textural difference is and if there are any other changes to product X as a result of the supplier change. Our existing supplier will stop producing at the end of December so it is critical we find a new supplier.’

The sensory scientist may well have chosen to ask the client how likely they thought a difference might be between the new supplier’s ingredient and the existing supplier. If it was likely that there would be a large difference they may have decided to go directly to conducting a full profile to understand what the differences were. These differences would be critical in understanding the effect of this change on consumers’ reactions to product X. If the change threatened key drivers of liking, further discussions with the new supplier may be required to achieve a match. AS3: ‘If the sensory test confirms that there is a textural difference between the new supplier and our existing supplier for product X, we will need to understand what the textural difference is and if there are any other changes to product X as a result of the supplier change. We also need to understand how this difference is related to the natural variance in our product.’

In this case the sensory scientist may well decide to carry out some batchto-batch variability tests and determine where the new ingredient batch fits. The difference from control method might be a useful starting point to gather data for this test as several batches may be included in one test. AS4: ‘Is there a textural difference between the new supplier and our existing supplier for product X?’

The example above (AS4) is an example of a poor action standard. The next stage after understanding if there was a difference or not is not documented and therefore the choice of test is a difficult one. It is more likely that the wrong test would be chosen and then, when the results are reported, the client would be questioning what the next steps should be: the sensory scientist will probably not have the information at hand to guide the client. These examples indicate how knowing the objective and knowing what the next steps will be as an outcome of the sensory study, are vital in the choice of sensory test. Some of the method categories below are not suitable for day-to-day quality assessments but can be used in the set up of a QC programme, so have been included in this discussion (Costell, 2002). In each case it is not the test method alone which will result in the desired outcome, but also the manner in which the test is conducted and how the results are analysed and used (Costell, 2002). Table 4.1 gives an overview of all the tests and the relative complexity of each. Table 4.2 gives the number of panellists and the level of training and experience they might require for each method.

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Method

Descriptive specification ‘In/out’ (or pass/fail) Difference from control A not A Paired comparison (e.g. 2AFC) Scaling (including targeted scaling) Ranking Triangle test Quality scoring/grading/rating Magnitude estimation Duo–trio In-house methods DIY

4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.11 4.12 4.12

Overview of sensory methods

Section

Table 4.1

High High High Medium Medium Medium Medium Low High Low Low High High

QC relevance Medium Low Medium Low to medium Low Low to high Low Low Low Low Low Variable, generally low Variable

Time to conduct test

High Medium Low to medium Medium Low High Low Low Medium to high Medium Low Variable, generally low Variable

Time to set up methodology

High Low Medium Low Low Medium to high Moderate Low Medium to high Low to medium Low Moderate Variable

Level of detail gained from results

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Table 4.2 Recommended number of panellists Recommended number of panellists (highly trained panellists)

Panellist training and experience

(10) 25 (10) 30 (18) 20 (10) 30 (20)

High Medium Low to medium Medium Low

Variable

High

30 (5) 24 (18) 8–12 (5)

Low Low Medium to high

4.11 4.11 4.12

Descriptive specification ‘In/out’ (or pass/fail) Difference from control A not A Paired comparison (e.g. 2AFC) Scaling (including targeted scaling) Ranking Triangle test* Quality scoring/grading/ rating Magnitude estimation Duo–trio In-house methods

Variable 32 (15) Variable

4.12

DIY

Variable

Medium Low Variable generally low Variable

Section

Method

4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10

* See ISO 4120:2004 for more details on number of panellists.

4.2 Descriptive specifications (DS) method The descriptive specifications (DS) method is also known as the comprehensive descriptive method (Muñoz et al., 1992) and descriptive analysis method (Lawless and Heymann, 1999). The basis for this popular method lies in the development of sensory specifications for finished products. A sensory specification is similar to other specifications and is a vital part of ensuring product quality. Specifications detail exactly what the product should look like, smell like and taste like and can easily be extended to texture measurements where necessary. An example semi-quantitative sensory specification is given in Fig. 4.1 and a fully quantitative example is given in Fig. 4.2. The sensory specification is built around those attributes which are known to contribute to consumer acceptance of the product and it is this aspect of the method which requires large resources during its conception. The method is very objective as it does not require the panellists to make any subjective judgements on the product’s quality as such. This judgement is made by the sensory scientist in the interpretation of the data. The method gives very actionable results which can be correlated to both instrumental and consumer measurements. A well-trained sensory panel of around 10 screened panellists is required to measure the levels of a selection of attributes, generally for finished

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Date: Product:

Fruit drink 1

Pack type and size:

380 ml PET bottle

The product must be free from off-odours, taints and foreign particles Intensity scale:

None Slight Moderate Strong Very strong

Appearance

Evaluated by looking at the product in a clear sampling cup under artificial daylight before and after swirling. A very strong orange coloured liquid that is bright, clear and still. The liquid appears thin and does not leave a residue upon swirling.

Aroma

Evaluated by smelling from the sampling cup before and after swirling. A moderate lemon and moderate orange aroma that is moderately sweet. A slight aroma of vitamin C tablets and slightly floral.

Flavour

Evaluated by taking sips from the sampling cup. A moderate lemon, moderate orange flavour that is also moderately acidic and moderately sweet. A slight flavour of vitamin C tablets and also slightly bitter in flavour.

Mouth-feel/Texture

Evaluated at the same time as the flavour and by taking more sips from the sampling cup. Slightly drying mouth-feel. The liquid feels slightly thicker than water in the mouth.

Aftertaste/Afterfeel

Evaluated after swallowing – no extra sips taken. A moderate citrus aftertaste that is also moderately acidic and moderately bitter. Moderately drying and a moderate teeth/mouth-coating afterfeel. A slightly sweet aftertaste.

Glossary of terms example Flavour/aftertaste Orange A bitter orange flavour. Like the flavour of Seville oranges. Lemon A sour lemon flavour. Like fresh lemons Acidic The basic taste of citric acid solution. Sweet The basic taste of sucrose solution. Vitamin C tablets The flavour of vitamin C tablets. Like Haliborange. Bitter The basic taste of caffeine solution. Citrus The tangy flavour of general citrus fruits such as lemon, lime and orange.

Fig. 4.1 Example specification for a fruit drink.

products. Samples are usually taken from daily production batches. The quantity required is based on each panellist making one assessment of all the modalities under consideration in the specification. This can be done with a fairly simple paper ballot or using standard sensory software. The method can be semi-quantitative (see example questionnaire given in

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Product 7, batch 123 Example data and specification (used for decision making and not seen by panellists) Attribute Appearance: colour intensity Appearance: brightness Aroma: lemon Aroma: orange Aroma: vitamin C tablet Flavour: lemon Flavour: orange Flavour: vitamin C tablet Flavour: acidic Flavour: sweet

Results for sample 123

Sensory specification

5.2 7.0 3.0 4.3 1.5 3.0 6.1 1.0 3.0 8.2

4.5–6.0 6.5–9.5 2.0–4.0 4.0–6.0 0–1.5 2.0–4.0 5.0–7.0 0–1.5 2.5–4.5 7.5–8.5

Fig. 4.2 Example data and specification for the fully quantitative descriptive/ specifications method.

Fig. 4.3) or fully quantitative by adding line scales for each attribute measured (an example questionnaire is given in Fig. 4.4). In the fully quantitative method, if the attribute measurements are out of specification then the product is deemed unacceptable by the sensory scientist. The panellists are not aware of the attribute intensity levels built into the specification and are therefore not making a judgement on product quality. They are acting as an instrument and the data they produce is used to help decide if the product meets specification. In the semi-quantitative method the panellist is checking if each attribute is present at the correct level. The information from each panellist is passed to the sensory scientist to decide if the product meets specification. The semi-quantitative method is useful for line-side assessments and can be particularly useful for checking preliminary products. The fully quantitative method is recommended for final product evaluation. The sensory specification can be set by management or by the additional use of consumer data. The setting of sensory specifications with input from consumer data is incredibly useful as it gives information about the attributes that drive product liking (and also disliking) but also gives information about the tolerances consumers have to changes in the product. This allows the quality team to make recommendations to management based on these tolerances, rather than rejecting products that may have been acceptable or not rejecting products that were unacceptable. For full details on setting up consumer-led specifications please see Muñoz et al. (1992). An additional dimension of this technique is that it lends itself very nicely to the use of statistical process control (SPC) (Oakland, 2007). This allows production to be monitored over time and drifts in quality highlighted before the product goes out of specification. An example of this is given in Fig. 4.5. For more details on the use of SPC please see Oakland (2007).

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1. Refer to the sensory specification documentation before completing this assessment 2. Tick the box if the descriptor is present 3. Product must be assessed as per make-up instructions Appearance very strong orange coloured liquid bright clear still thin (like water) not leave a residue upon swirling Aroma moderate lemon moderate orange slight vitamin C tablet

Aftertaste moderate citrus moderately acidic slightly sweet moderately drying If there are any additional descriptors or if any descriptors listed here are not present, please consult your manager ................................................................... ...................................................................

Flavour moderate lemon moderate orange moderately acidic moderately sweet slight vitamin C tablet slight bitter

................................................................... ................................................................... ...................................................................

Texture in the mouth slightly drying feels slightly thicker than water in the mouth

Fig. 4.3 Example of the ballot paper for the semi-quantitative descriptive/specifications method. Product 7, batch 123 Instructions: Please rate each of the attributes below according to the standard protocol for assessment, and with reference to the attribute definitions list and intensity training programme. Appearance Colour intensity

|

|

0 Brightness

10

|

|

0

10

Aroma Lemon

|

|

0

10

Fig. 4.4 Example of the ballot paper for the fully quantitative descriptive/specifications method.

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Orange

|

|

0

10

Vitamin C tablet

|

|

0

10

Flavour Lemon

|

|

0

10

Orange

|

|

0

10

Vitamin C tablet

|

|

0

10

Acidic

|

|

0

10

Sweet

|

|

0

10

The attribute list would continue with additional flavour, texture and aftertaste attributes Example definitions Lemon: the fresh lemon aroma/flavour as found in freshly peeled lemon segments Orange: the orange juice aroma/flavour as found in freshly peeled Jaffa orange segments Natural sweetness: basic taste of a sucrose solution Acidic: basic taste of a citric acid solution Example data and specification (used for decision making and not seen by panellists) Attribute

Results for sample 124

Appearance: colour intensity Appearance: brightness Aroma: lemon Aroma: orange Aroma: vitamin C tablet Flavour: lemon Flavour: orange Flavour: vitamin C tablet Flavour: acidic Flavour: sweet

5.2 7.0 3.0 4.3 1.5 3.0 6.1 1.0 3.0 8.2

Fig. 4.4 Continued

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Sensory specification 4.5–6.0 6.5–9.5 2.0–4.0 4.0–6.0 0–1.5 2.0–4.0 5.0–7.0 0–1.5 2.5–4.5 7.5–8.5

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Sensory analysis for food and beverage quality control 8

7

Intensity of raspberry flavour

6

5

4

Attribute intensity Upper control limit Lower control limit

3

2

1

0

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Batch number

Fig. 4.5 Statistical process control (SPC) example.

4.3 ‘In/out’ (or pass/fail) method The ‘in/out’ method is widely used in quality assurance (QA) and quality control (QC) sensory tests due to its ease of setting up and its simplicity in analysis (Muñoz et al., 1992). It can be used for a wide variety of purposes: raw materials, interim products and finished products. A trained panel assesses whether each sample type is ‘in’ or ‘out’ of specification. An example ballot paper is given in Fig. 4.6. The specifications must be documented to limit personal subjectivity. The method differs from the previous DS method in one main factor: the panellists actually make the decision on whether or not a sample is suitable or not. This is one of its main disadvantages as this decision can be quite subjective in nature and can cause problems – especially when wrongly linked to production bonuses, for example. Another main disadvantage is the lack of information provided about the reason for the product failure although this may be built into the method where necessary. Although the method is simple there are many industrial examples where its use could be improved. In some production facilities only one, or sometimes up to five people, take part in these types of assessments. These are informal and conducted verbally based on each person’s experience of

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Instructions Please evaluate the products below in the order shown. Evaluate each sample individually and mark whether it is ‘in’ or ‘out’ of specification in the box provided. Please use the specifications provided to help in your decision making. Sample 871 902 376 299

In

Out

Instructions Please evaluate the products below in the order shown. Evaluate each sample individually and mark whether it is ‘in’ or ‘out’ of specification in the box provided. Please use the specifications provided to help in your decision making. Where you have marked the product ‘out’ of specification please comment why you have made your choice Sample 871 902 376 299

In

Out

Comment .............................. .............................. .............................. ..............................

Fig. 4.6 Examples of the ballot papers for the ‘in/out’ or pass/fail method.

production quality and deviations. Problems can occur when these panellists do not agree on whether a product is in or out of specification (probably the reason why in some companies this job falls to one person) and can tend to lead to people making decisions based on their own personal preferences – not a recommended situation. Relatively easy additions and changes can be made to vastly improve the results. Firstly by documenting the production specifications, the deviations and personal preferences can be kept to a minimum (Carpenter et al., 2000). This is usually conducted by evaluation of a large number of samples and then determining important attributes: those which vary and those important for the product characteristics, and the limits of each of these attributes for successful production. The use of around 25 or more panellists will also improve the data collected by this method. Panel training, particularly in the form of examples of products both in and out of specification, can hugely increase the analytical nature of this method. Documentation of sample preparation and consistent serving methods can also go along way to improving the use of this method. As for many QA and QC methods, the use of action standards vastly improves the decision-making process. Generally the percentage of panellists rating each batch in or out is used for decision making.

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Obviously in an ideal situation the panellists would all rate an individual sample in the same manner, for example 100% of panellists would rate the batch as ‘in’ specification. However, in practice this does not happen and therefore action standards often take the form of ‘60% or more panellists accept the batch’ and therefore the batch is deemed to be in specification, or ‘40% or less accept the batch’ and the batch is deemed to be out of specification. Any batches falling in the area between 40 and 60% would be sent for further analysis to determine next steps (Muñoz et al., 1992). The use of panel monitoring techniques is critical for this type of method and can be easily implemented by the use of ‘hidden control’ products – usually kept from previous rejected batches for this purpose. Further additions, perhaps more complicated and expensive, can be made by the addition of consumer information. However, specifications for this method are generally prepared by management, although the addition of any consumer data where available would be beneficial.

4.4 Difference from control (DFC) method The difference from control (DFC) method is another popular method in daily quality assessments (Muñoz et al., 1992; Lawless and Heymann, 1999) and can also be used for the assessment of batches on a regular basis for ambient products. However, one of the disadvantages is the need for a ‘control’ product. For some food products this is less of an issue as control products can be stored effectively for several months at a time and a new control selected at regular intervals. Where a control cannot be stored the control for each test must be representative of standard production. One solution for this can be to use the descriptive specification method (DS method – see Section 4.2 above) to select the control product and then use this control to assess several batches over the usability period of the control using the DFC method. As the panellists for this test do not need to be as highly trained as those for the DS method, the DFC and the DS methods can be used in conjunction for resource and time saving in a production environment. The test is fairly straightforward to set up and screened panellists require only a short training period to get used to the test: but it is recommended that panellists are monitored in each test by the use of a hidden control (see below). Data analysis can be difficult where statistical significance is employed; however, many companies rely on the mean scores alone for each judgement. The test can be run by a technician but is best analysed with input from a sensory scientist where statistical analysis is required. Although a greater quantity of the control is required there only needs to be enough sample of each batch for the panellists to assess once. If statistical tests such as analysis of variance are to be conducted, the number of panellists required is around 18. However an understanding of batch conformity

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can be gathered with around ten or more highly trained panellists if the data are validated by the measurements for the hidden control. Each panellist is presented with the control product and several other batches coded with three-digit numbers. The number of batches that can be assessed in each test will depend on the product type, but if there is little carry-over or aftertaste issues, up to five different batches could be assessed in one test. The panellist assesses the control product first and is then asked to determine how different each individual sample compares with the control on a scale: generally 0 to 10 where 0 is no difference and 10 is an extreme difference. See Fig. 4.7 for an example questionnaire. The batches themselves are not compared – only compared with the control – therefore making it very resource friendly as there are two to five comparisons to the control in just one test. The DFC method relies on the use of ‘hidden’ control to prove its validity. This hidden control is another portion of the control batch but instead of being identified as such, it is hidden along with the various batches by a three-digit code. The hidden control should be rated by the panellists as being the same as the control with a score of 0 or 1, or perhaps 2 depending on the production variability between batches and between controls. If the hidden control is rated outside these limits the test results are rejected. The panellists must never be aware of the existence of the hidden control nor must its correct identification be used as panel monitoring in a feedback situation. The reason for this is that the panellists

Instructions Assess the sample marked control first. Assess the first sample marked with the 3-digit code. Assess the overall sensory differences between the two samples using the scale below – mark the scale to indicate the size of the overall difference. Difference Scale Code: 123

No 0

2

4

Mod

Difference Scale Code: 456

No 0

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Difference Scale Code: 789

No 0

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6

8

Extreme 10

6

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Extreme 10

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Extreme 10

Mod

Mod

Notes For a computerised system, each sample would be presented on a different ‘page’ or screen. For each coded sample a question about the difference may also be asked and this can also be presented in the form of differences about all modalities if desired.

Fig. 4.7 Example of the ballot paper for the difference from control method.

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will be focused on identifying the hidden control rather than identifying any differences between batches: not the objective of the test at all. This method can be used for production where there is inherent product variability due to its components, in preparation or in serving: for example baked and snack products. The use of standard difference tests would result in significant differences where in fact the difference is just due to the product’s inherent variability. Where the control batches can also be variable, the method can be adapted (Pecore et al., 2006) and two controls are presented. For this example there are four pairs for each panellist: Control 1 versus Control 1 (also called the hidden control), Control 1 versus a second control, control 1 versus the test batch and the second control versus the test batch. The first three comparisons are simply part of the original difference from control test and it is only the fourth comparison that makes up the control variability test. This allows for the test batch to be within the controls’ batch variability and easily detects where the test batch is outside the control batches variability. A further adaptation of this method also considers the variability of the test product, for example in ingredient substitution, by the introduction of a balanced design to eliminate panellists’ fatigue and the need for more sampling (Young et al., 2008). The DFC can also be used as a targeted DFC (TDFC). This is particularly useful if there is prior knowledge about the attributes or modalities that change within production batches. For example if differences are generally seen in sweetness level, then a TDFC can be employed to determine the difference in sweetness between a range of batches. See Fig. 4.8 for an example questionnaire.

Instructions Assess the sample marked control first. Assess the first sample marked with the 3-digit code. Assess the sensory difference in sweetness between the two samples using the scale below – mark the scale to indicate the size of the difference in sweetness. Difference in sweetness No Scale 0 Code: 123

Mod 2

4

6

8

Extreme 10

Difference in sweetness No Mod Extreme Scale 0 2 4 6 8 10 Code: 456 Notes For a computerised system, each sample would be presented on a different ‘page’ or screen.

Fig. 4.8

Example of the ballot paper for the targeted difference from control method.

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4.5 ‘A’ not ‘A’ method The ‘A’ not ‘A’ method is another popular method for QC and QA specialists because it is simple to train panellists for, conduct and analyse. It is particularly useful for production facilities where a small number of different products are made on a regular basis, as panellists become very familiar with these products and hence very familiar with ‘A’. It is also useful where the two samples cannot be exactly the same in appearance (obviously where this modality is not essential to the product quality) but the differences are subtle and only obvious if the two samples were presented together (Lawless and Heymann, 1999). This method is only useful where the inherent variability is low, otherwise it results in the rejection of too many production batches (Muñoz et al., 1992). The method only really gives the answer that the batch is different but does not give information as to the degree of difference or in what format the difference takes – for example, that the batch is sweeter or with a higher flavour intensity. However, the test is very simple to set up and train panellists and easy to analyse the results. The panellists are presented with a sample labelled ‘A’ to familiarise themselves with and then presented with a series of three digit coded samples, some of which are A and some of which are test batches. In training the panellists must become familiar with A prior to taking part in the tests so that the ‘sensory profile’ of A is familiar to them and the initial assessment during the tests just serves as a reminder (BSI, 1988). The presentation to each panellist should be random and different for each assessor. See Fig. 4.9 for an example questionnaire. The number of panellists required depends upon the test objective and the required significance level, but around 20 panellists would be used for

Instructions Assess the sample marked ‘A’ first, then pass back to the test coordinator. The coded samples consist of ‘A’ and ‘not A’ in a random order. All the ‘not A’ samples are identical. The respective number of each of the two kinds of samples is unknown to you. Assess the coded samples one by one and complete the form below Sample code

The sample is ‘A’

123 456 789 234 678

Fig. 4.9

Example of the ballot paper for the ‘A’ not ‘A’ method.

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a typical situation and each would assess five ‘A’ and five ‘not A’ samples. The results can be tabulated to indicate the number of panellists identifying ‘A’ and ‘not A’ correctly and the ratios are then analysed using χ2 (BSI, 1988).

4.6 Paired comparison methods (e.g. 2AFC, n-AFC, simple difference test) The paired comparison test is another simple method to set up, train and analyse but not always ideal for quality environments (Muñoz et al., 1992) as it can be very sensitive to small differences. The test can determine if two samples are different in a particular attribute (directional paired comparison or 2-alternative forced choice (2-AFC) method) or simply if the samples are different (simple difference test). For example in a test with biscuits, the sensory scientist may know that they differ in texture and therefore the panellists would be asked which biscuit is softer in texture: this would be a 2-AFC method (Lawless and Heymann, 1999). The simple difference test has limited usefulness as generally the triangle test or duo–trio are more suitable. However, it can be useful where there is limited sample quantity or where the presentation of three samples is not possible, for example for chewing gums or certain curried products.

4.6.1 2-AFC method There are two presentation orders (AB and BA) and the test is designed so that both orders are presented an equal number of times. The samples are both presented at the same time and the panellist is asked to identify the sample which is higher in the specified attribute. Figure 4.10 gives an example questionnaire for this test. The panellists must understand the attribute under consideration to be able to judge the difference effectively. The results give an indication of the direction of difference between the two samples; however, if the difference in one attribute affects several other attributes (for example the sugar level in biscuits can affect the sweetness and hardness) then this would not be the test of choice.

4.6.2 Simple difference test The samples are again both presented at the same time. Little training is needed as people generally find it easy to decide if the samples are the same or different. Around 20 to 50 presentations are required (Meilgaard et al., 1999) but each panellist assesses each sample only once. Therefore around 60 panellists for a QC application are sensible. There are four presentation orders in this example (AA, BB, AB, BA) and these will be randomised across panellists with an equal number of each order presented. Figure 4.10

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2-Alternative forced choice method Instructions There are two samples for you to assess: please assess them in the order shown below. Please assess the force required to bite off half the biscuit. Please ring the code of the hardest biscuit. 123

456

Thank you for taking part.

Simple difference test method Instructions There are two samples for you to assess: please assess them in the order shown below. Please bite off half of each biscuit. Are the two samples the same or different? Please tick the relevant box. Pair 123 and 456

SAME

DIFFERENT

Thank you for taking part.

Fig. 4.10 Example of the ballot paper for the paired comparison methods.

gives an example questionnaire for this test. The results can only tell the sensory scientist if the samples are different or not – no reason for difference or intensity of difference can be obtained from this method.

4.7 Scaling method (including targeted scaling) Scaling can be useful where a quality unit is closely assigned to a Research and Development (R&D) unit and therefore has use of the R&D quantitative descriptive profiling panel. The method can be used with a quality panel if the time is available for training and panel monitoring. The method is closely linked to quantitative profiling but generally the sensory scientist selects only the attributes that are known to change in production for daily monitoring of the key sensory characteristics. The method is not very popular due to the amount of panel training required when there is no access to an R&D panel, and the statistical data analysis element requires time and statistical training for the test coordinator. It can be useful though for the measurement of particular attributes that are known to change but yet are key to consumers’ liking of the product. This is

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particularly helpful where the product’s characteristics may be changed by blending or reworking as there is quantitative data to help with these adjustments. Screened and trained panellists rate the specific attributes on line scales for each sample. Samples are coded with three digit numbers and presented monadically to each panellist in a randomised balanced design. The rating can be performed using paper questionnaires but where sensory data collection systems are in use this method is much easier to gather and analyse data. An example questionnaire is given in Fig. 4.11. The panellist assesses each sample and marks on the line scale the intensity of the given attribute. For panellists that are not part of a quantitative panel it can be useful to give a warm-up sample with a given intensity and a further known-intensity sample can be used for panel monitoring. A useful adaptation of this method is the scaling of only one attribute. This is known as targeted scaling. The method can be especially useful where an R&D panel is not available as QC panellists can become very skilled in the understanding of the attribute and its scaling.

Instructions: Please rate each of the attributes below according to the standard protocol for assessment Appearance Colour intensity

|

|

0

100

Brightness

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|

0

100

Aroma Lemon (the fresh lemon aroma/flavour as found in freshly peeled lemon segments)

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100

Orange (the orange juice aroma/flavour as found in freshly peeled Jaffa orange segments)

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Vitamin C tablet (the aged orange aroma as found in Co-Op brand Vitamin C tablets)

|

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0

100

Fig. 4.11 Example of the ballot paper for the scaling method.

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4.8 Ranking test The ranking test is very simple to set up, panellists need little training and the analysis is simple, but in terms of quality assessments it is not especially popular. This is due to the manner in which the sensory question is asked in this test. Panellists are asked to put the samples in order (rank) of some attribute (BSI, 1989). For example they may be asked to rank the samples in order of sweetness or creaminess. As potential quality issues may be linked to several attributes (and not just creaminess alone, for example) this can limit the usefulness of this test. However, if the production variability is known, and that generally creaminess variability is the main issue, then this method can be very useful. Screened and trained panellists are presented with four or five samples in a random order and asked to rank them in order of the specified attribute or give the samples equal rank. See Fig. 4.12 for an example questionnaire. The use of data collection software can be very useful here as the panellists can simply drag and drop each sample in rank order or drop equal ranked samples into the same ‘box’. For quality methods one of the samples must be the verified control and it can be useful to have a hidden control on certain occasions. The hidden sample can also be useful to check panel performance. Please see notes on the use of a hidden control in Section 4.4 above. The minimum number of panellists required for this test is five (BSI, 1989), but this is not recommended as the more panellists you have the

Instructions Assess the samples in the order shown below. Note the intensity of the creaminess of each sample. Write ‘1’ in the box of the sample which is the least creamy. Write ‘2’ for the next creamy and ‘3’ for the next and ‘4’ for the most creamy. If two samples appear the same please rank them with the same number. Handy tip: as you assess each sample, place it in front of you in the order of creaminess – this makes it easier to fill in the boxes below when you have finalised your decision. Sample code

Rank Order

123 456 789 234 Thank you for taking part.

Fig. 4.12

Example of the ballot paper for the ranking method.

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greater the quality of the data. Carpenter et al. (2000), recommend a minimum of 30 panellists and analysis of the data with the Friedman rank test (O’Mahony, 1986).

4.9 Triangle test Muñoz et al. (1992) state that the triangle test is not an ideal method in QC as it can be very sensitive to small differences and can therefore create too many false positives. However, where a product can withstand very little difference, and consumers require low variability, it can be useful. Screened and trained panellists are presented with three coded samples. They are told that two of the samples are the same and one is different and then asked to identify the ‘odd’ sample. There are six possible presentation orders (AAB, ABA, BAA, BBA, BAB, ABB) and therefore it is recommended to conduct this test with groups of six assessors (6, 12, 18 . . .) to include all the presentation orders in each case. The ISO standard (ISO4120:2004) for triangle tests gives a very detailed overview of the number of panellists required dependent upon the objective of the test. An example questionnaire is given in Fig. 4.13. An additional question may Instructions There are three samples for you to assess. Two of the samples are the same and one is different. Please assess them in the order shown below. Please ring the code of the odd sample. 123

456

789

Thank you for taking part.

Fig. 4.13

Example of the ballot paper for the triangle test method.

be added to the questionnaire to gain information about the nature of the difference. It is not recommended to give direct feedback on whether the panellist detected the ‘correct’ odd sample, as in the situation where samples are generally not different (as decided by the whole panel), this can lead to the panellists feeling that they always fail in this test situation.

4.10 Quality scoring/grading/rating method The quality scoring method is a common method for quality control and is often developed for the company’s specific product(s). The training and

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experience levels of the panellists taking part in these tests are generally high as the method relies in part on the panellist’s memory of the ideal product (often seen in scales of ‘typicality’) and also the panellist generally needs experience of the day-to-day issues and changes occurring within the product. Another factor in the panellist’s training requirements with this method is the fact that they will be making the decision about the product quality: usually by scoring, grading or rating the effect of the changing attributes on the end quality of the product. This type of method can often be seen in use for commodity products such as milk and fish and are often supported by an industry consensus. For example the American Dairy Science Association developed a scale used for milk products: 10 and 9 (excellent), 8 (good), 7 (fair), 6 (poor), to less than 6 being ‘unacceptable, a probable consumer complaint’. In some cases specific modalities or attributes are measured and the scores or grades summed to give an overall indication of product quality. Figure 4.14 gives an example of a quality score based on ‘typicality’. There can be many disadvantages of this method. Firstly, because it relies on the expertise of the panellist, the results may not be directly linkable to consumers’ opinions of the products. Panellists can also drift into their own scoring system based on their own likes and dislikes. For new product development it can cause issues as there is generally little understanding of consumers’ opinions of the new product at the first production stage. In some cases the method is very poorly used with small numbers of panellists without the necessary training experience – this tends to lead to panellists making their own personal judgements on the products. If an overall scoring system is used this does not give the information required to fix the problem for the next production run or if the product might be re-blended or used for a different product as there is no information about which sensory

Please assess each coded sample below and score according to the table below. Scale

Definition

1 2 3 4 5

Fresh, typical, full flavour, no aged, no stale, no rancid flavours Fresh, typical, slightly lacking flavour, no off notes Relatively fresh, typical, however dull flavour Flavour slightly unbalanced with ageing, stale notes Aged, stale, rancid, not typical Code

Score

123

_____

456

_____

789

_____

Fig. 4.14 Example of the ballot paper for a quality score method.

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characteristics are causing the quality issues. With the correct controls in place (Muñoz et al., 1992) this method can be fast and economic to use and, when backed by industry standards and linked to consumer acceptability scores, can be useful for management in making quality decisions. However Muñoz et al. recommend that unless the rules are adhered to, companies would be better placed if they chose another recommended method (such as DS or in/out) owing to the inherent disadvantages of quality scoring and grading.

4.11 Magnitude estimation and duo–trio methods The magnitude estimation method is very simple but rarely used for dayto-day quality ratings except for specific products such as the assessment of chilli peppers. The method is based on Steven’s law. Panellists are given a reference sample and told, for example, that the reference would score 50 for a specific attribute (e.g. sweetness, crispiness, chilli heat). Subsequent samples are then scored in comparison to this reference. For example if the next chilli was twice as hot as the reference, the sample would score 100. For more information see the ISO standard 11056:1999. Muñoz et al. (1992) state that the duo–trio test is not an ideal method in QC as it can be very sensitive to small differences and can therefore create too many false positives. The test was developed by Peryam and Swartz in 1950 for quality control in distilleries. It was proposed as an advantage over the triangle test as it was thought to be easier for panellists to match rather than compare three unknowns (Stone and Sidel, 2004). The test measures if there are any differences between two products but three products are assessed. This method is similar to the ‘A’ not ‘A’ and the triangle test in that a reference sample (A) is given to the panellist and then they are presented with a pair of samples and asked which of the pair matches ‘A’. Unlike the ‘A’ not ‘A’ test, all three samples are presented simultaneously. The test does not give any indication as to the nature of the difference. An advantage of this test is that the analysis of the data from this method is very simple: the scientist looks up the number of correct answers in statistical tables and reports the result depending on the significance. More than 15 panellists are required: the ideal minimum number being around 30 with equal numbers of each possible combination. There are two formats to the duo–trio test: where the reference is the same for each panellist (constant reference) and where the reference is a balanced representation of both of the samples in the test (balanced reference). Where panellists are familiar with the reference sample the first option appears more sensitive (Lawless and Heymann, 1999). An example questionnaire for both methods is given in Fig. 4.15. The constant reference method, where panellists are familiar with the reference, can be very sensitive to differences attributable to ingredient substitution.

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Constant reference duo–trio Instructions There are three samples for you to assess. One of the coded pairs is the same as the reference. Please assess the reference first, then the two coded samples in the order shown below. Please ring the code of the sample which is most similar to the reference Reference

456

789

Thank you for taking part.

Balanced reference duo–trio Instructions There are three samples for you to assess. One of the coded pairs is the same as the reference. Please assess the reference first, then the two coded samples in the order shown below. Please ring the code of the sample which is most similar to the reference Reference

456

789

Thank you for taking part.

Fig. 4.15 Example of the ballot paper for the duo–trio test method. The questionnaire is identical for both tests; it is the presentation of the samples that differs.

4.12 In-house and do-it-yourself (DIY) methods There are many in-house sensory methods developed for quality control and some of these have become very popular and used by other industries: for example the duo–trio test. Many companies have internal grading systems based on typicality, product quality (using scales such as bad to good) and even undocumented assessments where the line-staff simply assess the product and tick a box to say if the product is satisfactory or not. As mentioned in the introduction to this chapter, these subjective methods are not ideal, and the use of the objective, consumer-led methods are recommended. However, some in-house methods have been developed over many years, have detailed Standard Operating Procedures and Work Instructions and are based on sensory specifications and consumer data, resulting in more objective and controlled results. Hybrids of the recommended methods can be used to produce a detailed programme of tests for the different situations a quality control sensory scientist might find themselves in. For example the sensory scientist might

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choose the difference from control method for monitoring batch variation and for ingredient substitution tests, the descriptive specifications method for passing daily batches and the ‘in/out’ method for passing raw materials.

4.13 References bsi (1988) British Standard Methods for Sensory Analysis of food, Part 5 ‘A’ not ‘A’ test, BS 5929: Part 5. bsi (1989) British Standard Methods for Sensory Analysis of food, Part 6 Ranking, BS 5929: Part 6. carpenter, r p, lyon d h and hasdell, t a (2000) Guideline for Sensory Analysis in Food Product Development and Quality Control, Aspen. costell, e (2002) ‘A comparison of sensory methods in quality control’, Food Quality and Preference, 13, 341–353. iso Sensory Analysis – Methodology – Triangle test, 4120:2004. www.iso.org. iso Sensory Analysis – Methodology – Magnitude estimation method, 11056:1999. www.iso.org. lawless, h t and heymann, h (1999) Sensory Evaluation of Food. Principles and practices, Aspen. meilgaard, m, civille, g v and carr, b t (1999) Sensory Evaluation Techniques, CRC Press. muñoz, m, civille, g v and carr, b t (1992) Sensory Evaluation in Quality Control, Van Nostrand Reinhold. oakland, j (2007) Statistical Process Control, Butterworth-Heinemann. o’mahoney, m (1986) Sensory Evaluation of Food: Statistical methods and procedures, Marcel Dekker. pecore, s et al. (2006) Degree of difference testing: a new approach incorporating control lot variability, Food Quality and Preference, 17, 552–555. stone, h and sidel, j l (2004) Sensory Evaluation Practices, Academic Press. young, t a et al. (2008) ‘Incorporating test and control product variability in degree of difference tests’, Food Quality and Preference, 19, 734–736, doi:10.1016/j. foodqual.2008.04.002.

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5 Establishing product sensory specifications C. J. M. Beeren, Leatherhead Food Research, UK

Abstract: To obtain a consistent product quality, with characteristics as desired by the consumer, detailed product specifications are vital. Product specifications can be created by using different methods, but will always need the input from consumers and from different disciplines within the organisation such as product development, manufacturing and quality control. Any product attributes identified should be well defined, unambiguous and well understood by assessors to ensure consistent use and results. Once a reliable sensory specification has been developed, production, competitor and development samples can be compared against the specification to check sensory quality. All aspects from development, to implementation, use and follow-up of the product sensory specifications are discussed in this chapter. Key words: product specification, sensory characteristics, quality, key attributes.

5.1 Introduction A consumer’s first purchase of a food or beverage product is probably influenced by information gathered from different sources, such as data available from commercials, recommendations by family or friends and details from promotion in store, or the decision may be influenced by the price. At this point of the first purchase, little information would be available about the sensorial characteristics; a consumer would not know yet whether he or she personally would find the product as tasty as a friend made it sound, or whether the product really looks as good on the table as the commercial made it look. A few exceptions, though, exist; as for some products the human senses of sight and or smell may play an important factor influencing this first purchase. With our sense of sight we measure visual aspects of products, and as such many products benefit from an appealing product packaging, and position on the shelf. In addition, for a

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smaller number of products, the product itself is visible to the consumer, for example a pizza may be seen through a window in the packaging, or a cheese could be seen at the counter. For these occasions, sight would play a major role in the first purchase decision. The aroma of the food or beverage plays an important factor during a first purchase for only a minority of products, but where it does, it will be vital, such as the smell coming from a bakery aisle or from a roasted chicken in a delicatessen. When a food or beverage is re-purchased, the influence of the product’s sensory characteristics is obvious; the consumer knows how the product looks, smells, tastes and feels, and will have certain expectations about the sensorial characteristics of the food or beverage. Consequently, a consistent sensory quality is inherent for continued consumer support and thus to make a product successful. Naturally, consumers vary from individual to individual, and also within individual; thus they will make their food choices based on numerous factors, including their mood state, the time of day, the social surroundings, etc. The total food concept is therefore not only based on the sensorial product characteristics, but is a combination of these individual product characteristics and the environment in which the product is bought and eaten (Earle et al., 2001). The different factors influencing the purchase introduce many possibilities for manufacturers, but also a need to clearly communicate the food brand, characteristics and values to inform consumers and ultimately to convince them that the product is the right choice. To develop and position a product accurately, the involvement of the target consumer is vital. The consumer’s needs or wishes in terms of product ideas, concepts and potential product characteristics must be known by the product developer. Prototypes should be evaluated and once a product is launched and in the marketplace, ongoing consumer research should be carried out, ensuring continuing product fulfilment and measuring any possible desired changes to the product characteristics. Ideally, consumer data should be correlated to specific emotional or environmental conditions as this will give an enhanced insight to the consumers’ view of the product and may give potential for further or new developments.

5.1.1 Instrumental measurements Usually quality control (QC) uses a combination of different analyses, likely to include both instrumental and sensory analysis. The exact test types used will be dependent on different factors such as product type, amount of samples to be evaluated, staff availability and experience. In a study by Gimeno et al. (2000), the use of colour measurement by the L a b (lightness, red/green, blue/yellow) system and instrumental pH measurement is demonstrated in chorizo samples; the b variability of the colour measurement indicated different amounts of paprika used and

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the pH correlated with a textural sensory property; the cohesiveness of the chorizo samples. This example illustrates that for some attributes an instrumental method could be successfully used to measure a sensory characteristic of the product. The advantages of using an instrumental specification for colour measurements above sensory measurements is described by Hutchings (1999); the instrumental method is not susceptible to eye fatigue, poor colour memory, suitability of assessment condition – including uniform lighting – availability of trained graders and in addition, instrumental colour measurements may also be less time consuming than measurements carried out by sensory assessors. The effectiveness of the instrumental technique should, however, always be validated first by correlating the results obtained from the instrument with the perception of the consumer. Often a combination of sensory and instrumental measurements is used to define product quality; this is illustrated by Bruwer et al. (2007) who carried out a study for a tortilla chip producer to characterise the textural properties and variations of the food product. Instrumental data and data obtained from a trained sensory panel were used, resulting in two robust quality variables for developing online sensors for process measurements.

5.1.2 Sensorial measurements Although for some characteristics it may be appropriate to use instrumental measurements, it should always be ensured that the instrumental method correctly reflects the character as perceived by the human senses and thus by the consumer. Instruments and the human senses may respond in different ways to the same input, resulting in a different output. Additionally different product characteristics will interact with each other, resulting in a different perception. For example, the colour perception of white and yellow corns appears affected by the variation of the pericarp thickness and the glossiness and thus instrumental methods appear to be not as effective as evaluation by sensory evaluation to distinguish subtle colour differences of this product (Hutchings, 1999; Floyd et al., 1995). Another example pointing out the importance of sensorial measurement is described by Paganuzzi and Carozzi (2000). The results of their paper describe the significance of the sensory measurements and the subsequent request for EU approval to change the product specification of an extra virgin olive oil DOC (denominazione d’origine controllata). Muñoz (2002) indicates that companies that do not use sensory practices in their QC operations and have not confirmed the correlation between sensory and instrumental methods, either are unable to detect the sensory issues in their products, and may thus fail to check products comprehensively, or may not have had a major quality problem involving sensory issues to acknowledge the value of sensory measurements in QC. Furthermore, Feria-Morales (2002) indicates that many foods are sold according to their

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sensory quality, which is not easily measured by conventional analytical techniques. More information on the correlation between sensorial and instrumental methods can be found in Chapter 6 of this book.

5.2 Rationale using sensory specifications The importance of the sensory characteristics of food and beverage products for consumers clearly shows the requirement for the manufacturer to produce products with consistent characteristics meeting the demand of the consumer. It is not only end consumers who expect a consistent sensory quality; food and beverage manufacturers also expect a reliable, constant quality for the bought ingredients. Product specifications are often supplied by the ingredient suppliers to communicate the specific product quality characteristics between businesses. Moreover, specification sheets appear the most common service given by ingredient suppliers and are also one of the most important factors in choosing a vendor for the buyer (Berglind, 2003). Not only are product specifications created by ingredient companies supplying products to other food manufacturers, product sensory specifications are an essential tool to aid with product consistency and represent one of the most important tools of a QC program. Well-defined product sensory specifications can be reliably used for all subsequent assessments to monitor the sensory quality, comparing real product characteristics with the set specifications (Metheringham and Rodway, 2001). Short-term benefits of using sensory product specifications include the provision of fast, objective and focused results, whilst long-term benefits include helping reduce quality fluctuations and the identification of the sensory critical control points within the production process. The use of sensory specifications also promotes awareness of the sensory quality of products by staff, tracks product consistency and aids communication with the industry (Metheringham and Rodway, 2001).

5.3 Defining sensory specifications It may be difficult for consumers to identify and articulate the specific sensory attributes that influence their acceptability for particular food products. The interaction of different, even simple tastes, such as sweet and acidic taste, or colour and fruit flavours can make product evaluation and identification of exact characteristics complex (Earle et al., 2001). Developing terms to describe products in aroma, appearance, flavour and texture is therefore often carried out by trained sensory panels or by panels of product experts. The terms derived should be specific enough to be useful for

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product development and for efficient usage for QC purposes; to merely describe a tomato sauce as ‘red’ will not give enough information, as there are many varieties of a red colour (Staniforth, 2004) or to identify a ‘typical’ flavour does not define the product’s flavour and thus more detailed descriptions are required. After creation of well-defined product descriptors, the descriptors should be correlated with the product liking of consumers. This will create objective measurement criteria for consumers’ ideals and will identify the key quality attributes required for product development, quality control testing and to aid with shelf-life determination of products.

5.3.1 Target product When creating the sensory specification, the following factors should be considered: • • • • • • • • • • •

target product; available consumer information; product ingredients; production process; storage conditions; packaging; brand; function of product; product quality requirements; product transportation; product marketing.

The basis of the creation of the sensory specification will be the target product as this was created by product developers based on the desires of the consumer. This target product should be sensible and viable within production, cost and shelf-life constraints and the produced and marketed food or beverage should mirror this reference product. Information gathered from consumers during the development stage may also prove very useful as guidance for consumers’ requirements. This could include the desired consumer characteristics obtained from product brainstorming and from any consumer product research carried out on concepts, prototypes and/or final versions of the newly developed product. The process that a food or beverage will undergo plays a major role on the end target product, and as such (key) product ingredients, production processes, packaging and storage conditions should be studied when creating the sensory specification, as these process factors may have an influence on the sensory profile of the end product. Ideally, several product variations would be made available during the generation of the product sensory specification, using minimum and maximum levels of particular

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key ingredients or processes, storing samples under different conditions, (if relevant) in different packaging materials and products of different ages. During the creation of the product specification, all factors influencing the total quality of the target product should be taken into account; product, price, place and promotion, referred to as the four Ps in the marketing mix. The product in this mix is in a condition that consumers expect to receive it (Blythe, 2008). Within the four Ps, the following factors should be included in the considerations: brand, function, quality, packaging, safety, pricing decisions, storage, transportation, market coverage, advertising and promotional strategies (Internet Center for Management and Business Administration, Inc., 2009).

5.3.2 Identification of critical consumer attributes Attributes used for the sensory product specification should be the ones that are key to consumers’ acceptance to ensure that the product reflects the consumers’ desire. Hence, consumers should be involved when setting up the product sensory specifications. Data generated during product development and product testing may be considered first, which may avoid excessive expenses. It should be noted, though, that even a minor change in product, packaging, promotion or price may have affected consumers’ expectations and thus consumer insight should be obtained from the target product in case earlier testing was carried out on a different product. Furthermore, consumers’ liking is an evolving process: consumers may change their mind, at shorter or longer time frames, as other products or variants come on the market or as people change and thus frequent testing of consumers’ opinion is recommended. Identification of critical consumer attributes includes also removing any obsolete attributes, which are the attributes that are not important to consumers. Too extensive product sensory specifications may lead to overtesting and a waste of time and resources.

5.3.3 Creation of the product sensory specification Key quality attributes defined should be based upon human perception, capturing the different consumption stages. Visual attributes are generally evaluated first, followed by aroma, taste/flavour, texture/mouth-feel and at the end of the consumption, after swallowing, specific aftertastes and an afterfeel may be perceived. For products where aroma can change rapidly or where the aroma can be perceived readily, for example, hot served products such as coffee, the aroma may be evaluated prior to the appearance to ensure all important aroma characters are captured. Creation of the sensory product specification by highly experienced product specialists may not always be most optimal. The product experts

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could be inclined to incorporate their own likes and dislikes, biasing the outcome. Trained, pre-selected assessors objectively describing the main product characteristics and setting realistic, measurable sensory standards may therefore be more appropriate as the main method creating the attribute lists (Metheringham and Rodway, 2001). It is, however, recommended that different disciplines are involved when creating the product sensory specification, including, but not exclusively, product development, ingredient purchasing, production, process development, and sales and marketing. Input from client, retailer or food manufacturer may be essential at this stage too. The different experts are able to identify possible product variability. Ingredient variability should be taken into account also; some ingredients subjected to variability will inherently alter the end product, also indicating the need for proper raw material/ ingredients QC and thus ingredient sensory specifications. Quality limits for raw materials must be set to allow for the finished product to meet its specification; however, limits should be broad enough to allow for some flexibility in purchasing (Matz, 1992). Sensible quality limits for changes occurring during shelf-life and storage should also be set and measured against. Any probable deviations of the standard product quality should be captured during the evaluation of the product and the degree of variability of products’ sensory characteristics should be known. When these are covered in the sensory specification, it would be less likely that these would be overlooked and assessors can be trained on acceptability levels. Unfortunately unexpected events happen, leading to quality deviations such as taints or off-flavours which were not considered at any stage. Including an attribute named ‘other’, ‘off-note’ or the possibility of giving open comments prevents assessors’ dumping the perceived difference into another attribute. The final product specification however, should reflect ‘conformance to customers’ requirements’ and the unit operations should function to be in compliance with this specification, as a product that fails to reflect the features required by the customer will lead to rejection of the product and a product that exceeds expectations may be compromising economical viability (Bonnel, 1994) or raising consumer expectations, which may not be met at a re-purchase of a standard quality product. To summarise, the generated list would; • contain the key quality attributes vital to consumers; • contain attributes which may vary (due to ingredient or process variation); • ask assessors to describe any perceived characteristics not mentioned in the standard list, if relevant; • be written in a sensible order, similarly to the consumption experience of these products.

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Attribute definitions and ranges All product attributes generated should be clearly defined, to avoid any ambiguities and to ensure all assessors would have a clear understanding of the characteristic to be evaluated. Attributes such as fresh, natural or typical can easily be misinterpreted and should thus be avoided. Also simple attributes such as hardness of a chocolate sample could be misinterpreted if this was without any further clarification; some respondents may, for example, assume that the attribute evaluates the hardness when the product is first bitten into, while other assessors could assume that a hard chocolate would be a chocolate which would stay hard, even after repetitive chewing. An example of a product attribute list, including definitions, is shown in Table 5.1. Following the creation of critical measurable sensory attributes, viable acceptability ranges for each of the attributes must be established. The

Table 5.1 Sensory attribute list – chocolate product Appearance Brown colour Gloss Evenness of surface Air

Brown chocolate colour intensity Light scattering on surface The presence of pits/damage on the surface The presence of air after breaking the chocolate

Aroma Overall aroma intensity Cocoa Stale Off

Total aroma strength Aroma of cocoa powder Musty, cardboard aroma Atypical aroma, e.g. rubber

Texture Hardness on first bite Smoothness Mouth-coating Body

Force required to shear sample on first bite Initial smoothness assessed when tongue yields chocolate Coating of mouth Thickness of sample

Flavour Overall flavour intensity Sweet Bitter Cocoa Nutty Stale Off

Total flavour strength Basic taste of sucrose Basic taste of caffeine Flavour of cocoa powder Flavour of almonds/nut skins Musty, cardboard flavour Atypical flavour, e.g. rubber

After-effects Sweet Bitter Cocoa Stale Off flavour

Sweet aftertaste Bitter aftertaste Cocoa powder aftertaste Musty, cardboard Atypical aftertaste, e.g. rubber

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range should be viable within production and buying constraints and reflect consumers’ expectations of the product. Similarly to the definition of the attributes, the acceptable ranges for each attribute should be unambiguous. ‘Few to many’ raisins in an apple pie may be perceived in a different way by different assessors and should be more accurate, unless dealing with very trained assessors evaluating the products. 5.3.4 Role of quality control during establishment of product specification The main roles for sensory testing within the QC department are to maintain consistent product quality, the prevention of taints and the assessment of product shelf-life. As part of the development of the sensory product specification, QC should oversee the progress and ensure a complete list of attributes and ranges capturing all critical elements, whilst creating a workable specification for the manufacturer. Usable and actionable results are required to ensure follow-up of any products not falling within the specification. Product results should also be observed and specifications be amended as necessary.

5.4 Reference samples To assist with the evaluation, suitable reference samples will aid the understanding of sensory attributes and ranges. Reference samples will guide as a baseline and reduce assessment variability. Suitable reference samples often include (frozen) factory obtained samples, different aged, ‘spiked’ or abused samples and chemicals to illustrate particular attributes and visual references, illustrating the range of product variability. Identification of visual references, such as colour charts or photographs, is appropriate to assist with the appearance attributes, exact colours, hues and intensities are often difficult to remember for assessors and are easily referred to with colour charts. Good quality charts and images would remain unchanged and allow faultless use over time unlike the use of standard photographs/prints which may be subject to variability with processing and change over time. Any attributes with which assessors may be less familiar would benefit from demonstration using reference products, preferably shown at different intensities, e.g., different cooking times and different levels of ingredients. 5.4.1 Gold standard Reference products may be a gold standard of the product, where assessors can compare the product ‘like for like’ and compare directly for any differences. If this method of gold standard is used, QC must ensure a consistent reference, which may be freshly prepared or stably stored, e.g. frozen.

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Refreshment of gold standards on a frequent basis could lead to a drifting reference sample; a very gradual change may not be perceived on a one-to-one comparison between successive batches, but could add up to a change in quality over time. More robust testing of replacement of the gold standard could prevent this gradual drift. 5.4.2 Competitive material samples as references When a gold standard is not at hand, ‘equivalent’ competitive products could be acquired with which to compare production samples. It should be noted that this comparison, although often helpful, is relative to the consistency of the competitive material. Advantages of comparing with competitive products include building up knowledge of differences between manufactured and competitive products on the market, establishing a benchmark. 5.4.3 Rejected or manipulated samples as references To illustrate different ranges of attributes, rejected or manipulated products may prove very valuable. Any rejected or borderline products should if possible be kept for training or reference purposes of the sensory evaluation. These products are direct illustrations of non-ideal products, and assessors will immediately understand the product limits. To demonstrate particular product ranges, specifically to clarify attribute ranges possibly occurring during manufacturing or from ingredient supply, manipulated products may be very useful. Smaller or larger volumes of specific ingredients could be added, products could be exposed to specific aromas in an enclosed environment, additional ingredients can be added or products can be prepared differently. It will speak for itself that only food grade materials and products with a known history can be used and that any products, including any rejected or manipulated samples given to assessors, should be safe to consume and be within national regulations. 5.4.4 Daily products as references To describe specific product characteristics, such as certain aromas, flavours or textures, well-known daily products could be referred to. The daily reference products do not have to be similar to the evaluated product; for example, vanillin sugar to illustrate the vanilla aroma note in cookies and cinnamon powder to illustrate a spicy flavour characteristic in a beverage.

5.5 Implementation of sensory specifications To optimally use the created product sensory specifications, good sensory procedures should be used for the assessment of products. A simple but

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robust method to measure the desired product characteristics would normally be favourable within the QC environment. Naturally, availability and viability of instrumental and sensorial measurements and the company’s capability to use the measurements should be taken into account, using solely the most appropriate systems for measurement of product quality.

5.5.1 Sensory assessors Several respondents are required for sensory product assessments. These respondents could be recruited internally or externally. The advantages of using internal assessors may include their presence in case of urgent need for evaluation and being more cost effective, and the disadvantages are likely time constraints and subjectivity. Focusing on externally recruited assessors, a personal interest in the outcome of the test would be less probable and they would spend more time focusing better at the job at hand. Sensory screening potential assessors Assessors used for sensory evaluation of quality control checks are typically screened and selected for their ability to distinguish small differences in appearance, aroma, taste, flavour and texture and to verbalise these differences. Generally, screening tests include a basic taste recognition test, whereby four or five basic tastes would be presented and evaluated, consisting of sweet, salt, sour, bitter and possibly umami. In some instances astringent could be included as an additional sensory characteristic; for example, for chocolate or wine panels. As not all respondents would be familiar with all basic tastes, a prior exposure to all components is appropriate. Other usual screening tests include detection and recognition of aromas, whereby generally approximately five different components are evaluated and assessors try to recognise and describe the perceived smell. To be selected, assessors would at least be able to smell the samples and to describe products in the direction of the exact material. The ISO standard (ISO 8586-1) suggests using materials such as benzaldehyde for almond/ cherry aroma and vanillin for a vanilla aroma. Incorporating common taint samples, such as trichloroanisoles (TCA) and trichlorophenols (TCP) is common too, as about 70% of taints are caused by these two components. Taint issues should be picked up during the QC sensory evaluation and it is thus important that assessors are sensitive to these components. To ensure that assessors would also be able to describe any deviations from the sensory specification, term derivation of products is also a common part of a screening procedure. Term derivation could be of any specific food, beverage or illustration. Other tests that may be used for assessor screening are dependent on the role of the assessors, and could include specific discrimination tests, such as triangle or paired comparison, memory tests, tests to identify visual

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impairments, ranking tests, etc. ISO standards do give further details on screening of assessors (ISO 8586-1). It is important that the sensory analyst knows any personal details of assessors, such as allergy information or strong likes or dislikes. Further, any assessor who is part of a sensory panel should be motivated and not be forced into it. Sensory training potential assessors To fully comprehend the test products and entirely understand the test methods and procedures, sensory training for assessors is required. Any assessors not trained prior to the assessment in the methods or products used may be less confident and are unlikely to deviate from any standard results, and thus score any product as acceptable. Exposure to products will help identifying the varieties of the products concerned; Heckel and Wilson (2002) describe the recognition of deviation from the norm in terms of appearance and flavour by a trained person; samplers not exposed to several samples may not catch the potential variation and problems. Training may include background details on the product itself, such as details on ingredients, and the process to discover the sources of specific product characteristics and exposure to the several test products and variants, including tainted test samples. The test method should be explained and practised, and test procedures should be discussed. Any customer feedback, case studies and unacceptable samples could be shared and discussed to gain a better understanding of the product quality. During training, it is important to evaluate and discuss acceptability ranges, to ensure all assessors would fully understand the acceptable levels. It is useful to compare training results with results from already trained panels and with instrumental data. Correlation between the sensory and instrumental data is not always straightforward, as human perception may be influenced by different factors. Any correlations or deviations both with already trained sensory assessors and with instrumental data, would be valuable information for discussions between assessors and the panel leader(s). When carried out properly, training also tends to work very well to motivate assessors.

5.5.2 Sensory test methods Test procedures chosen to maintain product quality should consider the viability within company constraints. Testing should be practical and consider factors such as the assessors’ availability, test frequency, and the test facility to provide useful and reliable sensory data. To evaluate the product against the created sensory product specification, different types of sensory testing could be applied.Whilst Chapter 4 described different test methods which can be used in more detail and specific test

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methods are extensively described in textbooks (Lawless and Heymann, 1998; Brinkman, 2002; Meilgaard et al., 2007), a brief overview of specific methods relevant for testing against sensory specifications is now given. Tests commonly used within QC departments for product comparison against sensory product specifications include: • • • • •

in/out method; grading; quality ratings; difference from control; descriptive testing.

In/out method The in/out method is a simple method, benefiting from its ease of use by assessors. When using the in/out method, also referred to as In-spec/out of spec or pass/fail, assessors are asked whether a specific sample is in or outside the defined sensory specification. Examples of ‘In-specification’ criteria for potatoes are uniform off-white colour, potato flavour, earthy flavour and soft texture after cooking for a specific time. Out-of-specification examples for a potato variant may be blackening/greying as illustrated with photographs, glassy appearance and off-flavour. Results can be tracked visually over time. Grading method The grading method is an extension of the in/out method, whereby samples are classified in different grades; generally three different grades, A, B, C, are employed, with ‘A’ usually describing a product within the defined specification, ‘B’ a product that is ‘just acceptable, but needs improvement’ and ‘C’ descibing a products which is ‘out-of-specification’ and should be refected. Table 5.2 shows an example of a grading of fish samples. Illustrations can be created to to view trends over specific periods (Figs. 5.1 and 5.2). Quality rating method Quality rating methods are used to rate specific quality attributes. Assessors are trained to recognise different quality levels of important attributes, defined in the product sensory specification. Each attribute will be rated by the assessors, based on its perceived sensory quality. Samples will be rated on line (unstructured scale) or category scales, examples of which are shown in Fig. 5.3 (line scale) and Fig. 5.4 (category scale). The quality rating method is a semi-quantitative method and statistical treatment of rated data is possible. Difference from control The difference from control test is used to establish whether a difference exists and to get an indication of the size of the difference between two or

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Table 5.2

Aroma

Raw fish grades – Atlantic ground fish A

B

C

A detectable raw fish odour

Neutral odour

Presence of taint or off-odour, such as sour, ammonia, bilge Appearance Colour of bled fish Colour of bled fish Yellow or brown (chart A) (chart B) colour (chart C) No single blood clot or Blood clot(s) Blood clot(s) up to 0.5 cm in total with dimension exceeding 4 cm in maximum dimension between 0.5 and total dimension 4.0 cm Bruising/discoloration Bruising/ Bruising/discoloration not exceeding 2.0 cm discoloration exceeding 5.0 cm in total dimension between 2.0 and 5.0 cm Mouthfeel/ Firm Slightly soft Excessively soft Resilient; up to 10% Between 10 and More than 40% of texture surface area may 40% of surface the surface shows show gaping area may show gaping gaping Adapted from Bonnel (1994).

25 A

B

C

Frequency

20 15 10 5 0 1

2 Period

Fig. 5.1 Grading – visualisation over time period (1).

more products. A consistent control is required as a reference sample; this control would be the ‘gold standard’ as described in Section 5.4.1. Some statistical treatment is possible to identify whether the test samples are different from the reference sample. Descriptive testing With descriptive testing the nature and size of the difference of specific sensory product attributes are compared. Different types of descriptive

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Sensory scoring

Grade

3

2 Year 1 Year 2

Dec

Oct

Nov

Sep

Aug

Jun

May

Apr

Mar

Feb

Jan

1

Month

Fig. 5.2 Grading – visualisation over time period (2).

Sweetness None

Very

None

Strong

Dry

Very moist

Lemon Flavour

Moistness

Hardness Soft

Fig. 5.3

1 None

Hard

Examples of line scales (adapted from Meilgaard et al., 2007).

2 Very Slight

3 Slight

4 5 6 7 8 9 Slight/ Moderately Moderately Strong Strong– Very Strong Moderately /Strong Very Strong

Fig. 5.4 Example category scale.

testing are available, such as quantitative descriptive analysis (QDA), flavour profiling (FP), texture profiling (TP), free choice profiling (FCP), spectrum descriptive analysis, consensus profiling and time-intensity methods. Most methods require extensive training and are usually more time consuming than earlier described test methods. The information

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Off-flavour* Honey flavour

Yeast flavour

Fruit aroma* 60 50 40 30 20 10 0

Citrus aroma* Yeast aroma*

Overall flavour*

Sweet taste*

Citrus flavour* Fruity flavour*

Sour taste* Bitter taste 1

2

3

*Indicates a statistical significance difference between samples at 95% confidence level.

Fig. 5.5 Spider graph displaying sensory attributes.

obtained from descriptive testing, however, will be more in-depth, showing full profiles of evaluated samples and identifying very specific differences, visually (Fig. 5.5) and in terms of statistical significance.

5.5.3 Sensory test protocols Efforts must be made by food and beverage companies to measure and deliver a product with a consistent sensory quality; clearly defined test procedures and protocols will assist in keeping consistent quality and reducing bias during evaluation. Assessment area, number of assessors, frequency of assessment, product sampling procedures, product storage, sample size, sample presentation, sample treatment, data analysis and actions should all be considered carefully. Firstly, the testing objectives should be clearly defined, and agreed upon with all involved parties. Production, quality control, processing, product development and sales departments should be in agreement to the purpose and thus the objectives of the tests carried out. Test protocols should then be developed with the aim of reaching the set objectives. Sensory environment The environment in which the sensory testing is carried out has an influence not only on product perception, but also psychologically, indicating the importance of the testing. A dedicated room for testing shows the company’s commitment to sensory testing. Using the same dedicated area

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for all tests would also lead to more consistent results; the room has an effect on the presentation of products and the appearance of the visual characteristics, and can be a comfort to assessors. Influences of the environment should be considered (ISO8589, 1988), such as: • consistent lighting, i.e. artificial light, and same types of lights throughout the room, ideally artificial daylight/north light fluorescent should be considered for optimal colour assessments; • noise may distract assessors and thus influence performance; • odours coming from production or preparation area should be kept to a minimum; • decoration may influence the perception of the visual appearance – neutral colours, such as matt off-white or light neutral grey are recommended; • individual testing booths to limit distraction and to avoid communication between assessors; • furthermore, preparation and storage area adequate for testing purposes should be in place to present samples appropriately and the testing area should be easily accessible. Assessors Screened and trained assessors can take part in the evaluation of products. Panel leaders should keep records of assessors’ sensitivity levels. In particular sensitivity results for any potential taint materials would be of interest in case an off-note were identified during the evaluation. Assessors should be told not to eat, drink or smoke prior to an evaluation session, not to use any (strong) fragrances and not test when they have a heavy cold. Assessors may respond differently towards test products depending on the individual, likes/dislikes, sensitivity, knowledge of the test (products), mood, familiarity of products, time of day, etc. A panel of assessors should therefore be employed to reduce personal factors. Training will aid establishing more objective evaluation of products and thus, generally, the more trained assessors are, the fewer assessors are required. However, panel leaders should always consider that however well trained, some variability will still exist between assessors, not least in sensitivity levels, knowledge of test and motivation. Other factors influencing the panel size include the amount of test samples, consequences of wrong decisions, etc. A panel of approximately six people for each assessment is not atypical. Samples The presentation of samples should be consistent; using consistent preparation methods delivering the same volume of sample(s), at the same

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temperature and in the same manner during every evaluation. Assessors should handle the samples in the same way during each test. The test procedures to sample products from production, to store, prepare and present products should be documented. Within one test, samples are ideally prepared by the same person, avoiding differences due to preparation. Test procedures on how to handle and evaluate the food or beverage should be readily available for the assessors, ideally within the product specification. For example, the texture of products may be assessed by using the fingers, cutlery or in the mouth; each one may lead to different results. Records should be kept during testing, factors such as length of storage, product preparation, serving temperature and evaluation time can be utilised if any abnormalities appear or any questions arise at a later stage. The amount of samples tested should be kept to a minimum, particularly samples with a strong flavour, or acidic products. It may also be difficult for assessors to spend too much time away from their normal activities, and thus amount of time spent should be considered. More frequently a panel with fewer samples would be favoured above larger sample numbers. Samples should be presented blind, reducing biased judgements. Appropriate palate cleansers should be used to refresh assessors’ palates between each test sample. Water should always be available prior to a new sample. Further useful palate cleansers could include water biscuits or bread, lemon juice for fatty products, cucumber, yogurt for spicy products, milk for acidic products or carrots for soft textured products. Assessors should always drink water after any other palate cleanser and before evaluating the next product. The weakest flavoured samples should be presented first, and during preparation it should be ensured that all components of products are given to each of the assessors when multi-component products are evaluated. To reduce carry-over effects and adaptation, samples – of a similar strength – are randomised. Test methodology and protocol To avoid confusion by assessors, the same methodology should be utilised and well explained and understood by assessors. Sampling at regular times, ideally appropriately to the product – but not at a time that assessors are hungry or have just eaten – will help establish a consistent product assessment. Exact methods and protocols used during testing should remain as consistent as possible and assessors should be comfortable using these. When products are shared, good hygiene practices should be considered by assessors, and sufficient tools should be available for assessors, i.e., a sufficient amount of cutlery to use a new set for each assessment. The way the products are handled by assessors, including how products are manipulated in the mouth, should be clear from the sensory specification as this may affect the sensory perception. Some attributes may be judged on a first bite, others

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after a specific amount of chews. Products are ideally swallowed to ensure all characteristics are well perceived. In some cases this may not be practical; then it would be advisable that all assessors should sip and spit and spittoons must be available.

5.6 Maintenance and follow-up When a product is recognised as out of spec, immediate action should be taken. Follow-up actions should thus be formulated at the time that the quality system is set up. It may be possible to reconsider the intended use of the product: products could be blended or the further processed for improvements. Heckel and Wilson (2002) describe the possibility of rebleaching or re-refining oils with high peroxide values: this process is then followed by the stabilisation by hydrogenation, and the alteration of oil blends in cases of different melting points. In other cases, however, products cannot be reworked and should be disposed of. Any form of deviation from the specification should be logged and investigated, to prevent reoccurrence. The investigation should focus on the root cause, and commonly will include further sensory evaluation with expert assessors to identify irregularity of the product and possible further analytical testing to identify the root cause. Seasonal variation can be an issue for natural products. Specification tolerances may need to be amended depending on the season. Analysis of trends will help identifying the variation: Fig. 5.2 shows a grading scheme of a particular product, whereby the first months in a year are liable to decline in quality. Trend analysis is also useful for identification in gradual reduction in quality. Figure 5.2 shows an indication of dropping quality in year 2. Management support and commitment are vital in decision making and ensuring subsequent actions are carried out. Tracking of sensory data over time may highlight potential issues in an earlier stage and will give a good overview on the performance of products. Table 5.3 illustrates different options in relation to test types. Accurate detailed records of all testing and of actions performed should be kept as evidence to prevent or reduce claims based on presumably defective products (Matz, 1993). Records should show that appropriate action is taken on products not meeting the sensory product specification. As consequences can be very serious, senior management should be involved in any decision making and creation of action standards on rejected products. Successful QC programs require ongoing technical support from R&D sensory professionals, support from the factory and factory management for guidance, training, creation of awareness on the importance and decision making (Muñoz, 2002).

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Table 5.3 Tracking data by sensory methodology Sensory methodology

Tracking data

In/out method Grading Quality ratings Difference from control Descriptive testing

Visualisation of in or out over time Visualisation of classification over time (Fig. 5.1) Statistical difference per attribute over time and visually Statistical difference or size of difference from reference over time Statistical difference between products and visually (Fig. 5.5)

5.7 Case study Consistent, high-quality products are vital in the competitive market in which retailers operate. To ensure optimum and consistent quality of products, and to assist the suppliers in defining and measuring product quality, a tool to measure product quality is used. The product quality tool is a sensory product specification comprising a list of key attributes and a quality grading levels of each attribute. The product quality tool is part of the wider product specification agreed between the supplier and the retailer.

5.7.1 Set up product quality tool To create the product quality tool, the following process is applied; 1. Key attributes are identified by the supplier based on extensive product and process knowledge. 2. The list of attributes is rationalised based on consumer input. 3. Graphical images of different stages of the product are included, e.g. prepared and unprepared, frozen and defrosted, fresh and end of life product, start and end of season. 4. Graphical images are provided showing different quality levels of all visual attributes of the food or beverage and of the packaging, including preferred and inferior product quality. 5. Definitions and quality ranges of all aroma, flavour, texture and aftereffects attributes are created. 6. The supplier and retailer will discuss all available information and agree on the final product quality tool.

5.7.2 Using the product quality tool Once agreed, the product quality tool will be used by both the supplier and the retailer. The supplier will evaluate the finished products using the product quality tool before sending the product out to the retailer. The

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retailer will test the products upon arrival to ensure the transport has not affected product quality. At each evaluation, a panel of assessors will score each attribute in one of the following three criteria: acceptable (A), borderline (B) and reject (R). The total score will be the lowest given score. If a borderline or reject score is given, an investigation will be carried out to find the cause for a (slightly) lesser quality. If the tests result in a borderline or reject score, the supplier and retailer will discuss these findings and agree on corrective actions. Assessors A minimum of three assessors carry out the evaluation individually. Each of these assessors is screened in their sensory perception, is familiar with the product quality tool and trained in the products, attributes and scoring system. Protocols Specific protocols for evaluation will be defined ensuring testing is carried out using the same principles, at supplier and at retailer and also over time. The protocol will include the following items: • exact product preparation instructions 䊊 equipment used, 䊊 length of time and temperature in microwave, oven, etc. • precise product serving instructions 䊊 serving temperature, 䊊 size of sample, 䊊 display of sample; • location of testing; • instructions for tasting 䊊 palate cleansers to use, 䊊 swallow sample or spit.

5.7.3 Results from product quality tool If a product is scored as acceptable, the product is ready to be distributed to the retailer. If a product is scored as borderline, a discussion with the supplier will take place and corrective actions will be taken. If a product is scored as rejected, it cannot be dispatched. The problem has to be solved first, and re-sampling will have to take place. Over time, the results are collected and frequent updates are issued to see trends occurring in the data. The results will highlight any trends over time, e.g. gradual increase or decrease in quality, seasonal variability and differences in ranges. The results kept are useful in case of consumer complaints and provide in-depth information on product quality.

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5.8 References berglind, v. (2003). ‘Value-adding vendors’, Prepared Foods, vol. 172 (1), pp 69–70, 72 blythe, j. (2008). Essentials of Marketing, 4th Edition, Harlow, Pearson Education bonnel, a.d. (1994). Quality Assurance in Seafood Processing: A Practical Guide, New York, Chapman & Hall brinkman, j. (2002). Proeven van Succes, Sensorisch Onderzoek: Technieken, Procedures en Toepassingen, Houten, Keesing Noordervliet BV bruwer, m.-j., macgregor, j.f. and bourg jr., w.m. (2007). ‘Fusion of sensory and mechanical testing data to define measures of snack food texture’, Food Quality and Preference, vol. 18(6), pp 890–900 earle, m., earle, r. and anderson, a. (2001). Food Product Development, Cambridge, Woodhead Publishing Limited feria-morales, a.m. (2002). ‘Examining the case of green coffee to illustrate the limitations of grading systems/expert tasters in sensory evaluation for quality control’, Food Quality and Preference, vol. 13, pp 355–367 floyd, c.d. rooney, l.n. and bockholt, a.j. (1995) ‘Measuring desirable and undesirable color in white and yellow food corn, Cereal Chemistry, 72(5), pp 488–490 gimeno, o., ansorena, d., astiasarán, i. and bello, j. (2000). ‘Characterization of chorizo de Pamplona: instrumental measurements of colour and texture’, Food Chemistry, vol. 69, pp 195–200 heckel, c.b. and wilson, e. (2002). ‘Characterization of fats and oils, specifications and technical bulletins’, The Manufacturing Confectioner, vol. 82(6), pp 43–49 hutchings, j.b. (1999). Food Color and Appearance, Gaithersburg, Aspen Publishers internet center for management and business administration, inc. (2009). ‘The marketing mix (the 4 P’s of marketing), [Online], Available: http://www.netmba. com/marketing/mix/ [02 Feb 2009] iso 8586-1 (1993). Assessors for Sensory Analysis, Part 1. Guide to the Selection, Training and Monitoring of Selected Assessors, International Organization of Standardization (ISO) iso 8589 (1988). Sensory Analysis – General Guidance for the Design of Test Rooms, International Organization of Standardization (ISO) lawless, h.t. and heymann, h. (1998). Sensory Evaluation of Food, New York, Chapman & Hall/International Thompson Publishing matz, s.a. (1992). Cookie and Cracker Technology, 3rd Edition, New York, Van Nostrand Reinhold/AVI matz, s.a. (1993). Snack Food Technology, 3rd Edition, New York, Van Nostrand Reinhold/AVI meilgaard, m.c., civille, g.v. and carr, b.t. (2007). Sensory Evaluation Techniques, 4th Edition, Boca Raton, CRC Press metheringham, t. and rodway, l. (2001). ‘Quality in practice using proven sensory techniques to aid quality control’, New Food, issue 2, pp 19, 21–23 muñoz, a.m. (2002). ‘Sensory evaluation in quality control; an overview, new developments and future opportunities’, Food Quality and Preference, vol. 13, pp 329–339 paganuzzi, v. and carozzi, s. (2000). ‘Organoleptic characteristics of the extra virgin olive oil of the “Riviera ligure” designation’, La Rivista Italiana Delle Sostanze Grasse, vol. 77(1), pp 5–10 staniforth, j. (2004). ‘A question of consistency and quality’, New Food, Issue 2, pp 20–22

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6 Combining instrumental and sensory methods in food quality control D. Kilcast, Consultant, Food and Beverage Sensory Quality, UK

Abstract: Sensory evaluation is the primary method of acquiring valid quality information; however, for practical reasons instrumental methods are frequently used, but are also commonly misused. This chapter describes the various types of instrumental measurements that are relevant to appearance, texture and flavour, and then outlines how relevant data can be extracted and correlated with sensory data, using different types of statistical procedures. The chapter concludes with descriptions of more advanced testing methods, including in vivo methods, non-destructive procedures and also the use of electronic noses and tongues. Key words: sensory-instrumental, statistical analysis, correlation, method validation, electronic noses and tongues.

6.1

Introduction: the perceptual basis of food quality

Consumers fortunate enough to live in prosperous societies have the choice of an enormous and ever-increasing range of foods, and manufacturers find themselves in an intensely competitive situation. In less well-developed societies, hunger will be the constant driving force, and our diet will be determined by availability of any food that satisfies our basic nutritional needs. It is increasingly clear that if we are to understand what drives consumer choice of food, no single factor can be considered in isolation from other factors. For some years, psychology researchers have been developing models to understand consumer behaviour (e.g. Shepherd and Sparks, 1994). Although there are many circumstances under which non-sensory factors such as price, advertising and nutritional image can have strong effects, delivering the sensory characteristics of foods is required by consumers central to continued purchase of foods. The importance of a holistic approach is also becoming more clear when the components of sensory perception are considered. During the sequences

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of actions that constitute food consumption we perceive a whole range of different characteristics relating to the appearance, flavour and texture of the food. Traditionally, it has been common industrial practice to consider these characteristics individually when analysing and designing food sensory quality, and this can be seen in the development of sensory methods that are specific to certain characteristics, for example the flavour profile method and the texture profile method (details of both methods can be found in Lawless and Heymann, 1998). Current sensory measurement systems, however, are increasingly focused on assessing all sensory factors that are likely to be important to perceived quality, and on understanding how these interact at both physiological and psychological levels. Numerous sensory methods have consequently been developed to assess various aspects of sensory quality for both research and quality control purposes, and these have been described in more detail in this volume. The relevance of these sensory quality measurements (made by trained panels) to likely consumer response should ideally also have been established by carrying out appropriate correlation studies. The information secured by such research is vital in maximising product success, but can be out of reach of smaller companies operating on limited budgets, and there is a danger of falling prey to the temptation of extrapolating too far from a limited number of non-validated quality measurements. Similar considerations can also lead to the uncritical use of instrumental measurements that are assumed to be relevant to sensory quality.

6.2 The role of instrumental measurement The development of applied sensory techniques for evaluating the quality of consumer goods has been most extensive in the food and beverages industry, almost certainly reflecting the intimate contact that users have with the finished product. In contrast, until relatively recently other manufacturers of consumer goods have relied almost exclusively on using various types of appropriate instrumental measurement methods to ensure that any important perceived sensory characteristics of the product are as intended. The extent of the use of instrumental methods in different industries therefore reflects the difficulties inherent in the availability of validated sensory techniques. Given the wide range of sensory techniques available to the food and beverage sector then, why is there such extensive usage of instrumental measurement methods in quality control functions, and why is there a growing demand for the development of new methods? Many possible answers to these questions can be proposed. For example: • Logistical difficulties (especially in terms of time and cost) in setting up sensory panels, especially in small companies. • Staff downsizing policies, giving rise to difficulties in securing adequate panellist numbers.

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• The realisation that in order to maintain even a basic sensory quality control (QC) system, resources in terms of facilities and panellist training require investment. • Instability of results from sensory panels over long time periods. • Possibility of contamination (accidental or malicious) of product by toxic chemicals, especially when investigating consumer complaint returns. • The manufacturing business produces large numbers of small batches of different products, and key customers demand 100% batch testing. • An unfounded expectation that there will be a simple and invariant 1 : 1 correlation between an instrumental parameter with a key sensory characteristic. • Lack of appreciation of the power and relevance of formal testing procedures, and a failure to recognise that uncontrolled informal sampling procedures are not an adequate substitute. • A naive faith in data that is generated by modern electronic instrumentation. Lawless and Heymann (1998) have also pointed out that instrumental measurements should be used for evaluations that are repetitive, fatiguing and dangerous, and when decisions made with the data are not business critical – again, providing that a correlation can be established. Irrespective of these concerns, consumer enjoyment of foods and beverages will be determined principally by a wide range of responses from the senses, and no instrument (or set of instruments) will be able to mimic these in the foreseeable future. However, the concerns listed above are not trivial, and although all companies must take all possible steps to employ sensory methods in QC, instrumental methods will continue to provide valuable quality input, provided that steps are taken to establish that the measurements relate to relevant sensory characteristics.

6.3 Sensory analysis of quality Details of the use of sensory methods in a QC environment are covered in other chapters in this volume, and this section will be restricted to an overview of the type of sensory characteristics that instrumental methods are designed to relate to. 6.3.1 The human senses It is generally accepted that humans have five senses in operation, namely sight, smell, taste, touch and hearing, although warmth, cold, movement and pain may also be considered as senses of importance in a food context. Foods are complex mixtures of chemical compounds, arranged into structural units. The perception of the sensory characteristics of foods results

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from the stimulation of all our senses to some extent by the physicochemical properties of the foods. The sensory characteristics of food are generally grouped into three categories, namely appearance, flavour and texture. These categories are, however, not independent of one another. For example, colour, which is obviously an important appearance characteristic, can be shown to have an influence on flavour perception; consumers will assign higher scores for flavour intensity to darker foods than to lighter foods. The interaction between appearance and flavour is referred to as ‘visual flavour’. Similarly, textural characteristics such as viscosity can influence the perception of flavour, and some flavour characteristics, e.g. acidity, can affect textural characteristics. One means of defining flavour, texture and appearance is by taking into account the fact that each can be attributed to the stimulation of one or possibly two of the senses. On this basis the International Organization for Standardization (ISO, 1992) has proposed working definitions for flavour, texture and appearance, as given below. • Appearance: sensory characteristics of foods perceived largely by way of the visual sense. Input from other senses, especially smell, may contribute. • Flavour: the combination of taste and odour. Pain, heat, cold, tactile and visual sensations may also contribute. • Texture: sensory characteristics perceived largely by way of the senses of movement and touch. Input from other senses, especially vision and taste, may sometimes contribute.

6.3.2 Sensory test procedures The main sensory test procedures available to sensory analysts are shown schematically in Fig. 6.1. There is a fundamental distinction between the analytical methods, which use trained panels as an instrument to measure sensory properties, and the hedonic methods, which measure consumer responses to the sensory characteristics. Whilst the availability of such a wide range is of great value to sensory researchers, for practical reasons those used for QC purposes need to be chosen and adapted to the restrictions imposed within most manufacturing environments (See Chapter 4 and Muñoz et al., 1992; Costello, 2002). The methods most commonly used for QC purposes range from simple procedures that are easy to operate, but which have limited information content, through to complex systems with high information content, but which are expensive and time-consuming. A schematic diagram of the relationship between some key methods is shown in Figure 6.2. Whilst all these methods have their place in sensory QC, if they are to be complemented, or even replaced, by instrumental methods, then the more quantitative methods (especially quality ratings and profiling) are of the greatest importance if numerical relationships between instrumental

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Fig. 6.2 Key sensory test methods used in quality control.

and sensory data are to be established. The range of sensory techniques that are of potential value has been summarised by Hugi and Voirol (2001).

6.4 Instrumental measurement of quality factors 6.4.1 General principles It is possible to identify an enormous range of measurement variables that can be used to quantify aspects of product quality and product safety, in addition to those used to quantify factors such as production efficiency and compliance (Kress-Rogers, 2001a). Uncritical choice of the types of instrumental measurements to be used will generate substantial difficulties in data handling and interpretation. Further, as a consequence of the development of sophisticated software used for instrument control and data analysis, a given instrument will frequently generate a large number of numeric parameters. If confusion is to be avoided, and erroneous conclusions are to

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be minimised, careful consideration must be given both to the choice of instrumental measurement and to the selection of measured parameters. Steps that should be taken in the use of instrumental measurements are outlined below. 1. Identify the sensory quality factors that the instrumental measurements are intended to mimic. 2. Select appropriate sensory methods for quantifying the sensory quality factors, and any necessary data manipulation and formatting. 3. Select appropriate instrumental test procedures and measured parameters, and any necessary data manipulation and formatting. 4. Correlate the instrumental measurements against sensory measurements using appropriate methods. 5. Validate any sensory–instrumental relationships developed for their value as a predictive tool. One important distinction between instrumental and sensory assessments is that the instrumental measurements comprise measurement of discrete, well-defined physicochemical properties, whereas sensory perception is rarely discrete, and different stimuli (within or across different sensory modalities) interact at both physiological and psychological levels. As a consequence, whilst an instrumental measurement of flavour components can usually be assumed to be free from the influence of other product characteristics, it has long been established that the sensory perception of flavour can be strongly influenced by product colour (for example see Zampini et al., 2007). One notable and important exception, however, lies in the measurement of flavour, in which case the physicochemical structure of the products can influence the release of flavour components, with profound effects on both perceived flavour and on the measured flavour components (for example, Taylor, 2002; Taylor and Roberts, 2004).

6.4.2 Appearance measurement For many food products, the visual senses are the first to be used by purchasers, consumers and trained sensory assessors. If a negative impression is communicated at point of sale, then purchase might not go ahead. More subtle influences can also influence the perception of non-visual sensory attributes through interactive mechanisms. Even if the product is packaged at point of sale and not directly visible, then visual information associated with the packaging system, including product images, product descriptions and ingredient lists will generate an expectation of product quality. Colour is usually regarded as the most important visual product characteristic (Francis and Clydesdale, 1975), and many instrumental systems have been developed for colour measurement, varying in sophistication from colour reference atlases to highly sensitive electronic instruments (detailed information can be found in, for example, MacDougall, 2001,

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2002). The most common ones in practical use that are capable of generating quantitative data are based on the Hunter Lab system, in which colour is measured in terms of three parameters: L (lightness), a (red/green) and b (blue/yellow). Many foods carry important visual cues other than colour. For example, the glossiness of chocolate, turbidity of beverages and the visual composition of prepared meals can all influence consumer liking, and many systems have been developed for measuring this wider range of visual characteristics (Kress-Rogers and Brimelow, 2001). A more holistic view has also been taken in appearance assessment, through the concept of total appearance (Hutchings, 1999). In addition to direct visual information, the importance of indirect visual information about the product should also be considered, commonly available in printed form on packaging as photographic images, product labelling and ingredient information.

6.4.3 Texture measurement Texture perception is complex, with two major components: a tactile, surface response from skin (somesthesis) and a deep response from muscles and tendons (kinesthesis or proprioception) (Kilcast, 2004). In addition to perception in the mouth, manipulation of products by fingers and hands can generate textural responses, together with visual information (visual texture) and information arising from sounds released when handling and chewing products. As a consequence, many types of instrumental measurements have been devised to cover food categories (for more detailed information on measurement methods, see Rosenthal, 1999; Bourne, 2002; McKenna, 2003; Kilcast, 2004). The majority of these methods measure a wide range of mechanical characteristics of food which, although related to texture, do not give a complete picture of textural characteristics. As an example, the frictional properties of foods that are related to the perception of attributes such as roughness and creaminess have received relatively little attention, and measurement methods have not been developed to the extent of those used for other textural characteristics, but in recent years the importance of understanding such processes has been stressed (de Wijk and Prinz, 2006; Engelen and van der Bilt, 2008). Although the incomplete nature of instrumental texture measurement is widely recognised, the use of texture measurement in QC protocols is extensive, and this in part results from the greater degree of difficulty often encountered in using sensory panels for texture assessment in comparison with other sensory modalities. The types of texture measurement employed have been categorised as empirical, imitative and fundamental. Empirical methods measure often ill-defined variables that are indicated through practical experience to be related to some aspect of textural quality, and are frequently dedicated to a specific product type. Imitative methods mimic conditions that the product

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is subjected to during eating. Fundamental methods measure well-defined physical properties of the product which can be independent of the measurement method. Both imitative and fundamental methods can usually be applied to a wide range of food types, and instrument manufacturers supply a wide range of test cells for this purpose. In general, empirical methods generate a single measurement parameter, whereas imitative and fundamental methods can generate a wide range of measurement parameters, some of which might be correlated. Selection of appropriate parameters is important if valid correlations with sensory data are to be achieved.

6.4.4 Flavour measurement Flavour is conventionally regarded as a combination of sensations derived from several distinct types of chemical stimuli. Tastes, detected by receptors on the tongue and other oral surfaces, are involatile chemical stimuli that are carried in solution by saliva from the food to the receptors. It is now widely accepted that there are five basic tastes – sweet, salt, bitter, acid, savoury (umami) – although this list is sometimes extended to include other sensations. Odours (aromas) are volatile chemical stimuli detected by receptors located in the olfactory epithelium in the nasal cavity. These are transmitted to the receptors directly through sniffing (orthonasal route), or from the mouth during eating (retronasal route). The odour response is complex, with around 2500 odorous chemicals found in food (Taylor and Roberts, 2004). A third component of flavour, a chemical sense that stimulates the trigeminal nerves, is responsible for sensations such as burning and cooling. Trigeminal sensations can arise from both chemicals in dissolved in saliva, for example the tingling sensation from carbonic acid in fizzy drinks, and from volatile chemicals, for example pungent thiocyanates in mustard and horseradish. Measurement of flavour components is consequently strongly influenced by the widely differing volatilities of flavour-active chemicals. The relatively large number of volatile chemicals contributing to flavour has been reflected in the wide range of instrumental methods that are now commonly used for volatile analysis, in particular gas chromatography/mass spectrometry systems (for example, see Kress-Rogers, 2001a). More recently, considerable publicity has been given to the development of so-called ‘electronic noses’, which are more correctly volatile sensors operating on a pattern recognition basis. Although these are finding numerous uses in other fields, relatively few routine uses have been recorded within the food industry (Röck et al., 2008). Measurement of taste chemicals has relied predominantly on traditional methods of chemical analysis for salty and acidic stimuli, with high-pressure liquid chromatography being used for less volatile chemicals such as sugars. However, ‘electronic tongues’ have now appeared on the market, and whilst considerable research is being carried out, very few practical applications have been reported.

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6.4.5 Other measurements In addition to physicochemical measurements that can be related directly to the sensory quality of products, other measured data can be used to help build a model of likely sensory quality. This can take the form of data such as solution concentrations of components, pH, process temperature and emulsion droplet size. Depending on the type of data incorporated, care needs to be taken in the use of correlation methods: this is discussed further in Section 6.5.4. 6.4.6 Selection of instrumental measurement methods When developing instrumental–sensory relationships, careful consideration must be taken in both R&D and QC environments in selecting the instrumental methods to be used. Researchers often fall prey to the temptation to list all the instruments that might generate data relevant to sensory perception, leading to probable problems in data analysis and interpretation. On the other hand, QC methods are often those that are inexpensive, rapid and convenient, but which are not necessarily the most appropriate. In either situation, an additional danger is that methods will be selected on the basis of outdated information (or, worse, hearsay) regarding their relevance to perceived sensory characteristics, and correlations assumed rather than being checked and validated. In analytical investigations of aroma, Reineccius (2006) has stressed the importance of giving careful consideration to the sample, volatiles of interest, analysis time and study objectives in selecting analytical procedures, and has pointed out that analytical objectives such as those listed below will strongly influence the choice of procedures: • Obtain a complete aroma isolate to accurately identify and quantify all aroma constituents. • Identify key components responsible for the characteristic aroma. • Identify any off-notes. • Monitor aroma changes with time. • Predict sensory attributes. • Determine if a food flavouring is adulterated. The validity of all instrumental–sensory data correlations found in published or internal company literature should always be questioned, especially if product design factors such as ingredient composition, physical structure, processing conditions, storage conditions and packaging have changed substantially since the reported investigations.

6.5 Analysis and validation of instrumental measurements Section 6.2 listed some of the driving forces underlying the use of instrumental measurement of food quality. In a QC environment, the most

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pressing requirement is to find instrumental methods that are rapid and inexpensive and which can reduce the dependence of the company on sensory panels (or even replace the use of sensory panels, although fortunately regulations in most developed countries recognise the importance of sensory quality assessments). One consequence is that all too often instrumental data are used uncritically, and several steps are needed to ensure that any instrumental measurement(s) used are valid.

6.5.1 Data inspection Most authorities on statistical data analysis stress the need to carry out appropriate visual inspection of numeric data before any statistical or correlation analysis is carried out. This applies not only to instrumental data, but also to sensory data used for correlation studies. The primary purpose of this stage is to check for any anomalies in the data that would compromise the quality of any data associations achieved. This could take the form, for example, of an instrument recalibration during an experiment, an uncorrected temperature change, or a simple transcription error. Inspection of small data sets in tabular form is feasible, and for many instruments, such as pH meters and empirical texture measuring instruments generating just a single-point measurement, this will give a good indication of data anomalies. Increasingly, however, multi-purpose instruments are used that carry out a continuous recording during a test, for example deformation-force measurements in texture assessment. Instrumental software will then often calculate a summary parameter that experimentation has shown to relate to sensory characteristics. Although convenient to the user, care should always be taken to inspect the form of the data recording to ensure that valid parameters are being measured. As an example, testing of gels and solid foods for firmness usually involves penetrating the product with a probe of defined geometry, and recording the force continuously during penetration. By convention, firmness is usually measured as the force recorded at a set penetration distance. Relatively minor changes to the gel structure and the probe geometry can result in distinctly different force–deformation curve shapes, as shown in Fig. 6.3. In the case of a simple brittle gel (e.g. gelatin) penetrated by a cylindrical probe, a break in the gel structure occurs at a short penetration distance, giving a discontinuity to the smooth curve. This has two consequences in practice. First, this initial break occurs at the 4 mm penetration distance conventionally used for gel firmness testing, and unwanted variability on this measurement. Secondly, this initial break results in chaotic breakdown patterns at higher penetration distances, and as a consequence high variability in any parameters measured at these penetration distances (Kilcast et al., 1984; Kilcast, 2001). This initial break does not occur when using a hemispherically ended probe, and firmness measurements associated with the simpler breakdown pattern show lower variability.

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Fig. 6.4 Adhesive failure and cohesive failure in stickiness measurement.

A further example of the importance of visual observation of product behaviour during instrumental testing can be seen in research on devising instrumental measurements to measure stickiness in foods (Kilcast and Roberts, 1998; Kilcast, 2001). The perception of oral stickiness during sensory testing relates to the force needed to remove product from the teeth (ISO 5492, 1992). Instrumental testing of stickiness commonly uses a procedure in which the product is placed between two plates, is compressed, and then the force recorded as the plates are separated (Fig. 6.4). Perceived stickiness can then be related to the force when the product separates from the plate. In some situations, however, the product remains stuck to the plates and undergoes an internal failure, termed cohesive failure. Whilst this is an important characteristic in some contexts, for example in the sticking

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of unwanted material to equipment surfaces, it is less likely to relate to perceived stickiness. As the behaviour of products during such tests can be influenced by a range of factors, such as product rheology, test conditions and the surface energy of the materials used for testing, observation can help to minimise the risk of misinterpretation.

6.5.2 Correlation analysis The most common objective in the use of instrumental relationships is to set up an empirical statistical model that relates the intensity of a sensory characteristic to a measured instrumental parameter, or to a set of instrumental parameters. Another relationship that is sometimes considered is to use instrumental data to directly model consumer liking. This requires, however, reliable consumer liking data that is relevant to the intended market, and an understanding of consumer segmentation patterns. A stepwise approach is therefore usually taken, first to relate instrumental data to key sensory attributes, and then to relate the sensory attributes to consumer liking (examples of different approaches to modelling consumer liking can be found in MacFie, 2007). An important prerequisite to carrying out any statistical analysis of instrumental data is to carry out a visual inspection of the data using scatter plots, usually by plotting one measure on the x-axis against a second measure on the y-axis. The visual form of the resulting plot will often give useful information on the data relationship, and guidance on further data analysis. In addition, the plots will often highlight problems with the data set. Examples of the form of plots that might be seen are shown in Fig. 6.5. Figure 6.5a shows a plot in which it is difficult to discern any structure in the data set, and a significant correlation is unlikely to be seen in such a plot. In Fig. 6.5b, there is sufficient indication of a possible linear relationship that would warrant further investigation. Evidence for a relationship can also be seen in Fig. 6.5c, but in this case the curvilinear form of this plot points to using non-linear modelling. The form of the plot in Fig. 6.5d is found occasionally, and indicates a possible change in the product structure (especially in texture testing) or some environmental factor such as temperature during the test. In this case (sometimes called a broken-stick model) two different linear relationships are evident, with the intersection occurring at the presumed change. If the scatter plots reveal possible linear relationships, then the next step is usually to calculate Pearson product moment correlation coefficients (r). A perfect positive correlation gives r = +1, a perfect negative correlation gives r = −1, and no correlation gives r = 0. However, it should be noted that these coefficients are relevant only to linear correlations, and strong data relationships can exist in which the correlation coefficient is very low. This is illustrated in Fig. 6.6. The scatter plot shown in Fig. 6.6a will give a correlation coefficient that is close to zero. The plots shown in Fig. 6.6b indicate

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near-perfect positive and negative correlations, and will give correlation coefficients close to +1 and −1, respectively. The scatter plot shown in Fig. 6.6c shows a very strong non-linear correlation, but which will give a very low correlation coefficient. (This inverted-U relationship is very commonly encountered in relationships between consumer liking and sensory attributes.) Figure 6.6d shows a situation in which this is a strong linear correlation, but an outlying point reduces the correlation coefficient. This situation often occurs through data transposition errors, and can also indicate a step change in a measurement. The square of the correlation coefficient (r2, or coefficient of determination) gives a measure of the data variance accounted for by the linear correlation. For example, a correlation coefficient of 0.7 indicates that 49% of the data variance is accounted for in the correlation. Statistical software packages commonly available will often associate a significance value to the correlation coefficient. Lawless and Heymann (1998) have described the use of the so-called Anscombe data sets in demonstrating the dangers of using correlation coefficients without first examining the form of the data.

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6.5.3 Regression analysis Linear regression is used to fit a linear mathematical function between two variables, and which takes the general form: y = a + bx As with Pearson product moment coefficients, the regressions will be invalid for highly correlated but non-linear data sets, such as that shown in Figure 6.6c. Statistical software packages can conveniently be used to generate such a regression equation, together with an associated coefficient of determination. Many packages can also be used to generate confidence bands, for example a 95% confidence band means that there is a 95% probability that the ‘real’ trend is in that band. Confidence bands should not be used for predictive purposes, however – prediction bands are wider than confidence bands, but are not generated by some software packages. A further limitation of such an equation is that the relationship might only be valid for a limited range of measurements. For example, there is a linear relationship between sweetness intensity and solution concentration for a wide range of bulk sweeteners, but this relationship can become curvilinear at both low and high sweetener concentrations (Portmann and Kilcast, 1996).

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Multiple linear regression (MLR) methods are used to find a linear relationship between a variable and a set of variables, for example between a sensory attribute (y) and a set of instrumental variables (x1, x2, x3 . . .). The relationship developed takes the form: y = a + b1x1 + b2x2 + b3x3 + … Most statistical analysis packages have available a range of MLR methods. For example, all variables can be included in the relationship, or variables can be added (and removed) and the effect on the calculated R2 observed to find the best correlation. However, problems can arise if a large number of instrumental variables are included. Firstly, as the number of instrumental variables rises, this requires a larger number of sensory observations. Secondly, if there are inter-correlations between the instrumental variables (which is likely if several parameters are measured from a graphical plot) then the regression relationship can become misleading. Thirdly, if intercorrelations exist, then the order in which they are introduced into the equation can influence the relationship (Lawless and Heymann, 1998). Consequently, the temptation to include every measurable instrumental parameter into a predictive relationship must be resisted, and careful consideration given to the choice of parameters. Many statistical software packages are also able to carry out non-linear multiple regressions, for example using square terms or log terms. Such regressions should only be used if there is a good logical reason for including non-linear terms, for example in investigating a sensory characteristic such as creaminess, in which fat droplet size is thought to be important, and in which case the cube of the mean droplet size (i.e. fat particle size volume) might be included. Again, the temptation to develop complex non-linear relationships that are easily set up with readily available software packages must be resisted, and the simpler relationships investigated first – the visual form of a valid relationship of practical value will very frequently have a clear logical basis.

6.5.4 Multivariate methods Multivariate statistical methods are used almost routinely by the sensory analyst, reflecting the complex multidimensional character of sensory data, and the need for tools to help rationalise and understand sensory phenomena. Instrumental data can be equally complex, and unsurprisingly many multivariate approaches have been developed to examine the relationship between these data sets. Reineccius (2006) identifies two approaches in the use of multivariate methods: firstly, the unsupervised approach is taken, in which all the data are entered into a statistical program that searches for relationships, trends or groupings of samples; secondly supervised methods, in which there is a priori knowledge of the sample groupings. The main unsupervised methods used are principal component analysis (PCA), factor

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analysis (FA), cluster analysis and multidimensional scaling (MDS). Supervised methods include multiple regression analysis (MRA, discussed above), principal component regression (PCR), canonical correlation analysis (CCA) and partial least squares regression (PLS). The use of some of these methods in the analysis of flavour data has been described by Qannari and Schlich (2006). PCA is a data reduction technique that replaces a large number of original variables by a smaller number of linear combinations, whilst still explaining a substantial proportion of the original variation in the data. Essentially, PCA projects an n-dimensional space onto a two-dimensional plot. PCA analyses the correlation structure in the data set and identifies the axis along which the maximum variation occurs. A second principal axis is then identified orthogonal to the first axis, corresponding to the second greatest amount of variation, and so on. The new axes are linear combinations of the original axes, and the coefficients, or loadings, measure the importance of the original variables on each principal component. A useful reduction will often retain 70–80% of the variation in the first three dimensions. PCA is now a routine statistical procedure for analysing sensory profile data, and increasingly used to examine for structure in combined sets of sensory and instrumental data. When analysing such data sets, it is important to carry out the analysis using the correlation matrix to compensate for the different measurement scales used. An example of the use of PCA on combined data sets is shown in Fig. 6.7, redrawn from Kilcast and Clegg (2002). 0.4 0.3

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PCR takes the first few principal components from a PCA carried out on the instrumental data, and then uses these in a multiple linear regression against the sensory variables. One pitfall in the use of this method is that those principal components not used in the regression might contain information relevant to the sensory data. This defect is addressed in the use of CCA and PLS. Both these methods analyse the structure of each data set, and then measure the association between these structures – CCA uses the correlation coefficient as a measure, and PLS uses the covariance. PLS is currently one of the most popular methods used to relate two data sets (Qannari and Schlich, 2006).

6.6 Future trends The traditional approach described above to devise instrumental measurement systems that can be used as predictors of sensory quality has been extended in recent years to developing instrumental measurements that give a more direct measure of sensory response. In addition, different modelling procedures have been investigated.

6.6.1 In vivo measurements One of the most important milestones in texture measurement was the development of the General Foods Texturometer (Friedman et al., 1963), which was designed to mimic as far as possible human chewing actions. Although it has subsequently been replaced by more general-purpose instruments, the principle behind mimicking oral action was taken further to measuring electrical activity in chewing muscles during mastication using electromyography (EMG). The technique has been used to measure changes in texture during mastication (e.g. Kilcast, 2001; Brown et al., 1998) and to investigate differences in human chewing behaviour and texture perception (e.g. Brown et al., 1994). Other oral texture measurement methods have been reviewed by Smith (2004). The principle of in vivo measurement has also been applied to the measurement of flavour. Systems have been developed for extracting and analysing volatiles released from the food in the mouth (e.g. Linforth and Taylor, 2006; Cook et al., 2005; Taylor and Hort, 2004), and these have been extended to the in vivo measurements of tastes (Taylor and Hort, 2004). Whilst in vivo measurements have proved of great value for understanding the physical processes underlying sensory perception of food, they are generally complex and have yet to see extensive use in a QC environment. Related research utilising various types of measurement of brain activity during eating (e.g. Rolls, 2005) are again generating important information on the understanding of sensory perception mechanisms, but are again unlikely to see practical use for QC purposes.

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6.6.2 Non-destructive testing Many instrumental test methods, and particularly those used for texture measurement, are inherently destructive, and for many purposes, for examples in assessing the ripeness of fruits, there is considerable interest in developing non-destructive test methods (Irudayaraj and Reh, 2008; KressRogers and Brimelow, 2001). Examples of non-destructive methods for texture measurement include sound input techniques, particularly for fruit (Duizer, 2004) and near-infrared techniques applied to a wide range of foods (Millar, 2004). Other techniques that have potential applications in the QC laboratory are nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) (Thybo et al., 2004).

6.6.3 Electronic noses and tongues As has been stated previously, a substantial amount of research has been carried out to develop electronic noses for volatile measurement, and electronic tongues for involatile measurement (Kress-Rogers, 2001b; Röck et al., 2008). Extensive publicity given to such systems has led to an overexpectation of their potential, and many companies that have invested in these instruments have not found their expectations realised. Some applications have been reported, mainly in the beverage and water areas (e.g. Deisingh et al., 2004; Marti et al., 2005), but routine usage is currently not high. Specific applications that might be expected in the future include screening incoming ingredients such as raw milk for taints, and screening packaging materials for residual volatiles.

6.6.4 Data analysis The traditional data analysis methods described above continue to prevail in most of the industry, but other (non-statistical) approaches to relating instrumental and sensory data are under active investigation, and it is highly likely that some of these will be developed into practical methods. Artificial neural networks (ANN), which are designed to mimic the structure and functionality of the biological nervous system (Kress-Rogers, 2001b), can be trained to relate complex instrumental data sets to sensory quality data. They have been used in conjunction with both electronic nose systems (Yu et al., 2008) and with electronic tongue systems (Chen et al., 2008) in the quality evaluation of green tea. Fuzzy logic analysis has been used to investigate the relationship between instrumental parameters and sensory quality of mango drinks (Jaya, 2003), and ANN and fuzzy logic systems have the potential to be used in combination (Kress-Rogers, 2001b). A criticism that is often levelled at ANN systems is their lack of transparency, and their use as a black box. An alternative approach that is being made is the use of novel belief rule-based (BRB) models (Wang et al., 2009; Yang et al., 2009) which can be used to support quality analysis and con-

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sumer acceptance prediction in rapid retro-design and testing of new food and drink products. 6.6.5 Conclusion Almost certainly, considerable efforts will continue to be made to identify instrumental methods that can be used to give valid measures of sensory quality. It is equally certain, however, that these efforts will fail to find a single instrument, or combination of instruments, that will be capable of measuring the full range of sensory information that the human senses respond to and interpret as quality. This does not diminish the value of instrumental measurement as a valuable adjunct to sensory assessment, provided that the instrumental measurements can be validated satisfactorily. A key issue is that any useful set of instrumental measurements should be compatible with the working constraints of a busy quality function.

6.7 Sources of further information Sensory methods used in a QC environment are described in other chapters in this volume, in standard sensory texts (e.g. Lawless and Heymann, 1998; Meilgaard et al., 2006) and texts focusing on QC methods (e.g. Muñoz et al., 1992; Costello, 2002). Instrumental methods tend to be classified according to sensory modality, e.g. appearance in Hutchings (1999) and MacDougall (2002); texture in Bourne (2002) and Kilcast (2004) and flavour in Taylor and Roberts (2004) and Voilley and Etiévant (2006). A broader picture of instrumental methods for food quality measurement can be found in KressRogers and Brimelow (2001). Correlation studies are less well covered in a comprehensive form, and the reader should examine published papers, in conjunction with standard statistical texts.

6.8 References bourne m. c. (2002). Food Texture and Viscosity, Concept and Measurement (Second Edition), Academic Press. brown w. e., langley k. r., martin a. and macfie h. j. m. (1994). Characterisation of patterns of chewing behaviour in human subjects and their influence on texture perception. Journal of Texture Studies, 25(4), 455–468. brown w. e., eves d., ellison m. and braxton d. (1998). Use of combined electromyography and kinesthesiology during mastication to chart the oral breakdown of foodstuffs: relevance to measurement of food texture. Journal of Texture Studies, 29(2), 145–167. chen q., zhaoa j. and vittayapadunga s. (2008). Identification of the green tea grade level using electronic tongue and pattern recognition. Food Research International, 41(5), 500–504. cook d. j., hollowood t. a., linforth r. s. t. and taylor a. j. (2005). Correlating instrumental measurements of texture and flavour release with human perception. International Journal of Food Science and Technology, 40(6), 631–641.

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costello e. (2002). A comparison of sensory methods in quality control. Food Quality and Preference, 13, 341–353. de wijk a. and prinz j. f. (2006). Mechanisms underlying the role of friction in oral texture. Journal of Texture Studies, 37, 413–427. deisingh a. k., stone d. c. and thompson m. (2004). Applications of electronic noses and tongues in food analysis. International Journal of Food Science and Technology, 39(6), 587–604. duizer l. (2004). Sound input techniques for measuring texture. In Texture in Food. Volume 2: Solid foods, ed. D Kilcast, Woodhead/CRC Press. engelen l. and van der bilt a. (2008). Oral physiology and texture perception of semisolids. Journal of Texture Studies, 39, 83–113. francis f. j. and clydesdale f. m. (1975). Food Colorimetry: Theory and Applications, AVI, Westport. friedman h. h., whitney h. and szczesniak a. s. (1963). The Texturometer – a new instrument for objective texture measurement. Journal of Food Science, 28, 390–403. hugi a. and voirol e. (2001). Instrumental measurements and sensory parameters. In Instrumentation and sensors for the food industry, ed. E. Kress-Rogers and C. Brimelow, Woodhead/CRC Press. hutchings j. f. (1999). Food Colour and Appearance (second edition) Aspen. international standard iso 5492 (1992); bsi 5098:1992. Glossary of terms relating to sensory analysis, ISO, Geneva. irudayaraj j. and reh c. (eds) (2008). Nondestructive Testing of Food Quality, Blackwell. jaya s. (2003). Sensory evaluation of mango drinks using fuzzy logic. Journal of Sensory Studies, 18(2), 163–176. kilcast d. (2001). Modern methods of texture measurement. In Instrumentation and sensors for the food industry, ed. E. Kress-Rogers and C. Brimelow, Woodhead/ CRC Press. kilcast d. (ed.) (2004). Texture in Food. Volume 2: Solid foods, Woodhead/CRC Press. kilcast d. and clegg s. (2002). Sensory perception of creaminess and its relationship with food structure. Food Quality and Preference, 13(7–8), 609–623. kilcast d. and roberts c. (1998). Perception and measurement of stickiness in sugarrich foods. Journal of Texture Studies, 29(1), 81–100. kilcast d., boyar m. m. and fry j. c. (1984). Gelation photoelasticity: a new technique for measuring stress distributions in gels during penetration testing. Journal of Food Science, 49(2), 654–655. kress-rogers e. (2001a). Instrumentation for food quality measurement. In Instrumentation and sensors for the food industry, ed. E. Kress-Rogers and C. Brimelow, Woodhead/CRC Press. kress-rogers e. (2001b). Sensors for food flavour and freshness: electronic noses, tongues and testers. In Instrumentation and sensors for the food industry, ed. E. Kress-Rogers and C. Brimelow, Woodhead/CRC Press. kress-rogers e. and brimelow c. (eds) (2001). Instrumentation and sensors for the food industry, Woodhead/CRC Press. lawless h. t. and heymann h. (1998). Sensory Evaluation of Food. Principles and Practices, Chapman & Hall. linforth r. and taylor a. j. (2006). The process of flavour release. In Flavour in Food, ed. A Voilley and P Etiévant, Woodhead/CRC Press. macdougall d. b. (2001). Principles of colour measurement in food. In Instrumentation and Sensors for the Food Industry, ed. E. Kress-Rogers and C. Brimelow, Woodhead/CRC Press.

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macdougall d. b. (2002). Colour in Food. Improving Quality, Woodhead/CRC Press. macfie h. j. h. (2007). Consumer-led Product Development, Woodhead/CRC Press. mckenna b. m. (2003). Texture in Food. Volume 1: Semi-solid Foods, Woodhead/CRC Press. marti m. p., boque r., busto o. and guasch j. (2005). Electronic noses in the quality control of alcoholic beverages. Trends in Analytical Chemistry, 24(1), 57–66. meilgaard m., civille g. v. and carr b. t. (2006). Sensory Evaluation Techniques, CRC Press. millar s. (2004). Near infrared (NIR) diffuse reflectance in texture measurement. In Texture in Food. Volume 2: Solid foods, ed. D Kilcast, Woodhead/CRC Press. muñoz a. m., civille g. v. and carr b. t. (1992). Sensory Evaluation in Quality Control, Van Nostrand Reinhold, New York. portmann m-o. p. and kilcast d. (1996). Psychophysical characteristics of new sweeteners of commercial importance for the EC food industry. Food Chemistry, 56(3), 291–302. qannari e. m. and schlich p. (2006). Matching sensory and instrumental data. In Flavour in Food, ed. A Voilley and P Etiévant, Woodhead/CRC Press. reineccius g. (2006). Choosing the correct analytical technique in aroma analysis. In Flavour in Food, ed. A Voilley and P Etiévant, Woodhead/CRC Press. röck f., barsan n. and weimar u. (2008). Electronic nose: current status and future trends. Chem. Rev., 108, 705–725. rolls e. t. (2005). Taste, olfactory, and food texture processing in the brain, and the control of food intake. Physiology and Behavior, 85, 45–56. rosenthal a. j. (1999). Food Texture: Measurement and Perception, Aspen. shepherd r. and sparks p. (1994). Modelling Food Choice. In Measurement of Food Preferences, eds. H.J.H. MacFie and D.M.H. Thomson, Blackie A&P, Glasgow. smith a. c. (2004). Texture and mastication. In Texture in Food. Volume 2: Solid foods, ed. D Kilcast, Woodhead/CRC Press. taylor a. j. (2002). Release and transport of flavors in vivo: physicochemical, physiological, and perceptual considerations. Comprehensive Reviews in Food Quality and Food Safety, 1, 45–57. taylor a. j. and hort j. (2004). Measuring proximal stimuli involved in flavour perception. In Flavor Perception, ed. A. J. Taylor and D. D. Roberts, Blackwell. taylor a. j. and roberts d. d. (2004). Flavor Perception. Blackwell. thybo a. k., karlsson a. h., bertram h. c., anderson h. j., szczypinski p. m. and donstrup s. (2004). Nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) in texture measurement. In Texture in Food. Volume 2: Solid foods, ed. D. Kilcast, Woodhead/CRC Press. voilley a. and etiévant p. (eds) (2006). Flavour in Food, Woodhead/CRC Press. wang y-m., yang j-b., xu d-l. and chin k-s. (2009). Consumer preference prediction by using a hybrid evidential reasoning and belief rule-based methodology. Accepted for publication by Expert Systems with Application. yang j-b., wang y-m., xu d-l. chin k-s. and chatton l. (2009). A belief rule-based methodology for mapping consumer preferences and setting product targets. Accepted for publication by IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans. yu h., wang j., yao c., zhang h. and yu y. (2008). Quality grade identification of green tea using E-nose by CA and ANN. LWT – Food Science and Technology, 41(7), 1268–1273. zampini m., sanabria d., phillips n. and spence c. (2007). The multisensory perception of flavor: Assessing the influence of color cues on flavor discrimination responses. Food Quality and Preference, 18, 975–984.

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7 Statistical approaches to sensory quality control C. Findlay, Compusense Inc., Canada and A. Hasted, QI Statistics, UK

Abstract: Statistical issues are discussed within the context of understanding the practical considerations that apply to quality control. The concepts of risk and power are presented and related to specific test types. Worked examples are provided for four of the most common sensory quality control procedures. Key words: statistics, risk, power, confidence, proficiency, validation.

7.1 Introduction It should be made clear at the outset that statistics are simply a tool to improve decision making about quality. In professional organizations the decision process is based upon well-defined standards that strive to deliver objective outcomes that are science-based. In any case, the decision should be valid, robust and consistent. This provides a framework for product improvement and for strong and positive relationships throughout the supply chain. It is essential that suppliers understand that they are being evaluated using criteria that are well understood and contribute to the success of the product in the marketplace. This means that all targets that are set for product properties, or sensory attributes, must be validated through consumer research. Sensory quality standards are valuable only if they reflect the expectations of consumers. This point cannot be overemphasized. If the sensory standard is truly representative of the consumer’s perception of quality, value and product identity it will reinforce the relationship of trust that must exist between the producer and their consumer. The supplier–client relationship applies equally within organizations. Manufacturing, production or operations must be provided with sensory

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standards that reflect the expectation of the ultimate consumer. An internal supplier has the same demands as any external supplier and it is in everyone’s best interest to ensure sensory quality for success in the marketplace.

7.2 Statistics defined Let’s start at the beginning. It is useful to define what we mean by quality control (QC) and statistics. According to Wikipedia (http://en.wikipedia. org/wiki/Quality_control): Quality control is involved in developing systems to ensure products or services are designed and produced to meet or exceed customer requirements. Traditional statistical process controls in manufacturing operations usually proceed by randomly sampling and testing a fraction of the output. Variances of critical tolerances are continuously tracked, and manufacturing processes are corrected before bad parts can be produced.

Again, according to Wikipedia (http://en.wikipedia.org/wiki/Statistics): Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. Also with prediction and forecasting based on data. It is applicable to a wide variety of academic disciplines, from the natural and social sciences to the humanities, government and business. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and are then used to draw inferences about the process or population being studied; this is called inferential statistics. Descriptive, predictive, and inferential statistics comprise applied statistics.

7.2.1 Function of statistics In summary, there are three major roles that statistics can play in helping us make decisions about the data we collect: • Descriptive function: ° Descriptive statistics help to summarize raw data, ° Plots may be used to visualize data. • Inferential function; ° Determine if any observed effects are real or simply due to chance variation. • Measurement function; ° Estimate degree of association between the experimental variables and the attributes.

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7.2.2 Hypothesis testing All statistics are based upon assumptions. If the assumptions are valid, the statistics are useful and meaningful. One way of testing our assumption is to state a hypothesis. This is most frequently presented in the form of a logical statement ‘If X then Y’. The hypothesis can also be thought of as a story. For example, ‘There are two products, one is the control, and the other is the test product. Our test objective is to determine if the test product isn’t perceived as different from the control.’ In statistical terms, we are stating the ‘null hypothesis’. Mathematically this is expressed by the equation ‘μ1 = μ2’ or that the mean for sample 1 is equal to the mean of sample 2. However, we have a small complication. The mean is derived from a range of individual measurements that vary. This variance creates a need to understand the precision of each of the measured mean values, usually expressed as a confidence interval around the measured mean value, which will depend both on the variation in the individual measurements and the number of individual measurements. To declare two means to be significantly different, statistical hypothesis testing forces us to estimate the probability that the means could come from populations where there is no difference between test and control. That probability is expressed as the p-value. Statistical methods provide us with tools for estimating the risk involved in making business decisions. There are really two main concepts that define the consequences of any judgment. The first is drawing the conclusion that there is no difference when there really is a difference. The second is concluding that there is a difference when in fact there is no difference. These possibilities are typically defined as Type I and Type II error respectively (Fig. 7.1). The statistical method that is used to protect against these

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sources of error is based upon estimating acceptable levels of risk of the incidence rate. 7.2.3 Alpha risk, beta risk and power Alpha risk • Type I error. • Reject a true null hypothesis of no difference and conclude that a difference exists between the products when there is none. • Commit Type I error with probability α. Beta risk • Type II error. • Fail to reject a false null hypothesis and conclude that no difference exists between the products when there is one. • Commit Type II error with probability β. Typically, in food products a 95% confidence level is used. This is normally expressed as setting the alpha (α) at 0.05. The implication of this decision prior to actually conducting the product test and measuring the test statistic is that 19 times out of 20 a correct decision will be made if products with p-values less than 0.05 are declared different. Conversely, it may be assumed that for every 20 events, one will be erroneously judged to be different when it is in fact not detectably different. The declaration of difference is an easier task than the declaration of similarity. So controlling for β risk requires significantly larger numbers of judgments. Power • Sample size is associated with both Type I and Type II error. • A larger sample size is needed to control Type II error (which is why a larger sample size is needed for similarity testing). • Generally, with a large sample size, you profit from an increase in power (power = 1 − β). • With higher power, it is easier to find existing differences. 7.2.4 Notes on the p-value The p-value ≠ α. The value of α is a predetermined level of risk, the probability of committing Type I error. This value represents acceptable risk levels for the business. The p-value is an observed probability based on the actual data collected. This means it is calculated after the test. The range of p-values is between 0 and 1 (p ∈ [0,1]). A value of 1 tells us that there is absolutely no difference between samples under the conditions of the test. Conversely a value approaching 0 tells us that there is a significant difference between samples under the conditions of the test.

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7.3 Managing risk It must be stated that we cannot eliminate risk altogether. All we can do is attempt to manage risk and keep it at an acceptable level that meets the needs of the business. When risk involves a matter of life or death, the statistical measures become very stringent, in keeping with the consequence. In those cases the α may be set at 0.0001, or a 99.99% confidence. Typically in sensory control α is set at 0.05 or 95% confidence.

7.3.1 Statistical significance versus commercial importance Many organizations have hard-edged rules of statistical significance. If the test does not meet the p < 0.05 criteria, the product is rejected. This implies that there is a drop-off point where catastrophic consequences occur. In reality, there is a gradual slope that requires some judgment in managing commercial outcomes. The curve that describes the decrease in p-value is not like falling off a cliff (Fig. 7.2a), but more like rolling down a hill (Fig. 7.2b). The slope of this curve is affected by many factors. The number of judgments has a large role to play. In Fig. 7.3a a simple difference test outcome is plotted for 100 respondents. In the case of correct judgments, the alpha of 0.05 is reached at 59. If the number of respondents is reduced to 50, 32 correct judgments are required to declare a significant difference (Fig. 7.3b). This is 64% of respondents compared with 59% for the 100 person test. Statistical significance does not infer commercial importance. With larger and larger sample sizes, smaller and smaller differences will be flagged as statistically significant.

7.3.2 False alarms It is important to understand that although the selection of alpha and beta levels provides some level of protection there is still a significant risk of mistaken decisions. In the case of 95% confidence, the chance of a Type I error is 5%. That is that just by chance, one batch in 20 will be erroneously judged as different when in fact the difference is negligible. Conversely, typical sampling levels and judgment numbers result in effective beta levels of 20%. This means that in one case in five the decision that the products tested are not detectably different will be wrong (Lawless and Heymann, 1999).

7.4 Knowing your product It is essential that before any sensory quality standards are established the product be well understood. The performance characteristics, shelf-life,

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Falling off a Cliff

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product variability, end-use expectations and distribution system must be investigated. The standards that are set for the product must be attainable and representative.

7.4.1 Action standards and consumer validation The biggest challenge to any organization is making sure that the QC decision points are appropriate. They must be stringent and sensitive enough to assure the reputation of the supplier at the same time as being realistic

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measures that reflect the variations in agricultural commodity or manufacturing method. It is clear that fresh product in season will likely be considered the ‘gold standard’. However, apples that have been in storage for 5 or 6 months cannot be expected to be the same and their standards must reflect that reality. In any case, it is impossible to set a meaningful sensory standard without validation by appropriate consumer panels. The range of acceptance must be established in the marketplace. It is then possible to correlate the consumer data with the sensory quality method that will be applied to the product. This is a difficult subject for product experts. They have different

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Product sensory quality space

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Fig. 7.4 Gold standard sensory properties within a product space.

criteria for the measurement of quality than consumers. They may be attempting to control sensory properties that have a relatively small importance to real consumers. Consequently the relative importance of sensory attributes is required to develop meaningful action standards. 7.4.2 Establishing the gold standard Although a gold standard is not a statistical concept, it is important to understand that for most products there is not a single set of sensory specifications, but a range or zone of product properties that are all acceptable. Figure 7.4 illustrates the lack of symmetry that is common in sensory dimensions. In some products, texture may be far more important than aroma. The shape of the ‘gold’ zone reflects this. It may also be that the product is more acceptable if it is ‘too soft’ than if it is ‘too hard’. Sampling plans must be implemented to insure that products are representative of actual production and the variability found in the process.

7.5 Methods of measurement and practical examples According to Muñoz in the 2002 Food Quality and Preference special issue on quality, there are four main methods that are used in sensory QC. Each of the methods has strengths and weaknesses. The following point form summary provides the framework to compare the methods and examine specific examples. 7.5.1

In/out or pass/fail

• Assessors or judges are trained to reliably detect defects that have been defined using clear standards.

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• This applies very well to simple products that have few variables, i.e. water. • There is no diagnostic capability, since the product either passes or fails. • There is no measurement of trend or tendency except rising or falling failures numbers. • The decision is made on the frequency count of a number of judges. Example 5.1 Five trained quality control technicians evaluate a sample of product from the production line, taken from the two production shifts in a day. The judgment is made as ‘pass or fail’ by each judge independently. The product is deemed to pass the test if the majority of judges (three or more out of five) pass the product. The results from one week’s tests are given in Table 7.1. Overall eight out of the ten samples tested pass, and the two fails were on different days and from different shifts. Judge 4 passes all ten samples, whereas the other judges all fail at least two samples. This suggests that judge 4 may need further training in how to discriminate between good and poor quality samples. It is clear that this method does not provide any specific reason for a product failing. The simplicity and low cost of the test are an apparent advantage; however, a test that measures more sensory properties would lead to diagnostic information that would help the business correct any problem with the product.

7.5.2

Rating systems: degree of difference or difference from control

• Scales are used to measure a small number of key attributes compared to a range of references. • References are ideally based upon consumer acceptance data. • A cut-off value is determined and acceptance is based upon a majority of trained judges scoring within the cut-off. • Blind controls are used to determine the repeatability and reliability of the judges. • Attribute score permit some diagnostic information to be obtained. • Trends can be followed by the numeric values for each attribute over time. Example 5.2: Difference from control (DFC) test Assessors are presented with pairs of samples, an identified control and a test sample and asked to rate the difference of the test sample from the control sample using a simple category scale: • no difference; • very slight difference; • slight difference;

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Week total

Friday

Thursday

Wednesday

Pass Fail

A B A B A B A B A B

Monday

Tuesday

Shift

7 3

Pass Fail Pass Pass Fail Pass Pass Fail Pass Pass

Judge 1

8 2

Pass Pass Fail Pass Pass Pass Pass Fail Pass Pass

Judge 2

Results from one week of pass/fail tests

Day

Table 7.1

7 3

Fail Pass Pass Pass Fail Pass Pass Fail Pass Pass

Judge 3

10 0

Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass

Judge 4

6 4

Pass Fail Pass Pass Fail Pass Pass Fail Pass Fail

Judge 5

4 3 4 5 2 5 5 1 5 4

Pass total

8 2

Pass Pass Pass Pass Fail Pass Pass Fail Pass Pass

Conclusion

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Sensory analysis for food and beverage quality control Table 7.2

Mean scores of five samples in DFC test

Product

Mean score

No. of scores

Blind control Sample 1 Sample 2 Sample 3 Sample 4

1.5 2.6 2.6 2.2 2.7

12 10 8 6 4 2 0 0

1

2

3

4

5

Difference from control

Fig. 7.5 Blind control sample scores for a DFC test.

• moderate difference; • large difference; • very large difference. Twenty-two assessors were presented with five paired samples to a design balanced for presentation order. Each pair comprised a test sample paired with an identified control. The five samples comprised four test samples and a blind control sample. The mean scores for the five samples were as given in Table 7.2. The blind control is included in the test as a benchmark sample and is rated as different from the control by most assessors; the perceived differences may be due to assessor uncertainty in the use of the scales or in production variation in the control product. The individual scores of the assessors for the blind control are given in Fig. 7.5. The samples are compared in their level of difference from control using Friedman’s test which gives a non-parametric analysis of the rank of the difference scores for each assessor (the normality assumptions required for analysis of variance or t-tests are not considered valid for data on a 5 point scale). Friedman’s test (Table 7.3) gives a statistically significant difference in rank sums with p-value <0.0001 (Q = 24.5 tested against a chi-squared distribution with four degrees of freedom) and pairwise comparisons are tested using a non-parametric equivalent to Fisher’s LSD test (Conover, 1999).

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Table 7.3 Friedman’s test on ranking of averages in DFC test Sample

Average rank

Blind control Sample 3 Sample 1 Sample 2 Sample 4

1.89 2.59 3.39 3.52 3.61

Groups A A

B B B

C C C

Samples 1, 2 and 4 are rated as most different from the control, with differences significant at p-value <5%; sample 3 is not distinguished from the blind control in its average level of difference from the control sample.

7.5.3

Quality ratings – an integrated quality score

• Frequently applied to commodities and traditional foods. ° dairy product grading ° meat product grading ° fruit and vegetable grading ° wine judging schemes ° soya sauce evaluation • Judges are trained to evaluate specific modalities and attributes and assign a score for each item. • Typically, the scoring is based upon deduction from a perfect score, for example 100. In cheese grading, it is unusual to find a score higher than 95 and lower than 90. Consequently the real range of results is very limited. • Depending on the product, attributes are given different weights that apply to the score. Defects can be defined as critical and be sufficient to reject the product. Other attributes may be weighted to reflect the trueness to type of the product or its typicality. • Scores are ideally calibrated with consumer studies to validate the levels being used. Example 5.3 A simple quality assessment exercise was carried out on biscuits. A panel of 20 assessors were presented with two biscuit samples, a ‘gold standard’ benchmark sample and a production sample from the manufacturing line. The assessment was designed so that half the assessors tasted the benchmark sample first and half tasted the production sample first. The biscuits were scored on four acceptability attributes on a 9 point hedonic scale (1 = dislike extremely, 9 = like extremely) and an overall quality rating on a 10 point scale. The data were analyzed either by a paired

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Table 7.4 Analysis of variance (ANOVA) for appearance Source

DF

Type I SS

Mean Squar

F-Value

Pr > F

Assessor Sample Error

19 1 19

31.48 1.23 36.27

1.66 1.23 1.91

0.87 0.64

0.6199 0.4330

Total

39

68.97

Table 7.5 Summary table for multiple attributes Attribute Appearance Aroma Flavor Texture Overall quality

Benchmark sample

Production sample

p-value

LSD (5%)

6.95 7.45 7.05 7.25 7.25

6.60 7.05 6.70 7.10 6.75

0.43 0.09 0.47 0.72 0.29

0.91 0.47 0.99 0.88 0.95

t-test or a two-way analysis of variance with factors assessor and sample. These two analysis approaches are exactly equivalent when two samples are compared. The results are summarized by the mean scores together with the significance of the test for differences in means taken either from the F-test in the analysis of variance or the paired t-test. For illustration the analysis of variance of one hedonic attribute (appearance) is given in Table 7.4. The F-test for differences in sample means has an F-value = 0.64 with a p-value (Pr > F) of 0.4330, indicating that the variation in the sample means is not significant when compared with the assessor noise variation measured by the error mean square. A similar analysis is then carried out for the other attributes and the results as tabulated in Table 7.5. Based on the assessments of this small panel of 20 assessors there is no evidence of a statistically significant difference in appearance, flavor and texture acceptability between the production sample and the benchmark. The difference in mean scores for aroma are significant at p = 0.09, suggesting reasonable evidence that the aroma acceptability of the production sample is lower than the benchmark and process factors affecting aroma should be investigated. The LSD (5%) column gives the level of difference in means that will be detected as significant at the 5% level of significance and so gives a measure of the sensitivity of the assessment panel. The difference in means for the overall quality score must exceed 0.95 before a difference in quality is detected as statistically significant at the 5% level. To decrease this test threshold a larger panel of assessors will be required.

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Descriptive analysis – leading to a single score

• Specific attributes are chosen to describe the quality of the product. • Judges are trained to evaluate the attributes using appropriate scales. • References are developed and used to determine the profile and tolerances for the attributes for each product. • One ballot may be used to evaluate a very wide range of products. • Products are defined by their target values for each of the attributes. • Data are analyzed using ANOVA and variance is measured on all responses. • Extensive statistical tools may be applied to this kind of data. • Examples of this approach may be observed in the quality index method (QIM) for seafood evaluation developed in Iceland and the Liquor Control Board of Ontario’s technique for the evaluation of wines in Canada. Example 5.4 A routine quality assessment is carried out on a seasoned rice dish. Five samples of product produced at sequential time points through the day are tested by a trained sensory panel. The panel consists of 11 assessors and the samples are tasted in three replicates. Ten sensory attributes are scored on an unstructured line scale anchored at 0 and 15. The data are first analysed attribute by attribute using a mixed model analysis of variance fitting terms assessor, sample and assessor by sample interaction, with assessor defined as a random effect and sample as a fixed effect for attributes where analysis of variance is valid. Attributes for which a high proportion of the scores are zero, thus invalidating the assumptions required for analysis of variance, are analyzed using Friedman’s non-parametric rank sum test (see Table 7.6). The table contains the percentage of zero scores for each attribute. The characteristics burnt flavor, pepper flavor and rancid flavor are not detected in the product samples by most assessors. The p-value gives the significance of overall differences in sample means, measured by the F-test in the analysis of variance, or by Friedman’s rank sum test for the three starred attributes. The mean scores for each sample are given; means with the same letter for an attribute are not detected as significantly different using the Tukey HSD test at 5% significance. (Other pairwise significance tests could also be used here, for example Fisher’s LSD.) If the product quality is consistent we would not expect to see any significant differences in mean scores. Overall flavor, salt, dehydrated vegetable flavor and, to a lesser extent, dried green herb flavor are all showing evidence of a decrease over the assessment period. In any product some sensory characteristics will be more important than others. A quality index can be constructed, measuring the deviation in quality from the ideal (target) product weighted by a predefined level of

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* Friedman’s test p-value.

0.015 0.017 0.206 0.068 0.001 0.016 0.061* 0.413* 0.101* 0.597

1% 85% 81% 81% 37%

p-value

0% 0% 2% 2% 0%

% zero scores

0.4

0.3

0.3 0.4 0.3 0.4 0.4

Tukey HSD (5%)

2.4 0.3 0.2 0.2 0.9

a a a a a

6.1 a 5.1 a 2.7 a 2.9 a 3.6 a

Sample 1

2.3 0.2 0.2 0.2 0.7

a a a a a

5.8 ab 4.8 ab 2.5 a 2.9 a 3.3 ab

Sample 2

2.3 0.1 0.2 0.1 0.9

a a a a a

6 ab 4.8 ab 2.5 a 2.8 a 3.4 a

Sample 3

2.1 0.2 0.2 0.1 0.8

a a a a a

5.9 ab 4.7 ab 2.5 a 2.7 a 3.3 ab

Sample 4

2.1 0.1 0.1 0.1 0.7

5.7 4.5 2.4 2.6 3

a a a a a

b b a a b

Sample 5

Mean scores and significant differences (Tukey HSD test)

Summary table for ten attributes analyzed using Tukey’s HSD test

Overall flavor Salt Sweet Savoury Dehydrated vegetable flavor Dried green herb flavor Burnt flavor Pepper flavor Rancid flavor Dairy flavor

Attribute

Table 7.6

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Table 7.7 Ideal profile value and attribute weighting factors Attributes

Ideal profile

Factor weight

6.0 4.5 2.5 2.5 3.5 2.5 0.0 0.2 0.0 1.0

5 2 1 1 2 2 10 20 10 10

Overall flavor Salt Sweet Savoury Dehydrated vegetable flavor Dried green herb flavor Burnt flavor Pepper flavor Rancid flavor Dairy flavor

Table 7.8 Calculation of quality index for the rice product Attribute Overall flavor Salt Sweet Savoury Dehydrated vegetable flavor Dried green herb flavor Burnt flavor Pepper flavor Rancid flavor Dairy flavor

Sample 1 mean

Ideal profile

Attribute weighting

Absolute deviation

Deviation × weighting

6.08 5.05 2.70 2.94 3.57

6.0 4.0 2.5 2.5 3.5

5 2 1 1 2

0.08 1.05 0.20 0.44 0.07

0.40 2.10 0.20 0.44 0.14

2.37 0.25 0.16 0.17 0.89

2.5 0.0 0.2 0.0 1.0

2 10 20 10 10

0.13 0.25 0.04 0.17 0.11 Sum Quality index

0.26 2.50 0.80 1.70 1.10 9.64 90.36

importance of each sensory characteristic. These ideal values and the weightings for this product are given in Table 7.7. The quality index is calculated by first subtracting the actual values from the ideal profile and then multiplying the absolute value of this difference by the factor weighting and summing the results. This sum is then subtracted from a nominal optimal quality index set at 100. The calculations for Sample 1 are outlined in Table 7.8. A product will fail if the quality index drops too low. The lowest acceptable value will be set based on experience and critical evaluation of product quality. The quality index and key individual sensory characteristics can then be tracked over time.

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Index

134

100 98 96 94 92 90 88 86 1

2

3

4

5

Sample Quality Index

Fig. 7.6

Fail

Rice product quality index for five samples of production.

The graph of the quality index by sample (Fig. 7.6) indicates that, based on assessment by the trained sensory panel, the product being produced has deviated from its target sensory properties and further investigation is required. In addition to these four approaches, there are many hybrid methods that have been created to address specific quality issues.

7.6 Practical considerations Clearly there has to be some trade-off in the choices of methods and of the level of risk that is acceptable in commercial operations. The most important element on any QC program is the commitment of senior management and the directors of the company. In effect, the impact of the level of commitment can be measured in statistical terms. Clearly, there are levels of complication associated with the choice of quality program.

7.6.1 Cost The biggest barrier to implementation of sensory quality is cost. The simplest methods, in/out or pass fail, are relatively inexpensive. There are three cost elements; samples, number of assessors and the assessor’s time. In the case of products that are costly, destructive testing can be quite expensive. Premium wines and spirits come in sealed bottles that cannot be returned to production. Conversely, agricultural commodities are inexpensive and abundant, so sampling is not a major cost contributor. The number of assessors is a key part of delivering statistical robustness to the test. The larger the number of assessments, the smaller the impact of any individual judge on the

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Low

Confidence

High

Statistical approaches to sensory quality control

High

Low Cost Methods:

In/out

Ratings

Scoring

Descriptive

Fig. 7.7 Confidence versus cost.

outcome of the test. The selection and training of assessors represent an investment that increases in expense in relation to the depth of the method used for assessment. Ultimately, it is the cost of product failure in the market that should inform the decision of how much should be spent on attaining a level of quality measurement that mitigates risk. In general, greater confidence can be attained at a higher cost (Fig. 7.7).

7.6.2 Speed In any manufacturing and distribution operation the timeliness of quality results is essential. Particularly short shelf-life fresh products benefit from rapid assessments that do not detract from the commercial distribution of the product. In the case of retorted shelf-stable products, it is normal to quarantine product while awaiting the results of sterility testing. This provides a much longer period of time to conduct QC testing. Many businesses choose to conduct rapid release testing and then conduct a quality assurance review on a periodic basis to audit the process. Unfortunately it takes longer and costs more if the test is more sophisticated (Fig. 7.8).

7.6.3 Confidence The confidence in the result will be a function of the sampling plan, the methods of assessment and the number of assessments. As the method moves from a simple pass/fail to a full descriptive analysis, the information obtained becomes more comprehensive and improves the quality of decision making. In addition, the ability to track levels of specific attributes delivers a powerful diagnostic tool for product and process improvement.

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Slow

Speed

Fast

136

High

Low Cost Methods:

In/out

Ratings

Scoring

Descriptive

Slow

Speed

Fast

Fig. 7.8 Speed of test versus cost.

High

Low Power Methods:

In/out

Ratings

Scoring

Descriptive

Fig. 7.9 Speed of test versus power of test.

7.6.4 Power In the case of sensory quality, we are usually attempting to establish that the current product being tested is not significantly different from our control. In effect we are looking for a measure of similarity. Consequently we are interested in reducing beta-risk. Reducing beta-risk can be achieved through having larger numbers of assessments. In general, this means that greater power comes at the cost of speed (Fig. 7.9). By contrast, the slower tests that have greater power also tend to provide much greater diagnostic information. So if a product fails, or is found to have deficiencies in sensory

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Diagnostics Weak Strong

Statistical approaches to sensory quality control

High

Low Power Methods:

In/out

Ratings

Scoring

Descriptive

Fig. 7.10 Diagnostic strength versus power of test.

properties, the test produces actionable results that can be used to improve the product (Fig. 7.10).

7.7 Assessor proficiency and validation Sensory evaluation for quality control is meant to be an analytical measurement. This means that the measurement must be both accurate and precise. The value measured must be reproducible and repeatable. This can be achieved only through training of assessors. Whether the individual is being trained to make ‘pass/fail’ judgments or to be part of a highly refined descriptive analysis panel, they have to be trained to a well-defined level of proficiency. They must be calibrated periodically and their performance must be tested for proficiency routinely. We would never consider operating a pH meter without first calibrating the electrode. So it must be for the sensory quality panelist and panel. A wide range of statistical tools are available for proficiency measures. It is worth mentioning that PanelCheck, a software tool developed by Nofima Mat in Norway is freely available for download over the Internet. Similar products have been developed in ‘R’ the open-source statistical programming language and can be located on the web. Individuals have different inherent sensitivities to sensory stimulus. Most notable are ‘colour-blindness’ or the ‘super-taster’ classification. There should be a measure of distribution of ratings. Since it is likely that certain judges will be more sensitive to some defects than others, they may constitute an early warning system or provide the ‘canary in the mine’ for

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potential problems. This way the ‘outliers’ can provide a benefit, providing this has been assessed and found to be reliable.

7.8 Sensory instrumental correlations Under ideal circumstances we can dispense with sensory evaluation entirely and use instrumental methods to evaluate products. However, the same rules apply to instrumental methods as to the establishment of gold standards. The instrumental measure must be correlated with consumer response or correlated with a sensory measure that in turn is correlated to a consumer response. Textural evaluation with instruments has been demonstrated to correlate quite well with sensory evaluation. However, flavor and aroma have been more difficult. The electronic nose has been shown to be useful in some QC applications. Once the instrument has been well trained to detect a defect it can be very reliable and convenient. The biggest problem occurs when a defect appears that is outside the range of products that have been used to train the instrument. Where humans can generalize and draw parallels, the instrument is limited to the sample set that it has been developed with. There are three concerns with instrumental methods: • Some measures are more sensitive than human senses. Texture instruments often detect small changes in hardness that are far below human threshold. • Some measures are significantly less sensitive. Human noses can detect part per trillion amounts of odorants where the instruments can only detect parts per billion. • Some measures are completely irrelevant and do not correlate with the complex multimodal sensory response that humans experience.

7.9 Product matching Any manufacturer of existing food or beverage products will be confronted with several challenges. If the market is expanding in both volume and geography there will be demands to find ingredient supplies that will support market growth. When a new category appears in the market, competitors will want to imitate the innovative product to maintain market share. Retailers who rely on private label brands as a key part of their customer offerings will need to refine their ability to match products. They will need to have a good alternative that delivers the key benefits of the national brand, at the same time as being significantly less expensive or delivering greater value. In addition, the retailer will require security of supply through the use of multiple suppliers.

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Sainsbury’s is an excellent example of a retailer that has created a sensory quality program for its suppliers to assure customer satisfaction. Training courses are available to provide suppliers with a thorough understanding of the procedure.

7.9.1 New suppliers, processes and ingredient replacement In all cases where an existing product is to be produced in a new location, by a new process or using different ingredients, testing must be conducted to determine the impact of the change. The simplest approach is to perform difference tests between products that have been prepared with the previous ingredient or process and the proposed alternative. Any of the methods discussed in this chapter may be used to detect difference. The decision must be made on data that allows some measure of the impact on the final consumer.

7.9.2 Equivalency When we compare any two items, it is useful to know whether one product is equivalent to another. Also it is often desirable to know whether one product can perform as a substitute for another. Equivalence testing is applied to recipe or formula modifications of manufactured products, modifications of products in response to government regulations like trans-fats or salt reduction, or cost reductions. Advertising claims concerning equivalence require careful study and involve legal opinion. The ASTM Standard Guide for Sensory Claim Substantiation (E-1958-07) is an excellent starting point if a company wishes to demonstrate parity or superiority in the market. Further reference in equivalency may be found in Ennis and Ennis (2009) who have used an open interval to define equivalence and provided exact and approximate methods for testing a null hypothesis of non-equivalence.

7.10 Conclusions Statistical treatment of sensory quality control data is limited to the nature of the tests being performed. There are several rules that will improve the data of any test. The samples should be assessed in as unbiased a way as possible. Identity should be disguised, order should be balanced, sufficient measurements must be made and blind reference and control samples should be tested to measure panelist performance. If good sensory practices are applied to any of the approaches, the results will be more robust. The greater the degree of comprehension in the test methods with more attributes and more assessors, the more useful the outcomes will be. Unfortunately the more sophisticated the test, the longer it

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takes and the costlier it becomes. Ultimately it is the producer that must make a business decision about the cost of risk and the benefit of proper control.

7.11 References and further reading bonillaa, a.c., sveinsdottir, k. and martinsdottir, e. (2007) Development of Quality Index Method (QIM) scheme for fresh cod (Gadus morhua) fillets and application in shelf life study. Food Control, 18: 352–358 conover, w.j. (1999) Practical Nonparametric Statistics (3rd edition). Wiley ennis, d.m. and ennis, j.m. (2009) Equivalence hypothesis testing. Food Quality and Preference, 21: 253 lawless, h.t. and heymann, h. (1999) Sensory Evaluation of Food. Principles and Practices. Aspen Publishers muñoz, a.m. (2002) Sensory evaluation in quality control: an overview, new developments and future opportunities. Food Quality and Preference, 13: 329–339

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8 Using sensory techniques for shelf-life assessment L. L. Rogers, Consultant, UK

Abstract: This chapter is concerned with the sensory shelf-life of consumer products. An introduction to what is meant by shelf-life, why sensory techniques are important and how they compare to other aspects of the end of shelf-life is included. The chapter continues with a comparison between setting and confirming shelf-life and then gives details of a case study for setting up shelf-life studies for confirmatory tests. This includes aspects such as developing the plan, considerations, resource, accelerated methods, analytical methods and finishing with the analysis and reporting of shelf-life data. Key words: sensory science, shelf-life, quality, case study, ambient product, confirming shelf-life.

8.1 Introduction It is a legal requirement to assign a shelf-life, either a ‘use by date’ or a ‘best before date’, to a food product. Food businesses must guarantee the safety, legality and quality of the product through its shelf-life: Food Labelling Directive (2000/13/EEC). The Directive requires that a ‘use by’ rather than a ‘best before’ date should be used on those pre-packed foods ‘which, from the microbiological point of view, are highly perishable and are therefore likely after a short period to constitute an immediate danger to human health’. A best before date is the ‘date up to and including which the foodstuff will retain its optimum condition (e.g. it will not be stale)’. In this respect the sensory quality of products is not as critical as the microbiological quality or the legal requirements for labelling for example. Microbiological safety will always come first for shelf-life determination and should always be considered when devising the sensory assessment plan to ensure the safety of the sensory panellists and consumers taking part in any tests. However, sensory quality comes in a close third place due to the fact that

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consumers will not repurchase a product that they deem low in quality. In fact they will probably not even take the shelf-life into consideration when making this judgement: particularly for ambient products where they expect good quality at all times. For example with bread, a consumer may well note that the bread is very close to the end of its shelf-life and decide that in this case the slight dryness is acceptable, but for a pack of biscuits, a soft drink or a bag of sweets, they will probably not even note the best before date. On their first try of the product they will be unimpressed and may never purchase it again – even if it was only one week before the end of its ninemonth shelf-life. In order to assign shelf-life, a review of data from previous experiments or new shelf-life studies would be conducted, to determine the stability profile of the product so that appropriate storage conditions and storage period are recommended. This can also include open-shelf-life for products that are opened and then used over a period of time. For example, ultra-heat-treated juices have a long ambient shelf-life, as much as two years, but once opened require storage in a refrigerator for a very short period – usually less than a week. A review of similar products on the market can give guidance as to the potential shelf-life of a brand-new product. It is well known in the sensory community that the use of both quantitative descriptive profiling and consumer liking or acceptance methods provides both research and marketing teams with valuable consumer insight into product behaviour over time, and it is recommended that this approach is taken to set shelf-life. For example, once the key drivers of consumer liking over shelf-life are known, a descriptive panel can be used to predict the end of shelf-life for any changes or adaptations to existing products.

8.2 What is shelf-life? For highly perishable food a ‘use by’ date is required to indicate for how long the food is safe to consume if stored correctly. The ASTM (ASTM, 2005) state that sensory shelf-life is ‘the time period during which the products’ sensory characteristics and performance are as intended by the manufacturer’ and they also refer to this as being set to ‘manage business risk and meet business needs’. A stronger definition is given by the Institute of Food Science and Technology (IFST): shelf-life is defined as the time during which the food product will: (i) Remain safe (ii) Be certain to retain desired sensory, chemical, physical and microbiological characteristics (iii) Comply with any label declaration of nutritional data when stored under recommended conditions’ (IFST, 1993).

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Depending on its physical and chemical properties and the storage conditions, there will come a point when either the product quality will become unacceptable or it will become harmful to the consumer. These two outcomes may happen in the opposite order or may well happen at the same time. The IFST definition leaves one point for discussion, however: ‘desired sensory . . . characteristics’. This will depend upon the product, the product type or classification, the branding (premium or own label for example), and the management decisions around the setting of shelf-life in each individual company. This type of shelf-life, generally for ambient products, uses the ‘best before’ or ‘best before end’ date stamps on products to indicate to the consumers that if consumed after the date there might be some deterioration in product quality and any nutritional claims (such as vitamin content). The shelf-life of products is affected by many different aspects of the product ingredients, processing, packaging and storage conditions. For example preservatives, water activity, heat treatments, temperature control, oxygen ingress, light, humidity and pack integrity. The IFST group these effects into intrinsic and extrinsic factors. Intrinsic factors are the properties of the final product (e.g. water activity, pH) whereas extrinsic factors are the external effects on the product such as the time–temperature profile of the product, exposure to light and handling through the supply chain. These factors can all interact – often unpredictably – and should be taken into account when planning a sensory shelf-life study: particularly in respect of food safety.

8.2.1 Reasons for determining shelf-life using sensory methods It is not sensory quality alone that sets the end of shelf-life for food products. The microbiological safety and nutritional aspects of the product generally come first. The microbiological safety and nutritional aspects are indeed heavily regulated, whereas the sensory aspects are not, unless one includes groups such as the Trading Standards in the UK into consideration: where misleading consumers, for example calling a herb tea ‘peach’ flavour where at the end of shelf-life there is no peach flavour left. However, the sensory aspects of the food product: its flavour, texture and appearance for example, will play a huge role in the consumer purchase behaviour – both for first and repeat purchase decisions. The only way to measure this consumer ‘acceptability’ is through sensory methods, be they analytical (triangle tests, profiling, difference from control) or consumer (hedonic, just about right scales, survival analysis) sensory methods. However, predictions of sensory shelf-life may be possible through purely analytical methods. This is generally conducted through the choice of ‘marker’ chemicals, identified through gas chromatography–mass spectrometry (GCMS) methods, often linked to GC–olfactometry (Reineccius

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and Heath, 2005). This latter method allows the effluent from the GC column to be ‘sniffed’ in an attempt to identify the key compounds responsible for the flavour of the product. Often the marker chosen is representative of the ageing process and gives a cut-off point for the end of shelf-life when it reaches a certain level. However, as with accelerated methods, this type of predictive method requires validation before use, using real-time storage conditions and sensory analysis to determine the level at which the marker compound results in an unacceptable product.

8.2.2 Determining the end of shelf-life It is rare for a company to use the criterion of no detectable change in the sensory characteristics over shelf-life, but this is generally set as an action standard for premium products. This is mainly because, as the consumer has paid a premium price, they will not expect any deterioration of the product over shelf-life. Often the end of shelf-life is established at a certain number or a specific drop in consumer acceptability. This can be set dependent upon the product type: for example for a premium product the limit might be set as ‘less than a 0.5 drop’ on the 9 point hedonic scale, whereas it might be set as ‘no greater than a 1.0 drop’ on the same scale for a commodity product such as breakfast cereal or instant coffee. Another action standard for shelf-life testing can be to state that: ‘the product should fail when it no longer represents the product concept’. This can be very useful in products such as biscuits or crisps, which when they change in texture, and no longer match the concept, are outside a shelf-life that is deemed acceptable by consumers. Another similar action standard lies in the knowledge of the product’s overall sensory profile: if this changes then this is the end of shelf-life (Muñoz et al., 1992). For example if a drink product has a certain intensity of ginger flavour and this drops by, say 10%, and the sweetness also drops by 10%: the point at which this change happens is the end of shelf-life. In fact the developer may deem that this is actually beyond the end of shelflife and set the shelf-life to be shorter. The overall product profile approach can also be adapted with consumer knowledge, in that only product attributes that are known or suspected to be key to the consumers’ perception of the product are taken into account (ASTM, 2005). For example, in a fruit biscuit where the raisins develop a very slight ‘off-note’ but this is not detectable by consumers, the change is not used to set the end of shelf-life. But as soon as the texture starts to soften, consumers detect this change and this analytical sensory measurement can be used to set the end of shelf-life. Some companies set the end of shelf-life when the acceptability of the product is too low but this method is not recommended as it can effect future purchase behaviour of the fresh product as mentioned above.

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8.3 Setting or confirming shelf-life? For quality teams heavily involved in new product development and the scale-up to factory production, there will be involvement in the actual setting of shelf-life for the product. This will require the setting up of perhaps several tests to determine when the end of shelf-life has been reached using certain predetermined action standards (see above). For other quality aspects, such as the change in an ingredient supplier, process changes or packaging changes, the involvement will be in confirming shelflife in comparison with the current standard product (Meilgaard et al., 1999). This can be a much simpler affair, as previous work involving the consumer and the changes to key attributes that drive consumer liking have already been determined and so more simple sensory difference tests might be used to confirm previous findings. However, if the changes under consideration shorten or lengthen the product’s shelf-life, consumer validation might be required, resulting in a complex experiment involving not only the product under consideration but also the control product or products.

8.3.1 Reasons for confirming shelf-life during production There are a multitude of reasons for confirming shelf-life during production. Firstly confirmation of shelf-life is generally carried out on a regular basis on a certain number of batches a year for the simple reason of ensuring that the consumer is still receiving the high-quality product they were expecting. Secondly, changes to ingredients and ingredient suppliers will result in the need to confirm these changes do not affect the product quality or the product quality over shelf-life. Other production changes such as temperature changes on processing, cost-saving experiments, mixing times, equipment changes, for example will also result in the need to confirm no effect on shelf-life. Packaging changes can also have a major effect on shelflife. Quite often packaging changes may be brought in to extend shelf-life as advances in packaging technology become more cost effective.

8.3.2 Additional considerations for setting shelf-life Within different companies there will be additional considerations for shelflife setting. The supply chain network will probably play a role. For example if a product has a shelf-life of seven days but it does not reach the supermarket until day five, then the product quality for the two days the product is on sale will be critical and will probably result in the requirement for an extension to shelf-life as the two day sales period will not be sustainable. Supermarket requirements will also play a part: many will not accept a product with less than 70–80% of its shelf-life remaining. This may result in a requirement to speed up time to market or extend shelf-life by some means. The type of date-stamp will also come into consideration. For

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example for an ambient product the use of the month-end stamp may reflect to the consumer a certain lack of freshness, but the change to a dated stamp may improve the consumers’ perception of the product’s freshness.

8.4 The case study: Setting up shelf-life confirmation studies for an ambient product 8.4.1 Introduction As discussed in Section 8.3.1 above, there are many reasons for the confirmation of shelf-life on an ambient product. For short shelf-life and chilled products this is mostly controlled by microbiological safety for obvious reasons, but for ambient products the end of shelf-life is more often determined based on the sensory quality of the product, unless it is first based on nutritional claims. This is reflected in the terminology of the date stamping: ‘best’ before or ‘best’ before end. The development of the shelf-life plan for each experiment can be conducted using a risk-based approach. This is a simple way of making the best uses of resource and facilities by only conducting comprehensive shelf-life testing where required. All other plans will be based upon the amount of risk associated with each individual experiment. For example, for a new product in a new range the risk assigned would be ‘high’ as there is no existing data to base the shelf-life determination on. But for the change to a powdered ingredient supplier from an existing supplier, the risk of the shelf-life being affected is probably very low and therefore the minimum amount of testing could be conducted. For the confirmation of shelf-life for ambient products the risk is generally low to medium, but within this range the sensory scientist may wish to assign low to high categories, to further utilise resources effectively. When confirming shelf-life, a control product of some description is generally required for comparison. This control would be the original product prior to any changes, if any changes are to be made. Ideally this control should be made at the same time as the trial product and from the same batches of ingredients, where possible. This will help eliminate any additional changes to the product other than the change under consideration. The control product will be aged in the same way as the new product but generally a fresh control is included in the tests for comparison. For ambient products, storage under chilled conditions is generally suitable for the ‘fresh’ control. Sometimes the control is simply data gathered for the existing product and used in the current experiment for comparison.

8.4.2 Considerations and preparations There are many points around the product’s shelf-life to consider before developing the plan of approach.

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Is the product stable or for how long is the product expected to be stable? This is an important starting point, as the length of the expected shelf-life will determine how many time-points are considered, what sensory tests might be used and when decisions will be able to be made. For example, if the product is a powder, stable for one year under usual production conditions, and a change to the packaging is required which is expected to lengthen its shelf-life, then conducting tests every month would seem to be rather excessive. If, however, the product has a shelf-life of four months and a change to an ingredient is expected to shorten its shelf-life, then early, and regular, testing would be recommended. When does the product start to change? This knowledge is very useful as it can determine the start of the testing procedure apart from, of course, the initial test on the fresh product to set the baseline data. If a product with a shelf-life of ten months starts to change at eight months generally, and the planned ingredient change is expected to shorten shelf-life, the best plan may be to select two or three time-points up to say, seven months and then test weekly or fortnightly around the expected change point. Then, if the data indicates the shelf-life will be shorter, the time-points will be readily available from which to select the new end of shelf-life. What attributes change and how? What new attributes are introduced (if any)? What further changes happen after the initial change? The answer to these questions will help determine the choice of sensory test. For example, if several attributes change over the product’s shelf-life it might be worth considering a rapid profile at each time-point particularly if this method is also used for other quality measurements (Lawless and Heymann, 1999). This profile would be conducted using the attribute language drawn up during product development phases or may be an existing attribute language for a standard product. The addition of one or two ‘blank’ attributes for each modality can be useful to monitor the introduction and level of new attributes over shelf-life. If only one attribute is known to change over shelf-life, then several methods are available. Ranking of the changing attribute may be useful, or a simple difference test to determine when the change is happening. Difference from control tests can also be very useful in confirming sensory shelf-life, particularly as a direct comparison with the original control product is made within the test. The main consideration here in the choice of sensory test is whether or not quantitative data are required on the planned product change, as the data can be very useful in determining the reasons behind any problems and perhaps working towards eliminating these. If information is only needed about the fact a change has happened, then one of the simple

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difference tests may be the best choice. If the initial change is then followed quickly by a change that it is known to affect consumers’ overall liking of the product it is important to look for this initial change to help decide the end of shelf-life: any later may be too late for the consumer. Can the changing attributes be explained? This can be helpful to help eliminate changes that severely affect the product’s shelf-life. If the changes are due to, for example, flavour loss, tainting, colour changes or packaging effects, then further development work might be required to slow down or eliminate these changes – particularly if the ingredient or packaging change under consideration itself is the main culprit. How long are the products on the shelf? At what point in a product’s life is it consumed? How many people have access to older products? These questions are important when determining the time-points to test. For example if the majority of a product with nine months shelf-life is consumed within three months then there is little requirement to test comprehensively after this point. But if a fair proportion of people may well purchase the same product when it is seven or eight months old, then testing should be extended over the complete shelf-life. When do consumers notice the product change? What effect do the changes have on consumer measures? As soon as the product starts to change, as indicated by the sensory tests at each time-point, the sensory scientist will need to determine if this is noticed by the consumer. This might be carried out in any number of ways. Firstly data may already exist about the sensory attributes that are key drivers of consumer liking and if it is one of these that is changing in a negative manner then maybe the end of shelf-life has already been reached. If the data exist but due to the experiment, for example a packaging change, additional attributes have been introduced for which no consumer data exist, it might be wise to conduct a small-scale consumer test to determine if consumers notice the difference. If there are no data on whether the change will be detected by consumers or what effect it will have on their overall liking, again a small-scale consumer test can give valuable information. In this particular example it will be worthwhile planning a consumer test at each time-point when the sensory attribute data have been gathered. The action standards developed by the project team will be useful here in determining the end of shelf-life. When are the data required? At what project stage does the shelf-life need to be set? It can be very cost effective to conduct sensory shelf-life tests by collecting samples throughout shelf-life and conducting all the analysis at the hypoth-

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esised end of shelf-life. This technique is often referred to as reversed shelflife or single-point shelf-life tests. This can be conducted through storage of one batch or through production of several batches. For example, if it is known that the product type stores well under chilled conditions with very little change to sensory characteristics, samples can be put on store in the conditions under study and then taken off store at the various time-points in the sensory plan and put into chilled storage. This has the effect of ‘pausing’ storage and although the product will be 26 weeks old at the end of the shelf-life it is effectively 13 weeks old as it was stored in supermarket conditions for 13 weeks and chilled (paused) conditions for 13 weeks. In this way all samples can be saved for a quantitative profile at the end of the 26 week period. This is obviously very cost and resource effective but not particularly useful if the project manager needs to make decisions at 13 weeks and 20 weeks of storage. In this case, especially if the project manager has to actually set the shelf-life at, say 13 weeks, for a product that has a hypothesised shelf-life of 26 weeks, predictive and accelerated methods might prove useful. If, however, storage of the product chilled is not feasible, another approach is to take production batches at certain time-points and collect them for the final profile. In this example production at week 0 would serve for the 26-week-old product, production at week 7 would serve for the 19-week-old product and production at week 13 would serve for the 13-week-old product and so on. In this case the production batches must be similar and this method will not work for products with batch-to-batch variation, unless this can be taken into account during analysis. In cases where the project manager needs information throughout shelflife, the sensory test will be conducted at each time-point. This method is generally referred to as standard shelf-life or multi-point shelf-life testing, and is generally more resource intensive than the previous sampling methods. Any accelerated samples would also be included at each timepoint to allow information on the end of shelf-life to be available as soon as possible. Are there any accelerated methods for storing the product? Accelerated shelf-life testing (ASLT) (Kilcast and Subramanian, 2000) can be incredibly useful when setting a shelf-life for an ambient product, as the developer does not need to wait for the whole of the product’s shelf-life to determine its end. For example, storage at a higher temperature can result in a prediction in, say, half the time of the real shelf-life, although some methods claim to deliver the information in substantially less time. In some cases the developers may use the worse-case scenario to help with the prediction of shelf-life, e.g. heat cycling the pack that is most susceptible to change, or the injection of oxygen into the pack combined with heat treatment. However there are many critics of accelerated shelf-life testing. In a recent review (Hough et al., 2006) Harry Lawless was quoted: ‘accelerated

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testing is mostly useless. If it worked, you could make fine aged Bordeaux in an oven. You can’t!’ The main issue is with the production of artefacts: chemical components that would not usually occur under normal storage conditions. The other problem is with the amount of resource it takes to validate the accelerated method. The product must be kept at real time storage conditions for the required length of time to compare with the accelerated test results: therefore resulting in a long lead time before the accelerated test can be used with confidence. For confirmatory shelf-life tests this investment will be very worthwhile, but for new product development the resource is less warranted, until the product has been launched and becomes part of standard production. The quandary though is that during the new product development phase, the accelerated test is at its most useful. In some cases companies have developed methods that are applicable to a certain group of products, and therefore predictions from accelerated tests are known to give a good enough approximation of the shelf-life, until the confirmatory tests are finished.

8.4.3 Developing the sensory plan A protocol should be prepared for each shelf-life study and it can be very useful to discuss this with the project team prior to commencement of the study. This could include: • • • • • • • • • • • • • •

the purpose of the stability study; previous data about the product or similar products; packaging information; project stage, shelf-life decision requirements; expected shelf-life; accelerated tests availability; business risk/product risk; storage conditions for each individual sample; batches to be selected: number and type; which pack type (where there are numerous sizes for example); sampling plans and time-points; the testing to be performed at each time point; testing specifications and action standards; the number of packs required to conduct the specified testing (i.e. how many are required to be put on store and is there sufficient quantity); • any special requirements (e.g. open shelf-life tests, colour assessments). The purpose of the stability study To confirm shelf-life for product X, an ambient juice product sold in three different pack sizes. Production at new contract packer who currently pack other products for the business.

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Previous data about product or similar products Current product, complete data sets available for all three pack types. The data are in the form of sensory profiling data, consumer acceptability tests, difference from control production records and analytical data. Previous trials at contract manufacturer have indicated that production is equivalent to production at the current site and no issues are expected. The contract manufacturer’s equipment and processes are very similar to those on the current lines. Packaging information Current packs being used at contract packer. Generally the smaller pack has more shelf-life ‘issues’ due to the increased contact with the pack and subsequent contact with heat and light. Cleaning system for the packaging is similar but not identical. Project stage, shelf-life decision requirements Contract manufacturer required for next year whilst current lines will be out of use due to refurbishment plans. Decision on shelf-life required in nine months, however an early warning of any issues is requested. Expected shelf-life 6 months as per current smallest pack, 12 months for largest pack. Accelerated tests availability Available and validated. Predicts at double storage time (i.e. 6 months’ storage under accelerated conditions is equivalent to 12 months’ real time storage). Business risk/product risk Business risk is high as all product will be produced at contract packer. Product risk low as the product changes very little over shelf-life. Storage conditions for each individual sample Reversed shelf-life plan. All samples will be collected and placed on store at the start of shelf-life. Samples stored at chilled, shelf and accelerated conditions. They will be taken off store and placed into chilled storage at the required time-points. The majority of analyses will be conducted at the end of shelf-life. Spare product will be stored at shelf if consumer tests are required. Batches to be selected, number and type Three batches will be selected from the large-scale trial to be conducted. Each batch will be packed into the different pack sizes. Which pack type? Only the smallest and largest pack sizes will be tested at the end of shelflife. The medium pack size will be kept on store in case the analysis highlights any issues.

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Sampling plans and time-points The product has a shelf-life of 52 weeks in the current pack and 26 weeks in the smallest pack. Shelf-stored samples will be taken at 0, 22 and 26 for the smallest pack and 0, 26, 39, 48 and 52 weeks for the largest pack. Accelerated samples will be taken at 11 and 13 weeks for the smallest pack and 24 and 26 weeks for the largest pack. The testing to be performed at each time point All analysis will be conducted at the end of shelf-life as it is expected that the move to the new contract packer will not cause any shelf-life issues. A full quantitative profile will be conducted using the current attribute language; however, the panellists will be presented with all samples to update language prior to conducting the three replicates on the stored samples. A simple difference from control test will be conducted with accelerated smaller packs compared to chilled stored controls, three months into the storage time to give an early warning of any issues. Testing specifications and action standards All products should have the same shelf-life as per current specifications. The action standard is: shelf-life will be the same as per current production. Any issues in the sensory profiling results will be confirmed by consumer tests. If the shelf-life is less than current production the move will not take place. Refurbishment will be postponed until another contract packer is identified. However, this is an unlikely event. The number of packs required to conduct the specified testing (i.e. how many are required to be put on store and is there sufficient quantity) The sensory tests planned will require five product units at each time-point. The time-points have been kept to a minimum therefore there are no store room issues. There will be sufficient quantity of product, as the trial is full scale. There will be a requirement to put on store 100 spare units in case the sensory profiling tests indicate any concerns and consumer tests are required. Any special requirements (e.g. open shelf-life tests, colour assessments) No special requirements for shelf-life testing.

8.4.4 Collection, analysis and reporting The raw data, depending upon the sensory method(s) chosen, should be analysed according to standard methods (e.g. ANOVA, Friedman, simple means) (O’Mahoney, 1986) and reported as per business standards. Decisions would be made in relation to the action standards and if these were met or not. The action standards would also detail the plan to be taken if the action standards were met as well as if they were not met. Reviewing

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the information with the project team can help decide a detailed plan of attack when action standards are not met. The team can include staff from marketing, packaging, market insights, production, planning and quality teams and research and development. The information will also be valuable for similar projects or for current understanding of product performance. It can also help with further areas to work on such as representative storage conditions, continuing validation of accelerated methods and predicting shelf-life for future experiments. In summary, the sensory scientist needs to determine how far the product has moved from its original concept and then confirm the end of shelf-life for this product. Before deciding upon a plan for the shelf-life testing, the scientist needs to take into consideration the amount and type of resource available for the testing and also the facilities for conducting the tests. If the confirmation is required due to a simple change in ingredient supplier for example, a simple difference from control test might be required for a limited number of time-points. The resource for this test may be readily available. However, if a change to packaging is required and it is expected that taints may well be an issue at any point over shelf-life, the resource required may well be considerably more. If the change under consideration might result in a lower shelf-life and consumer testing might be required to help with the determination of a new end of shelf-life, the resources available might not meet the requirements of the test and further discussions might be needed before the experiment can be conducted.

8.5 References and further reading astm (2005), Standard Guide for Sensory Evaluation Methods to Determine the Sensory Shelf Life of Consumer Products, E 2454-05 hough, g et al. (2006), ‘Workshop summary: sensory shelf-life testing.’ Food Quality and Preference, 17, 640–645 doi:10.1016/j.foodqual.2006.01.010 ifst (1993), Shelf life of foods – guidelines for its determination and production. London: Institute of Food Science and Technology. kilcast, d and subramaniam, p (2000), The stability and shelf-life of food, CRC Press lawless, h t and heymann, h (1999), Sensory Evaluation of Food. Principles and practices, Aspen meilgaard, m, civille, g v and carr, b t (1999), Sensory Evaluation Techniques, CRC Press muñoz, m, civille, g v and carr, b t (1991), Sensory Evaluation in Quality Control, Van Nostrand Reinhold o’mahoney, m (1986), Sensory Evaluation of Food: Statistical methods and procedures, Marcel Dekker reineccius, g and heath, h b (2005), Flavor Chemistry and Technology, CRC Press stone, h and sidel, j l (2004), Sensory Evaluation Practices, Academic Press

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9 Sensory quality control for taint prevention D. Kilcast, Consultant, Food and Beverage Sensory Quality, UK

Abstract: The chapter describes the chemical nature of taints in food and beverages, and the importance of guarding against their occurrence. The main sources of taints are described, together with their chemical nature. The main sensory test procedures for detecting taint are described, and chemical analysis procedures are outlined. Preventive testing procedures are described, and the chapter concludes with a selection of case studies. Key words: food and beverage taint, taint sources, chemical analysis, sensory testing, taint prevention.

9.1 Introduction The definition of taint in food and beverage ISO standards is ‘a taste or odour foreign to the product’ (ISO 5492, 1992), but this does not necessarily imply unpleasantness. The same ISO standard defines an off-flavour as ‘an atypical flavour usually associated with deterioration’. These definitions do not make a clear distinction between taints and off-flavours, and do not reflect the highly unpleasant nature of taint and to the serious consequences to the manufacturer and retailer if tainted products reach consumers. For practical reasons, more specific definitions have been developed to assist businesses in developing preventive procedures. Whilst there is no general agreement, the following are proposed: • Taint: An unpleasant odour or flavour caused by contamination from sources external to the product. • Off-flavour: An unpleasant odour or flavour resulting from internal deteriorative change.

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Some care is needed in using these definitions. For example, neither definition covers the situation in which contamination or deterioration produces a change that is not perceived as unpleasant. In addition, some processes cannot be conveniently categorised as either taints and offflavours. However, one of the principal values of these definitions lies in addressing quality problems, and in directing investigations down the correct route. These definitions also differ from dictionary definitions in two other important ways. Firstly, there are no implications that the chemical species responsible for food taints are associated with any toxicity hazard. Secondly, food taints are perceptible by the human senses: foreign chemicals present at high concentrations that can be measured using instrumental methods fall outside these definitions unless they can be detected by the human senses. This does not diminish the undesirable nature of chemical contamination of foods, but focuses on those contaminants that can be perceived, particularly by their odour or flavour, frequently at extremely low concentrations, for example parts per million (ppm) 106, parts per billion (ppb) 109 or even parts per trillion (ppt) 1012. Although the two types of process can render food equally unpleasant, this distinction is of great assistance in identifying the cause of taint problems. These difficulties result from a number of sources. Firstly, consumer descriptions of taint are, with a few exceptions, notoriously unreliable, partly from a lack of any training in analytical descriptive methods but mainly from unfamiliarity with the chemical species responsible for taint. One possible exception is taint resulting from chlorophenol contamination, which is commonly described by UK consumers as antiseptic, TCP or medicinal, this reliability being a consequence of familiarity with products characterised by these sensations. Secondly, the extremely low concentrations that can give rise to taint present immense difficulties for the analyst who tries to identify the chemical nature of the taint. Thirdly, taint can occur at all stages of the food manufacture and supply chain, and from many different sources at each stage. Consequently, the detective work needed to identify the cause of taint-oriented consumer complaints can be quite different for taints and for off-flavours. The highly unpleasant nature of food taints can generate severe business problems for retailer, producer, ingredients supplier, farmer, equipment supplier, packaging producer and even building contractor. Some of the major problem areas are listed below.

Lost stock This is the most immediate and obvious consequence of a taint problem, especially if some time has elapsed before a problem is the detected, and the source of the taint is still present. Tainted product should never be reworked into production – the tainting material will still be present, at a

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lower concentration. Reworking tainted product would also be ethically questionable. Production disruption Difficult decisions are needed if a taint is detected by quality control (QC) tasting or through consumer complaints. Taint problems can be transient, and production maintained, but if a source of tainting material remains in situ, then all continuing production is at risk. Product recall costs In recent years, there have been increasing numbers of product recalls based on suspected taint problems, although the majority of recalls continue to be associated with safety concerns. Examples of taint-related recalls reported by the Food Standards Agency in the UK (FSA, 2008) are: • consumer complaints of sulphur or plastic odours in bottled mineral water; • a cheesy snack tainted with chlorine during the production process; • unpleasant smell and taste in orange juice (the recall was issued on the basis of only two consumer complaints); • ‘unusual taste’ in packs of breakfast cereal; • unpleasant taste in fresh Scottish salmon. Lost consumer confidence If taints are not detected until consumer complaints are received, there can be a severe negative impact on consumers, even with respected brands. In one case, this has resulted in the discontinuation of an entire brand. Insurance claims Insurers will commonly require evidence that the product was unfit for purpose, and will also require evidence of the chemical nature of the taint. Insurance claim procedures can often take several years, but it is important that this evidence is acquired as soon as possible after the problem occurrence. Litigation At this stage expenses become extremely high. Success depends on having sufficient evidence through sensory and chemical testing, and the availability of this evidence at critical points in the whole supply chain. The principal difficulty in protecting against taint is the extremely low levels of contamination that can give rise to unpleasant characteristics. Examples of the low concentrations at which the most commonly encountered tainting materials (halophenols and haloanisoles) can be perceived are shown in Table 9.1. (Interpreting taint threshold data will be discussed in more detail later in this chapter.)

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Table 9.1 Taste thresholds (in water) of halophenols and haloanisoles Threshold: parts per billion (109)

Compound 2-Chlorophenol 2-Bromophenol

0.1 0.03

2,6-Dichlorophenol 2,6-Dichloroanisole 2,6-Dibromophenol

0.3 0.04 (odour) 5 × 10−4

2,4,6-Trichlorophenol 2,4,6-Trichloroanisole 2,4,6-Tribromophenol 2,4,6-Tribromoanisole

2 0.02 0.6 8 × 10−6 (odour)

9.2 Chemistry of taint A summary of the most common chemical taints, their origins and typical sensory descriptors is shown in Table 9.2. A more complete discussion of the chemistry of taint can be found in Saxby (1996). Chemicals incorporating the phenol structure form the most frequently encountered source of taint problems. Simple phenols themselves can originate from a number of sources such as waterproofing materials and epoxy flooring compositions, and can also be a product of microbial degradation of potatoes. In general, these simple structures cause few problems owing to their relatively high threshold values. A notable exception is guaiacol (2-methoxyphenol), which is a product of microbial degradation of vanillin, and degradation of lignin, and which has a distinctive smokey character and a taste threshold of around 50 ppb (μg/l) in water. Considerably greater risks are posed by the halophenols, which in general have considerably lower thresholds than the corresponding phenols (Table 9.1). The chlorinated derivatives are the most widespread, but brominated derivatives have increasingly become recognised as common tainting materials, with lower thresholds than the corresponding chlorinated compounds. A common source of bromophenol taints in fruit juice arises from the action of the microorganism Alicyclobacillus acidoterrestris, which produces a mixture of bromophenols, chlorophenols and guaiacol (Whitfield, 2003). For all halophenols, higher levels of halogenation result in lower taste thresholds. The halophenols can in turn be converted by a wide range of microorganisms present in the environment to give haloanisoles, in which the hydroxy group of the phenol ring is methylated to a methoxy group. The haloanisoles have, in general, considerably lower thresholds than the corresponding halophenols. Consequently, even a low conversion of trace quantities of a halophenol that is present at well below threshold levels can generate sufficient haloanisole that is delectable by its musty character. Taints from these phenolic compounds are probably responsible for up to 70% of the incidences encountered in foods and beverages. Of the

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Table 9.2

Chemical sources of common taints and their typical descriptions

Chemical type

Common sources

Phenols

Epoxy flooring Potato degradation Microbial degradation of vanillin

Guaiacol Halophenols

Haloanisoles

Geosmin, isoborneol

Sulphur-containing Acrylates Hydrocarbons

Halogenation of phenols Bleached paper/board materials Disinfectants Wood treatments Herbicides Chemical discharges Microbial action on halophenols Wine corks Wood pallets Bleached paper/board materials Algal and mould growth in water

Solvent/food reaction Fumigation by methyl bromide UV-cured inks and varnishes Styrene Board surface coatings Microbial action on sorbic acid Microbial action on cinnamaldehyde

Typical sensory descriptors Phenolic Carbolic Smokey Phenolic Disinfectant Antiseptic Phenolic

Musty Earthy

Musty Mouldy Muddy Earthy Cat urine Cabbage Plastic Acrid Plastic Petrol Chemical

remaining taint problems, a substantial number originate from packaging systems, but a wide range of different chemical structures can be responsible for these problems. In view of the innovative nature of the food and beverage packaging business, the risks of transfer of tainting materials have increased considerably in recent years, and the trend to manufacturing in remote countries with little or no experience of taint will increase these risks further.

9.3 Sources of taints Taint sources can be broadly classified into four main groups, although there can be substantial overlap: direct contact, water supply contamination, aerial (vapour phase) contamination and internal chemical reaction. These are summarised in Table 9.3.

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Table 9.3 Sources of taint General classification

Specific sources

Direct contact

Packaging systems Disinfectants/cleaning materials Wood pallets (and other wood components) Process line components Fumigants Wine corks Pesticides/herbicides

Water supply contamination

Water treatment systems Microbiological Process line components Peat from river water Effluent

Aerial (vapour-phase) contamination

Transport containers Flooring and paint materials Disinfectants Insulating materials Diesel exhaust fumes External chemical release

Internal chemical reaction

Autoxidation Enzymic action Precursor reactions

9.3.1 Direct contact Intimate contact between taint source and product required for direct transfer can occur through a wide range of mechanisms, but packaging materials represent the most widespread risk, for both liquid and solid products. The risks of transfer can be influenced by many factors, but particularly high contact surface area and long exposure times; slow transfer can occur over long time periods for long shelf-life products. Transfer is also facilitated if the product contains components that can act as a solvent for the transferring chemical species, which will generally be involatile. The main taint risks are associated with plastic materials and paper and board materials; few taint risks are associated with glass and metals. The wine industry is also at risk from tainting materials present in cork bottle closures. High molecular weight polymers are unlikely to migrate, but can contain residual monomers and by-products of the manufacturing process that can cause taint, for example free styrene in polystyrene. Other additives such as antioxidants, stabilisers and slip agents that are used in plastic compositions can migrate, however. Paper/board packaging that has undergone chlorine bleaching are common sources of taints, and risks have increased as more packaging is recycled, although other non-tainting bleach treatments are now available. Probably the most common sources of packaging

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taints, however, are printing inks, varnishes, adhesives and coatings, which can be used for both plastic and paper/board packaging. As innovative design is a feature of the current food and beverage packaging, this can be expected to be an increasing source of problems. Comprehensive discussions of packaging taints can be found in Tice (1996) and in Lord (2003).

9.3.2 Water supply contamination The quality of potable water is strictly controlled in most developed countries, but it is unfortunate that the chemical most commonly used for disinfection – chlorine – is also the source of many taint problems. In the presence of phenolic materials, either from the original water source (such as river water) or from the food itself (many products contain components with phenolic structures), chlorine can react to produce chlorophenols. For example, during hot summers in the UK, high levels of chlorine used to ensure safety have been known to react with phenolics when consumers dilute orange juice concentrate, producing antiseptic taints. Bromination or combined chlorination/bromination water treatment systems that can be used for process water treatment (but not product water treatment, owing to the risk of forming carcinogenic bromates) can give even more severe problems through bromine vapour contamination. Chlorine can also react with phenolic components present in machine dispensing systems, particularly in hot beverage dispense systems, in which the boiler acts as a reaction vessel. In hot, dry seasons with poor water quality, Actinomycetes and cyanobacteria (blue-green algae) can produce geosmin and 2-methylisoborneol, which give an earthy taint. Effluent from industrial locations and run-off from farming activities are also potential sources of taint.

9.3.3 Aerial (vapour-phase) transfer Contamination by volatile tainting chemicals can occur at almost every stage of food and beverage production. Arguably, the most common source of tainting by volatiles occurs by cross-contamination from product components within the production environment itself, especially when different strongly flavoured products are manufactured either simultaneously or in sequence. This type of tainting can also occur during storage prior to distribution, during transportation, during retail storage and also during domestic storage. Whilst one of the most common tainting mechanisms, this is less likely to generate the serious problems encountered when the tainting volatiles are foreign to the ranges of food and beverage products. Some of the earliest and most important series of investigations of the origin and nature of taint problems caused by external chemical contamination were carried out by research from the UK retailer Marks & Spencer (Goldenberg and Matheson, 1975). One of the findings of this research was

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that taints can be caused by chemical releases from factories up to 8 km distant, depending on wind direction. This is a continuing problem, mainly for food and beverage manufacturers located in heavily industrialised areas, but in addition those located in rural areas where chemical releases from farming practices can occur. Contamination can occur during the distribution chain, and the likelihood of contamination rises with the length of the chain. Taint can occur from the use of containers that have been used previously for odorous products, or from containers that have been cleaned using unsuitable disinfecting materials. Containers that have previously been used for nonfoods, and which have been sanitised using unsuitable disinfecting chemicals, pose the greatest risks. Materials used within production and storage areas are also known taint sources. One of the most common sources of taint problems in manufacturing, farming and storage environments is the use of unsuitable chemical disinfection systems, especially those using chlorophenols. Most companies aware of the dangers of taint operate a positive list of non-tainting disinfectants. However, even companies using common non-tainting materials such as those based on sodium hypochlorite can suffer taint problems if reactions occur with phenolic compound present in the product or in components used in manufacturing. Major problems have occurred through the use of flooring compositions that have not been tested for their food tainting potential. Whilst most volatile chemicals from many flooring compositions carry the risk of taint, especially if insufficient curing time has been given to ensure complete reaction of volatile components, epoxy flooring materials containing phenolic components have given particularly serious problems. Paint also carries potential taint risks, although these are rarely encountered, possibly as a consequence of the lower quantities of volatiles released in comparison with flooring. Fumes from diesel engines can also cause taint problems. This is sometimes observed if the engines of delivery vehicles are left running during loading and unloading. In a typical scenario, this can result in the contamination of packaging materials left near the loading bay doors, and subsequent product contamination.

9.3.4 Internal chemical reaction Problems caused by internal deteriorative change are strictly defined as off-flavours, but there are situations in which external chemical contamination by non-tainting chemicals can produce taint through reaction with other non-tainting chemicals to generate taint. A classic example is the production of chlorophenol taints from reaction of chlorine in tap water (which is usually not detectable) with phenolic food or drink components. Another documented reaction producing taint is the reaction of compounds

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such as mesityl oxide, present in trace quantities in solvents, with hydrogen sulphide, present in trace quantities in meat and vegetable products, giving rise to an extremely pungent smell of tom cat urine (Saxby, 1996). This problem has been encountered in the meat and vegetable industries, and in the canning industry, where the source of the mesityl oxide has been the can lacquer. An additional reaction known to give rise to taint is the reaction of methyl bromide used as a grain fumigant with methionine residues to give a sulphury note (Saxby, 1996).

9.4 Detection and analysis of taints As taints are by definition perceived by the human senses, the use of selected and trained sensory panels is a logical starting point for testing for the presence of taint. An important secondary consideration is that the extremely low levels of chemicals coupled with the wide range of chemical types that can cause taint present enormous difficulties to the analytical chemist, and there is a consequential need to use sophisticated and expensive analytical instrumentation that would be available to relatively few companies. Sensory panels are in practice the primary means of assessing whether a taint is present and giving some direction to the possible chemical nature of any taint detected. Further information on the role of sensory taint testing can be found in Kilcast (1996a,b, 2003).

9.4.1 Perception of taint The chemical species that cause taint problems in food are usually volatile chemicals that are released from the food during eating and which are detected by the sense of smell. Some tainting chemical species (especially those producing bitter responses) are less volatile, and are detected by taste receptors on the tongue and other oral surfaces. Both taste and odour stimuli can be detected only if they are released effectively from the food matrix during the course of mastication. In situations in which it is not possible to ingest the test food product (for example, when investigating consumer complaint returns), then mixing the food with water can often enhance the release of taint volatiles.

9.4.2 Thresholds The interpretation of the term threshold must be clarified, as misuse of threshold data such as those shown in Table 9.1 can result in serious difficulties when investigating taint problems. A common means of quantifying response to chemical stimuli is through the use of a threshold, commonly defined as the concentration in a specified medium that is detected by 50% of a specified population. This definition is widely used in describing sensory

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perception of stimuli, but unfortunately is frequently misused and misunderstood. Thresholds indicate the level of stimulus that is sufficient to trigger perception but, contrary to common usage, a number of thresholds can be defined, none of which is invariant. Sensory standards give the following definitions for thresholds relevant to taint testing (ISO 5492, 1992): • Detection threshold: ‘the lowest physical intensity at which a stimulus is perceptible’. • Recognition threshold: ‘the lowest physical intensity at which a stimulus is correctly identified’. Detection will be much lower than recognition thresholds for stimuli that are unfamiliar, whereas for stimuli that are more familiar, such as sweet compounds, the thresholds will be much closer. In dealing with taints, which are generally unfamiliar, we are generally concerned with detection thresholds, but literature data rarely identifies whether detection or recognition thresholds are being quoted thresholds. (Note: as detection thresholds do not depend on prior experience, these are, in principle, more stable than recognition thresholds.) Literature data also frequently fail to cite the methodological variables, such as number of test subjects, degree of experience of test subjects, nature of instructions to test subjects, test procedure and whether replicated, and details of any statistical analysis. The medium in which the stimulus is present has a substantial effect on the measured thresholds, through masking effects from other flavours and from the different rate and extent of release that can occur. These omissions may serve to explain the wide range of numerical values found by different researchers for the same thresholds. Even if threshold measurements utilised the same test methodologies and exercised careful control over experimental variables, variations in measured thresholds must be expected as a result of the enormous range of human sensitivities. Typically, there is a million-fold difference between the 1% most sensitive and 1% least sensitive consumers, and as fewer than 10 consumer complaints can trigger a product recall it must be assumed that taints present at concentrations well below the quoted thresholds can be detectable by consumers. Taint detection testing should therefore use as many human subjects as possible.

9.5 Sensory testing procedures Any effective sensory quality system needs to satisfy a number of interrelated requirements. These have been described in other chapters in this volume, and more detailed discussions can be found in standard texts (e.g. Meilgaard et al., 2006; Stone and Sidel, 2004; Lawless and Heymann, 1998; Muñoz et al., 1992). The procedures that are most relevant to taint testing are described briefly in the following sections.

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9.5.1 Discrimination tests Discrimination tests are perceived as one of the easiest classes of sensory testing to apply in an industrial environment, and are consequently heavily used. In practice, their interpretation is more complex than commonly assumed, and care must be taken in their operation. The tests can be used in two ways: to determine whether there is an overall difference between two samples, or to determine whether one sample has more or less of a specific attribute than another. The tests have limited information content and can be unwieldy when many product comparisons are to be made. In such circumstances, alternative methods, such as quantitative scaling or profiling, are often superior, but the high sensitivity of well-designed difference tests can offer the best protection against taint problems. Difference tests are most commonly used to ascertain whether two samples are different, not to ascertain whether two samples are the same, and it should be noted that, if a difference is not found, it does not prove that samples are the same. However, recent revisions of ISO standards advise sensory analysts on how to use the tests for the latter purpose. Paired comparison test In the most common form of the test, two coded samples are presented either sequentially or simultaneously in a balanced presentation order (i.e. AB and BA). There are two variations on the test. In the directional difference variant, the panellists are asked to choose the sample with the greater or lesser amount of a specified characteristic. The panellists should be instructed to make a choice (forced-choice procedure), even if they have to make a guess; useful information can also be obtained by asking them how certain they were in their selection. In the directional form test (sometimes referred to as the 2-AFC, alternative forced choice, test), it is important that the panellists clearly comprehend the nature of the attribute of interest. This test has limited applications in taint testing, as the question asked implies that both samples could be tainted to some degree, and this would not be a practicable scenario. Duo–trio test In the most common variant of the duo–trio test, the panellists are presented with a sample that is identified as a reference, followed by two coded samples, one of which is the same as the reference and the other different. The panellists are asked to identify which sample is the same as the reference. The duo–trio test is particularly useful when testing foods that are difficult to prepare in identical portions. Testing such heterogeneous foods using the triangle test, which relies on identical portions, can give rise to difficulties, but in the duo–trio test there are no inherent difficulties in asking the question: Which sample is most similar to the reference?

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Triangle test Three coded samples are presented to the panellists, two of which are identical, using all possible sample permutations. The panellists are asked to select the odd sample in a forced-choice procedure. The increased number of samples compared with a paired comparison test can result in problems with flavour carry-over when using strongly flavoured samples, making identification of the odd sample more difficult. This can be a particular problem in taint testing, especially in permutations in which the first two samples carry the suspected taint. Consequently, an unbalanced variant of the test is sometimes used, in which the only permutations presented comprise two control and one test sample. In this situation, however, the validity of the common statistical analysis methods for discrimination tests becomes questionable. Difficulties can also be encountered in ensuring presentation of identical samples of some foods. R-index test This short-cut signal-detection method (O’Mahony, 1979, 1986) is less well used in industrial practice, but an application to taint testing has been described (Linssen et al., 1991). The test samples are compared against a previously presented standard, and rated in one of four categories. For difference testing, these categories are standard, perhaps standard, perhaps not standard and not standard. The test can also be carried out as a recognition test, in which case the categories are standard recognised, perhaps standard recognised, perhaps standard not recognised and standard not recognised. The results are expressed in terms of R-indices, which represent probability values of correct discrimination or correct identification. The method is claimed to give some quantification of magnitude of difference, but its use has not been widely reported in the literature. One important limitation is that a relatively high number of judgements is needed in this form of test, leading to the risk of severe panellist fatigue, and, in the case of some important taints, severe sensory adaptation that can results in non-identification. Difference from control test The test can be of particular value when a control is available; the panellists are presented with an identified control and a range of test samples. They are asked to rate the samples on suitable scales anchored by the points ‘not different from control’ to ‘very different from control’. The test results are usually analysed as scaled data.

9.5.2 Descriptive test procedures The major advantages of discrimination tests are their relative simplicity to set up and operate, and their high sensitivity. However, they have two important limitations. Firstly, only two sample treatments are compared

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together. Secondly, the information content of discrimination tests is limited, even when operated in an extended format, incorporating a range of questions. More informative tests can produce more quantitative data, which can be subjected to a wider range of statistical treatments. Quantification of sensory data is needed in many applications, and the recording of perceived intensity of attributes or liking requires some form of scaling procedure. These procedures should be distinguished from quality grading systems, which are used to sort products into classes defined by a combination of sensory characteristics. Such systems are not open to quantitative numerical analysis. Scaling procedures are mainly used to generate numeric data that can be manipulated and analysed statistically. Before this can be carried out, however, thought must be given to how the scales used are seen and interpreted by the assessors, and how this may influence the type of analysis that can be safely applied. In practice, establishing a trained sensory panel can often proceed from a category scale with a small number of scale points (e.g. 5), through a category scale with more points (e.g. 9) to an unstructured line scale. Sensory analysts should be aware of difficulties that panellists have in using scales, and careful training is needed to ensure that scales are unambiguous and can measure the intended response. Scaling may be used to quantify a single, well-defined attribute. However, it should be established that there is no ambiguity in the attribute of interest. If it is likely that several attributes require quantification, then there are several descriptive profile procedures that can be used, although these require extensive panel training if they are to be successful. If scaling is used to measure taint intensity, in most cases it is sufficient to use a single intensity scale, but there are circumstances in which if might be necessary to define the description of specific individual taints, and record these on separate intensity scales. In this case, appropriate panel training is needed.

9.5.3 Panel selection and training The primary objective in using sensory procedures in taint testing is to acquire analytical information, using the panel as the measuring instrument. As a consequence, it is essential to select panellists who can be shown to have the perceptual skills to detect taint at low levels, and to describe the taint. If a company does not have a sensory panel already operating, then as a starting point, the procedures described in ISO 8586-1 (1993) should be used to set up a basic sensory panel. The general scheme for recruitment and training is shown in Fig. 9.1, and suitability of panellists for taint protection should be built in to any panel formation for QC purposes (see Section 9.7). An important feature of this scheme is that the use of panels is a dynamic activity. It is essential to monitor performance and, if necessary, take corrective actions. In addition, there is likely to be a steady loss of

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Recruitment (advertising, questionnaire) Screening (pre-selection) Training (general) Selection (for specific tasks) Training (specific) Monitoring (performance)

Fig. 9.1

Performance improvement

General scheme for panellist recruitment and training.

panellists, and new panellists will need to be recruited and trained up to the standard of the ongoing panel.

9.5.4 Selecting and operating sensory tests Selecting sensory testing procedures for taint testing encounters a fundamental problem. A taint that could spell commercial disaster may be detectable only by a few per cent of consumers, so can sensory tests that, for practical reasons, using only small numbers of panellists, be designed to guard against this occurrence? No procedure can give any guarantee that a taint will be detected, but steps can be taken to minimise the risk of not identifying a taint stimulus. The most important of these are the following: • For all test procedures, if the identity of the tainting species to be tested for is known, use panellists who are known to be sensitive to that species. Unfortunately, it cannot be assumed that a panellist sensitive to one specific tainting species will also be sensitive to other tainting species, even if the chemical structures are similar. • If a high-sensitivity panel is not attainable, and especially if the nature of the taint is unknown, use as many panellists as possible in the hope of having someone present who is sensitive to the taint. Practical constraints will limit the number used, but, if possible, this should not be less than 15. There is little value in using a smaller number repeatedly in replicated tests if their sensitivities are not sufficiently high.

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• Use a high-sensitivity test procedure. Discrimination tests are generally more suitable than descriptive scaling tests, as they are more rapid and do not require intensive training. In addition, a difference test against an appropriate, untainted control is a relatively easy task for the panellist. Triangle tests are commonly used, but if there is a risk of flavour carry-over, a duo–trio test using an untainted reference should be considered. For rapid screening of a relatively large number of samples, scaling of taint intensity on either a category scale or an unstructured line scale can be used, but sensitivity is likely to be lower then that of triangle tests. • When using difference tests, maximise the information content of the test by using an extended format. A rigorous approach to sensory analysis would dictate that identification of a difference is the only information that should be elicited from panellists, the reason being that any attempt to elicit other information will require different psychological processes that may invalidate the test. As a minimum requirement, descriptive information on the nature of any identified difference must be recorded. In addition, two other types of information are frequently elicited. Firstly, since taints are by definition disliked, preference information is recorded. As indicated previously, this is a unique exception to the general rule that hedonic and analytical tests must not be mixed. The preference information is not interpreted as a likely measure of consumer response, but is used purely as a directional indicator in conjunction with descriptive information. Secondly, panellists are asked to rate how confident they were in their choice of the odd sample on a 4-point category scale (absolutely sure/fairly sure/not very sure/only guessed). Confidence levels weighted toward one end of the scale or the other can help resolve indeterminate results by indicating to what extent panellists may be guessing. Such a scale may be formalised by assigning scores to the scale points. An important point to note when using such ancillary data, however, is that these data are valid only from panellists who have correctly identified the odd sample. Data from panellists who have made incorrect identifications are invalid and must not be used.

9.5.5 Analysis of sensory taint test data It is essential when analysing the results of sensory taint test data to minimise the risk of not identifying a taint that is present, and to use statistical tests that are appropriate in this context. A fundamental problem is apparent here, as discussed by O’Mahony (1982, 1986). Conventional hypothesis testing involves testing the experimental data against a null hypothesis (H0) that no trend, or difference, exists in the data. A probability value is calculated that represents a difference occurring by chance. If this value is low, it is unlikely that the null hypothesis is true, and the alternative hypothesis (H1) is accepted, which states that a difference is present. On the other

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hand, a high value indicates that the result could have occurred by chance, and the null hypothesis is not rejected. A probability value of 0.05 (5% significance) in a difference test can then be interpreted as indicating that a difference does appear to exist, but with a 5% (1 in 20) probability that the result could have been due to chance. If we require more assurance that we really have found a difference, a lower significance level of 1% could be used, giving a 1 in 100 probability of a chance result. Unfortunately, the more assurance of a real difference that we seek, the greater the risk of not identifying a real difference that is present (Type II error). By increasing the significance level to 10%, 15% or even 20%, the risk of not identifying a real difference diminishes, but the risk of incorrectly identifying a difference (Type I error) increases. The choice of an appropriate cut-off point depends on how prepared you are to be wrong; even 1% would be too high a risk in medical experiments, and values of 0.1% or 0.01% may be more appropriate. In sensory testing, however, and in particular in taint testing, the consequences of incorrectly saying that a difference exists are relatively minor, against the consequences of not identifying a difference and allowing tainted product to reach consumers. Consequently, levels of up to 20% should be used to minimise this risk, but accepting that, by using a 20% cut-off, there will be an expectation that overall 1 in 5 will be incorrect. It should be noted that, in interpreting probability levels, there is little practical difference between probabilities of 4.9% and 5.1%, but that, if a rigid cut-off of 5% were used, different interpretations would result. Consequently, it is preferable to calculate exact significance values and use common sense in their interpretation. Regardless of the results of statistical tests, take careful note of minority judgements, particularly from panellists of established reliability, and retest for added assurance. As stated previously, formal statistical analysis methods can only be carried out on data produced using appropriate test procedures. If these are changed substantially, for example in triangle tests using only one test sample in each triad, then statistical analysis using formal procedures might not be valid.

9.5.6 Chemical analysis of taint Instrumental analysis of taints and off-flavours is a complementary technique to that of sensory analysis and presents its own interesting blend of certainties and challenges, largely governed by the sensory characteristics of the compounds under study. The presence of a tainting compound in a sample is apparent from the change in odour or flavour. The analyst begins, therefore, by knowing there is something to find. The description of the taint provides an additional parameter, since any target compound identified in the sample must have the same taste and odour characteristics as those derived from sensory analysis. Many of the compounds that cause taint do

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so because they are very potent and can be perceived at extremely low concentrations by human senses. This presents a challenge to the analyst, given the need to detect these very low concentrations of the tainting compound, often in the presence of much higher concentrations of other naturally occurring materials from the sample. Threshold data are also important criteria for the analysis, since any candidate compound identified by the chemical analysis must be present in the sample at a high enough concentration to cause the perceived taint, and as discussed earlier there can be problems in interpreting threshold data. Four key stages can in general be identified in the chemical analysis of taints: 1. Use of preliminary sensory tests to generate information on the possible chemical class of the tainting material(s). This will normally require the use of an experienced sensory panel that has been trained to detect tainting chemicals and to provide reliable descriptive information that might give clues as to possible structural types. In some cases, sample availability issues will mean that the only descriptive information available is from consumer complaints. If so, this information must be viewed with a extreme caution, as consumers are in general totally unfamiliar with the chemical nature of taints and their descriptions are frequently unreliable. Possible exceptions include the halophenol class (medicinal, antiseptic), as these are commonly used as mild disinfectants and, in the UK, as a well-known mouthwash with the name TCP (TriChloroPhenols). Even with this familiarity, however, this class if often confused with other classes. 2. Selection of a suitable procedure to extract the tainting chemicals. Prior knowledge of the chemical structure from descriptive information will help guide the analyst. Whilst most taint problems are caused by volatile compounds, involatile compounds can frequently cause problems. These commonly include those compounds responsible for bitterness. The basic problem encountered for all compounds is that sufficient quantities need to be extracted to facilitate chemical analysis, but frequently there is relatively little material available, especially if retained samples are unavailable. Extraction systems commonly used include solvent extraction, combined steam distillation and solvent extraction (as typified by Likens–Nickerson extraction, probably the most widely used method for extraction of volatile taints), headspace extraction (static and dynamic) and solid phase microextraction (SPME). 3. Selection of a suitable chemical analysis technique. In principle, many techniques exist, but the most important techniques rely on a combination of gas chromatography and mass spectrometry (GCMS). Different options are available when using the mass spectrometer in conjunction with gas chromatography. In full scan mode, which is particularly useful when there is enough material in the extract, a scan is carried out over

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the full mass range to record complete mass spectra. This is often used as a survey analysis, to identify volatiles present in a sample. Selected ion monitoring (SIM), in which the mass spectrometer is set to record data from a small number of ions characteristic of target compounds, is used to identify and quantify compounds present. The SIM method has a much higher degree of sensitivity than the full scan method. 4. Correlation of analytical data with sensory data using a specific set of analytical criteria. These criteria are: • the compound is present in the test sample but not in the control (or is present in substantially greater concentration in the test sample), • the chemical nature of the compound is consistent with sensory descriptions, • the compound is present in sufficiently high concentration as to be perceived as taint (but note the previous arguments on the problems in interpretation threshold data). Detailed information on the instrumental analysis of taints and offflavours can be found in Maarse and Grosch (1996) and in Reid (2003).

9.6 Diagnostic taint testing Taint problems continue to be widespread in spite of considerable effort and expense on the part of the food and associated industries. These problems frequently involve insurance claims or litigation, and in such cases correct sensory (and also chemical analysis) procedures must be adhered to rigorously. The first indication of a taint problem is frequently through consumer complaints on sensory quality. One consequence of the commonly low level of taint detection is that the complaints may come in at a low rate over a period of time, and recognition of a taint problem may not be immediate. In addition, investigation of a sensory quality complaint arising from a single customer return requires care owing to possible safety problems (including malicious contamination), and examination should be restricted to odour and, if feasible, chemical composition. Examination of batches of suspect product should be carried out as a means of investigation, but again, care must be taken to guard against possible safety problems. The suspect product to be tested should be drawn from the same batch coding as the complaint material, and as far as possible should have gone through the same distribution channels. In addition, suitable control material of similar age should be available. Availability of retained samples from points in the production and distribution chain is invaluable. In circumstances in which the complaint pattern suggests nonuniform distribution within a production batch, testing can be carried out to a suitable statistical sampling plan, but such testing can often prove prohibitively time-consuming and expensive.

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Consumer descriptions of most taints cannot be relied on as a means of focusing chemical analysis investigations, and sensory testing of suspect batches should be carried out to generate reliable descriptive information. However, care is needed in relating descriptions to possible chemical species. If the presence of a taint in complaint batches can be established, efforts must be made as quickly as possible to isolate affected product and to identify the source of the taint. Sensory testing can be used to investigate whether the problem is associated with a single transport container, production run, ingredients batch or packaging material batch. If the problem appears to be continuing over a period of time, however, possible sources such as new building materials, process line components or water-borne contamination must be examined. If ingredients (including water supply) are suspected as continuing sources of taint, small test batches of product can be prepared and compared against appropriate controls. Materials suspected as sources of taint can be tested using taint transfer tests, as described in the next section. Particular care must be taken in gathering evidence and setting up test procedures if, as must frequently be assumed, insurance claims are likely or, even more importantly, litigation is likely. Companies supplying tainted materials may face litigation by their customers, and in turn may enter into litigation against their own suppliers. It is frequently advantageous to contract out testing work to an experienced third-party organisation in order to establish impartiality in generating data to be used as evidence. Care should, however, be taken to establish the scientific credentials and expertise of such organisations. A number of suggestions can be made in initiating such investigations if the timescales and costs of litigation are to be minimised: • Have in place documented systems for rapid identification of the nature and source of the taint. • Isolate affected product batch codes. • Use both sensory and chemical analysis to establish both the occurrence and the identity of the taint – do not rely on one type of information only. • If feasible, store both suspect and control samples under conditions suitable for future testing. • Carry out sensory testing according to international standards procedures and use as many assessors (preferably sensitive) as possible. • Extract as much information from the tests as possible, but do not compromise the test quality. • Have the tests carried out and interpreted on a double-blind basis, especially if the tests are to be sub-contracted to a third-party organisation. • Ensure that the names and addresses of panellists are held, as presentation of sensory data in a court of law may require the presence of the individual panellists as witnesses.

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9.7 Taint prevention 9.7.1 Taint transfer testing Taint transfer testing is a powerful, but frequently misapplied, means of limiting problems arising from the introduction of new materials and changes in environmental conditions. The tests seek to expose food or food simulants to potential taint sources in an exposure situation that is severe but not unrealistic. Severity factors of up to ten times are usually used, but higher factors can be used for critical applications. However, the level of severity can often be restricted by limitations associated with the test design, and by safety considerations associated with sensory testing. An outline protocol for such tests is shown in Fig. 9.2. The design of the exposure system varies considerably depending on the nature of the test. For example, taint testing of pesticide residues requires a full-scale field trial with rigidly defined crop growing, pesticide application and crop sampling procedures. In testing packaging systems, the model system may need to simulate either direct contact or remote exposure, and, in testing process line components, factors such as product residence time and product temperature must be considered. An example of a simple model system that can be used to test various types of construction materials is shown in Fig. 9.3. A wide range of factors need to be considered in designing model systems for testing materials such as flooring, paints and packaging. Specific protocols will depend on the nature of the material under test, and the

Design exposure system

Select food simulant(s)

Expose for appropriate time and conditions

Test against control sample (high sensitivity test/panel)

Low-risk analysis and interpretation

Fig. 9.2 Schematic diagram of the steps in taint transfer testing.

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Aluminium foil

Test material

Food simulant

Glass dish

Bench

Fig. 9.3 Schematic diagram of a simple model system for taint transfer testing of suspect materials.

consequent risk of taint transfer, but the following factors are typical of those that might be considered: • type, structure and composition of food or food simulant; • ratio of the volume or surface area of the material to the volume of the vessel; • ratio of the volume or surface area of the material to the volume or surface area of the food/food simulant; • stage of exposure (e.g. at what stage during curing of a flooring material exposure is to start); • length of exposure; • temperature and humidity during exposure; • exposure method (e.g. direct contact if the product is to have intimate contact with the test material, or vapour phase transfer if no intimate contact is likely); • exposure lighting conditions (especially when rancidity development may occur); • ventilated or unventilated exposure system – whilst most construction and repair operations should be carried out with full ventilation, this is frequently not adhered to; • temperature and length of storage of food/food simulant between exposure and testing; • sensory test procedure and interpretation. Choice of appropriate foods/food simulants is an important consideration, with two possible approaches. Where a specific ingredient or product is known to be at risk, the test can be focused on that material. Where the purpose of the test is more general, however, simple foods or food simulants are often used. Solvent or adsorptive properties are the most important physicochemical considerations in selecting appropriate general simulants. Oils and fats will tend to absorb water-insoluble tainting species, and mate-

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rials such as butter are known to be sensitive to taint transfer. High surface area powders with hydrophilic characteristics have also been found to be sensitive to taint transfer, and tend to absorb water-soluble taints. Use of such materials will simulate a large proportion of the solvent and adsorptive characteristics of real foods. An additional requirement for suitable simulants, however, is that they should be relatively bland to enable easy detection, and also of acceptable palatability. This latter consideration, unfortunately, renders some simulants recommended for packaging migration tests, for example 3% acetic acid, unsuitable for taint transfer testing. Still mineral water can be used to simulate aqueous liquids, and 8% ethanol in water to simulate alcoholic drinks. In this author’s experience, however, the characteristic ethanol flavour has been found to be rather unpleasant, and a bland vodka has been used, diluted down to 8% ethanol. Some suitable materials for general-purpose use are given in Table 9.4.

9.7.2 Standardisation of preventive test methods Standard procedures for taint transfer testing have been published in several countries, mainly aimed at food packaging materials (for example BS 3755, 1964; OICC, 1998; DIN 10955, 1983; ASTM E619-84, 1988). The British Standard (BS) and the American Standard (ASTM) deal with taint transfer from packaging films in general, and the OICC standard (‘Robinson test’) deals specifically with taint transfer to cocoa and chocolate products, although it is frequently used for other products. The German DIN standard also refers to food packaging, but contains much useful information for setting up tests on other materials. All the early published methods are, however, deficient in their use of sensory testing methods, although they continue to find uses. The test procedures described in this chapter are compatible with those described in a more recent ISO standard on packaging

Table 9.4 Foods/food simulants for taint transfer testing Type

Food/simulant

Comments

Fat

Unsalted butter

Mixed prior to sensory testing, or outer surfaces only used for severe test Bland variety (white or milk)

Chocolate Hydrophilic powder

Sugar Cornflour Rusks/crispbread

Combined

Biscuits Milk

High surface area preferred (e.g. icing sugar); test as 5% solution Test as blancmange formulation (but can get textural variation) Expose crushed High-fat, e.g. shortbread Full-cream; for short-term exposure tests only, or rancidity problems can interfere

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testing (ISO, 2003), and this document is strongly recommended for the purpose of formalising test procedures. No standardised methods of general applicability have been published for any other potential taint sources. In seeking to maintain high food quality and to minimise the risk of taint problems, Marks & Spencer has developed Codes of Practice referring to the use of packaging films, plastics and paints (Goldenberg and Matheson, 1975). These guidelines stress the importance of testing by the packaging supplier before dispatch and by the food manufacturer before use. This important principle is, unfortunately, rarely recognised by the food industry in general. Food manufacturers frequently rely on suppliers to provide some general form of certification or test evidence that a material is free from taint, but the material is seldom tested under the conditions in which it will be used. Information provided by suppliers can be regarded as useful screening information, but users must protect themselves by re-testing under more realistic and rigorous usage conditions.

9.8 The role of sensory quality control (QC) in taint prevention Company procedures used to protect businesses against taint can be broadly grouped into three categories: routine product (and ingredient) testing, diagnostic testing in the event of a taint, and preventive testing. The last two categories have been described in Sections 9.6 and 9.7, and in each case sensory testing using a trained panel is an important component. Such testing falls ideally within the remit of a research panel carrying sufficient training and experience. Routine ingredient screening product and product testing will clearly fall into the remit of a sensory QC function. In practice, however, it is highly likely that only the larger companies will have the resources to operate a research panel effectively, and in most companies taint-related testing in all three categories will then default to the QC panel(s). Even in larger companies, and especially those running occasional trained panels using external panellists, the rapid responses that are needed to act on a possible taint problem will inevitably demand local expertise that can be assembled quickly. This then raises the question of the requirements needed for a QC panel that will be able to fulfil this function. The panellists used for routine and other taint testing will normally be those used for routine sensory QC testing, and the testing will most conveniently form part of the standard QC procedures. Consideration should also be given to using sub-sets of the standard panel who have been shown to be sensitive to key taints, and even to using individuals who do not participate in routine QC testing but who are known to be able to recognise and detect taint. As a consequence, there are considerable difficulties in using formal sensory testing methods for taint protection, especially in routine testing, and simplified procedures are often employed. These depend sub-

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stantially on local requirements, but a screening form that has been used by Leatherhead Food International for a range of clients is shown in Fig. 9.4. This form can be adapted to focus on specific taint-related descriptions, and can include distractor terms used to disguise the test objective and to reduce errors of anticipation. Regardless of whether research or QC panels are used however, panellists must be screened to establish their suitability for taint detection. The screening procedures will depend largely on the nature of any anticipated taint problems, but in view of the prevalence of halophenol taints, a minimum requirement should be to establish sensitivity to chlorophenols. This can conveniently be achieved without ethical difficulties in the UK using the TCP brand mouthwash. Further screening of sensitivity to haloanisoles should also be considered. However, great care must be taken in handling materials such as 2,4,6-trichloroanisole (and other potent tainting chemicals) in a food manufacturing environment, and unless there is substantial expertise in handling serial chemical dilutions, screening should be carried out on an odour basis to avoid potential ethical concerns. One difficulty commonly encountered in manufacturing and other companies lies in identifying those activities such as building modifications that lie well outside the responsibilities of a sensory testing function, but which could have a substantial impact on product quality. Staff outside the technical functions cannot necessarily be expected to have the chemical and sensory knowledge that would enable them to identify activities that might introduce a taint risk. If this is the case, then two approaches should be taken. Firstly, staff with the appropriate chemical and sensory backgrounds should be given the authority to monitor any operations within the company (and even in the surrounding environment) that might introduce a taint risk. Secondly, appropriate personnel from all relevant company functions should attend taint awareness training courses, examples of which have been running in the UK for several years.

9.9 Ethical aspects Any sensory evaluation operation using human subjects as a means of acquiring information on the sensory characteristics of foods must have ethical procedures in place designed to protect panellists from hazards associated with consuming unsafe food, and these must form part of general safety practices operated by the company management. Consuming or testing food that may be contaminated with unknown tainting species carries a specific toxic risk, and additional measures may be needed to protect panellists against such risks and also company staff against subsequent litigation. The fundamental basis for any ethical system using human subjects for sensory testing lies in a written Company (or Organisational) Ethical Policy,

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TASTE EVALUATION Test Date: Please taste the samples in the order provided, and indicate the intensity of any taste that you would not normally associate with ******. Please rinse your mouth out with the water provided between samples. Please tick any of the descriptors which describe the taste of the ****** sample. Please add any additional terms which you feel are appropriate. Sample Taste strength None Trace Weak Medium Strong

A

B

C

D

E

F

G

Descriptors Antiseptic (medicinal) Astringent Bitter Burning Cardboard Chemical Chlorine Earthy Metallic Mouldy Musty Oily Painty Petrol Plastic Sulphury Other (please state) Total descriptors SUMMARY Evidence for taint None Weak Medium Strong

Fig. 9.4 Example of screening procedure questionnaire used for taint testing large sample numbers.

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designed to reduce the risks to health of participants, whether company employees or external personnel. Guidelines originally drawn up by the UK Government have now been adopted by the EU (ACNFP, undated), and these should be referred to, in conjunction with guidelines published by the Institute of Food Science and Technology in the UK (IFST, 2005).

9.10 Case studies Most occurrences of taint have legal implications, and consequently relatively information reaches the public domain. The case studies listed below have been amended to protect the identities of companies concerned, but reflect the way in which taint investigations can proceed.

9.10.1 Case study 1: taint from flooring A long-term supplier to a major retailer of manually filleted chicken breasts was upgrading the factory premises, including changing from concrete to composite flooring. The company was using hypochlorite to decontaminate the chicken breasts. The company was aware of the need to leave new flooring to cure sufficiently, and allowed seven days before restoring production, but not of the dangers of using phenolic-based epoxy resins. In the first day of production, all product had an intense antiseptic smell that was confirmed by analysis to arise from chlorophenols, which had been formed from reaction of residual phenolic monomers in the flooring with hypochlorite splashed during usage. As the contamination could not be readily removed from the flooring, the business relationship was terminated. Note: The company involved in this case study had no effective sensory QC system that might have identified product defects before supply to its customer. Although the company showed some awareness of possible taints from flooring, it had not taken steps to establish that the flooring was taint-safe.

9.10.2 Case study 2: taint from water treatment A milk processor was one of the first users of bromination for cooling water treatment in UK, to cool sterilised milk from the production line. The company’s normal practice was to store approved sanitisers in a cellar, linked to the production areas by an elevator, but non-approved external sanitisers with phenolic components had also been stored, and a spillage had occurred. It was established that bromine vapour from the cooling tower had been transmitted to the cellar via the elevator, brominating phenolic sanitisers. Complaints of antiseptic taints were received from retail customers. Attempts had been made to clean the cellar, but the company was warned that bromophenols in the drains could convert to anisoles. Subsequent

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complaints of musty taints in milk were received several weeks later, and the presence of bromoanisoles confirmed by chemical analysis. Note: This company was a pioneer in the use of modern water treatment. As with case study 1, any change from an established production system, even to a clearly superior system, carries the risk of a new taint hazard. The company again did not have an effective sensory QC system in place.

9.10.3 Case study 3: taint from wood preservatives A snack food company had built a new warehouse complex to store potatoes. This comprised eight interconnected buildings, fully insulated with polyurethane foam, and with a forced ventilation air-conditioning system. New wooden crates (costing £0.5 million) were purchased, and as the company had a high awareness of taints, they specified that no chlorinated wood preservatives should be used. The new crates were delivered in the autumn, and left outside over the winter. The next summer potato harvest was stored in the new crates in the warehouses. Consumer complaints of musty taints in potato snacks were received and, following sensory testing, chemical analysis confirmed the presence of bromoanisoles. It was established that the wood for the crates had come from South America, and had been treated with bromophenol wood preservatives. During the winter storage in wet conditions, the bromophenols had been converted to bromoanisoles by microorganisms. The volatile anisoles were then transported throughout warehouse by the air flow system, and chemical analysis showed that these were present to a depth of 8 cm in the insulation, and were also present in electrical wiring insulation. All these materials had to be stripped before the warehouses could be cleaned and rebuilt, and as a consequence the complex was out of action for several years, at a cost to the company of many millions of pounds. Note: This company was very well aware of taint problems, and in general took all reasonable steps to test out new materials before use. It was sufficiently aware of taint problems from wood preservatives in its specifications, but at the time of the incident the risks from brominated compounds were only just emerging.

9.10.4 Case study 4: taint from ingredients Sensory tests on a batch of a key ingredient carried out by a confectionery manufacturer identified a possible antiseptic taint. A tainting chemical was identified by chemical analysis, and a new delivery of the ingredient was cleared for production. Production over the next two weeks raised sporadic alerts over possible antiseptic taint in the finished product, but this was not confirmed on more extensive testing. Production was finally halted when a loading bay operative carrying out an informal but approved sampling raised an alarm regarding disinfectant taste. It was subsequently found that

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an entirely different ingredient was contaminated with a tainting material. The reason for the failure of the QC system was that an R-index procedure was being used, involving repeat testing, and that desensitisation by the tainting chemical had falsely overridden the initial, and correct, identification of taint. Note: This company had put into place a very advanced sensory QC system, but which in the event was too sophisticated. Desensitisation by chloroanisoles had been reported previously, but the effects of chlorophenols had received less attention in published literature. The company was also very unlucky in facing two completely independent taint problems at about the same time.

9.11 Future trends Assuring the sensory quality of foods is a goal for the entire food industry, but until recently detailed specifications for food quality have relied almost entirely on non-sensory factors. A potential adaptation of sensory methods, driven by the retail sector in the UK, is the development of detailed sensory specifications for foods, and incorporates a simple assessment of product quality against specification. Although relatively crude, such systems offer the opportunity for low-cost sensory appraisal of perceived quality on a qualitative or semi-quantitative basis, and should be of great assistance in ensuring freedom from taint. The development in instrumental methods is likely to follow the route exemplified by the ‘electronic nose’ systems, more correctly described as volatile sensors (Schaller et al., 1998). At present, these systems are detection instruments, and cannot easily identify specific volatiles, although more recent instruments are more correctly regarded as developments of mass spectrometers. However, they are more usefully used as pattern recognition devices, using multivariate or neural network software systems. These can detect changes in volatile patterns that can potentially be related to foreign volatile components. Research has shown potential applications in the detection of taint in several areas, for example cork stoppers (Rocha et al., 1998) and boar taint in pork (Vestergaard et al., 2006). There are indications that some food manufacturers are now using electronic noses to screen incoming packaging materials for odour level and raw milk for halophenol contamination, but at the time of writing these remain unconfirmed. Companies operating in the UK are likely to face increased pressures on taint prevention following a change to the interpretation of UK Food Law. In 2007, The Food Standards Agency in the UK issued Guidance Notes for Food Business Operators on Food Safety, Traceability, Product Withdrawal and Recall (FSA, 2007) that included foods of ‘. . . unacceptable taste or odour . . .’ as unfit, and which might result in prosecutions if tainted food is supplied to consumers.

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9.12 Sources of further information The main published sources of information on taint and taint testing are as follows: • M.J. Saxby (Ed.). Food Taints and Off-flavours, Second Edition, Blackie, London (1996). • B. Baigrie (Ed.) Taints and Off-flavours in Food, Woodhead Publishing (2003). Many laboratories have the capability to carry out high-quality sensory testing and chemical analysis, but relatively few have experience in the combined use of both types of procedures for the purpose of taint investigations. The main laboratories in the UK are: • Leatherhead Food International, Randalls Road, Leatherhead, Surrey KT22 7RY, UK. • Reading Scientific Services, Lord Zuckerman Research Centre, Whiteknights, PO Box 234, Reading RG6 6LA, UK. • Campden and Chorleywood Food Research Association, Station Road, Chipping Campden, Gloucestershire GL55 6LD, UK.

9.13 References and further reading acnfp (undated). Guidelines on the conduct of the taste trials involving novel foods or foods produced by novel processes. http://www.acnfp.gov.uk/acnfppapers/ inforelatass/guidetastehuman/guidetaste. astm E619-84 (1988). Evaluating foreign odors in food packaging. American Society for Testing and Materials, Philadelphia. bs 3755 (1964). Methods of test for the assessment of odour from packaging materials used for foodstuffs, London. din 10955 (1983). Testing of container materials and containers for food products, Berlin. fsa (2007). Guidance Notes for Food Business Operators on Food Safety, Traceability, Product Withdrawal and Recall. fsa (2008). www.food.gov.uk/news/newsarchive goldenberg, n and matheson, h r (1975). ‘Off-flavours’ in foods, a summary of experience: 1948–74. Chemistry and Industry, 551–557. ifst (2005). Guidelines for Ethical and Professional Practices for the Sensory Analysis of Foods. http://www.ifst.org/documents/policystatements/practicesforsensoryanalysis_policystat.pdf. iso 5492 (1992). Glossary of terms relating to sensory analysis. iso 6658 (2005). Sensory analysis. Methodology. General guidance. iso 8589 (1988). Guide to design of test rooms for sensory analysis of food. iso 8586-1 (1993). Assessors for sensory analysis. Part 1. Guide to the selection, training and monitoring of selected assessors. iso 13302 (2003). Sensory analysis. Methods for assessing modifications to the flavour of foodstuffs due to packaging. kilcast, d (1996a). Sensory evaluation of taints and off-flavours. In Food Taints and Off-flavours, Second Edition, ed. M.J. Saxby, Blackie, London, 1–40.

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kilcast, d (1996b). Organoleptic assessment. In Migration from Food Contact Materials, ed. L.L. Katan, Blackie, London, 51–76. kilcast, d (2003). Sensory analytical methods in detecting taints and off-flavour in food. In Taints and Off-flavours in Food, ed. B. Baigrie, Woodhead Publishing, 5–30. lawless, h t and heymann, h (1998). Sensory Evaluation of Food. Principles and Practices. Chapman & Hall, London. linssen, j p h, janssens, j l g m, reitsma, j c e and roozen, j p (1991). Sensory analysis of polystyrene packaging material taint in cocoa powder for drinks and chocolate flakes. Food Additives and Contaminants, 8(1), 1–7. lord t (2003). Packaging materials as a source of taints. In Taints and Off-flavours in Food, ed. B Baigrie, Woodhead Publishing, 64–111. maarse, h and grosch, h w (1996). Analysis of taints and off-flavours. In Food Taints and Off-Flavours, Second Edition, ed M J Saxby, Blackie, 72–106. meilgaard, m, civille, g v and carr, b t (2006). Sensory Evaluation Techniques, CRC Press. muñoz, a m, civille, g v and carr, b t (1992). Sensory Evaluation in Quality Control, Van Nostrand Reinhold. o’mahony, m a p d (1979). Short-cut signal detection measures for sensory analysis. J. Fd. Sci., 44, 302–303. o’mahony, m a p d (1982). Some assumptions and difficulties with common statistics for sensory analysis. Food Technology, 36(11), 76–82. o’mahony, m a p d (1986). Sensory Evaluation of Food: Statistical Methods and Procedures. Marcel Dekker Inc. o’mahony, m a p d (1995). Who told you the triangle test was simple? Food Quality and Preference, 6(4), 227–238. oicc (1998). Transfer of Packaging Odours to Cocoa and Chocolate Products. Analytical Methods of the Office International du Cacao et du Chocolat, Verlag, Zurich. reid w j (2003). Instrumental methods in detecting taints and off-flavours. In Food Taints and Off-Flavours, Second Edition, ed M J Saxby, Blackie, 31–63. rocha, s, delgadillo, i, correia, a j f, barros, a and wells, p (1998). Application of an electronic aroma sensing system to cork stopper quality control. J. Ag. Food Chem., 46(1), 141–151. saxby, m j (1996). A survey of chemicals causing taints and off-flavours in food. In Food Taints and Off-Flavours, Second Edition, ed M J Saxby, Blackie, 41–71. schaller, e, bosset, j o and escher, f (1998). ‘Electronic noses’ and their application to food. Lebens.-Wiss. u.-Technol., 31, 305–316. stone, h and sidel, j l (2004). Sensory Evaluation Practices, Academic Press. tice p (1996). Packaging material as a source of taint. In Food Taints and OffFlavours, Second Edition, ed M J Saxby, Blackie, 202–235. vestergaard, j s, haugen, j-e and byrne d (2006). Application of an electronic nose for measurements of boar taint in entire male pigs. Meat Science, 74(3), 564–577. whitfield f b (2003). Microbiologically derived off-flavours. In Taints and Offflavours in Food, ed. B Baigrie, Woodhead Publishing, 112–139.

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10 Sensory quality definition of food ingredients A. Van Biesen, C. Petit and E. Vanzeveren, Puratos N.V., Belgium

Abstract: The quality of a product should be defined according to consumer expectations and, more particularly, to those of target consumers. Producing for international markets also means it is necessary to carry out various studies to define the specific quality required by each market. This chapter presents two case studies done in the context of the industrial bakery world. They correspond to two different stages of quality assessment of soft bread texture through sensory evaluation techniques: a sensory profiling and a consumer study. Key words: food ingredients, expert panel, free-choice profiling, consumers, hedonic evaluation, preference mapping.

10.1 Introduction Whether for an ingredient or a food product, quality control (QC) is meaningful only if the definition of quality is pertinent. Today the investment involved in the development and launch of a product is so significant that mistakes are unaffordable. It is no longer acceptable to base the decision of what to produce on the feelings of a few professionals. The quality of the product will therefore be defined and validated according to consumer expectations and, more particularly, those of target consumers. Producing for international markets also means it is necessary to carry out various studies to define the specific quality required by each market.

10.2

Developing good quality ingredients in a consumer-oriented approach

10.2.1 Specificity of sensory testing on food ingredients The development of ingredients, just like that of foods, is steered by various sensory analysis techniques. We should remind ourselves here of what an

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‘ingredient’ is. From a quantitative point of view, ingredients, mixed in correct quantities with raw materials according to a well-defined method will produce a food. The increasing demands of consumers, production methods and conservation, among other factors, have caused the food industry to use ingredients which allow either the properties of a food to be modified or to be maintained for a significantly longer period. With the exception of microbiological conservation technologies, the ingredient therefore always has a sensory justification. However, it is usually a matter of interaction with the raw material. That is why it is pointless in a consumer-driven approach to perform sensory evaluation on the ingredient outside the food matrix. The ingredients for bakery and patisserie are tested in the end product. The consumer or the expert judge will react to the properties of the food, modified by one or several ingredients present in different doses. The processed data are, therefore, the results of a measurement or a ranking of sensory stimuli provoked by a food whose properties are modified by one or several ingredients acting alone or interacting (Sieffermann, 1995; Urdapilleta et al., 2001). Studying the hedonic or descriptive human responses allows us to describe the effect of an ingredient in a given application. The responses obtained for the product containing the ingredient have no significance when considered alone: they must, at the very minimum, be compared to a reference product, which is generally the same food manufactured in strictly identical conditions but without the ingredient in question. Ideally, the simultaneous effects of several ingredients are revealed by measurements resulting from an experiment plan. This allows the effects of dosages and interactions to be integrated. We will see later that it is not sufficient to define the beneficial effect of a single ingredient; the objective is to determine the optimal dose of all the ingredients or improvers. There are therefore two avenues of work which are generally consecutive and complementary steps in the same development process: sensory evaluation and hedonic evaluation.

10.2.2 Sensory evaluation The first step consists of describing the effects of an ingredient which humans can perceive. As part of this first stage, there may also be prior verification which aims to confirm the sensitive effect of the ingredient. In other words, to verify if, for a given dose of the ingredient, trained judges are able to detect a modification in the properties of the food. Triangular tests in particular are to be recommended in this case. Other discriminative tests using different combinations may also be justified. It is, on the other hand, essential to examine the meaning of the responses given by this test. The interrogation must focus on the opportunity to pursue the development of an ingredient whose effects are revealed only under strict experimental conditions, which will probably not be verified in normal

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consumption situations (for example the simultaneous consumption of two different products). It is also evident that minimal differences will not polarise consumer preferences. We often use discriminative testing to evaluate the impact of modification in some processes or formulas, for instance for enzymes, yeasts and margarines. Those tests are part of a quality control procedure, but do not belong to a standardised control plan used for all ingredients. The effects of ingredients can then be described by different methods. The methods derived from free profile techniques are particularly indicated to describe modifications to texture (cf. case 1). At this stage these semiquantitative results are satisfactory to the extent that the goal is essentially to verbalise the effect of the ingredient.

10.2.3 Preference assessment The second stage consists of measuring the hedonic reactions of individuals, groups and populations to modifications to the properties of the food. These measures are meaningful only if they are established by a significantly large group of naive consumers. ‘Naive’, in this case, signifies that the consumers taste the food without being informed of the ingredients used (cf. case 2). Only under these conditions is it possible to validate an improver or the optimal combination of different ingredients. In a second phase, it is possible to judge the influence of the sensory and cognitive perception of the different alternatives, for example, labelling, claims and packaging.

10.3 Case study 1: What’s your texture? It is clear that to define quality of food ingredients, it is important that we have a good overview of the expectations of the customers for a specific product. It is the responsibility of the producer to discover these expectations, try to understand them and practise them. It is also the responsibility of the producer to inform their customers about the evolutions in their business because food quality is not a static idea but a dynamic notion. Because of this last consideration, in a company there needs to be a guideline relating to continuous improvement. To improve a product, process and ingredients must be improved. Some changes in ingredient levels have been made which have caused several simultaneous changes in product characteristics. Some of these changes are difficult to mask and thus tend to make sensory analysis difficult. Characterisation of texture commonly falls into two main groups, based on sensory and instrumental methods of analysis. A specific method is used to understand better the relation between sensorial and physical texture measurements.

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10.3.1

Definition of different ingredients used to improve texture in soft products Improvers optimise all aspects of the bread and give bakers the required tolerance and flexibility during all stages of the baking process: mixing, fermentation, baking and shelf-life. Improvers also help bakers to move the volume, crumb and crust and freshness of their breads to the next level. Enzymes are proteins that enhance the functionality of doughs, like tolerance and water absorption, and improve the characteristics of bread (volume, softness, colour, etc.). As the discoverers of the benefits of xylanase, the cornerstone of the world-renowned S500 improver, Puratos pioneered the use of enzymes in baking applications. The researchers and bread experts’ mission is to discover, produce, combine and apply new enzyme functionalities for bakery applications. The enzyme solutions are based on the optimal synergies between xylanases, amylases, lipases, glucose-oxidases and proteases – not to mention more complex matters such as customer needs and consumer expectations. In recent years, there have been many opportunities to combine various types of emulsifiers to explore new synergies. Puratos also manufactures and applies distilled monoglycerides, sodium stearoil lactilare (SSL) and specialised compounds. The expert panel evaluated five breads which are a mix of these different ingredients, in order to characterise the texture of these breads.

10.3.2 Evaluation of soft bakery goods Soft bakery goods are characterised by three important criteria: 1. Flavour: aroma and taste. 2. Baking performances, which contain: process tolerance, volume and shape, crust colour and aspect, crumb structure and crumb colour. 3. Texture performances such as softness, moistness, stickiness, cohesiveness, resiliency, freshness, bite and mouth-feel. We will look more in detail at this last criterion, texture performances. Texture refers to the properties held and sensations caused by the external surface of objects received through the sense of touch. Texture is sometimes used to describe the feel of non-tactile sensations. Texture can also be termed a pattern that has been scaled down (especially in the case of two-dimensional non-tactile textures) where the individual elements that go on to make the pattern become indistinguishable.

10.3.3 Sensorial analysis of soft products by the expert panel Sensory analysis includes use of the sense of smell, taste, sound and touch. Evaluation of food texture by touch includes use of the fingers, as well as the lips, tongue, palate and teeth in the mount. As would be expected,

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sensory methods of analysis are subject to wide variability, though this variability can be reduced by using trained assessors (experts). The sensorial characterisation was achieved in Belgium by a panel of expert judges using a method of free-choice profiling (Williams and Langron, 1984). The originality of this method comes from the fact that the judges are free to choose individually the number and the nature of the descriptive terms they will evaluate. Consequently, there is no semantic consensus among the judges. The data are analysed using generalised procustes analysis (GPA) (Gower, 1975), a method which, from the individual configurations, allows an eventual consensus among the judges to be found over the relative positioning of the products. Eight expert judges have thus characterised the five soft breads from our product space. Each judge received the five products simultaneously, and generated descriptive terms to differentiate the breads from each other. Then, each judge ranked the five breads in an ascending or descending order on each descriptive term. Figure 10.1 shows the relative positioning of the products on the first two axes of the GPA (90% information). This map highlights the products according to their texture level, and the breads are well discriminated on this characteristic. The perceived sensory differences in texture between the breads can be interpreted as follows:

Resilient Chewy 5 Crumbly

3 Soft

Axis 1: 72% Dry Hard

Moist 1

- Sensorial - Instrumental

Claggy/Sticky Axis 2: 18%

Hard

Sticky 2

4

Short bite

Fig. 10.1 Texture profiling of soft products by expert panel and instrumental characterisation.

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• Product 1, reference without any additional ingredient, is perceived as dry, hard and crumbly. • Product 2, with improver, is characterised is a shorter soft bread. • Product 3 is softer, and in the same area but to a lesser extent product 4, is characterised as moist and sticky. This by adding emulsifiers in combination with different concentrations of improver. • By adding gluten in combination with improver, product 5 is characterised as the most chewy product. After several profiles, the experts agree on the main texture attributes such as softness, moistness, stickiness, crumbliness, resiliency, shortness, melting and freshness. The tactile and sensorial evaluation was assessed to see if perceptions differed: • Softness/hardness: force required to compress the bread between the molars, or between tongue and palate. • Resiliency: speed and degree (springiness) at which the baked product returns to its original shape after a certain deformation. • Elasticity: speed and degree (springiness) at which the crumb of a baked product returns to its original shape after a certain deformation. • Moist – dry, sticky crumb: sensation strongly related to freshness and staling of the crumb. Excessive moistness results in stickiness; this is adherence of the crumb to the fingers and more to the palate upon chewing. • Cohesiveness: the degree to which the baked crumb holds together when rubbing or folding. • Crumbliness: the degree to which the baked crumb disintegrates easily in fine crumb particles. • Chewy (short): reflects the force to break a sample and the number of chews to masticate a sample to a consistency ready for swallowing. • Easy to swallow (melting): ease at which a bolus of bread can be swallowed from the moment it is put in the mouth. Results from a combination of short bite and moist mouth-feel. • Freshness: overall sensation of a fresh baked product. In case of soft packed bakery goods, this is a combination of soft and moist texture and flavor.

10.3.4 Bread under pressure with texture profile analysis (TPA) Texture is an important attribute that affects processing and handling and affects shelf-life and consumer acceptance of products. It is sometimes preferable to use instrumental methods of assessing texture rather than sensory analysis because they can be carried out under more strictly defined and controlled conditions. Furthermore, problems of experimental variability are more likely to be caused by sample heterogeneity than by

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instrumental imprecision. Another reason for instrumental analysis may be that often changes in ingredient levels cause several simultaneous changes in product characteristics. Some of these changes are difficult to mask and thus tend to make sensory analysis difficult (Peleg, 1983; Szczesniak, 1987). Therefore a main goal of many texture studies is to devise one or more mechanical tests with the capacity to replace human sensory evaluation as a tool to evaluate texture. Scientific texture analysis provides quantifiable, repeatable and accurate data on the physical properties of food, cosmetic, pharmaceutical and chemical products. It is now an established procedure in research, and a valuable tool in the quest for improved quality control methods. Measurements that yield both fundamental and empirical product characteristics are well developed and wide-ranging imitative test procedures are becoming increasingly important. TPA (texture profile analysis) is suitable for use in quality control; as a means of quantifying quite subtle aspects of texture, it commends itself as a tool both for new product development and for research. Texture analysers are used to measure many properties, such as hardness, brittleness, fracturability, adhesiveness, elasticity, bloom and strength, on a vast range of products. Analysing the texture of food using equipment and software provides processors with an objective measurement, which is an alternative to tasting panels traditionally used to evaluate the sensory experience (http://www. stablemicrosystems.com/tahome2.htm). Specific methods are used to define the most important bread characteristic. This procedures include compression, puncture/penetration, tension, fracture/bending, extrusion, cutting/ shearing. The physical criteria of soft bakes products correlate very well with human impressions. (Fig. 10.1).

10.3.5 Conclusions In food, texture is one of the most important organoleptic attributes for consumers: • It is directly connected to preference, mainly in baked goods; the right texture is the most difficult parameter for R&D. • As it is a parameter that changes from one group to another, it means that what is good for one might be bad for another. Soft baked goods represent 60% of the world bakery market where the freshness image of a supermarket is judged by the freshness of its bakery isle. Second to healthy ingredients, fresh keeping generates the most innovative ingredients for bakery. Some 90% of consumers have a positive buying intention when ‘softer’ is mentioned on the packaging.

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10.4 Case study 2: A toast bread for Chinese consumers Developing a toast bread appealing to Chinese consumers is a challenge for a European company. Indeed, bread does not belong to the culinary tradition in China, where rice is the basic food product. Moreover, food preferences and choices are mainly influenced by culture and consumption habits, and there is no guarantee that a Chinese consumer will appreciate the same sensory qualities in a bread as a European consumer. Considering single ingredients, the objective is not only to know which sensory characteristics are appreciated by Chinese consumers, but also – and above all – to link these characteristics to the presence of one or several ingredients to defined dosages. To do so, we used a methodology in four steps (Fig. 10.2): 1. Definition of a group of toast breads presenting different colour, texture and flavour characteristics. 2. Sensorial characterisation of those breads by a panel of expert judges (description and quantification). 3. Hedonic evaluation of those breads by Chinese consumers. 4. Link between consumer preferences and sensorial characteristics of the breads and indirectly ingredients.

10.4.1 Definition of a toast bread’s product space For each given characteristic – colour, texture and flavour – three levels are defined by varying the dosages of several ingredients: 1. Colour: pale–average–intense. 2. Texture: soft–average–hard. 3. Flavour: weak–average–strong. The toast breads for evaluation are defined by combinations of these different levels by an experimental design. Amongst all possible combinations, eight are selected to form the final product space (Fig. 10.3).

We deduce

Ingredients We know

Colour Texture Flavour

Preferences

We measure

Fig. 10.2 Methodology to define a good quality toast bread for Chinese consumers.

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Fig. 10.3 Combinations of colour, texture and flavour levels to construct the ‘toast breads’ product space.

10.4.2 Sensory characterisation The sensorial characterisation was achieved in Belgium by a panel of expert judges using a method of free choice profiling and data were analysed using GPA. Nine expert judges have thus characterised the eight toast breads from our product space. Figure 10.4 shows the relative positioning of the products on the first two axes of the GPA (68% information). The breads are well discriminated on colour, texture and flavour levels. By studying the correlations between the descriptive terms and the GPA axes, we can interpret the perceived sensory differences between the breads (Fig. 10.5): • products 1 and 2 are perceived as softer and stickier, whereas products 6, 7 and 8 are perceived as harder and more elastic; • products 1, 5 and to a lesser extent 6, are characterised by a more pronounced butter flavour; • product 2 has a more intense yellow colour and a softer and stickier texture; • product 6 has a more pale colour and a harder and more elastic texture; • product 8 is characterised by a fruity flavour and a harder and more elastic texture.

10.4.3 Hedonic evaluation The hedonic evaluation of the eight toast breads has been achieved in China directly with Chinese consumers. Indeed, this is of upmost importance for a consumer test to get the opinion of ‘native’ people belonging to the target population for the tested food product. Tests have been performed in three different cities located in different regions of the country: Shanghai, Beijing and Canton. This in order to determine if preferences for toast breads were homogeneous over the whole country, or variable from one region to the other. The preferences of

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Product positioning for all judges 8-DV 8-KV

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Consensus product positioning

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Fig. 10.4 Sensory mapping obtained by GPA on the eight toast breads – product positioning for all judges and consensus.

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uit

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Fig. 10.5 Sensory mapping obtained by GPA on the eight toast breads – consensus product and attributes positioning.

549 consumers have been measured, with an equi-proportion on the three cities (183 in Shanghai, 180 in Beijing, 186 in Canton). Each consumer evaluated the eight toast breads one after the other on a hedonic 9 point scale: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Extremely unpleasant Very unpleasant Unpleasant Fairly unpleasant Neither unpleasant, neither pleasant Fairly pleasant Pleasant Very pleasant Extremely pleasant

Figure 10.6 shows the results obtained by analysis of variance on the hedonic evaluations given by the consumers for each of the three cities. Results are similar but not identical from one city to the other: • In Shanghai, product 3 is significantly less appreciated, and products 2 and 5 are significantly preferred. • In Beijing, products 3 and 4 are significantly less appreciated, and products 1 and 5 are significantly preferred. • In Canton, product 5 is significantly preferred. In order to determine which sensory characteristics of the breads induce these preferences, we need to link the sensory data obtained from the free profile to those consumer data.

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Shanghai Ext pleasant

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Ext unpleasant Product 3 Product 6 Product 7 Product 4 Product 8 Product 1 Product 2 Product 5

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Ext unpleasant Product 3 Product 4 Product 6 Product 8 Product 7 Product 2 Product 1 Product 5

Canton Ext pleasant

Pleasant

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Ext unpleasant

Product 7 Product 3 Product 6 Product 4 Product 8 Product 2 Product 1 Product 5

Fig. 10.6 Average ratings of the eight toast breads for consumers in Shanghai, Beijing and Canton. Note that products linked with a straight line are not significantly different at the 5% level (ANOVA).

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10.4.4 Preference mapping Preference mapping is a method of data analysis which allows the comparison of a pool of products whose hedonic character has been evaluated by consumers, and whose sensory characteristics have been described by an expert sensory panel (profiling data). This method is based first on the construction of a sensory map, obtained by a multidimensional analysis on the profiling data. The postulate form of preference mapping is that if two products are close together on the sensory map, consumers will give them close hedonic scores. Preferences of each consumer, considered individually, are then modelled by regression, as a function of the relative positioning of the products on the sensory map. After this design step, as many preference mappings as the number of consumers are obtained. The last step is then to build a consensus map to visualise the preferences of the whole group of consumers. The method used in this study determines on the sensory map a ‘zone of optimal appreciation’ which corresponds to the maximum number of satisfied consumers. The model cumulates the individual regions of preferences to obtain a final surface which is expressed as a percentage of the total number of consumers (Danzart, 1998; Elmore et al., 1999; Guinard et al., 2001; Helgesen et al., 1997; Hough and Sanchez, 1998). Figure 10.7 shows the preference mappings obtained for the whole consumer groups in Shanghai, Beijing and Canton: • In Shanghai and Beijing, the zone of optimal appreciation is located on the right-hand side of the map, and corresponds to products with a texture between average and soft, with an intense yellow colour and a pronounced butter flavour. • In Canton, the zone of optimal appreciation is broader and shifted towards the lower side of the map, incorporating products 1 and 5; it corresponds to products with a texture between average and soft, and a pronounced butter aroma. Colour is a less important characteristic for those consumers.

10.4.5 Clustering of consumers The interpretation of the results at the global population level can lead to misleading conclusions if the preferences of the consumers are rather heterogeneous. It is advisable to always verify the existence of sub-groups of consumers with different preferences between each others. To do so, we use a method called hierarchical ascendant clustering (HAC), which allows segmenting the consumers in sub-groups according to their preferences. The data collected in Beijing reveal that there are actually two groups of consumers inside the respondents, with different preferences for toast breads (Fig. 10.8). Using the preference mapping for each sub-group separately, we can note that:

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Shanghai 55%

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50%

35% 45% P4

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35% 50% 0

P3

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45% P5 65%

40% 40% 60% P1

P6

Fig. 10.7 Preference mappings of the eight toast breads for consumers in Shanghai, Beijing and Canton.

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Canton P8

45%

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50% 40%

P2 P4 P3

P7 40% 40%

P5

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65% P1

Fig. 10.7 Continued

14 12 10 8 6 4 2 0 First group (48%) (86 consumers)

Second group (52%) (94 consumers)

Fig. 10.8 Hierarchical ascendant clustering of consumers in Beijing.

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• 48% consumers from Beijing prefer products 5, 1 and 2, and appreciate product 6 less; • 52% consumers from Beijing prefer product 6 to the other products. For both groups, consumers appreciated products with a pronounced butter flavour. However, colour and texture characteristics are less consensual: some consumers appreciate a pale colour and a harder texture (group 1 – 48%); others appreciate a more intense color and a softer texture (group 2 – 52%). A similar exercise realised for the cities of Shanghai and Canton gives conclusions that are close to those obtained in Beijing. Thus, it is important to highlight that the differences of appreciation are more pronounced between the consumers of a given city than from one city to another. In other words, the intra-city variability is greater than the inter-city variability.

10.4.6 Conclusions Designing products for specific target populations requires the ability to link the sensorial effects of ingredients in the end product to the preferences of the consumers. This four-step methodology has been applied with success in the food industry to develop good quality ingredients in a consumer-oriented approach.

10.5 References danzart m (1998), ‘Statistique’. In SSHA, Evaluation Sensorielle: Manuel Méthodologique, Paris, Technique et Documentation Lavoisier, 217–300. elmore j r, heyman h, johnson j and hewett j e (1999), ‘Preference mapping : relating acceptance of creaminess to a descriptive sensory map of a semi-solid’, Food Quality and Preference, 10, 465–475. gower j c (1975), ‘Generalized procustes analysis’, Psychometrika, 40 (1), 33– 51. guinard j x, uotani b and schlich p (2001), ‘Internal and external mapping of preferences for commercial lager beers: comparison of hedonic ratings by consumers blind versus with knowledge of brand and price’, Food Quality and Preference, 12, 243–255. helgesen h, solheim r and naes t (1997), ‘Consumer preference mapping of dry fermented lamb sausages’, Food Quality and Preference, 8, 97–109. hough g and sanchez r (1998), ‘Descriptive analysis and external preference mapping of powdered chocolate milk’, Food Quality and Preference, 9, 197– 204. peleg m (1983), ‘The semantics of rheology and texture’, Food Technology, 11, 54–61. sieffermann j m (1995), Etude comparative de méthodes descriptives en analyse sensorielle – Application à l’évaluation de l’efficacité du profil sensoriel libre. Thèse de Sciences Alimentaires: ENSIA, Massy, France. szczesniak a s (1987), ‘correlating sensory with instrumental texture measurements – an overview of recent developments’, Journal of Texture Studies, 18, 1–15.

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urdapilleta i, ton nu c, saint denis c and huon de kermadec f (2001), Traité d’Evaluation Sensorielle: Aspects cognitifs et métrologiques des perceptions, Paris, Dunod. williams a a and langron s p (1984), ‘The use of free choice profiling for the evaluation of commercial ports’, Journal of the Science of Food and Agriculture, 35, 558–568.

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11 Sensory quality assurance in the chilled and frozen ready meal, soup and sauce sectors M. Swainson and L. McWatt, University of Lincoln, UK

Abstract: This chapter discusses the use of sensory evaluation in the assurance of product quality within the food production sectors of ready meals, soups and sauces. The chapter methodically reviews typical food processing stages, from recipe development through to end product supply, and considers how sensory assessment methods can be utilised to help assure the quality of the end products within these selected high-risk chilled food sectors. Key words: sensory analysis, organoleptic assessment, key sensory points (KSPs), quality assurance, quality control, taste panel, ready meals, soups, sauces.

11.1 Introduction Multi-component foods such as ready meals, soups and sauces by their very nature comprise a diverse range of ingredients. The final quality of the foods produced will typically be heavily influenced by the quality of these raw materials and the consistency of the production processes involved. With particular regard to these factors, sensory assessment has a key role to play in ensuring product quality at each stage of the food manufacturing operation. This chapter considers the many development and processing stages of a typical ready meal, soup or sauce manufacturing operation and provides detail upon the use of sensory assessment within each phase of the production process. It should be noted that during the manufacture of food products there are usually many checks that have to be conducted to help ensure product safety, quality and legality. This chapter particularly seeks to focus upon the use of sensory evaluation within the quality assurance (QA) aspects of food production. A reputable food manufacturing business QA system will view

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sensory evaluation as one of many useful tools to be utilised in the monitoring and control of product quality, safety and legality throughout the manufacturing operation. The reader is therefore encouraged to consider how such checks may complement the QA system of their own manufacturing operation.

11.2 Sensory quality assurance (QA) in the recipe development process Recipe creation and development within a multi-component food production business are typically the role of a new product development (NPD) department. This department is often considered to be the lifeblood of a business as the extent of customer acceptance of its new product creations will be reflected in the total sales of the business. A very high proportion of the customer’s engagement with food products is based upon organoleptic factors (e.g. appearance, aroma, taste and texture) and therefore it is vital that the NPD department utilises a wide range of sensory skills and techniques in order to create products which will satisfy the expectations of the end consumer. It is generally accepted that consumers will have subjective reactions to food products and describe new foods in terms of their ‘likes’ and ‘dislikes’, whereas food manufacturing operations will often benefit from objectivity in identifying and defining sensory attributes. By combining consumer reactions with well-defined sensory attributes it is possible to gain an insight into those foods which have attributes that consumers will accept and those which the consumer will reject. Such skills at the NPD stage include the ability to be able to physically create the product envisaged and this skill is greatly helped by experience. Once the required product sensory characteristics (including appearance, aroma, taste and texture) have been clearly defined, the development chef can then reflect upon how other products with similar characteristics are made. Such an approach will often help to then define the ingredient list and the likely production process for the new product. These skills will certainly help during the following typical NPD scenarios: • Blue-sky creativity: sometimes during the development process the NPD team will dream-up ideas for new products, utilising their sensory skills to then create the product which has been envisaged. This is a skill that can take years to refine, and the process is greatly supported by an ability to define and then achieve the desired product sensory attributes. • Customer brief: often the business NPD team will receive a ‘brief’/ description from the customer upon the type of product or product

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range they are interested in procuring. Sometimes these briefs can be extremely detailed, providing the desired key sensory points (KSPs) for each individual recipe, and other times the ‘customer brief’ may only outline a general range of products required and just state the product names or target consumer groups, in which case the NPD team has a wide degree of artistic licence to formulate product samples designed to gain the customer’s business. • ‘Me too’/‘copycat’ approach: sometimes the customer or internal business drive will wish to move into a product category/market that already has the product types in it that they also wish to sell. In such cases businesses will often take product samples of the competition, carry out well-structured benchmarking sessions, decide upon suitable production methods for each product, and ideally find a way to make the products better than the competition and at a lower cost. Usually in such circumstances the NPD team will have the added benefit of being able to review the food packaging label of the product that they are seeking to copy, and therefore may benefit from knowledge of aspects such as the ingredient declarations and nutritional data of the competitors products. During the NPD process there are a number of techniques that can be utilised to aid the selection of the best products/recipes. These techniques include the following: • Difference testing: selected business sensory evaluation panel members are each in isolation (to avoid any influence) presented with a set of individually labelled product samples (typically three), all at the same time. One sample is different from the other two (i.e. the single sample may be a proposed new recipe version or the current standard recipe), and the panellists are asked to pick out which sample is different. The results of the panel will inform the business whether there is an actual consistently distinguishable difference between the current recipe and the proposed new recipe. • Preference testing or ‘consumer testing’ has limited use in the early stages of the NPD function, but is vital once a product has been developed to gauge consumer reaction to the new product. Members of the business sensory assessment panel are individually presented with samples of the current and the proposed new recipe (which are unmarked to ensure that it is unclear which is which) and are asked to select the sample which they prefer. It should be noted that when selecting such a testing method the manager should ensure that it is appropriate for the recipe being assessed. For example, the product being assessed may be for eventual sale as an accompaniment to a product (e.g. a pour-over sauce for steak), and in such circumstances the benefits of assessing the product in the context of its end use should be considered.

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Whilst evaluating a proposed new recipe it is important to understand your customer and the aspects of the product that particularly matter to them (i.e. the KSPs of the product). Such an approach will help keep the NPD team focused upon delivering specifically what the customer wants. • Customer panels can be used to help define the product KSPs (e.g. appearance, aroma, taste, texture) and thereby ensure a business that the products being developed are likely to meet with the approval of the end consumer once launched. Customer panels can utilise sensory evaluation techniques including acceptance/preference testing, focus groups, central location testing and product placement or can simply request the panellist to taste the product and state what they like and what they dislike about the product, and ultimately would they purchase the product at the price that it is intended to be sold for? Sometimes it will be of benefit to ensure that the consumer panellists selected have experience of regularly eating the product types to be tested in order to ensure that their assessment is very finely focused upon the product KSPs. As a minimum requirement consumers should be non-rejectors of the product, and ideally they should be current users of the product. • ‘Old product development’ (OPD) is a process in which the development team is tasked with making particular improvements/adjustments to existing ‘live’ product recipes. These ‘improvements’ may often be related to aspects of product quality or cost (e.g. seeking a sales margin increase) and such work is an important phase of the product life cycle in terms of protecting product sales/operating margins. OPD also is often required as businesses seek to review their product ranges to meet the increasing customer requirements for healthier foods; for example, when seeking to develop product nutritional claims such as ‘reduced fat’ or ‘reduced salt’.

11.3 Sensory quality assurance (QA) in the post-development product scale-up phase Sensory assessment plays an important role within the product scale-up phase as it is vital to ensure that the product when manufactured on a full industrial scale still achieves all of the KSPs of the approved development sample (as this is what the end customer will have agreed and therefore will be expecting to be delivered as the final product). All too often during factory trials of a new product there are problems encountered when trying to match the factory product to the development kitchen samples which have been approved by the customer. The following are aspects of the scale-up process that can impact upon/ cause variations in the organoleptic properties of the factory-produced product when compared to the development kitchen sample:

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• Ingredients purchased on an industrial scale are typically not as ‘quality consistent’ as the development kitchen purchased and hand-prepared ingredients. Perhaps the ‘industrial scale supply’ meat/vegetable particulates contain more ‘off-cuts’ or ‘fines’ which affect the appearance of the end product. When preparing product samples for agreement with the customer, the development department should be encouraged to always use factory grade ingredients, as using hand-selected ‘perfect’ ingredients should be done only if such standards are consistently achievable by the supplier at the price point intended. The exception to this may sometimes be the use of hand-selected samples for product artwork, although care must be taken not to mislead the consumer at this stage. If such points are not controlled then the business is likely to overpromise and under-deliver in terms of product quality/consistency. • Production factors such as mixing/blending/cooking/holding times on an industrial scale in the factory typically take far longer than when making a very small quantity of product in the development kitchen. Such conditions can lead to an increased potential for product texture breakdown, colour deterioration and flavour changes. The technologist who oversees the scale-up operation should select the best factory methods to minimise such issues, make recommendations for more appropriate pieces of equipment and limit maximum batch sizes wherever processing time has an adverse impact upon product organoleptic quality. • The development sample may only have been cooked to a very limited extent in order to preserve texture, colour and flavour. However, in the full industrial process the product will also need to achieve certain shelflife aspirations which often involve having to cook the product at higher temperatures or for longer periods of time in order to ensure a sufficient level of microbiological reduction. Clearly there is therefore the potential for a reduction of organoleptic quality whilst seeking to achieve product safety/shelf-life. As product safety is a non-negotiable product requirement, the scale-up technologist must ensure that the process and operating times/temperatures selected achieve the required levels of safety and shelf-life whilst avoiding unacceptable levels of product organoleptic deterioration caused by over-processing.

11.3.1 Shelf-life assessment Shelf-life assessment will typically involve holding the new product within storage and handling conditions that reflect both the product supply chain and the holding conditions of the end consumer (most retailers/food service operations will have pre-set criteria detailing the expected times, temperatures and storage conditions of the shelf-life trials required for any products which are to be sold through their operations). Shelf-life testing usually requires both microbiological and organoleptic assessment over the course of the required shelf-life period (in some cases nutritional analysis will also

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be required to ensure that the nutritional performance of the product is as required over shelf-life). Often during the shelf-life assessment of a proposed new product, samples of the product will be despatched to a contract microbiological laboratory to be held at the required temperatures for the designated amounts of time between microbiological tests. The storage temperatures during such trials for chilled foods will usually be elevated to reflect the typically higher temperature storage conditions within the distribution chain and consumer storage (compared with the relatively low and consistent storage temperatures achievable within a closely controlled business chill store). It can sometimes be the product organoleptic performance that limits the total product shelf-life rather than the overall product microbiological performance. Therefore it is important to ensure that the organoleptic assessment of the product during shelf-life testing evaluates product samples that have been held in the same storage conditions (e.g. times and temperatures) as the microbiological test samples. To aid process efficiencies and reduce wastage there will often be business commercial pressures to apply as long a shelf-life to products as is possible. In such circumstances it can be tempting for a business to conduct its shelf-life testing using ‘best case’/optimum storage conditions. However, failure to take account of ‘worst case’ or even just ‘real world’ storage conditions/factors may well lead to a business applying a length of shelf-life to products which is based upon optimum storage control, rather than the real world storage conditions that the product is actually going to encounter within the supply chain. Application of an inappropriately long shelf-life may result in consumer complaints of product deterioration before the ‘use by’ date of the product has been reached and can lead to serious consumer health issues and product recalls based upon product quality or safety grounds. Often the simplest approach is to have the microbiological laboratory hold both the microbiological and the organoleptic shelf-life test samples within their storage incubators set at the required temperatures for the required amounts of time. Each time a product is due for microbiological assessment (at set points over the length of shelf-life to be assessed) the laboratory should also return the required number of samples back to the manufacturer for organoleptic shelf-life evaluation. It is also prudent to assess product shelf-life performance to a point which is past the length of shelf-life that will be typically applied to the product. Not only is this good practice in demonstrating due diligence that the shelf-life applied also allows for a margin of safety, but also by conducting such tests the business will gain an understanding of the product characteristics/features which are exhibited by the product as it deteriorates. In addition there may be times in the future when the business would benefit from an understanding of how long the product can actually last (e.g. perhaps helpful in circumstances when the customer has queried the poten-

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tial for a shelf-life extension due to excess stock holding and therefore having already conducted such tests will prove to be invaluable in enabling a swift response).

11.4 Sensory quality assurance (QA) in the production process 11.4.1 Definition of the required product organoleptic quality standard The end goal of the production process is to create a product which meets the customer’s expectations. Typically within the food manufacturing sector a sensory specification/standard for each product is used to aid product quality assessment. The product profile of organoleptic expectations is often written and agreed with the end customer at the time of product development or scale-up. This sensory description for each product should clearly define the specific organoleptic characteristics required to be achieved. This information is usually defined under sections including appearance, aroma, taste and texture (Fig. 11.1). Photographs of the product can also be incorporated within such sensory descriptions to further define the visual standard required. One issue with regard to the use of end product sensory descriptions is that at certain stages within the production process the product may require sensory assessment but will not yet be expected to meet the end product sensory description. For example, pre-fried onions may require an organoleptic assessment to confirm that they are sufficiently soft and caramelised prior to their addition to the sauce component of a chicken tikka masala ready meal. Therefore ‘intermediary stage’ sensory descriptions may be of benefit to the quality assurance of such product components. Often these sensory descriptions are written by the process technologist responsible for the scale-up of the product, as the technologist will know the performance criteria required of the component at this specific process stage.

11.4.2

Production process sensory evaluation techniques and useful equipment for product assessment The following section seeks to describe the many sensory related checks that can be conducted as part of a QA programme within a food manufacturing operation. Businesses will typically select particular sensory assessments and check frequencies based upon their staff and time resources available, the potential for product variability and the scale of the financial/ business consequences of failure to supply to the quality specification required. Sensory evaluation of ingredients and end products within the production processes of ready meals, soups and sauces often relies heavily upon

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Product Sensory Assessment Check-Sheet. Product Name: Chicken Tikka Masala Sauce. Product Code: XYZ123.

Batch / Use-by Code:

Date & Time of Assessment:

Assessed By:

Product Attribute

Sensory Standard

Appearance

A pulpy pale red/orange sauce of medium thickness with visible flecks of coriander. The sauce contains visible 20 mm diced chicken breast, 5 mm sliced onions and 3 mm sliced green chillies. Occasionally there will be pieces of 12 mm diced tomato present. There will be only slight visible oil separation. Colour chart reference: Acceptable range = ABC789 to ABC794.

Colour

Pass / Fail & Comments

Aroma

Aroma is of fresh coriander, mild garlic and almonds, with a background of tomato.

Flavour

Well-balanced flavours typical of tikka masala. Tomato, chilli, onion, coriander and garlic flavours dominate. Heat from the green chillies building during continued eating. Mildly spicy aftertaste and ongoing heat from the green chillies.

Aftertaste

Texture

A medium thickness sauce with slight oiliness. The sauce should have a pulpy consistency. Chicken pieces should be firm to the bite, but not tough or chewy. The sliced onions and green chillies should be soft but still clearly defined within the end product.

Viscosity

A medium thickness sauce. Bostwick consistometer check: Sauce sample must be sieved pre-test. Acceptable range = 10 − 12 cm / 30 s @80 °C.

Notes (including further comments / actions taken)

Document Version Number: 1.01

Document Issue Date: DD/MM/YYYY

Fig. 11.1 Example product sensory description/check-sheet.

the organoleptic assessment skills of the production operatives and supervisory staff. It is therefore vital that all staff who are to be placed within factory operation roles involving the sensory assessment of the food products must be:

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• screened to ensure that they can recognise to an appropriate degree the key flavours of sweet, sour, bitter, acid and umami – British Standard methods for sensory analysis of food are available to support taste identification and threshold testing; • screened for colour blindness – the Ishihara test technique can be used when screening for colour blindness; • trained to adequately assess the sensory performance of a food product – such training can include understanding of the standards to be attained (and where to find such sensory descriptions within the business operating system), the importance of not allowing personal preference to influence the tests completed/test results and also reviewing corrective actions to be taken in the event of a non-conformance. Whilst the use of a trained operator’s palate and visual assessment skills provides an excellent resource for quality assurance, such checks upon appearance, aroma, taste and texture can by their nature be quite subjective. Therefore it is also of great benefit to a manufacturer’s QA programme to also incorporate equipment which can provide objective, measured tests upon the acceptability of the food products. Test equipment typically used during the production processes of soups, sauces and ready meals include the following. Bostwick consistometer Products such as soups, sauces, dips and dressings are all viscous liquids. The Bostwick consistometer (Fig. 11.2) determines the food sample consistency by measuring the distance which the material flows under its own weight over a set period of time. This enables the assessment of liquid food samples against pre-set consistency/viscosity standards. It should be noted when using such equipment that product viscosity will vary with product temperature. Typically the hotter the product, the less viscous it will be. Such checks should therefore always be conducted at a set temperature point. Usually manufacturers will choose a set temperature close to the temperature that the product is likely to be at that point of assessment. For example a pasteurised sauce to be assessed at the stage of cooked batch completion may be assessed at 80 °C, whereas a cold blend sauce to be assessed at the point of batch completion may be assessed at 4 °C. To ensure accuracy a calibrated hand probe should be used to confirm that the product is at the required temperature at the point of assessment. Another point of potential variation in results is that when assessing soups and sauces which contain particulates, the amount of particulates in each small test sample will affect the flow rate/viscosity of the product. Therefore it is common business practice, when assessing the viscosity of particulate sauces/soups, to always conduct a ‘sieved Bostwick’ where the sample is sieved through a set size sieve to remove the particulates before viscosity assessment in order to eliminate the ‘particulate variable’.

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Fig. 11.2 Short and long Bostwick consistometers.

Brookfield viscometer Brookfield viscometers (Fig. 11.3) are often used within the food processing sectors where accurate bench-top analysis of product viscosity is needed. These viscometers use the principle of ‘rotational viscometry’, i.e. their measurement of product viscosity is based upon immersing a specifically selected spindle within a sample of the product followed by measurement of the torque required to rotate the spindle at a set speed whilst immersed within the product sample. As the torque required will be proportional to the quantity of viscous drag upon the spindle, this therefore provides an assessment of the product viscosity, reported in centipoise units (cP). Colour reference charts Colours can be described, but the use of colour charts enables the assessor to work back to a consistent standard, rather than having to envisage the expected colour defined by a written description. There are a number of

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Fig. 11.3 Brookfield viscometer.

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colour reference charts available to use within the food manufacturing sector, commonly used colour reference charts include Royal Horticultural Society (RHS) and Pantone. Many food companies now rely on photographic standards for colour assessments and often these standards will be reviewed on the computer screen at the relevant food manufacturing work-station. In addition to supporting in-house process control, the use of specific test equipment also enables the manufacturing operation to communicate and work to a common standard with suppliers/customers, helping to ensure that quality standards for raw material supply and end product acceptance are clearly and objectively defined.

11.4.3 Ingredients There are a number of steps that can be taken to help achieve the consistent supply of correct quality ingredients to the food manufacturing operation. The use of ‘approved suppliers’ (where the supplier is assessed for control of aspects such as product quality, safety and legality prior to being authorised to supply) is a good start to the assurance of consistent ingredient supply. Purchase to a pre-agreed ingredient specification which reflects the quality performance requirements of the ingredient is an important factor. By having a clear understanding and definition of the intended end product key sensory points (KSPs), checks can then focus upon ensuring that the ingredient KSPs are aligned with the end product specification. Definition of ingredient KSPs will include written description of the sensory aspects of the ingredient, including its appearance, aroma, taste and texture. These specified KSPs can then be checked to confirm conformance to requirements upon point of delivery. Consistent ingredient supply is highly reliant upon consistent processes and machinery at the supplier site; therefore, where possible supplier audits should seek to review the supplier’s ability to achieve a consistently correct ingredient quality. Sensory assessment of incoming ingredients should be conducted by a member of staff who has been trained upon such checks and confirmed to be capable of reviewing each ingredient against its specified quality criteria. These checks may include raw or cooked product tests in order to confirm that the ingredient’s appearance, aroma, taste and texture meet the requirements defined within the specification/sensory description. It is always important to ensure that a representative sample is taken from the incoming goods to be assessed. Staff can be trained upon sampling amounts and techniques to ensure that various points of the delivery are checked (including coverage of the range of supplier lot/batch/date codes present). Where cook tests are required (for example for the organoleptic assessment of raw meats) it is important to ensure that appropriate assessment

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facilities/cooking equipment are consistently available to the operator, otherwise there is a risk that checks will not be conducted. Also such tests can be time consuming and therefore it is important to ensure that operators are trained to focus upon the checks and do not rush. Sensory related checks upon incoming ingredients include the following: • Vegetables (typically assessed raw unless cook rate/texture is a critical factor to check in advance of processing): size (including dice or slice dimensions if applicable), texture, taste, aroma, colour, skin presence, extraneous matter and soil presence. • Meats (typically visually assessed in a raw state, and then fully organoleptically assessed upon cooking): aroma, size (including dice, slice or mince dimensions), visual lean, texture, colour, blood, gristle and extraneous matter. • Dairy products: colour, aroma, viscosity and taste. Food manufacturers should consider the proactive benefits of encouraging their suppliers to conduct pre-outload sensory assessment of the raw materials, in order that by the time the ingredient reaches the manufacturing site it has already received a recent confirmatory check that it meets all of the sensory criteria expected. Such a ‘right first time’ approach can help avoid a lot of disruption and cost to both operations. A relevant point of note is to ensure that when agreeing a specified standard for ingredient assessment pre-despatch (at the supplier site) and upon arrival at your manufacturing site, the same design/model of testing equipment should ideally be used at both sites to help reduce the potential for variances in the testing approach taken. Also the test methods/conditions need to be stipulated to ensure consistency between the two sites; for example, product viscosity will be affected by temperature and therefore should always be measured at a pre-agreed temperature to facilitate comparison. Over time a business may wish to vary its frequency of checks upon each ingredient, with the extent of assessment dependent upon the supplier track history of consistency of supply, the potential for major product/business impact in the event of a fault, and some ingredients may require extra focus upon particular ‘at risk’ times of year with regard to consistency/seasonality of supply. For example small dice/slice sizes of fresh processed carrots, because of their high surface area, can be far more susceptible to spoilage at certain times of the year. Such spoilage can result in an acidic flavour/ aroma which, if the ‘off’ carrots were then accidentally used, would render the end product unsalable. As a consequence manufacturers may choose to reduce the operating shelf-life of such ingredients at particular ‘known issue’ times of year. It is often useful to build up a catalogue of these potential supply issues that can be used within a food production business for staff training and advanced warning purposes.

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Often manufacturers will conduct incoming goods checks in the area in which they are going to store the product as long delays whilst conducting checks in ambient conditions can have an adverse effect upon both the organoleptic and microbiological properties of chilled/frozen ingredients.

11.4.4

Ingredient supply changes, cost improvement initiatives and ingredient substitutions There can be many circumstances which will drive a business to replace a currently supplied ingredient with another. Perhaps the supplier is unable to achieve the consistency of supply required due to the order volumes being too small, too big or too infrequent. Perhaps there is a business initiative to improve the nutritional status of the end product (e.g. reduced salt or reduced fat projects). Another potential reason is that of ‘cost control’ or ‘margin protection’, where a cheaper supply is therefore being sought. Also there is the possibility that the usual manufacturing site unexpectedly does not have the ingredient available and therefore the business needs to use a substitute ingredient in order to avoid significant disruption in their production/delivery plan. It is important to highlight at this stage that when seeking to make adjustments to the ingredients used a business must ensure that it is in control of key factors such as ingredient declaration changes, any food safety/allergen status changes and impacts upon end product specifications/ customer approval. Changes in some ingredient characteristics which initially appear to be minor, can sometimes make the difference between the end product being safe or unsafe. For example when evaluating a new chopped tomato product for use as a base ingredient within fresh, coldblended salsa sauces/dips, if the new chopped tomato supply is not as acidic as the previously used ingredient then there is the significant potential that the resultant end product will not be as acidic and will therefore be more susceptible to spoilage and potentially pathogen presence/growth. When considering proposed new ingredients it is also important to ensure that the end product quality is not going to be adversely affected by any such ingredient changes and therefore, in advance of progression to factory trials of any new ingredient, sample assessment using sensory evaluation techniques can help to ensure that the proposed new ingredient is likely to be successful. There is the temptation for suppliers to provide perfect hand-selected samples at the initial sales phases which can sometimes give an unrealistic impression of the quality and consistency of the ingredient to be supplied. It is therefore important to always request factory-produced samples from the suppliers of proposed new ingredients to ensure that the product being assessed is representative of the product to be supplied on an ongoing basis.

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Organoleptic assessment of the proposed new ingredient against the required ingredient specification and against a sample drawn from the current supply is a typical approach used by businesses when evaluating the adequacy of a proposed new ingredient. Such checks are usually conducted by a company panel, consisting of staff who have confirmed their competence in sensory evaluation (often quality and development staff), and involve the review (uncooked or cooked as appropriate) of aspects such as ingredient appearance, aroma, taste and texture (and also viscosity assessment for liquid products). In business often there can be resistance to change. Some staff will have their preferred suppliers, perhaps due to relationships that have been built over many years of supply. Other staff may be influenced by thoughts that if a proposed new ingredient is cheaper then it cannot possibly be as good as the currently supplied ingredient. Staff may be risk averse, feeling that any changes may have the potential to damage the end product/business reputation. In order to avoid such matters from clouding the fair evaluation of an ingredient, drawing together an ingredient sensory evaluation panel and applying ‘Difference testing’ or ‘Preference testing’ methods can be used to overcome any bias and provide objective rather than subjective responses. When conducting such evaluation panels often an ingredient may be distinguishable as different from the current supply when assessed in isolation, but when that ingredient is present within the intended multicomponent end products the difference in performance cannot be distinguished. A business that does not consider such aspects may be missing out on potential supply benefits and cost savings. Therefore sometimes the most appropriate way to evaluate a proposed ingredient change is via review of the ingredient performance in the end product and not in isolation. Evaluation of the proposed ingredient performance within the end product may also be appropriate when the characteristics of that ingredient make it very difficult to judge objectively when in isolation. For example extra-mature blue Stilton cheese crusts may be purchased for their excellent strength of flavour when added to certain soups and sauces, but when eaten on their own some may find such ingredients overpowering. As a consequence the assessor may select the mildest sample during preference testing, which actually would not provide the same extent of flavour performance as the stronger sample once added to the end product. Some ingredients within a business manufacturing multicomponent food products may be widely used across a large number of products. With regard to sauce and soup manufacture such ingredients may include tomatoes, milks and creams which are often used as the background for soup and sauce products, diced/sliced vegetables frequently used as a particulate or blended component, and herbs/spices which are typically used to add extra flavour.

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Clearly any change to an ingredient which is used within many different products has the potential to cause widespread problems if the change is not controlled and ensured to be appropriate. For these reasons it is important when considering such ingredient changes that ‘worst case’ scenarios are considered, i.e. conducting kitchen or factory production trials upon the products which contain the highest quantities or are most influenced by the ingredient that is being considered for change. Sometimes the result may be that a new ingredient is approved for use only in recipes where it is used at a low level (e.g. below a threshold of noticeability/influence upon the KSPs of the end product). During all testing and evaluation of ingredients and their subsequent performance within the end products it is important to focus upon the aspects of the end product that the consumer would miss/notice when influenced by a change in the characteristics of an ingredient. Will the end consumer notice the change? If so, will the consumer feel that the change makes the product better or worse, or will the consumer feel that the change has made no difference at all to their enjoyment of the product? It is important to bear in mind whether or not proposed changes are going to be communicated to the end consumer. If consumers are told that there is a difference then they will expect and seek to find a difference.

11.4.5 Packaging The delivery packaging of the ingredients can serve many functions including protection from physical damage, microbiological spoilage, contamination and flavour taints over shelf-life. Alongside these key factors it is worth ensuring that the ingredient delivery format facilitates a good range of ingredient assessment, including sensory evaluation, upon arrival. Factors such as ensuring that all parts of a delivery can be accessed for inspection always need to be considered (e.g. vegetable delivery suppliers may use Dolavs or cages which can sometimes lead to certain individual ingredient packs being inaccessible until the whole load is unpacked). In addition it may be costly to open packaging formats such as vacuum packed meats for evaluation immediately upon delivery if the ingredient is not to be used until many days after delivery (as opening will allow air into the pack which will consequently reduce the shelf-life of the amount of ingredient remaining in the pack following testing). For such reasons the manufacturing operation may decide to defer a full assessment of the ingredient quality until nearer the time of use (but not so near that there would be insufficient time to deal with any problems arising from this inspection), or perhaps arrange for a smaller ‘sample pack’ to be sent alongside the main larger delivery packs. Such arrangements send a clear message to the supplier that their customer is monitoring the quality of their supply upon delivery and therefore will heighten the supplier’s focus upon ensuring full adherence to the specified quality standards.

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With regard to the final products produced, the packaging of the end product (and in particular the direct food contact packaging) has the potential to impart flavour taints to the product if the packaging is of a sub-standard quality (i.e. not suitable for food contact) or reacts with the product under certain conditions (e.g. some acidic products can be quite chemically aggressive, leading to the increased potential for taints to occur). If the product is to be cooked/re-heated within the food contact packaging then businesses should also be mindful of the potential for packaging impacts upon the end product during such heating phases (e.g. reactions occurring upon microwave heating the end product where very high localised temperatures can be reached, particularly when in the presence of foods with a high oil content as the heat conductance of such oils can cause particularly high temperature hot spots to occur and therefore increase the potential for chemical reaction/deterioration). During the initial approval of packaging for product use it is vital that a full organoleptic analysis is conducted upon the food product which has been held within the packaging in a manner which reflects the worst case scenarios and timescales of the production process, storage, distribution and consumer end use. If the nature of the product would make it difficult to ascertain whether a flavour taint was being caused by the packaging (e.g. perhaps the product is a very spicy, aromatic dish which would mask any flavour taints if present) then consideration should be given to also running trials upon more sensitive products within the packaging. Such test products would ideally have quite bland flavours and therefore could include water, mild food oil or mashed potato. The manufacturer should select the most appropriate type of test product for the packaging and intended end use. Such packaging/product tests could be conducted by a food manufacturer on a routine basis in order to form part of a packaging quality monitoring programme, and should certainly be conducted upon any proposed change of packaging specification or packaging supplier.

11.4.6 Storage It is vital to product quality consistency and safety that all product ingredients are stored in a manner which reflects the supplier’s recommendations and good manufacturing practice upon aspects such as temperature control, relative humidity and the avoidance of physical damage (e.g. stacking/compression of goods). Whilst some ingredients (including chilled cut vegetables) will typically be used within a few days of arrival on site, many longer shelf-life ingredients may be stored for weeks or months before being required for use. It is therefore advisable for a business to monitor these ingredients during their storage phase for factors including organoleptic performance, as such checks will provide advanced notice of any developing

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quality deterioration issue and therefore ensure sufficient time to rectify the matter without disruption to the business production plan. Routine checks during storage can also include review of the condition of the ingredient packaging, as damage and poor seals can allow air ingress which may accelerate spoilage, drying or oxidative reactions. An everincreasing amount of foods are now reliant upon vacuum packaging or modified atmosphere packaging (MAP) for the achievement of their shelflives and therefore a small seal failure across a batch if unnoticed can soon lead to a major failure in organoleptic performance and possibly food safety issues. Ensuring that all ingredients are held within appropriate storage conditions is a key factor in assuring their consistent organoleptic performance over the course of their shelf-life. Optimal storage conditions will benefit each ingredient and typically a multicomponent ready meal, soup or sauce manufacturing business will have chilled product stores running at below +4 °C and frozen goods stores running at below −18 °C. With regard to chilled and frozen products, high air flow conditions can significantly dry any exposed product (in frozen products this is known as ‘freezer burn’ and can be protected against through thorough containment within the primary packaging). Higher than ideal storage temperatures can encourage microbiological growth which, in addition to the associated food safety issues, can also cause flavour taints and deterioration in product texture. Further assurance of organoleptic performance can be gained by installation of recording and alarm systems upon chills and freezers to confirm that the optimum running conditions are being consistently achieved. Relative humidity could also be monitored and controlled in dry goods stores, as too much moisture within the air can lead to clumping of powders and the potential for elevated levels of microbial spoilage.

11.4.7 Ingredient shelf-life extension Occasionally in food manufacturing operations there will be circumstances where the business has a surplus of a particular ingredient which when assessed against predicted usage rates would be at risk of exceeding its site process use by/best before date before being scheduled for use. Such circumstances can occur due to over-ordering (perhaps resulting from a mistake or due to planning to predicted orders which have turned out to be unrealistically high) or perhaps due to delays in the production plan caused by line breakdowns. If it is not possible to pull forward the next production date, in an attempt to save the cost of ingredient stock losses food production businesses will sometimes seek to extend the shelf-life of the ingredients at risk of going out of date through factors such as formal review and agreement of shelflife extensions with the particular ingredient supplier, or sometimes will consider the freezing of the particular ‘at risk’ ingredient stocks which

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would otherwise have perished before the date of the next scheduled use if continued to be stored in chilled conditions. In such circumstances the use of sensory evaluation plays an important role in confirming that the decision to extend the shelf-life of the ingredient does not adversely affect to an unacceptable extent the ingredient organoleptic properties, and as a consequence does not pose a threat to the quality of the end product. It should be noted that when considering the potential for ingredient shelf-life extensions, the primary concern must always be that of product safety. For example, during the additional shelf-life required is there a risk of the safety of the ingredient being compromised? If through a combination of detailed product/process knowledge and liaison with the ingredient supplier it can be ascertained that the application of additional shelf-life would not pose a threat to food safety, then it would be appropriate to conduct a thorough sensory assessment in order to also confirm that the extended shelf life ingredient will still deliver (and not threaten to damage) the required KSPs within the final product. Shelf-life extension checks should be conducted by experienced members of the technical, quality and development teams, who have first-hand experience of the usual organoleptic properties of the specific ingredient, and are well aware of the likely signs of deterioration or spoilage. Such signs can include off-aromas and off-flavours, colour deterioration and texture changes (e.g. perhaps a change to become slimy or dry). Points for consideration during such organoleptic assessments to underpin shelf-life extensions include ensuring that a representative sample size is being assessed from the ingredient stock in question, as the early signs of ingredient deterioration may be localised and not yet widespread across an ingredient batch. Businesses should also consider the balance of ‘risk to reward’. If by applying an ingredient shelf-life extension the business is saving only a small amount of ingredient or money and has plenty of ‘within standard shelf life’ material in stock, is it worth the time, trouble and end product quality performance risk to extend the shelf-life of the ingredient stock? It should also be noted that the end customer, be it a supermarket or food service business, may have a supplier policy upon whether they authorise (or need to be advised of) the procedure of controlled extensions to ingredient shelf-lives. Therefore with regard to ingredient shelf-life extensions it is always wise for the manufacturer to check their customer policies before considering how to proceed with the best interests of all parties in mind.

11.4.8 Recipe preparation phase This processing stage typically involves the removal of the ingredients from their primary packaging and measurement into their required recipe weights to await further processing. This is therefore usually the first point

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in the production process at which 100% of each ingredient can be handled and closely reviewed. Such circumstances therefore provide a key control point for ensuring that each ingredient meets the organoleptic quality required. Some intake checks may have been deferred until the preparation phase to avoid the potential for ingredient deterioration caused by opening the packaging at an earlier stage, or perhaps a supplier ingredient delivery comprised of numerous separate batches which for further assurance of product quality all require an individual check at this stage. Quality checks at this stage could be classed as ‘vigilance’ by the factory preparation staff who should be trained to ensure that each ingredient being prepared consistently appears to meet the quality standards required. Preparation staff should be made aware that each ingredient may have been stored on site for a significant amount of time and therefore could have deteriorated since delivery. Staff should also understand that whilst ingredients may have passed an inspection upon intake, the checker at that stage is only likely to have viewed a small percentage sample of that ingredient, whereas at the preparation stage all of the batch of that ingredient can be inspected to at least some extent. It is important therefore that staff see themselves far more as a key operators who are providing a vital QA role in monitoring product quality and questioning any issues, rather than as team members who have a relatively narrow remit of only weighing ingredients. In a manufacturing business where many product recipes are being processed on a daily basis it is unlikely that production staff will be able to remember the key attributes of each specific ingredient, and it is also unlikely that production staff would have the time to cross-reference every ingredient being processed against a written specification/description. However, the relevant production staff could be trained upon an appropriate ‘top five organoleptic quality points’ or ‘key quality criteria’ for each ingredient group (e.g. meats, dairy, vegetables, fruits, herbs, spices) to enable them to be particularly vigilant during the handling of every ingredient. Such quality check points could include: Does the ingredient match its name given upon the recipe sheet? (For example, Does the ingredient look like 10 mm Diced Streaky Smoked Rindless Bacon?) Are the appearance, colour and aroma as expected? Most companies prefer their operators not to taste test the ingredients during the processing operation as such practices can be linked to poor hygienic practice and can set a poor example to other staff. However, in some circumstances a taste check will provide a vital point of quality assurance and therefore each business should decide upon the appropriate amount of taste testing for their particular operation and the location at which the taste testing should take place. Potentially such testing could take place within a designated area of the factory, perhaps a tasting table/booth could be set up in order to further highlight to staff that the tasting of

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ingredients forms a key part of QA and should only be conducted in the designated area and at the appropriate stages of the process.

11.4.9 Work in progress (WIP) storage Manufacturing operations need to ensure that the selected prepared ingredient storage methods do not have an adverse impact upon the organoleptic performance of the ingredient. Often manufacturers of multicomponent foods will ensure that the ingredient (or ingredient mix) is stored in lidded food grade plastic containers. The term ‘food grade’ expects that the supplier of the container has selected/tested the material to ensure that it does not impart any flavours, taints or chemical compounds detrimental to the safety or quality of the ingredients to be contained. Clear labelling is important at this stage as the primary packaging is often no longer present and the ingredient will therefore usually have a reduced ‘prepared shelf-life’ which should be recorded upon the containers together with identification of the ingredient and the destination product/batch. Usually the prepared shelf-life of each ingredient is kept as short as possible to help ensure the ingredient’s quality at point of use. Whilst the quality monitoring of work in progress (WIP) is often confined to routine checks upon the prepared ingredient storage areas to ensure that the required holding conditions are being maintained and that none of the ingredients have exceeded its ‘prepared shelf-life’, sensory assessment will be required typically in the event of an ingredient quality query, or in the circumstances where the standard ‘prepared shelf-life’ has been exceeded (perhaps due to production delays or breakdowns) and the factory therefore requires a decision upon whether the prepared ingredient is still acceptable for use. Where shelf-life extension is to be considered the primary consideration must always be that of product safety. The points documented within Section 11.4.7 are equally as relevant in these circumstances at the WIP production stage.

11.4.10 Processing: mixing and cooking operations As with the preparation stage, the mixing/cooking stage is also a phase in the operation at which staff will have the opportunity to review and inspect all of the ingredients to be used within the production batch. This processing stage therefore provides another key QA point via monitoring that each ingredient meets the organoleptic standards required. Staff who are trained in the sensory review of ingredients at this stage will be of great advantage to the food manufacturing operation. Ensuring that each ingredient to be added to the batch is of the quality standards required is a key element of a food manufacturer’s QA system. During their training the processing staff should be briefed upon the need for ingredient quality awareness at all times, and the importance of

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not assuming that the ingredient quality is bound to be correct just on the basis that the ingredient has passed other check points to reach this particular stage of the process. Often it is beneficial to highlight to processing staff that if they do not spot an ingredient problem prior to use, that ingredient issue could then lead to an end product quality fault which may not be traceable back to that particular raw material fault, and consequently may leave the process operator open to suspicion that they may have incorrectly processed the product. Such an approach can help ensure that operators remain vigilant and always question any ingredient quality issues that they are not entirely sure upon. At the cooking/mixing stage there are a lot of ingredient physical and chemical interactions occurring (e.g. Maillard reactions, blending or softening of particulates, formation of oil-in-water emulsions). In an ideal world applying the same cooking/mixing process (times, temperatures, mixing/ blending speeds, etc.) would result in exactly the same end product on every occasion. However, as most food production operations are dealing with natural ingredients which can vary in variety, source and season, and also vary in factors such as their temperature and age upon addition to the product mix, such variations in the ingredients will often lead to variations in the processing performance of the end product. It is also not uncommon for manufacturing sites to possess a variety of processing equipment which can be used to produce the end products (e.g. dicers, slicers, mixers, blenders, homogenisers, cookers, packing machinery, chillers), and yet depending upon which equipment is available/selected, there can be variability in terms of the end results achieved (for example, mixers, agitators and pumps will vary in their degree of damage caused to the food product). As a result of the variables detailed above, in order to ensure that the end product meets the required sensory standards (e.g. appearance, aroma, taste, texture, viscosity), ideally the product sensory description will have been written and agreed in a form which allows for an acceptable range of product variability from one batch to the next. For example with regard to soups and sauces the colour reference may allow a shade either side of the ideal colour, and the product viscosity may allow for a set amount of deviation from the standard target. As variation is a fact of life for most processing operations handling natural ingredients, the processing team often have to assess the product at key stages within the production flow and make adjustments to the processing parameters in order to ensure that the desired end result is achieved. Such checks and corrective actions typically involve sensory assessment of the product/batch at the point of phase completion and before progression to the next stage of the operation (typically a cooling or packing stage). A sufficiently sized sample should be drawn from the most appropriate points in the batch (if the production batch is known to vary at certain points then all such points should be assessed) and the product should then

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be evaluated by the production operator or nominated quality assurance operative against the appropriate product sensory description (see Fig. 11.1 for an example sensory description for chicken tikka masala). The selected assessor should have been trained and screened in advance to confirm that they are capable of end product sensory analysis. Such staff training would typically take the form of the measures outlined earlier in this chapter. Upon sensory assessment, if the product meets all of the defined requirements then it can be allowed to progress to the next process stage. However, if a non-conformance is raised at this stage then corrective action will typically be required. Corrective actions in soups and sauces may include: • • • •

addition of water if the batch is too thick; extra cooking if the batch particulates are too firm; extra homogenisation if the batch texture is too coarse; addition of more thickening agent (e.g. starch) if the batch requires greater viscosity; • addition of extra particulates if the product texture has broken down. Many such corrective actions have an impact upon the product recipe and consequently the ingredient declaration. It is therefore prudent to have agreed the appropriate and acceptable corrective actions with the relevant authorities and customers in advance. Any product adjustments should also be logged upon the process records to ensure full traceability. A similar approach can also be taken when assessing the results of intermediate stage processes (e.g. pre-frying minced beef before addition to a bolognaise sauce, or perhaps pre-blending a starch/powder slurry mix before addition to a batch of soup). As previously mentioned, such assessments will require specific sensory standards to be written. At their simplest these standards could be one-line reminders placed upon the process sheets (for example ‘Check that the slurry mix is lump free before addition to the batch’), or could be a more complex full sensory description (e.g. appearance, aroma, taste, and texture guidance for a batch of par-cooked pilau rice which will then complete its cook at a later processing stage). As such product assessments and adjustments can be quite time consuming, and are typically conducted by production staff whose Key Performance Indicators often include ‘speed of operation’/‘throughput rates’, it is therefore important that these production operatives are encouraged not to rush their work related to QA. Production staff are often time pressured to complete the current product and move on to the next product in the plan. However, it must be stressed to the operators that the primary concerns of their role are the safety and quality of the food products and that therefore it is far more cost effective to take a little extra time to get the product ‘right first time’. Any batch recipe or process corrections/adjustments should be formally fed back to the team member responsible for setting the production process/

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production sheets (typically a ‘process technologist’) as the requirement to routinely have to adjust a particular product may suggest that the standard production process requires a permanent adjustment or re-trialling in order to increase the chances of first time success, and avoid the requirement for time-consuming corrective action every time that particular product is made. It is very important that where an operator has highlighted that there has been an issue which required ‘in-process correction’, the person responsible for adjusting future productions to avoid a recurrence of the issue should always feed back to the operator what corrective measures have been taken. The production operator will then feel that their feedback has been valued and will be focused upon highlighting other opportunities for improvement in the future. In circumstances where operators feel that their feedback is not listened to or that ‘we always have problems on this product’ there is a danger of acceptance of less than ideal quality standards and a loss of quality focus over time. With regard to the physical provision of sensory descriptions to the factory operators for reference against when conducting their product sensory checks, processing operations take different approaches, each of which can be effective providing that they are managed and monitored carefully with close focus upon document control and issue of updates as and when required. Common approaches include: • printing a summary of the product sensory description upon the relevant production process instruction sheets (e.g. the mixing/cooking sheet); • holding copies of all product sensory descriptions in files within the relevant processing areas; • maintaining an electronic database of sensory descriptions which can be accessed at a computer terminal or printed out when required. All batch assessment checks should be recorded to maintain full traceability within the operation and help prove in the event of an issue/ complaint that the batch was correct at that particular point in the operation. In addition the recording of such results and any further corrective actions can also be used to trend analyse the product over time for any routine problems or seasonal variances. (For example, perhaps variation with regard to colour, flavour or texture which could be linked to seasonally varying produce. If the extent of such seasonal variability is unacceptable then a solution may be to opt to use a frozen version of the variable ingredients, which has been harvested and frozen at one set point in the year, and is then available to be used consistently all year round.) In batch production processes, where a number of separate batches of the same product are to be produced sequentially, a technique that is often also used during sensory assessment is that of maintaining a reference sample of each of the previous batches for review against as each new batch is made. Such comparison enables a check to ensure that the product performance is not gradually drifting away from the required standard.

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11.4.11

Delays/holding times upon completion of the batch (or at key stages of the batch/product process) In many production processes, following operations such as cooking/mixing, and prior to packing there is the potential for a delay/holding time between such process stages. Sometimes this amount of time needs to be closely controlled to help ensure that the final end product quality will not be adversely affected. For example in ready meal manufacture, rice which has been cooked and chilled will ideally be used immediately but may be stored in a refrigerated area for approximately 24 hours before packing as a component into the final ready meal packs (e.g. ‘Sweet and sour chicken with egg fried rice’). In such a scenario there is the potential during this holding time for the rice to become dry/hard, especially if it has been stored uncovered and placed in a chill store area which has a high air flow. It is therefore important not only to set an optimised storage method and maximum amount of time before the component can no longer be used (in ready meal production such limiting of holding times can be as much related to product microbiological control as to product organoleptic control), but also to ensure that there is a confirmatory organoleptic assessment upon the component appearance, aroma, taste and texture before further processing/packing. At this stage if the component quality is found to have deteriorated beyond an acceptable point, the business would incur further significant product, time and packaging cost losses by continuing to process the product, only to realise later at final product analysis stage that a specific component has caused the entire product to be organoleptically unacceptable. Well-timed sensory checks serve to ensure product quality and help avoid incurring unnecessary costs by highlighting faults as early as possible.

11.4.12 Product packing The process of packing the product following the recipe mix/cooking operation can have significant impacts upon the product’s organoleptic performance. For example, when manufacturing soups and sauces the products will typically have been transferred from the recipe mix/cooking operation via a series of vessels, pipework and pumps (which may or may not have been designed to handle the product as gently as possible) and will often then be held in an agitated vessel during the packing process in order to ensure a consistent mix/blend. In addition, if the products are being hotfilled into the packaging then the products will be hot and therefore continuing to cook whilst awaiting being packed into the final product packaging. Heat and agitation factors will lead to organoleptic effects such as the softening of particulates, deterioration of starches/gels, colour and viscosity changes and therefore often the process technologist who has scaled-up the product will have set a maximum batch size related to the rate at which the

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product can be packed. (This packing rate is usually dictated by the speed of the packing machines, and sometimes by the cooling/freezing capacity of the operation post-packing.) The technologist may calculate and set such maximum holding times by organoleptically assessing trial batches during the course of the batch packing operation until the point is reached where the product has deteriorated past a point of acceptability. Any delays in the packing operation (perhaps due to machinery breakdown) may incur further deterioration of the food. In order to reduce the adverse impacts of such delays upon the end product in soup and sauce operations, factories will often choose to turn off the agitation of the batch during the delay to reduce the physical impacts upon the product, and where batches are held hot, a factory may also choose to reduce the holding temperature during the time of the delay (whilst ensuring that the temperature reduction does not fall below a point where microbiological growth may become an issue). In the event of delays, food businesses will benefit from use of a ‘delay procedure’ (a summary of all of the actions required to be taken in the event of a process delay) which not only seeks to minimise the organoleptic impacts upon the product, but also ensures that the product quality is monitored closely during the delay (typically via the requirement for routine product assessments conducted by key staff) in order that the factory is quickly aware of when the delayed batch has gone past the point of being acceptable to pack. Where particulate deterioration in soups and sauces is a key concern, samples of the product may be routinely sieved to enable closer visual examination of the particulates. Sometimes colour change may be the main concern, requiring routine comparison against colour charts during the course of the delay. The physical packing process through vessels, agitators, pumps, pistons and pipework can also have product quality impacts, which will be further magnified if a significant proportion of the product is being recycled within the process, perhaps due to product being reworked back into the batch pre-packing/holding system (e.g. if the packing of the batch is encountering machine problems causing a high level of pack weight or seal integrity rejects which are then being reworked to save wastage). The significant scope for product organoleptic variation at the packing phase usually leads to businesses placing a great deal of organoleptic scrutiny upon samples drawn from the final product at the start, middle and end of the batch. Sometimes such checks are conducted even more frequently/ throughout the packing operation if particular problems are being encountered; for example, excessive variability in particulate distribution which therefore requires further analysis to monitor and ascertain the root cause of the issue. In soup/sauce batch production, focus upon a ‘distribution issue’ may necessitate routine ‘washouts’ of packs to ascertain the consistency of distribution of particulates across the packed batch (i.e. whether all of the

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product components are evenly present throughout the batch). Some particulates may sink or float when the batch is being packed which can lead to their being present mostly at the start or end of the batch. Also if certain larger size particulates are becoming blocked in the feed pipes/dosing pistons this may lead to their sporadic distribution as every so often the pressure may build up until a burst of those stuck particulates are finally dosed into the packaging.

11.4.13

Pre- or post-packing ‘cooling/chilling’ phases and the sensory evaluation of the end product Organoleptic deterioration is also a consideration in the product cooling/ chilling phases. For example, if the product is cooled in an open state, the chilled air flow that it is exposed to may cause some drying of the product, whereas if a product is cooled in its packaging, although this may protect the product from the drying effect of chilled air flows, the product can sometimes be found to take longer to cool due to the insulating effect of the packaging, which can also cause organoleptic changes. In addition, some cooling/chilling operations may require the product to be agitated, or may require the outer edges of the product to be crust frozen. Such aspects of cooling processes will also impact upon the organoleptic performance of the end product. Only after the product has been packed and cooled can a business start to be confident that its organoleptic quality tests are assessing the product in a form that the end consumer is likely to experience. Therefore the final product sensory evaluation of aspects including appearance, aroma, taste and texture is a very important stage of the business QA system. At this stage businesses will often evaluate a number of samples drawn from the start, middle and end areas of the production batch, the number of which should reflect the potential for variability within the production process. Food manufacturers will typically ensure that these checks are conducted immediately upon the completed end product by an experienced member of the cooling/packing teams or by a QA technician.

11.4.14 Taste panel Most food manufacturers will also place all recently completed products upon a routine (often daily) business taste panel which is attended by a multidisciplinary cross-section of the business staff and management. It is usually beneficial for the end product taste panel to include members of the NPD department, process development and sales teams, as these team members will often be able to closely remember the customer’s expectations of the particular products and how these expectations should be reflected within the KSPs of each particular product. Also having members of the factory production and QA teams present at the formal end product

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taste panel will help ensure a good transfer of product sensory knowledge and provide the opportunity to discuss any particular processing issues/ problems being encountered. Most end product taste panels will assess a representative sample of each production batch against the sensory description (Fig. 11.1) set either internally or with the end customer. Some businesses choose to score the attendee’s assessment of each batch, while others choose a simple ‘pass/fail’ approach. The more data that can be captured at this stage, the greater the potential for trend analysis of results over time. Such results can be used to drive business focus upon product quality (e.g. trend analysis of end product taste panel results may show a gradual deterioration in a product’s colour or texture over time which, owing to the gradual drift in standard, may not have been picked up by the individuals regularly attending each taste panel). There is also a good case for insisting that during the taste panel product assessment phase, every panellist should evaluate each sample in silence or isolation from other panellists to ensure that their judgement cannot be influenced in any way by more dominant, opinionated or senior members of the panel. Potentially some panel members may be biased in their opinions upon the acceptability of products due to pressures such as production throughputs, financial impacts or customer demands. As the end product taste panel will typically be the most thorough organoleptic evaluation that the product is going to receive on site, businesses should seek to ensure that the taste panels take place before the particular batches of products are due to be despatched to the customer. This will ensure that if a product is found at the business taste panel to be unacceptable (or in need of further scrutiny), then the product will still be within the control of the business, rather than incurring the difficult situation of having to consider a withdrawal/recall from the distribution chain or customer.

11.4.15 Freezing If the end product is intended to be sold in a frozen format then there are often extra organoleptic factors to be considered and monitored via the use of sensory assessment during the product/process design phase and the subsequent production quality assurance phase. These considerations include the fact that the freezing of food products will form ice crystals within the food. Typically, the slower the freezing process, the larger the ice crystals formed and therefore the greater the potential to damage the food product structure (including deterioration of the physical texture of particulates and damage to product starch/gel suspensions which can lead to excessive product syneresis upon defrost). Other organoleptic quality issues that can arise at the product freezing phase are the potential for ‘freezer burn’ (quality deterioration typically

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caused by product dehydration and oxidation, often linked to the product not being sufficiently wrapped in protective packaging) and also the potential for product quality loss during storage, which is especially a concern for products which are particularly susceptible to deterioration (for example, high-fat meat products can be particularly prone to rancidity during frozen storage). Poorly maintained freezer stores can also increase the potential for product deterioration due to significant fluctuations in air temperature.

11.4.16 Storage (including monitoring over shelf-life) Business efficiency pressures to produce larger product batch runs less frequently can lead to end products being held in storage for a significant amount of time pre-despatch to the customer. During this holding time most products organoleptic performance will typically be deteriorating. This is especially so if the product bears a ‘use-by’ code rather than a ‘best before’ code (e.g. usually chilled products). Most chilled foods will deteriorate during storage due to a combination of microbiological, physical and chemical factors. Frozen foods may deteriorate due to ice crystal formation and aspects such as oxidative reactions and freezer burn. Ambient products could be susceptible to the absorbance of moisture from the environment over time (especially if not packaged in robust gas and moisture barrier packaging materials). External factors can increase the potential for such deterioration to occur, including higher than ideal chill store/frozen store temperatures. As a result of the potential for product deterioration during storage some businesses choose to conduct a sensory evaluation of the stored (stock holding) batches on a routine basis and at a frequency which reflects the potential for deterioration to occur. Such checks are important as it is better to be aware of the deterioration of a product batch early in order that a fresh batch run can be planned in time, rather than awaiting receipt of a customer order, only to then find during final quality checks at point of despatch that the product does not meet the quality criteria required due to deterioration which has occurred during storage.

11.4.17 Despatch Some food companies tend to focus their taste panels on the final product just before despatch to the customer. At this stage there is little that can be done to rectify any product problems which may have occurred earlier in the production process; however, such checks serve as a useful quality control check point and provide the major control of avoiding any substandard products from being despatched to the end customer. For these reasons the final assessment of product organoleptic performance (compared against the requirements defined in the agreed customer

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specification) is typically seen as the final ‘quality safety-net’ prior to despatch.

11.5 Sensory quality assurance (QA) after product despatch 11.5.1 Distribution depot and in-store inspection Sensory checks upon the product are likely to continue even after it has been despatched, usually in the form of ‘depot checks’ (typically conducted upon delivery as part of the customer acceptance checks) or in-store/stock inspections conducted by the customer. By having a clearly defined and agreed sensory description/specification for each food product, quality queries and problems at depot and in-store can be reduced. Without agreed product standards the manufacturer risks the potential for their foods to be judged and possibly rejected purely on subjective assessments.

11.5.2 End-of-life assessment/review On a routine basis many businesses review the organoleptic performance of their products at the very end of the product shelf-life as part of their QA program. These tests can involve comparison against newer stock of the products and focus upon comparison against the agreed/specified sensory profile (which should define the target and limits of acceptability upon aspects including product appearance, aroma, taste and texture). Typically such assessments will involve the use of a multidisciplinary panel including members of the quality, NPD, process development, production and sales teams. To best reflect the actual conditions that the product batches will encounter during distribution and consumer storage, businesses should seek to hold their end-of-life samples in temperature conditions which reflect the end customer shelf-life testing criteria, and this is most easily achieved through the use of storage incubators. However, many businesses choose to simplify this approach by purchasing samples of their products from the relevant retail/food service outlets, storing them in domestic fridges (set to temperatures recognised by industry research to reflect typical consumer fridge conditions) for the remaining days of their shelf-life, followed by taste panel evaluation on the last day of the shelf-life to confirm that the organoleptic shelf-life appears to be set correctly (i.e. the product quality has not yet deteriorated to a point of being unacceptable to the end consumer). As with the routine daily site taste panels the businesses may choose to score the attendee’s assessment of each ‘end-of-life’ sample whereas others choose a simple ‘pass/fail’ approach. The results can then be trend analysed over time and reacted to in the event of a problem issue being noted.

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11.6 Conflicts of interest Whilst organoleptic performance is very important to the commercial success of the product, the vital issue of ensuring product safety can sometimes lead to compromises being necessary with regard to the achievement of an optimum organoleptic quality. For example, many food products require cooking for a ‘longer than organoleptically ideal’ length of time to achieve the required levels of microbiological reduction, thereby achieving product safety and shelf-life, and in doing so the process may adversely affect the texture, consistency or colour of the product. Another example is that product acidification to help achieve microbiological control and increased shelf-life can consequently impact upon the flavour profile of the product and make product consistency (e.g. some soup/sauce starch suspensions) more prone to deterioration. As the customer and business consequences of a food safety issue can be catastrophic, it is vitally important that all departments work to the same site priorities, always ensuring product safety first whilst striving to attain the required product quality/consistency.

11.7 Conclusions As can be seen by the numerous examples given in this chapter, the manufacture of multicomponent food products such as soups, sauces and ready meals presents a wide range of potential for faults to occur with regard to the quality of the products being manufactured. It may be useful for the reader to reflect upon the use of sensory evaluation as a tool for assuring quality at each stage of the food production process. Such sensory evaluation in support of quality assurance could be categorised in distinct phases which include: • avoid: sensory techniques to help avoid product faults (e.g. checks to confirm the correctness of incoming ingredients); • detect: sensory techniques designed to detect product faults (e.g. checks upon the organoleptic quality correctness of intermediary and end products); • decide: sensory techniques to help make decisions and resolve issues (e.g. sensory assessments and panels can be utilised to help decide whether a product quality fault contravenes the product specification and/or renders the product unacceptable to the end consumer. Sensory panels can also help a business decide whether the reprocessing/reworking of excess or sub-standard product batches is a possible salvage/cost saving option, or whether such actions will merely result in creating further non-saleable stock). The key themes of benefit to businesses seeking to develop sensory control include:

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• focus upon ensuring the correct and consistent quality of all ingredient supplies; • ensure that all manufacturing processes are well maintained, robust and consistent; • continually seek to make sensory checks objective rather than subjective wherever possible; • always ensure that the sensory criteria required of the product are realistic and consistently achievable before being formally agreed/ committed to with the end customer; • ensure that the product sensory criteria are clearly defined within the agreed customer product specification, together with all acceptable tolerance limits; • constantly monitor (via supervisory checks and audits) that the required sensory standard checks are diligently and consistently applied at each key stage of the manufacturing process. The application of sensory-related quality assurance techniques within the ready meal, soup and sauce sectors can be viewed as a vast and complex area, yet the reader should be assured that the majority of the systems and checks applied are low cost, relatively straightforward in their design and application, and are best administered by trained staff who have sound experience of the products and good clear knowledge of the customer expectations.

11.8 Acknowledgements BOSTWICK CONSISTOMETERS. Christison Particle Technologies Ltd, Albany Road, Gateshead NE8 3AT, UK http://www.christison.com/ http://www.consistometer.com/ BROOKFIELD VISCOMETERS. Brookfield Engineering Laboratories, Inc. 11 Commerce Boulevard, Middleboro, MA 02346-1031, USA http:// www.brookfieldengineering.com

11.9 Sources of further information betts, g.d., brown, h.m. and everis, l.k., 2004. Evaluation of Product Shelf-life for Chilled Foods. Chipping Campden: CCFRA. british standards institution, 1989. British Standard methods for sensory analysis of food BS 5929. London: BSI. brown, m., 2008. Chilled Foods, A Comprehensive Guide. Combridge: Woodhead. campden & chorleywood food research association, 1996. Product Development Guide for the Food Industry. Chipping Campden: CCFRA. fellows, p., 1990; 1988. Food Processing Technology Principles and Practice. New York, London: Ellis Horwood. hutton, t., 2001. Food Manufacturing: An Overview. Chipping Campden: CCFRA.

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ishihara, s., 2004. Ishihara’s Tests for Colour Deficiency. Tokyo: Kanehara Trading Inc. kemp, s., hollowood, t. and hort, j., 2009. Sensory Evaluation: A Practical Handbook. Chichester: Wiley Blackwell. knight, c., stanley, r., jones, l., campden & chorleywood food research association and royal agricultural society of england, 2002. Agriculture in the Food Supply Chain: An Overview. Chipping Campden: CCFRA. saxby, m.j., 1993. Food Taints and Off-flavours, 1st edn. London, New York: Blackie Academic & Professional; Chapman & Hall.

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12 Sensory quality control in the wine industry S. A. Langstaff, Applied Sensory, LLC, USA

Abstract: Today there are more fine wines in the world, and in greater variety and of higher quality, than ever before in history. Wine is made in many different kinds, flavors and levels of quality. In addition to winemakers, there are others in the industry who are responsible for evaluating wine quality – including wine writers and critics, sommeliers and wine merchants. This range of backgrounds and the complex nature of quality are also reflected in a diversity of views on the topic. However, quality may be determined if there is precise definition and agreement as to what attributes constitute quality in a particular product. Key words: Davis 20-point scorecard, intrinsic and extrinsic factors, descriptive analysis, principal component analysis, sensory ‘drivers’ of wine quality.

12.1 Introduction Wine is a very complex liquid that is much more than just a dilute alcoholic solution. It is one of the only natural beverages capable of offering a multiplicity of complex smells and flavors that can be identified; truly great wines can be characterized by complexities and facets that are beyond the capabilities of descriptive language. The fascination with wine is perhaps attributable to the intricacy of its making, the diversity of results and the pleasure that it brings. The varieties of grape, the soil they were grown in, the weather that year, the yeasts that ferment them, the skills of the winemaker in handling them, the years they spend in oak or glass: all these elements and more enter into the quality of a wine and merit consideration. Quality assessment is an essential part of winemaking. Traditionally, the evaluation of wine quality has been performed by winemakers, who have training and experience to detect defective wines and to craft wines according to specific styles. Maynard Amerine, a professor in the Department of

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Enology and Viticulture at the University of California, famously claimed that ‘quality in wines is much easier to recognize than define’ (Amerine and Roessler, 1983). The influential French enologist, Emile Peynaud, suggests a definition of quality: A very simple, obvious and very clear definition is this: ‘The quality of a wine is the totality of its properties, that is to say the properties which render it acceptable or desirable.’ In effect it is the totally subjective pleasure provided by drinking the wine which conditions judgment (Peynaud, 1987).

The precise concept of quality or the degree of excellence of a wine is adjusted to the nature of the object being evaluated; ordinary wines and great wines are not judged by the same standards. Inexpensive, everyday ‘jug’ wines are judged acceptable when they are simply free from defects, and standards of quality for such wines are typically associated with the statutory enforcement of minimal standards. When we define quality in fine wines, we move out of the laboratories where wine defects are monitored and touch the realm of aesthetics – the branch of philosophy concerned with the nature of beauty, art and taste. In the appreciation of quality in wines, as in the appreciation of any aesthetic creation, a learning curve is involved. At first products are liked which are easily recognized and understood. Later, with more experience, we demand greater complexity in foods, music or art for aesthetic appreciation – just so with wines. Quality in wines is associated with complexity, and this is not easily achieved without special techniques. Complexity means that the wine has a very large number of pleasing aromas and flavors. Great wines can be savored repeatedly without losing interest. They can stimulate one’s palate, mind and heart. Great wines must have a harmonious, wellbalanced combination of sensory elements: each component belongs and is present in the correct amount relative to the other components. As a harmonious wine is tasted each sensory feature is revealed and is congruous with the ones that preceded it. There are no inconsistencies in the wine’s complexity and intensity of color, aroma, flavor and aftertaste. The wine is experienced as a well-integrated whole. The appreciation of quality is in the consumer, and a consumer’s experience with wines will determine their appreciation of them. Wine appreciation demands quality in the wine and critical experience and evaluation from the consumer.

12.2 Historical perspective Wine has been around for thousands of years; perhaps beer is the only older fermented beverage. Wine was important to ancient peoples because it was less likely to be contaminated than water and the pleasant effects were early noted. The effects of wine were somewhat quicker and greater than those

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of beer, a beverage of lower alcoholic content. Wine was a social beverage used for feasting, celebrating and entertaining guests. The great variability in quality and the type of the wine naturally led to rating and selection. The best wine was used for auspicious occasions or esteemed guests whereas the poor or ordinary wine was for everyday use. The use of wine in ceremonials was prominent in all early religions. The need of the Church for wine for sacramental purposes was an important factor of the preservation of the wine industry through the Middle Ages. Many of the important wine districts of Europe were either discovered or preserved by monastic organizations. In Bordeaux and Germany, where large monastic orders owned extensive vineyards and developed winemaking to a much higher level, wine was made for not only sacramental purposes, but also used as part of the diet and sold as part of the income of the monastery. Not only did the monasteries preserve the growing of grapes and the production of wine, but they also, for the first time, made a point of regional classification of wine. Since the monasteries had some stability, they could keep wines over a period of years, especially now that wooden casks were available. Some monasteries kept good records and this made it possible for them to classify wines and to compare them with wines from other districts. As the Middle Ages became more politically stable, trade developed and wines were shipped to various parts of the western world. The international trade in wine had an important effect on standardization and classification of wines. When wines were shipped long distances for foreign trade, it was necessary to have some guarantee of their quality. This forced the producers into classifying their wines and selling at prices appropriate to the quality. Wine became an item of commerce, with appropriate quality judgments, records and connoisseurship.

12.3 European standards of wine quality In many wine regions in Europe, quality wine is not only an expression widely and loosely used for any wine of good quality, it is also an official wine designation throughout the European Union. These ‘standards of quality’ have been defined by a long history of market approval resulting in a consensus of what is ‘typical,’ or ‘correct,’ for a given wine type in a given region. The European Union recognizes quality wine as the higher of its two general categories of wine: quality wine (which must be produced in a specified region) and table wine. If a wine is labeled geographically, the label usually carries not just the name of the region but also the name of one of the carefully regulated quality designations. Each member country has a different system to describe this designation. The quality designations specify very strict regulations about the exact land included, permitted

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grape varieties, yields, minimum ripeness levels, and grape growing and winemaking techniques allowed. Assessing quality within regional appellations makes tastings simpler, but this does little to promote quality improvement. When tasting concentrates on artistic quality, rather than stylistic purity, comparative tasting can be revealing. Comparative tastings are more popular in the UK and the New World, where artistic merit tends to be considered more highly than compliance with regional styles.

12.4 The concept of wine quality There are a series of related difficulties around the idea of wine quality. The first focuses on the form which a consumer’s perception of wine quality takes; this would be a psychophysical response and is concerned with whether the consumption of wine evokes a sensory reflex or cognitive analysis or an affective (emotional) reaction. Another issue is to know whether there are any common features in the way that different tasters evaluate the quality of a wine and, if so, what those features are. The third issue is whether or not our judgments of wine have an external validity, or if they are purely personal. Is wine assessment an objective or a subjective process? Generally, quality is related to satisfaction and occurs when the perception of the performance of the product meets the user’s prior expectations of that product (Parasuraman et al., 1988). These prior expectations of wine quality are based on extrinsic (brand, packaging, etc.) and intrinsic (flavor type and intensity) factors. However, if during product consumption, the intrinsic factors do not match the consumers’ perceptions of the extrinsic factors then the consumers may be disappointed and may not purchase the product again. Combris et al. (1997) has shown that the hedonic price of Bordeaux wines is essentially determined by the extrinsic factors while the quality scores given by wine expert panels is essentially a function of the intrinsic factors. In sensory science, the perceived intrinsic quality of a product is described in several ways. According to Lawless and Heymann (1998) ‘quality is the absence of defects.’ In this case the ‘lack of defect’ is likely to be true for most commercially produced wines yet there are clear differences between the sensory attributes of a simple inexpensive non-aged red wine and a complex oak-aged red wine. In some sense, these differences in the complexity of the sensory attributes, as perceived by a trained panel, should translate into a difference of perceived quality; however, the definition that quality is a lack of defects does not allow this. Another school of thought is that quality may only be evaluated by experts who have had long term experience with a wide range of the qualities of the products within the category. Lawless and Heymann (1998) found that when other wine tasters

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were asked to rate quality, the products were scored in terms of their subjective liking. The problem becomes finding and evaluating these ‘experts’ who have had long-term experience with both defective and non-defective wines. In the case of coffee and tea tasters, there are apprenticeships within these industries to educate the quality evaluators in terms of the qualities found in the product category. Owing to the extensive training, it is likely that the quality evaluation of successive tea tasters would be quite similar. In the wine industry, experts for wine competitions are frequently wine writers, winemakers, wine educators or chefs. These experts would not have had a similar ‘apprenticeship’ in evaluating wine quality and their judgments could be very dissimilar. Studies of reliability of wine expert quality scores have been undertaken and some recent examples are Gawel and Godden (2008), Hodgson (2008), Scaman et al. (2001) and Vaamonde et al. (2000). There is great interest in finding ways to determine wine quality. Ough and Winton (1976) and Kwan and Kowalski (1980) studied the scores of experts using wine quality scorecards; however, they did not compare these results with hedonic scores or with descriptive analysis data. Lawless et al. (1997) compared wine quality scores (using a 20 point scorecard) among three experienced and one wine consumer panels. They found that the experienced panels agreed well while the consumer panel was less reliable. Scaman et al. (2001) analyzed the data obtained from the 20-point quality scorecard for similarity using principal component similarity analysis and found that the technique was successful in classifying judges. Assessing wine quality requires a significant degree of training and experience on the part of the taster. Currently, the wine industry in the United States has no commonly accepted ‘tests’ to assure a certain degree of tasting expertise. However, the Australian Wine Research Institute (AWRI) conducts a 4-day Advanced Wine Assessment Course yearly. This course was developed to further train experienced wine tasters working within the Australian wine industry in the skills associated with formal show judging. For quality scores to be of value, an expert taster should first demonstrate an ability to reproduce that quality score over repeat assessments of the same wine. There is a dearth of literature on the repeatability of wine expert ratings; trade publications do not report these data. However, Gawel and Godden (2008) found that the ability of experienced Australian wine tasters to consistently rate wines for overall quality varied greatly between individuals, but was generally better for red wines than white. Hodgson (2008) surveyed approximately 65 wine judging panels between 2005 and 2008. Only 30 panels achieved anything close to similar results, with the data pointing to ‘judge inconsistency, lack of concordance – or both’ as reasons for the variation. Judges tended to be more consistent in what they did not like than what they did. An analysis of variance covering every panel over the study period indicated that only about half of the panels presented awards based solely on wine quality.

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Enologists are attempting to use scientific, analytical processes in order to try to predict wine quality ‘objectively.’ Grapes are being analyzed by near-infrared spectroscopy or by a technique known as G-G assay to determine their composition in an attempt to predict how a wine will turn out (Goldberg, 1997). Instrumental analysis has been linked to the final quality of wine by means of descriptive analysis (Abbott et al., 1991). All of these methods make unexpressed assumptions about what wine quality actually is. They are also based on grapes; the process of moving from grape quality to what is actually sold in the bottle is still multi-stage and subject to many variables. A number of scientists are seeking to analyze the finished product in chemical terms in an effort to determine its quality. An overview of such a perspective was given by Acree and Cotterell (1985). Unlike many others starting from a scientific perspective, they did offer a definition of wine quality as ‘an estimate of its aesthetic worth by a particular group of humans.’ They suggested that faults in wine – which are susceptible to chemical analysis – are only half of the issue of quality. The other is that the wine must offer aesthetic value to those consuming it. Clementi et al. (1990) used descriptive analysis performed by enology students and quality evaluation ratings by experts to optimize Chianti wine quality by response surface methodology. They claimed that the optimization of sensory characteristics enabled quality to be put on a quantitative scientific basis. McCloskey et al. (1996) had a panel comprising wine industry quality experts use a modified descriptive analysis procedure to evaluate Chardonnay wines from different California appellations. They found that the detection of regional ‘typicalness’ by professionals was detected in high-quality wines linked to grape attributes more than to winemaking attributes. A more precise, although complex, attempt to link sensory wine quality to an analysis of the wine’s chemical composition has been offered by Somers (1998). Somers argues that the quality of red wine can be predicted using UV spectrometry to analyze the total phenolic makeup of the wine within the context of its relationship to sulfur dioxide. His argument is complex and has not so far been widely adopted by the industry. Other consultants have sought to use other methods of chemical analysis to predict wine quality. In the United States, a company called Enologix has produced a computer program to measure the ‘quality metrics’ of wine, using a database of existing wines to compare the chemical makeup of a sample and predict its likely market position (Penn, 2001). They define wine quality as the ‘color–flavor–fragrance intensity of a given wine with respect to all the other wines in its appellation.’ This, again, is a production-led definition of quality and at present is no more than a guide to likely market position rather than the consumer’s perceived quality of the wine. The evaluation of wine quality is ultimately a sensory process. In summary, it can be noted that the wine industry differs on whether or not the evaluation of wine quality is a subjective or an objective process. A

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series of terms have been adopted both to grade quality and to benchmark it, but even on these there is no common agreement. Gawel (1999) has researched the process of wine show judging and he notes that each judge’s view of the quality varies slightly from the rest; some common markers for quality are used, but how they weight these will vary from judge to judge according to their background, experience and personal perspective.

12.5 Attempts to standardize wine quality evaluation While the subjective element of wine quality evaluation tends to focus on the activity of aesthetic engagement with wine, the objective element tends to focus on defined dimensions of quality. Wine may be approached systematically, with a checklist of points to be considered and/or a benchmark of what the wine should be like against which it can be evaluated. Such systems give these evaluators an ‘objective’ way into engagement with the product.

12.5.1 The Davis 20-point scorecard The Davis 20-point scorecard (Fig. 12.1) was created in the infancy of the California wine industry as an attempt to standardize and objectify general quality assessment. When the scale was developed, the goal of most California winemaking was to make varietally distinct wines, and the aroma was considered the clearest indicator of the grape’s varietal character. At the time, although some world-class wines were being made, many California wines had defects such as excess ethyl acetate, a lack of clarity, etc. The Davis 20-point scorecard was designed to stimulate the production of quality wine and to provide the standards by which quality wines could be recognized and identified by winemakers and wine judges. Another function of the Davis 20-point scorecard was to provide some measure of objectivity for the analysis of wine character at a time when there was hardly any. It also provided an intellectual framework for evaluating sensory impressions of wine. The method was to divide the whole, overall impression of a wine into its various sensorial ‘parts.’ It assigned a number value to each of these parts, with the sum to total the arbitrary number 20. The component parts were not equally weighted; rather they were assigned a different numerical value based on the perception of their relative importance. Amerine and Roessler (1983) observe that, in using this scale, there are inconsistencies in results unless the scorecard is used by professionals who are specifically trained to use it. They recommend that each tasting panel first go through the process of clearly establishing quality parameters in relation to the scorecard for each specific wine type to be tasted. When the 20-point scale is applied in practice in the wine industry, the recommended training is rare and establishing quality parameters at each tasting or even

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Wine Code: Appearance Cloudy 0 Clear 1 Brilliant 2

0–2

Color Distinctly off (for type) Slightly off Correct

0–2 0 1 2

Aroma and bouquet 0–4 Faint 1 Slight 2 Pronounced 3 Subtract 1 or 2 for off-odors Add 1 for noticeable bouquet from aging Acetic acid (vinegary) Obvious 0 Slight 1 None 2

0–2

Total acidity Distinctly low or high for type Slightly low or high Normal Sweetness Too high or low for type Normal

0 1

Body Too high or low for type Normal

0 1

0–2 0 1 2 0–1

0–1

Flavor Distinctly abnormal or deficient Slightly abnormal Normal

0–2 0 1 2

Bitterness and astringency Distinctly high for type 0 Slightly high 1 Normal 2

0–2

General quality Lacking 0 Slight 1 Impressive 2

0–2

TOTAL Ratings: superior (17–20); standard (13–16); below standard (9–12); unacceptable or spoiled (1–8)

Fig. 12.1 The Davis 20 point scorecard for wine quality.

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for each wine type is almost unheard of. There remains significant and persistent debate over the relevance of any quality scoring method owing to the difficulty of establishing concrete definitions of quality and lack of standardized evaluation of tasters’ training and expertise.

12.5.2 Other wine quality scales When wine critics write tasting notes, they usually accompany the notes with a point score – a judgment of the wine’s quality on a scale of 20 or 100. One famous wine critic, Robert M. Parker, employs a 50–100 point quality scale. He believes that the various 20 point rating systems do not provide enough flexibility and often result in compressed and inflated wine ratings. The numeral rating given is a guide to what he thinks of the wine compared with its peer group. His guide to interpreting numerical wine ratings is based on the grading system used in American schools: • 90–100 is equivalent to an A and is given only for an outstanding or special effort. • 80–89 is equivalent to a B and such a wine, particularly in the 85–89 range, is very, very good. • 70–79 represents a C, or average mark, but obviously 79 is a much more desirable score than 70. Wines that receive scores between 75 and 79 are generally pleasant, straightforward wines that lack complexity, character, or depth. • Below 70 is a D or F. For wine, it is a sign of an imbalanced, flawed, or terribly dull or diluted product that will be of little interest to the discriminating consumer. A similar scoring system is used by the influential periodical The Wine Spectator: • • • • • •

95–100 Classic: a great wine. 90–94 Outstanding: a wine of superior character and style. 85–89 Very good: a wine with special qualities. 80–84 Good: a solid, well-made wine. 75–79 Mediocre: a drinkable wine that may have minor flaws. 50–74 Not recommended.

While some have suggested that scoring is not well suited to a beverage that has been romantically extolled for centuries, Robert Parker believes that wine is no different from any consumer product. He believes that there are specific standards of quality that full-time wine professionals recognize, and that there are benchmark wines against which others can be judged. For Mr Parker, scoring wines is simply taking a professional’s opinion and applying some sort of numerical system to it on a consistent basis. Scores, however, do not reveal important attributes about a wine. The written commentary that accompanies the critic’s ratings is often a variety

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of expressive abstract terms (Quandt, 2007). Referential terms are sometimes used to describe what the wines smell and taste like. The commentary may contain information regarding the wine’s style, its relative quality compared with its peers, and its value and aging potential. The popularity of number ratings has spread. Many wine consumers do not bother to read the descriptions in a critic’s wine review – they simply rush out to buy the wines with the highest scores. Wines that receive high scores from the best-known critics sell out almost overnight as the result of the demand generated by their scores. Numbers do provide convenient shorthand for communicating a critic’s opinion of a wine’s quality. But number ratings are problematic, too, for various reasons. The sheer precision of the scores – one wine receiving a score of 86, for example, while another earns 88 – suggests that they have been assigned objectively, when in fact they represent either the subjective opinion of an individual critic or the combined subjective opinions of a panel of critics. A score of 86 may not be statistically significantly different from a score of 88 but the 88 is perceived in the public’s mind as a ‘better’ wine. Different critics may also apply the same scale differently. For example, some might assign 95 points only to wines that are truly great compared to all wines of all types, while others could assign the same score to a wine that is great in its own class. While checklists and benchmarks offer a framework for evaluating the quality of a wine, they do not necessarily guarantee enjoyment. Quality is a multidimensional concept and it can therefore not be measured with a single number.

12.6

Wine and the development of sensory evaluation as a science

The grading of agricultural commodities had an important influence on the movement to assure consumers of quality standards in the foods they purchased. The study of grape growing and winemaking began in Berkeley at the University of California, years before the Davis campus existed. After the establishment of the University Farm in 1908, viticulture shifted to Davis. The passage of Prohibition in 1919 suspended research on winemaking until it was repealed in 1933. One of the most important accomplishments of the Department of Viticulture and Enology has been educating students in scientific method and innovative thinking. In the 1960s, Professors Maynard Amerine, Rosemarie Pangborn and Edward Roessler developed methods for analytical sensory evaluation (Amerine et al., 1965). They were the first to use statistics to evaluate wine sensory information, making it possible to undertake wine sensory evaluation as a serious scientific endeavor. In 1984, Professor Ann Noble published the Wine Aroma Wheel, allowing wine researchers and consumers to better

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identify, define and discuss wine aromas in an easily understood language. Most aromas in wine are described through analogy, with the most popular coming from the realm of fruits, berries and flowers. Food analogies are also common since wine is made from a fruit or berry and is part of a vegetative process. Herbs and spices are also frequently used descriptors for wines. The Wine Aroma Wheel was modified several years later to take into account comments from the industry (Noble et al., 1987). While an important tool in the wine industry, the Wine Aroma Wheel has not become an accepted standard as the Beer Flavor Wheel is in the brewing industry because it does not include many of the abstract terms used by industry professionals. Although descriptive analysis is an analytical tool permitting the precise measurement of changes in sensory properties produced by any treatment or in describing differences among wines from different regions or ages, it cannot provide an overall integrated impression of wine flavor. Wine quality is an incredibly complex, integrated function of the numerous chemical components.

12.7 Factors affecting wine quality The typical fine wine contains hundreds of compounds in minute quantities which add or detract from its flavor or character. The basic constituents of wine are: • • • • • • •

water as part of grape juice; alcohol created during fermentation; acids inherent in the grape or produced during fermentation; extract and tannin from the grape; flavors from the grape; flavors from the yeast used for fermentation; flavors created by various interaction of the chemical compounds produced during the winemaking process; • flavors created by aging with wood. Wine quality involves the complex interaction of many factors: the grape variety and how it is grown, the climate and soil where it is grown and the winemaker’s creative or commercial objective and skill in making the wine. The winemaker has the dual role of scientist and artist. Technological advances over the years have provided winemakers with new alternatives, knowledge and analytical techniques that enable them to make good quality wine even at the lowest price level. There is no question about the importance of the grape for it is the essence of the wine, and without an ideal environment it would achieve only a certain level of quality. Although a great wine cannot be made from bad grapes, it is surprisingly easy to turn potentially great wine into very expensive ordinary wine. The decision of

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when to pick grapes is a winemaker’s responsibility. Judging at what point the grapes will produce the most flavorful and balanced wine is critical. Winemakers walk the vineyards; they sample the fruit and rely on both their sensory instincts and lab analyses to determine ripeness. They also need to be practical regarding the logistical constraints of harvest, as to how many tons of grapes can be picked in a day, how many tanks are available and how much their cellar crew can handle. During the last 20 years, the emphasis in California winemaking has shifted to the vineyards. A winemaker’s depth of knowledge regarding the vineyards that produce the fruit for wines is perhaps the most important aspect of the job. Whether working for a large winery that contracts with multiple growers or on a small vineyard estate, the winemaker needs to have enough experience and awareness to make decisions about the ripeness, flavors, acidity and condition of the grapes. Each vintage is different, and winemakers need to use their training and intuitive skills to work with every season. A winemaker’s relationship with growers or vineyard managers is usually one of close cooperation and communication. Many are longterm relationships built on trust and a shared vision of how to achieve certain parameters for making the finest wine possible from a given vineyard site. Tasting is an important facet of winemaking, from the beginning when the juice is in the fermenter to the final blend before bottling. Winemakers learn the technical aspects of tasting at school. However, it is through experience where they gain the ability to affect the taste of their wines through a myriad of daily winemaking decisions. These choices range from determining types of yeast to managing fermentation temperatures and times. Some winemakers taste alone. Oftentimes, there is a winemaking team that tastes together, and it is important to develop a common vocabulary so that everyone agrees on the basic concepts – sweet, sour, bitter and astringent – as well as more complex descriptors. Blending is another tool in the artistic palette of a winemaker. Each vineyard lot is usually kept separate, and yeast, fermentation and oak treatments can vary, depending on the winemaker’s intention. Often a winemaker will have 20–30 lots of a wine, such as Cabernet Sauvignon, that come from different vineyards with various vine maturities, aged in different types of oak, or differ in color from light to opaque with varying degrees of alcohol. It is up to the winemaker to decide how to blend this array of wines to create the final product that will be bottled. The vintner’s objective is the kind or style of wine they set out to make. It may be dictated by artistic or commercial considerations, frequently both. To that end, they will use certain techniques and processes and their success will be measured by whether or not they have achieved their intended goal. Almost as important as any of these is the expectation of the market: what the drinker demands is ultimately what the producer will produce.

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12.8 Levels of wine quality Table wines may be divided into three basic quality tiers as they relate to winemaking options and economics: 1. Everyday wines – basic beverage with a good flavor and no faults. Wines should have a certain amount of body, be balanced and have a direct, single or neutral flavor with a short finish or aftertaste. These wines are at the lower end of the quality range, they receive minimal treatment and they are generally marketed early and as inexpensively as possible. 2. Premium wines – more refined, more character and more complexity. The wine will have a harmonious balance and will linger in the mouth. Such wines may be recognized by the varietal or regional designations they bear on the label. 3. Luxury wines – these wines offer flavors that are recognizable and distinctive, yet accompanied by more character and complexity. These usually gain complexity with prolonged aging. The three quality tiers relate to a winemaker’s options which begin with the grape variety or varieties selected to make a wine. For example, Cabernet Sauvignon is grape that can be made into everyday, premium or luxury quality wines. In the inappropriate soil and climate, it seldom surpasses everyday quality and frequently will produce a lesser quality wine than other, more suited varieties. Everyday wines need not possess any nuance of flavors and therefore grapes of lesser quality will generally be used. To use finer grapes in everyday wines may improve the wine, but will also add a cost which frequently is significantly greater than the contribution in terms of added quality.

12.9

Approaches to determining wine quality

Quality control is the series of analyses and tests that verify a wine’s palatability, stability, compliance with regulations, typicity and freedom from faults and contaminants. Quality control of wines aims first at rejecting wines with defective aromas and flavors. There are four approaches to determine wine sensory quality: 1. Quality ratings performed by wine experts. These often occur in the context of sanctioned competitions intended to recognize the best wines in a given category by assigning medals. 2. Ratings for an overall degree of difference from a standard or control product. These methods are used in the context of a quality assurance program and are designed to evaluate how well the wine meets certain specifications. 3. Wine styles that are ‘liked/preferred’ by a defined population of consumers. Quality may be defined by some as what the consumer views

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as such. Therefore, an indirect measure of wine quality can be obtained by having consumers evaluate the wine and comment on their preferences, their purchase intent and other variables. 4. Descriptive analysis performed by trained and experienced panelists. Objective profiles of wine flavor are generated which permit analytical evaluation of the differences among wines. What are the sensory attributes that ‘drive’ wine quality? For complete sensory quality control, a statistically controlled tasting panel is required. Developing a proper sensory program at a winery requires some basic resources. Those resources include the availability of qualified panelists from the winery’s staff, who in many cases are staff from nontechnical positions, such as administrative departments. Its administration also requires a qualified sensory professional who has knowledge of the test methods (difference tests, descriptive analysis, preference tests, etc.), when and how to use the tests, and the statistical methods needed for interpretation of the results. Lesschaeve (2007) has generated a table outlining specific key components (tasting room, data collection, panel, methods and sensory personnel) necessary for implementing a sensory program at a commercial winery.

12.10 Current sensory quality control practices in winemaking Although strides have been made in sensory methods and data analysis, formal sensory techniques are not being used in most small to mid-sized wineries and in only a few large wineries. Most wineries have only a nominal sensory program and that was developed for testing corks.

12.10.1 Case studies Large California Central Valley winery producing a range of low-priced wines This winery produces everyday wines. The key characteristic of these wines is that they are made for immediate appeal. Everyday wines will not develop any beneficial characteristics with aging; they lack this ability. The grapes used for this winery’s products are high-yield varieties which give many berries high in sugar content. Vineyard sites are chosen with these objectives in mind; a hot climate and fertile soil are ideal. Grapes are machine harvested and once they arrive at the production facility, they are usually rushed through the most efficient procedure in terms of both time and equipment usage. Also, economics dictates procedure in the winery. Crushing is followed immediately by fermentation; no effort is made to sort out the good batches of grapes from the average. The must is channeled into

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the same, usually large, fermenter. No skin contact time for white wines prior to fermentation, or for red wines after fermentation, is desired. The juice is converted into wine, and those components of the grape which contribute to complexity or aging potential are deliberately excluded since these are usually bitter and astringent when the wine is young. The fermentation process is often as rapid as possible. The yeast used is generally an all-purpose, heavy-duty strain. If off-odors or off-characteristics develop from grapes or fermentation, they will be blended out with neutral wine or filtered out later. As quickly and efficiently as possible after fermentation, the wines are clarified and stabilized. The stabilization process insures consistent quality during a normal shelf-life of one to two years after bottling. Wines are corrected for any deficiencies and are evaluated for consistency using sensory difference tests. The corrections often relate to acidity, and the usual procedure is to add acid afterward for balance. Sometimes the sweetness is adjusted, usually by adding some grape concentrate or sweet blending wine. Another option is to blend wine batches to compensate for lack of acidity, sweetness, structure and/or balance. Just prior to bottling, the wines are filtered to safeguard against any potentially harmful changes which might occur in the bottle. Mid-sized California winery producing range of moderately priced wines Located in Mendocino County, this winery obtains grapes from a diverse combination of microclimate, soil, and cultural practices. The winemaker will walk each vineyard prior to harvest, tasting berries for ‘flavor and tannin development.’ This winery strives to produce wines in which one can ‘taste the vineyard,’ similar perhaps to the French idea of ‘goût de terroir.’ Each vineyard site is fermented and aged as a separate lot; experiments within these lots have helped the winery define its style. Most of the wines are produced by the French cuvée system. A cuvée is selected by blending the individual vineyard lots together to produce a single wine that is better than any of its separate components. To achieve a perfectly balanced wine, some lots are added in their entirety, while others are only partially added. The remaining lots of wine are used in the production of house wines. All wines are screened for quality by the winemaker, owner and sales staff. The cooperage used is traditional: 50 gallon (190 liter) Burgundian barrels for Chardonnay and Pinot noir. German and French oak ovals (300 to 1500 gallons; 1100–5600 liters) for Gewürztraminer, Pinot gris, Riesling and Muscat. The latest high-tech equipment is used in the production of white wines (e.g. stainless panels for temperature control inside every oak oval, stainless steel membrane press, progressive cavity pumps) but a Pinot noir is made by ‘Methode l’Ancienne’ (without modern machinery). The winemaking regimes are frequently a blend of traditional and modern techniques and have been arrived at by experimentation to best suit the grapes from the ranch vineyards. The winery sells much of its 45,000-case production directly to consumers.

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Napa winery producing only expensive Cabernet-based wines The wines from this winery are individually hand-crafted. They transcend commercial considerations – cost is no object in producing them – they are made for the connoisseur who is willing to pay the price of perfection. The quality begins in the vineyard. The grape varieties are planted in locations which are ideal in terms of microclimate, appropriate soil and exposure to the sun. Pruning for a small crop maximizes grape quality. When harvesting, pickers hand-select only the best clusters from each vine. Several passes may be needed through each vineyard. The grapes are hand-sorted at the winery to further weed out inferior clusters; grape lots are crushed and kept separate. Fermentation procedures may involve the use of ‘wild’ yeasts, extended maceration and pushing down the cap by hand. After fermentation the free-run juice and the press wine are usually kept separate but sometimes are allowed to age before being combined later. The press wine may not be used at all. At this point, the best of each component wine will be selected and the blend will be assembled prior to the barrel-aging regimen. Wines are aged in small casks, the most expensive, best coopered versions available. The wine is handled as little as possible and only when required. Clarification is accomplished often by racking, transferring the wine from one cask to another to leave behind the sediment; this is costly and labor intensive. Fining may be achieved by using fresh egg whites, an expensive procedure which requires skilled labor and great care. Wines are then bottled using natural bark corks which have been screened to remove corks containing off-aroma compounds such as 2,4,6-trichloroanisole. These luxury wines contain numerous aromatic and flavor compounds which provide these wines with an ability to change over time, creating new flavors with age. These flavor compounds are usually not present in everyday wines – they contribute to a wine’s character by providing an aromatic bouquet, a complexity of flavors and tactile sensations in the mouth and always with an aftertaste that continues the flavor and sensation of the wine after it is swallowed. These wines always offer a balance of components which is appropriate to the type of wine. Foster’s Wine Estates De la Presa Owens (2001) discussed initiating a sensory program to evaluate all research experiments using sensory evaluation techniques at Foster’s Wine Estates (then Beringer Vineyards). Routine sensory descriptive analysis of wines developed into strategic sensory research which included input from many departments such as winemaking, quality assurance, vineyard and new product development. At Foster’s Wine Estates, three different types of in-house sensory panels are run. Discrimination panels (duo–trio, pair tests, ranking test) are used to determine if there are significant differences among different wines. Each year they also conduct several descriptive panels using QDA® (Stone and Sidel, 1993) to profile different wines.

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Finally, they host two or three winemaker panels per year, using only Foster’s Wine Estates winemakers. Consumer research studies are also implemented. Wineries which have out-sourced sensory services to an independent consulting firm Many commercial wineries do not have the resources to manage a sensory panel and implement a sensory program. However, these wineries may still benefit from the services of a professional sensory scientist. A sensory consultant could help a winery develop the tasting skills of their staff, aid the winery in describing the product’s sensory characteristics, assist in developing procedures which facilitate the description of the products and develop experiments and methods of analyzing the results of these experiments. A sensory consultant may have access to a panel of tasters trained in descriptive analysis. Wineries would use this particular service because they desire an unbiased and objective evaluation of their wines, usually compared against their competitors. Scorecards have been developed which assist in the objective evaluation of wine. Figures 12.2 and 12.3 are descriptive analysis scorecards with preselected attributes for white wines and red wines, respectively. Judges rate the intensity of each wine characteristic using structured category scales for easy data entry. For some attributes, there are more detailed descriptors which the panelists may circle if they feel so inclined. Also, spaces are provided for judges to describe the defective flavor of a red wine (if it exists) and to comment on the flavors which linger. After the visual, aromatic and flavor attributes have been scored, the panelists evaluate how ‘true to type’ the wine is. In order to do this, they must have experience with specific blends, such as Bordeaux-type blends and specific varietals. Similarly, panelists must have some experience with specific wine types in order to assess ‘overall quality.’ The judge may write any comments about the wine (whether other attributes were present that were not on the main scorecard, hedonic comments, etc.) in the space for ‘overall impression.’ Owing to budget constraints, usually one replication is performed. Data are analyzed by analysis of variance and polar coordinate plots are generated to display differences among the wines. An advanced statistical method, principal component analysis (PCA), is often used to illustrate relationships between a reduced set of variables. Patterns in descriptive sensory data may be determined by analyzing the data by this multivariate statistical method. The number of variables is reduced using factor analysis such that the first principal component (PC) statistically identified explains most of the variability in the data. The second PC, which is not correlated with the first, explains the majority of the remaining variance. Additional PCs may be identified. The PC plots show the position of each wine as a single point. This single point represents the scores for each of the different sensory attributes. Points that are close together are wines that are sensorily similar

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WHITE wine type: ____________________ Code: ____________ Taster: ____________ Yellow color intensity

0 1 light

Overall intensity

1 0 weak

Sulfur/sulfides (SO2, rubber, H2S, onion/ garlic, cooked veg)

0 low

1

Aldehydic (acetaldehyde, sherry)

0 low

Floral (honeysuckle, jasmine)

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Fruity (citrus, peach/apricot, fig, tropical, green apple)

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Herbal/vege (grass, bell pepper, mint, green olive, dried herbs)

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Spicy (cloves, nutmeg, anise)

0 low

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Nutty (hazelnut, almond, coconut)

0 low

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Caramelized (caramel, honey, butter, butterscotch)

0 low

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Vanilla/sweet oak

0 low

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Oak/woody (toasted oak, smoky, green)

0 low

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Overall intensity

Flavor by mouth 1 2 0 3 weak

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Sweetness

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Fig. 12.2 Descriptive analysis scorecard with pre-selected attributes for white wines.

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Herbal/vege

0 low

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Oak/caramelized

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Mouth-feel/body

0 1 thinner

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Ethanol burn

0 low

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Duration of flavors (__________________)

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Trueness to type

0 1 not true

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0 1 2 3 4 5 6 7 8 low Overall impression:_______________________________________________

9 high

Other 2

Overall quality

Fig. 12.2 Continued

RED wine type: _________________________ Code: __________ Taster: ___________ Red color intensity

0 1 paler

Overall intensity

0 1 weak

Sulfides (rubber, H2S, onion/garlic)

0 low

1

2

Volatile acidity (ethyl acetate, acetic acid)

0 1 low

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Berry (rasp.,straw.,black.,blue.,) cassis, cherry, art. fruit)

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Jam/dried fruit (berry jam, raisin, prune)

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Herbaceous (eucalyptus, mint, weedy, tobacco)

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Fig. 12.3 Descriptive analysis scorecard with pre-selected attributes for red wines.

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Vegetative (bell pepper, green bean, asparagus, olive)

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Fig. 12.3 Continued

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and points that are far apart are wines which are different. Emanating from the central origin are vectors representing each attribute. The length of the vector may be interpreted as an indication of influence on that PC. Short vectors indicate attributes of relatively low importance. Close alignment of a vector with the PC axis indicates a high correlation between the attribute represented by the axis and the variability explained by the PC. Eight California Syrah wines were evaluated by six experienced wine tasters using the form in Fig. 12.3. Analysis of variance (data not shown) indicated that the wines differed significantly at the 5% level of significance in 5 of the 24 attributes scored: red color intensity, volatile acidity (VA), berry aroma, defective flavor and overall quality. At the 10% level of significance, the wines were significantly different in spicy aroma, acidity and trueness to type. In Fig. 12.4, the eight wines and the eight sensory attributes are plotted on the first two PCs. These first two PCs explain 85.0% of the variance. Wines separated along the first PC according to the intensity of volatile acidity aroma, defective flavor and acidity. Trueness to type, quality and berry aroma also contributed to the separation of the wines, but to a lesser extent, as indicated by the larger angle between these attribute vectors and the first PC. The position of the wines on the second PC is determined by red color intensity. Figure 12.4 shows that wines 751 and 764 were characterized by VA aroma, defective flavor, low berry aroma, and low quality. Wines 536, 422 and 308 formed a cluster of wines which were pale in red color intensity, low in defective flavor and VA aroma, and were of moderate quality. NC and CC Syrahs were distinguished by berry aroma, trueness to type and quality. Analysis of variance indicated that Syrah 751 was significantly higher in red color intensity than all other Syrah wines. Wine 764 was not significantly different in red color from the two controls, NC and CC Syrah. Wines 093, 536 and 308 were of moderate red color whereas 422 was relatively pale red in color. Color intensity is related to both viticultural and winemaking procedures. The riper the grapes, the more pigments are produced; more pigments are extracted the longer the juice is left on the skins. Three wines – 751, 764 and 093 – were higher in VA aroma and defective flavor than the other wines in this group. Volatile acidity would be considered a winemaking issue. Judges’ comments revealed that wines 751, 093, 536 and 422 all had aromas associated with Brettanomyces contamination. Wines 536, 422 and 308 were not as true to type or as high in quality as the NC and CC Syrah control wines. The controls had more berry and spicy aromas than did the other Syrah wines. Intensity of berry aroma would be considered a viticultural variable although winemaking practices, such as barrel aging, can mask this component. In this example, the ‘drivers’ of quality appear to be: trueness to type, presence of berry and spicy aromas, and lack of VA aroma and defective flavor.

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Red Int.

257

Plane 1–2 Axis 2

CC Syrah

Berry Quality True to type

701

Spicy NC Syrah Volatile acidity Acidity

764

Axis 1 Defect. flavor 308 093

536

422

Fig. 12.4 Principal component analysis of the mean ratings of eight Syrah wines for eight sensory attributes. Axis 1: 60.5%, Axis 2: 24.5%.

12.11 Future of sensory evaluation in the wine industry Meeting quality requirements in the future will require a better understanding of the biology of human perception, olfactory and flavor preferences, the relationship between composition and the sensory quality of wine, and the production of wine to changing market specification and sensory preferences. In the vineyard there has been a lot of interest in ‘hang time’ and physiological ripeness. People (Francis et al., 2004) are now using the sensory evaluation of berries and seeds to determine harvest date but is the time of harvest a major factor affecting wine quality? What are the best grapes to plant for given plots? In wine, winemakers are attempting to describe

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mouth-feel attributes and the effects of tannins described as: chewy, dusty, fine-grained, lush, silky, green, harsh. What are the chemical compounds that cause mouth-feel sensations and how can they be manipulated to affect the mouth-feel? Will it be possible to establish a usable industry-wide vocabulary for mouth-feel attributes? While expertise with wine is important in the winery to guarantee the production of defect-free wines, there appears to be no evidence that wine expertise can predict consumer liking scores or market success. Winemakers and consumers may vary in their preferences and, thus, there may not be one quality target. To a winemaker, quality may refer to the diversity and persistence of flavor and the wine’s ability to age. Broad measures of quality (visual and chemical) can be tested in the vineyard. Wine quality is about a series of objective measures rather than a single measure of quality. At a basic level, this refers to a wine that lacks faults or is sound. Sound wines are then measured by both objective and subjective attributes which define their relative greatness. Although zero tolerance of wine faults is the goal, it must be acknowledged that what is unacceptable for a wine expert can still be acceptable for some consumers. Knowing the taint concentration at which a wine is still acceptable for consumers has significant economic impacts in the search for remedial treatments when tainted wines are detected. If a wine is tainted by the spoilage yeast Brettanomyces or is oxidized it is precluded from being high quality, so that by extension it can be argued that a level of technical competence is essential if a wine is to be good. This is not to argue that a wine should be technically spotless; there are those who would argue that the limited Brettanomyces found in some red wines from Burgundy add to, rather than detract from, their quality. Wines made ‘from recipes’ may be boring; slight technical imperfections could result in a more interesting wine. Although technical perfection is not essential as a dimension of wine quality, technical acceptability, providing a minimum level of flavor without any substantial faults is necessary to underpin wine quality. Wineries need to meet consumer demands by generating and improving the supply chain to deliver wines of appropriate quality to consumers in different world market segments. It is increasingly important to produce wine consistently to definable aroma and flavor specifications. With this comes the need to integrate better wine and grape research and to focus on the management of sensory features of wine that attract the consumer. The wine consumer of today is quality-focused, image-conscience and price-sensitive and this has led to a change in the rules of the marketplace to a degree where quality can be defined as ‘sustainable customer and consumer satisfaction.’ The process of transforming the wine industry from a production-oriented to a market-driven industry results in an increasing dependence on analytical sensory evaluation techniques.

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12.12 Sources of further information 12.12.1 Major professional associations/research groups American Society of Enology and Viticulture (http://www.asev.org/) California Enological Research Association (http://www.cerawine.org/ ?q=node/19) ASTM Committee E18 on Sensory Evaluation of Materials and Products (http://www.astm.org/Standards/E1879.htm)

12.12.2 On-line certificate program ‘Applied Sensory Science and Consumer Testing Certificate Program’ through UC Davis Extension (http://extension.ucdavis.edu/unit/ agriculture_and_food_science/certificate/applied_sensory_science_and_ consumer_testing/)

12.12.3 Wine judging course in Australia AWRI advanced wine assessment course (4 days) (http://www.awri.com.au/ industry_support/awac/)

12.12.4 Sensory workshops Applied Sensory, LLC (http://appliedsensory.com/services.aspx) ‘Introduction to Sensory Evaluation of Wine’ 2-day seminar at UC Davis through UC Extension (http://extension.ucdavis.edu/unit/winemaking/ course/description/?type=A&unit=WINE&SectionID=146083&prglist= WAP) Tragon Corporation (http://www.tragon.com/) Alexander Schmitt (http://www.wine-olfaction.com/Olfacto/Workshops. html)

12.12.5 Books Amerine, M.A. and Singleton, V.L. (1976), Wine, An Introduction, 2nd edition, Berkeley, University of California Press. Baldy, M.W. (1995), The University Wine Course, 2nd edition, San Francisco, The Wine Appreciation Guild. Broadbent, M. (1988), Pocket Guide to Wine Tasting, New York, Simon and Schuster. Jackson, R.S. (2002), Wine Tasting: A professional handbook, San Diego, Academic Press. Johnson, H. (1999), Hugh Johnson’s Story of Wine, London, Mitchell Beazley. Peynaud, E. (translated by A. Spencer) (1984), Knowing and Making Wine, New York, John Wiley & Sons.

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Robinson, J. (1996), Jancis Robinson’s Wine Course, New York, Abbeville Press. Robinson, J. (1999), The Oxford Companion to Wine, 2nd edition, New York, Oxford University Press Inc.

12.13 References abbott, n.a., combe, b.e. and williams, p.j. (1991), ‘The contribution of hydrolyzed flavor precursors to quality differences in shiraz juice and wines: an investigation by sensory descriptive analysis’, American Journal of Enology and Viticulture, 42, 167–174. acree, t.e. and cottrell, t.h.e. (1985), ‘Chemical indices if wine quality’, in Birch G. and Lindley M. (eds), Alcoholic Beverages, London, Elsevier Science, 145–159. amerine, m.a. and roessler, e.b. (1983), Wines, Their Sensory Evaluation, 2nd edition, New York, W.H. Freeman and Company. amerine, m.a., pangborn, r.m. and roessler, e.b. (1965), Principles of Sensory Evaluation of Food, New York, Academic Press. clementi, s., cruciani, g., giulietti, g., bertuccioli, m. and rosi, i. (1990), ‘Food quality optimization’, Food Quality and Preference, 2, 1–12. combris, p., lecocq, s. and visser, m. (1997), ‘Estimation of a hedonic price equation for Bordeaux wines: does quality matter?’, Economic Journal, 107, 390–402. de la presa owens, c. (2001), ‘Making sensory evaluation work in a winery’, Proceedings of the American Society of Enology and Viticulture 50th Anniversary Annual Meeting, J. M. Rantz (Ed.), Davis, 12–14. francis, i.l., høj, p.b., dambergs, r.g., gishen, m., de barros lopes, m.a., pretorius, i.s., godden, p.w., henschke, p.a., herderich, m.j. and watters, e.j. (2004), ‘Objective measures of grape quality – are they achievable?’, Proceedings of the 12th Australian Wine Industry Technical Conference, Melbourne, 24–29. gawel, r. (1999), ‘Quality ratings – why they vary’, Winestate, 24. gawel, r. and godden, p.w. (2008), ‘Evaluation of the consistency of wine quality assessment from expert wine tasters’, Australian Journal of Grape and Wine Research, 14, 1–8. goldberg, d. (1997), ‘Fine wine takes glycosides’, Search, 28, 302. hodgson, r. (2008), ‘An examination of judge reliability at a major U.S. wine competition’, Journal of Wine Economics, 3, 105–113. kwan, w.o. and kowalski, b.r. (1980), ‘Data analysis of sensory scores – evaluation of panelists and wine scorecards’, Journal of Food Science, 45, 213–216. lawless, h.t. and heymann, h. (1998), Sensory Evaluation of Foods: Principles and practices, New York, Kluwer Press. lawless, h.t., liu, y.f. and goldwyn, c. (1997), ‘Evaluation of wine quality using a small-panel hedonic scaling method’, Journal of Sensory Studies, 12, 317–332. lesschaeve, i. (2007), ‘Sensory evaluation of wine and commercial realities: review of current practices and perspectives’, American Journal of Enology and Viticulture, 58, 252–258. mccloskey, l.p., sylvan, m. and arrhenius, s.p. (1996), ‘Descriptive analysis for wine quality experts determining appellations by Chardonnay wine aroma’, Journal of Sensory Studies, 11, 49–67. noble, a.c., arnold, r.a., buechsenstein, j., leach, e.j., schmidt, j.o. and stern, p.m. (1987), ‘Modification of a standardized system of wine aroma terminology’, American Journal of Enology and Viticulture, 36, 143–146.

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ough, c.s. and winton, w.a. (1976), ‘An evaluation of Davis wine-score card and individual expert panel members’, American Journal of Enology and Viticulture, 27, 136–144. parasuraman, a., zeithaml, v.a. and berry, l.l. (1988), ‘Servqual – a multiple-item scale for measuring consumer perceptions of service quality’, Journal of Marketing, 64, 12–40. penn, c. (2001), ‘What is quality? An American perspective’, Australian and New Zealand Wine Industry Journal, 16, 58–59. peynaud, e. (translated by M. Schuster) (1987), The Taste of Wine, San Francisco, The Wine Appreciation Guild. quandt, r. (2007), ‘On wine bullshit: some new software?’, Journal of Wine Economics, 2, 129–135. scaman, c.h., dou, j., cliff, m.a., yuksel, d. and king, m.c. (2001), ‘Evaluation of wine competition judge performance using principal component similarity analysis’, Journal of Sensory Studies, 16, 287–300. somers, c. (1998), The Wine Spectrum: An approach towards objective definition of wine quality, Adelaide, Winetitles stone, h.s. and sidel, j.l. (1993), Sensory Evaluation Practices, San Diego, Academic Press. vaamonde, a., sanchez, p. and vilarino, f. (2000), ‘Discrepancies and consistencies in the subjective ratings of wine-tasting committees’, Journal of Food Quality, 23, 363–372.

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13 Sensory quality control of distilled beverages J. R. Piggott, University of Strathclyde, UK and S. Macleod, John Dewar and Sons Ltd, UK

Abstract: The minor properties, the aroma and flavour, of distilled beverages are critical for consumer acceptance, and must be monitored and controlled by sensory analysis. This chapter considers the background and history of sensory analysis of spirits, the particular problems involved in nosing and tasting of high-alcohol products, procedures and precautions which must be followed to overcome the difficulties, and current practices in the Scotch whisky industry. Taints and off-flavours are an occasional problem, and common causes of taints and their detection are described. Key words: spirits, whisky, Cognac, taint, off-flavour.

13.1 Introduction Whisky and other distilled beverages are consumed primarily for their alcohol content, and for the pleasure derived from the sensations of consumption. The alcohol concentration varies little, so consumers have little interest in the gross composition of the beverage. The factors which are used as the basis for selection of one product rather than another can therefore be described as the consumers’ perceptions of the competing products. This includes the image of the brand and manufacturer created by advertising, packaging and popular culture, and the minor properties of the brand itself. It is these minor properties, the aroma and flavour, which must be monitored and controlled by sensory analysis. Distilled beverages offer some unique challenges to the sensory analyst, but also offer some major benefits. The greatest challenge is obviously the alcohol content, which severely limits the quantity which can be consumed in the course of a tasting session. More samples can be assessed if they are tasted and expectorated, but the majority of quality control and research

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sensory analysis is by nosing. The benefits include ease of preparation, presentation to the assessors at room temperature, and stability at room temperature both before and after preparation. This chapter will consider the background and history of sensory analysis of spirits, the procedures and precautions which should be followed to survive the challenges (and take advantage of the benefits), current practices in the Scotch whisky industry, and the detection and control of taints and off-flavours.

13.2 Origins of sensory quality control of spirits Scotch whisky as it is known today dates from the latter half of the nineteenth century (Daiches, 1969). Before that time some quality control was presumably exerted by distillers, but lengthy maturation was not routinely carried out. The development of the Coffey still, which could produce large quantities of cheap grain whisky, permitted the production of blended whisky. The introduction of blending, as a means of reducing the flavour strength (and cost) of whisky was the first systematic attempt to control the flavour of the product. The growth of the blended whisky industry required the parallel development of a quality control system which could ensure that the flavour of a particular blend did not vary excessively from batch to batch. This quality control system evolved in the form of the whisky blender. The blenders’ system and expertise have developed in the context of the industry, with apparently little input of outside knowledge and techniques. However, some formal sensory analysis has been used for many years (Peryam and Swartz, 1950). This has largely been in the form of simple difference tests such as the duo–trio and triangle tests, generally used for comparison of final product with a standard previously defined as satisfactory. Generally speaking the other major distilled beverage industries have followed similar paths of development. In the case of Cognac and Armagnac, the maître de chai (cellar master) performs an equivalent function to the whisky blender (Cantagrel and Galy, 2003), and Bertrand (2003) showed a tasting vocabulary for Armagnac control. Vodka and flavoured spirits such as gin, based on neutral alcohol, require very highly purified alcohol with very low levels of flavour compounds (Aylott, 2003), and must be carefully controlled. This is typically by an experienced product expert, but occasionally more elaborate scoring systems have been used. Wilkin et al. (1983) described a simple descriptive method which was required for the control of neutral spirit. Descriptive methods have more often been used for research and communication than for straightforward quality control, but have been developed for many products – and in some cases a number of different vocabularies for the same product. For whisky, Shortreed et al. (1979) proposed a vocabulary and ‘flavour wheel’, Piggott and Jardine (1979) developed a vocabulary of 35 terms, subsequently reduced to 24, while Jounela-Eriksson (1981) used

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14 terms and the Seagram company in the USA used 30 terms (Canaway et al., 1984). Comparison of the performance of the vocabularies with a single panel suggested that 14 terms were too few (Canaway et al., 1984). Lee et al. (2001) proposed a revised flavour wheel, with major flavour characters defined by reference standards, to assist in training of assessors, with a simplified version aimed at marketing and educational purposes. Flavour wheels are generally constructed from a core of 15–20 themes, with a second tier containing terms breaking down each theme into particular notes. A third tier of concrete examples may occasionally be added. Flavour wheels have become popular as an attractive way of illustrating the flavours likely to be encountered in a particular product, for training sensory assessors and for communicating with marketing and sales departments and consumers. Jolly and Hattingh (2001) published a brandy aroma wheel, and Bordeu et al. (2004) a wheel for Pisco and other Muscat distillates. Lists of descriptive terms have been published in the research literature for many other products.

13.3 Procedures and precautions Jack (2003) recommended traditional tulip-shaped glasses for nosing whisky, similar to ISO wine tasting glasses. Evidence that these are better is hard to find, though these or similar glasses are widely used throughout the distilled spirits industry. There have been several attempts to demonstrate that particular shapes of glasses are better for different products, but again convincing technical evidence is generally lacking. Brossard (2007) presented a series of differing glasses of a malt whisky to a panel, and asked them to nose the samples and score them for quality, or degree of liking. The panellists, blindfolded, were under the impression that they were nosing different whiskies from the same glasses. The tulip-shaped glasses, where the bowl containing the sample is broader than the opening, generally scored higher than straight-sided glasses, demonstrating at least that the shape of the glass had an effect on the sensation. Typically, a sample of spirit is diluted to 20–23% ABV and presented to assessors in a glass which is approximately 25% full, leaving a relatively large headspace. The glass is normally then covered with a cap or watchglass to retain the volatiles in the headspace. To reduce preparation time and cost, samples are often prepared in advance and nosed by more than one assessor. Aspects of this practice which could have an impact on sample conditions are (a) effects of advanced preparation; (b) effects of repeated nosing; and (c) time required for headspace regeneration (Jack, 2003). Triangle tests (11 to 16 assessors) showed that examples of malt and grain whiskies and new make spirits prepared eight hours in advance could be distinguished from freshly prepared equivalents, though blends could not. After six hours no differences were found. Time between preparation and

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use should clearly be minimised, but it seems that several hours should not make a substantial difference. One consequence is that, in comparisons such as difference tests, the whole set of samples should be replaced if for example one is spilled and must be replaced. Repeated nosing over six or eight hours caused small but detectable changes in some samples, suggesting that the number of assessors nosing the same samples should be minimised. No significant differences were detected when a sample which had been opened and nosed was immediately compared with two untouched samples, whether they were freshly prepared or had been prepared for six hours. Sample temperature will clearly affect volatility of flavour compounds and therefore the headspace composition, but an elevated holding temperature (30 °C for 4 hours compared with 21 °C) did not make a detectable difference as long as the samples were allowed to cool before comparison. Descriptive analysis of samples at 5, 21 and 30 °C showed a significant impact of temperature on intensity of perceived aroma, but approximately the same effect on all aroma notes so the flavour profile remained the same. As in any sensory analysis, it is important that samples are nosed at the same temperature, and that the temperature is neither low (low overall flavour intensity) nor high (excessive and uncomfortable ethanol pungency). Jack (2003) concluded with a set of guidelines for the preparation and handling of spirit samples for sensory analysis. Some points are good practice in any sensory analysis, but others are specific to the sensory analysis of distilled spirits. Most of the Scotch whisky industry noses samples in preference to tasting, as it is accepted that the nose recovers more quickly than the palate. Therefore, it is possible to nose more samples accurately than if tasting. However, if tasting is required to be carried out to assess the mouth-feel, or to detect bitter off-notes, for example in gin, then the number of assessors is restricted, and the liquid must only be tasted, and not consumed. Health and safety is the main reason for these restrictions as a panellist who is about to operate machinery, or drive must not be allowed to be under the influence of alcohol. The flavour wheel (Fig. 13.1), used extensively in the Scotch whisky industry, demonstrates that the largest proportion of the character of whisky is devoted to aroma and therefore it is correct that we concentrate most of our sensory efforts on the nosing of our products. Occasional research has compared orthonasal with retronasal perception (nosing and tasting), and has shown that there is little to be gained by tasting products such as whisky (Piggott and Jardine, 1979) or Pisco (Lillo et al., 2005). However, for other spirit categories such as vodka, tasting is in preference to nosing, as the dominant sensation in vodka is the mouth-feel and absence of off-notes. Sensory assessment areas vary in the industry from purpose-built ISO standard sensory facilities (ISO, 1988) to something as basic as a room with a desk. However, no matter the extent of the design, the basic requirement

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is a clean, odour-free area, removed from distractions of the site, with good lighting and spacious enough to accommodate the panellists and samples required.

13.4 Current industry practices 13.4.1 Sensory assessment introduction All spirit companies use some form of sensory assessment as part of their quality control protocols. However, the sensory support ranges in complexity, for example in the whisky industry, from the expert opinion of one individual, such as the ‘Blender’, to the averaged opinions of sensory teams

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trained to assess samples from different sections of the whisky making process. The large whisky companies have all but abandoned the former approach owing to its obvious disadvantages: the ‘Blender’ succumbs to a respiratory infection; has an unknown blind spot for certain aromas or is off-site when an important sensory decision has to be made! Scotch whisky and spirits companies in general, especially those that are ISO 9001 accredited, require a robust system of sensory assessment that can demonstrate comprehensive procedures, specifications, calibration records and most importantly corrective actions to reflect the action taken in the event of a taint issue.

13.4.2 Sensory integrity A robust spirit quality system is dependent on the identification of the points in the process that are critical to the sensory integrity of the product(s). This can be approached using HACCP (Hazard Analysis Critical Control Points) (Mortimore et al., 2001). Using Scotch whisky production as an example, here is a typical process from a sensory analysis point of view, starting with the beginning of the process, the malt whisky distillery. Malt whisky distilleries are fragrant with aromas of sweet-smelling malted barley, fruity and grassy aromas, and other aromas depending on the character of the spirit produced from each distillery, each one having its own unique set of aromas. Some operators are keenly aware that even subtle changes in the aroma of the distillery environment could be a warning sign of changes to come in the spirit character. Harnessing this awareness is invaluable and it is therefore important to train the distillery operators to think with their noses! Critical sensory points in the distilling process There are three main ingredients used in the Scotch malt whisky making process: malted barley, water and yeast. The intake of the malted barley is the first critical stage in the process: a batch of malted barley delivered with a higher level of peat than specified for example, off-loaded and processed unchecked, could and probably would have a detrimental effect on the flavour profile of the resulting spirit for several cycles of distillation. The distillery operators are therefore required to nose the malted barley before it is off-loaded to ensure that no off-notes are present. Following successful off-load of the malted barley, milling takes place to form grist, to which hot water is added in the mashing process. The water quality is also of immense importance, so again it is important that the operators are able to detect off-notes in the water. After the mashing process and separation of the wort from the draff, it is good practice to nose the wort for any off-notes. The next step is fermentation and the creation of the wash, the wash is also sometimes nosed, but mostly only by exception.

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Distillation is the next critical stage and the creation of new make spirit; operators in the still house will be constantly assessing the aromas in the environment of the still house, as a particular odour from the wash safe can herald the arrival of the low wines. However, that is useful but unofficial method of sensory assessment, and most malt whisky distilleries have a sensory team at each site, made up from the employees at that site. The new make spirit is assessed by this team at least once per week, and each charge produced that week is assessed against the reference sample for the distillery. The new make spirit is diluted with water, preferably demineralised, to approximately 23% abv, in tulip-shaped nosing glasses covered with watchglasses. Each team member noses the reference and then noses each charge, always returning to the reference, and states that the sample is either the same as the reference or different. If the sample is perceived as different then the panellist is encouraged to state why it is different. The distilleries also submit charge samples to the central lab for both chemical and sensory analyses. The sensory analysis is carried out by the expert nosing team, who assess each charge and assign attributes, rather than the ‘difference from control’ test employed at the distillery. These attributes should match previously used attributes; if consecutive charges have either an attribute missing or an extra undesirable attribute then an investigation is launched with the distillery. Distillery operator sensory training/calibration At Dewar’s a number of iterations of training and calibration of the distilleries’ sensory teams have taken place. The international standard (ISO, 2008) sets out general guidance for the training of a sensory team; however, there really is little value in testing the operators on the basic tastes if all they are required to do is nose. It has been found that the most effective training is that which is most relevant to the type of aromas the operators will be encountering in their daily work routines. Sensory training and calibration are performed annually, and comprise three distinct sections. The first section concentrates on malted barley: three samples of malt are presented to the operators, one is a reference malt sample, the second is the same as the reference and the third is deliberately different. The operator is invited to nose, blind, each of the samples, and state whether the two malt samples are the same as the reference or different, and why they are different. The same routine is followed for the process water, where one of the samples is deliberately spiked with a mouldy offnote. It is also important that the distillery operators have an awareness of off-notes in the co-product draff, which is sold for animal feed, and therefore a sample of draff is deliberately allowed to spoil and is then presented to the operators to assess against a reference and a sample exactly the same as the reference. To train the operators to detect off-notes in the new make spirit, new make spirit is spiked with varying concentrations of feints (sweaty aroma) and the operator is invited to rank them in order.

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Maturation Closely following on from distillation is maturation, which is another critical sensory point in the Scotch whisky making process. It is critical because the new make spirit will spend, at least, three years by law (SI, 1990) in oak casks of less than 700 litres. These oak casks have previously been used to mature Scotch whisky, bourbon, sherry or other spirit types traditional to the Scotch whisky industry. Rejuvenated casks are also used, which means that after at least three maturation cycles, when the maturation potential of the casks has diminished, the casks are sent to the cooperage. During rejuvenation, the inside char layer is scraped off and then set alight to create a fresh char layer. Rejuvenation, or dechar rechar as it is also known, does not return the cask to the quality of when it was first filled, but it does succeed in providing enough maturation potential for another couple of maturation cycles. With so much history contained within the casks it is critical that sensory analyses are applied, particularly to those casks not generated in-house from the blending process. Sensory analysis is therefore not just confined to nosing of spirit in tulip-shaped nosing glasses in the comfort of a nosing room. Casks are assessed, in situ, by turning the cask onto its bilge and nosing the headspace via the bung hole. Carrying out this rather uncomfortable, not to mention undignified, task ensures that casks with off-notes such as sour, mouldy or rotten eggs are removed from the system prior to filling with new make spirit. If the new make spirit is permitted to be filled into such casks, then there is a good chance that the off-notes will taint the spirit and so the matured product. Blending and bottling After the required period of maturation for the specified product age, all the required casks that satisfy the flavour profile are garnered from the maturation warehouses in the blending area. At this stage the blender may feel quietly confident of the quality of the blend, because prior to the maturation process the new make spirit was assessed and the casks were also assessed. However, the unexpected can sometimes occur during maturation and so for small batch blends it is often prudent for the blender to nose the matured spirit in the cask, before it is disgorged to the vat. In a small batch, one rogue cask could have a devastating effect on the flavour profile. However, no matter the size of the blend, once complete a sample is drawn and submitted to the central laboratory for chemical and sensory analyses. The blends, in their pre-bottling and finished product forms are assessed by the blenders and by the sensory team trained in assessing mature spirit. This team noses each of the pre-bottling and finished product samples against a standard sample for each product. A score of 1 to 10 is assigned to each sample, 1 being the same as the standard and 10 being very different from the standard. The data are then subjected to analysis of variance (ANOVA) and any samples that are statistically significantly different are investigated.

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Mature spirit sensory training The mature spirit sensory team is recruited from all areas of the business and is recruited and calibrated on an annual basis. Three tests are used to calibrate the existing and new panellists. The first test (recognition test) is to test the ability of the candidate to recognise and describe an odour, where ten odours (FlavorActiv) are dissolved in 23% new make grain spirit. The panellists are requested to describe each odour; to help them find the word, a list of possible descriptors is provided, obviously more words than samples. The second test rates the ability of the candidate to detect a sample that is different, using a reference test; here the whiskies may be spiked with gin, or a very mature blend is compared against very young, etc. The third test (ranking test) determines the ability of the candidate to detect and then rank varying concentrations of a contamination, in this case, increasing concentrations of whisky spiked with gin. Once all the data are collated, the candidates are shown their results and invited to nose again the areas where their results indicate development is most required. Although some individuals are anosmic for various reasons, and this will be highlighted at the screening/calibration stage, most candidates can improve their sensory skills provided they are enthusiastic and attend the nosing panel as often as required. As well as annual calibration of our nosing teams both at the distilleries and at the central laboratory, deliberate, blind differences are set out for the team amongst the production samples – this aids the lab in determining the progress of the panellists.

13.5 Taints and off-flavours Taints and off-flavours in a food or beverage are flavours which are not normally present in the product. Though the terms have often been used interchangeably, a distinction can be made between them. A taint is an atypical flavour caused by a contaminant from the environment, whereas an off-flavour is an atypical flavour caused by changes in normal constituents of the food due to chemical or microbiological action (Mottram, 1998), or by natural flavour compounds being present at unusual or undesirable concentrations. As described previously, many of the routine checks of materials and processes are carried out to protect the product from undesirable flavours which may have been picked up from the environment or may have developed during processing of ingredients or raw materials. Taints may provide particular problems for sensory analysis, because they may be detected by only a proportion of the population who happen to be particularly sensitive to the compound concerned (Lunde et al., 2009). It may then be difficult to identify the cause of complaints when a small group within the company cannot detect a taint.

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13.5.1 Causes and symptoms Typically taints will be caused by one or a few compounds present at very low levels (<1 μg kg−1) (Mottram, 1998). Sometimes the cause of a taint may be well known, for example a muddy-earthy note in farmed fish due to geosmin and/or 2-methyl-iso-borneol (Howgate, 2004; Robertson et al., 2006), or a musty-mouldy note in alcoholic beverages due to 2,4,6-trichloroanisole on cork stoppers (Maga and Puech, 2005). In these cases it is only necessary to screen materials with assessors of demonstrated sensitivity. However, by their nature taints may be caused by a wide range of volatiles, and checks and controls must be in place to protect the product at all stages. Some of the more common taints are described as disinfectant, mouldy, musty, earthy, faecal, metallic, fishy, painty and plastic-like (Mottram, 1998), and these may be due to a limited range of compounds. In some cases the cause of a taint may be obvious, for example where the process is accidentally contaminated with a foreign material. Generally investigations of taints are only reported in the literature when they make a scientifically interesting story. Many cases must be solved within the company concerned, possibly using information from the literature, but never published, so anecdotal evidence is all that there is. Halogenated phenolic compounds are common causes of taints in foods. Phenols are readily chlorinated by, for example, hypochlorite bleach, and chlorophenols are used in a wide range of cleaning and preservative products. In most cases these do not present a problem, but 6-chloro-o-cresol (2-chloro-6-methylphenol) has an unusually low odour threshold and sufficient quantities may be absorbed from the atmosphere around a food to present a problem (Griffiths and Land, 1973). Wood pallets, floors of shipping containers and packaging materials may also be a source of chlorophenols which can potentially contaminate foods. Many microorganisms can methylate chlorophenols to the corresponding chloroanisole, and these compounds can be the cause of powerful taints (Whitfield, 1998). Similarly, 2,4,6-tribromophenol used as a fungicide can be converted to the corresponding musty-smelling tribromoanisole (Whitfield et al., 1997), and is a sufficiently powerful odorant to contaminate food stored nearby. Geosmin, possibly derived from process water (Zaitlin and Watson, 2006), has recently been identified as a component of spirit of poor sensory quality (Plutowska and Wardencki, 2009). Catty-smelling compounds have been the cause of a number of cases of food taints and of odours from polluted waters. These compounds arise from the reaction of hydrogen sulphide with an unsaturated carbonyl compound, such as mesityl oxide (4-methylpent-3-en-2-one) used as a solvent (Mottram, 1998). The hydrogen sulphide adds across the double bond to produce the corresponding thiol, which in the case of mesityl oxide has an odour threshold of 10−2 μg kg−1. Bacterial metabolism under anaerobic conditions can generate the hydrogen sulphide, and the resulting catty odour was an occasional taint in whisky (Piggott, 1991).

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13.5.2 Investigative techniques Finished product taint complaints received from the marketplace should always be treated in a consistent manner. It is important firstly to establish where the taint could have entered the product: during manufacturing, or after it left the manufacturing site. The ability to make this determination hinges on a quality assurance scheme that requires an adequate number of reference samples to be collected and stored for a period of no less than three years. It is imperative that the reference samples are representative of the batch, with a mix of pre-bottling (vat samples) and finished product (capped and labelled, etc.) samples. This will ensure that for each day of production, for each product, on each line there is at least one reference sample to represent it. The premise for this is that if the taint is present in the complaint bottle, but not present in any of the reference samples, then the probability is high that the taint has occurred post- production. Once the date of production is established, using the lot code on the complaint bottle, the investigation process can begin. The garnering of evidence is the first step in the investigation process: interrogating the relevant bottling line records to establish any unusual occurrences on that particular day of manufacturing, laboratory records (both sensory and chemical), and of course all reference samples collected for that particular batch of spirit, on that particular day of production. All these activities should begin as soon as the lot code is received; do not wait for the bottle to arrive before beginning the investigation, as it may take several days to arrive from the marketplace. Once the bottle is received, before it is opened for analysis, take note of how much is left in the bottle, note any signs of tampering. A well-considered and executed quality assurance scheme ensures that adequate procedures are in place to detect taint resulting from contaminated raw materials, or from cross-contamination during the production process. The affected stock will then be identified and fully isolated from the good stock and the root cause investigated. The aim is that the contaminated stock does not reach the marketplace and any procedural gaps that have been identified during the course of the investigation are dealt with swiftly, to prevent a reoccurrence in the future. Some taints, however, develop post-production, once the finished product has left the manufacturing site en route to the marketplace; examples of such contamination were cited earlier. Taints found to have occurred after leaving the site are much more difficult to investigate, and almost impossible to determine the source, as the consignment will have passed through a number of different handlers. For example, each part of this process – the haulage company, the port handlers, the sailing vessel (if transported by sea), the entry port, the distribution warehouse, the customer warehouse, the on or off-trade premise where the product is sold and/or consumed, or the consumer’s home – has the potential for taint transference from the immediate environment to the product.

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13.6 Sources of further information The general sensory analysis books such as those by Lawless and Heymann (1998) and Meilgaard et al. (2007) will provide further general information, and books on alcoholic beverages or particular spirits will provide information on the production and characteristics of the products. The book compiled by Lea and Piggott (2003) contains chapters on the major spirits, descriptions of a range of spirits can be found in the proceedings of the Worldwide Distilled Spirits Conference (Bryce et al., 2008), and The Alcohol Textbook contains a section on beverage spirits (Ingledew et al., 2009). Technical books devoted to individual spirits seem to be unusual, but whisky is covered in those of Piggott et al. (1989) and Russell (2003) and there are numerous popular books about the major spirits. Trade associations and promotional organisations will normally publish background information for a popular market, including: Scotch Whisky Association (http://www.scotch-whisky.org.uk/); Bureau National Interprofessionnel du Cognac (http://www.cognac.fr/); Bureau National Interprofessionnel de l’Armagnac (http://www.armagnac.fr/); Gin and Vodka Association (http://www.ginvodka.org/); Consejo Regulador del Tequila (http://www.crt.org.mx); West Indies Rum and Spirits Producers Association (http://www.truerum.com); Associazione Degustatori Italiani Grappe e Distillati (http://www.adid.it/); Programa Brasileiro de Desenvolvimento da Cachaça (http://www.cachacadobrasil.com.br/pagina_inicial.swf).

13.7 References aylott ri (2003), ‘Vodka, gin and other flavoured spirits’, in Lea AGH and Piggott JR, Fermented Beverage Production (2nd edition), New York, Kluwer Academic/ Plenum Publishers, 289–308. bertrand a (2003), ‘Armagnac and wine-spirits’, in Lea AGH and Piggott JR, Fermented Beverage Production (2nd edition), New York, Kluwer Academic/Plenum Publishers, 213–238. bordeu e, formas g and agosin e (2004), ‘Proposal for a standardized set of sensory terms for pisco a young muscat wine distillate’, American Journal of Enology and Viticulture, 55, 104–107. brossard p (2007), ‘The effects of the glass on the perception of the whisky flavours’. Available from: http://www.whisky-news.com/En/reports/whisky-glasses.pdf [accessed 9 July 2009]. bryce jh, piggott jr and stewart gg (2008), Distilled Spirits Production Technology and Innovation, Nottingham, Nottingham University Press. canaway pr, piggott jr, sharp r and carey rg (1984), ‘Comparison of sensory and analytical data for cereal distillates’, in Nykänen L and Lehtonen P, Flavour Research of Alcoholic Beverages, Helsinki, Foundation for Biotechnical and Industrial Fermentation Research, 301–312. cantagrel r and galy b (2003), ‘From vine to Cognac’, in Lea AGH and Piggott JR, Fermented Beverage Production (2nd edition), New York, Kluwer Academic/ Plenum Publishers, 195–212. daiches d (1969), Scotch Whisky, London, André Deutsch.

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griffiths nm and land dg (1973), ‘6-Chloro-o-cresol taint in biscuits’, Chemistry and Industry, 904. howgate p (2004), ‘Tainting of farmed fish by geosmin and 2-methyl-iso-bomeol: a review of sensory aspects and of uptake/depuration’, Aquaculture, 234, 155–181. ingledew wm, austin g, kelsall d and kluhspies c (2009), The Alcohol Textbook (5th edition), Nottingham, Nottingham University Press. iso (1988), ISO 8589:1988 Sensory analysis – General guidance for the design of taste rooms. iso (2008), ISO 8586-2:2008 Sensory analysis – General guidance for the selection, training and monitoring of assessors – Part 2: Expert sensory assessors. jack f (2003), ‘Development of guidelines for the preparation and handling of sensory samples in the Scotch whisky industry’, Journal of the Institute of Brewing, 109, 114–119. jolly np and hattingh s (2001), ‘A brandy aroma wheel for South African brandy’, South African Journal of Enology and Viticulture, 22, 16–21. jounela-eriksson p (1981), ‘Predictive value of sensory and analytical data for distilled beverages’, in Schreier P, Flavour ’81, Berlin, Walter de Gruyter, 34–46. lawless ht and heymann h (1998), Sensory Evaluation of Food: Principles and practices, New York, Chapman & Hall/International Thomson Pub. lea agh and piggott jr (2003), Fermented Beverage Production (2nd edition), New York, Kluwer Academic/Plenum Publishers. lee kym, paterson a, piggott jr and richardson gd (2001), ‘Origins of flavour in whiskies and a revised flavour wheel: a review’, Journal of the Institute of Brewing, 107, 287–313. lillo mpy, latrille e, casaubon g, agosin e, bordeu e and martin n (2005), ‘Comparison between odour and aroma profiles of Chilean Pisco spirit’, Food Quality and Preference, 16, 59–70. lunde k, skuterud e, nilsen a and egelandsdal b (2009), ‘A new method for differentiating the androstenone sensitivity among consumers’, Food Quality and Preference, 20, 304–311. maga ja and puech jl (2005), ‘Cork and alcoholic beverages’, Food Reviews International, 21, 53–68. meilgaard m, civille gv and carr bt (2007), Sensory Evaluation Techniques (4th edition), Boca Raton, Taylor & Francis. mortimore se, wallace c and cassianos c (2001), HACCP, Oxford, WileyBlackwell. mottram ds (1998), ‘Chemical tainting of foods’, International Journal of Food Science and Technology, 33, 19–29. peryam dr and swartz vw (1950), ‘Measurement of sensory differences’, Food Technology, 4, 390–395. piggott jr (1991), ‘Selection of terms for descriptive analysis’, in Lawless H and Klein B, Sensory Science Theory and Applications in Foods, New York, Dekker, 339–351. piggott jr and jardine sp (1979), ‘Descriptive sensory analysis of whisky flavour’, Journal of the Institute of Brewing, 85, 82–85. piggott jr, sharp r and duncan reb (1989), The Science and Technology of Whiskies, Harlow, Longman. plutowska b and wardencki w (2009), ‘Headspace solid-phase microextraction and gas chromatography–olfactometry analysis of raw spirits of different organoleptic quality’, Flavour and Fragrance Journal, 24, 177–185. robertson rf, hammond a, jauncey k, beveridge mcm and lawton la (2006), ‘An investigation into the occurrence of geosmin responsible for earthy-musty taints in UK farmed rainbow trout, Onchorhynchus mykiss’, Aquaculture, 259, 153–163.

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russell i (2003), Whisky: Technology, production and marketing, Amsterdam, Academic Press. shortreed gw, rickards p, swan js and burtles sm (1979), ‘The flavour terminology of Scotch whisky’, Brewers’ Guardian, 108(11), 55–62. si (1990) No. 998. The Scotch Whisky Order 1990, London, HMSO. whitfield fb (1998), ‘Microbiology of food taints’, International Journal of Food Science and Technology, 33, 31–51. whitfield fb, hill jl and shaw kj (1997), ‘2,4,6-tribromoanisole: a potential cause of mustiness in packaged food’, Journal of Agricultural and Food Chemistry, 45, 889–893. wilkin gd, webber ma and lafferty ea (1983), ‘Appraisal of industrial continuous still products’, in Piggott JR, Flavour of Distilled Beverages, Chichester, Ellis Horwood, 154–165. zaitlin b and watson sb (2006), ‘Actinomycetes in relation to taste and odour in drinking water: myths, tenets and truths’, Water Research, 40, 1741–1753.

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14 Sensory quality control of fresh produce E. Costell, I. Carbonell, A. Tárrega and S. Bayarri, Instituto de Agroquímica y Tecnología de Alimentos, CSIC, Spain

Abstract: An important point in sensory quality control of fresh produce is to select those attributes with the highest incidence on perceived quality by consumers. This chapter deals with the study of the effect of three storage temperatures (2, 5 and 20 °C) during a five week storage period on the sensory quality of two apple varieties (Top Red and Golden) and with the selection of the sensory attributes that most influence stored apple quality. A reduced version of the sensory profile with only nine attributes is proposed to control changes in the sensory quality of apples due to temperature storage. Key words: apples, sensory quality control, storage temperature, sensory profile, acceptability.

14.1 Introduction Sensory characteristics (appearance, aroma, taste and texture) are the main reason why consumers purchase a particular type of fruit or vegetable (Jaeger et al., 2003; Wismer et al., 2005). The sensory quality of these products plays an important role in consumer satisfaction and can influence consumer choice at the moment of purchase as well as modifying the degree of pleasure experienced when the products are consumed. Therefore, providing consumers with fruit and vegetables that meet their sensory and quality requisites will undoubtedly increase consumption (Kader, 2008). For most plant products, and especially fruit, sensory quality is considered the key factor to acceptance; but its measurement and control present some problems. Traditionally, sensory quality control of fruit and vegetables was considered to be a secondary activity at most, and was usually based on the opinion and expertise of a reduced number of judges. Furthermore, these judges focused mainly on detecting the varieties or genotypes displaying important defects in colour, flavour or texture. At present, it is widely recognised that market success of any fruit or vegetable does not depend only

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on the absence of perceivable defects but also on the fulfilment of consumer demands (Hampson et al., 2000; Harker et al., 2003; Jaeger and Harker, 2005). Basically, whatever the method used in food quality control, it follows a common approach: first, specifications are defined and quality standards are selected and, second, reliable methods are developed and tested to evaluate whether a product complies or not with the previously established requirements. Following this approach, the first problem in sensory quality control arises when trying to select those attributes with the highest incidence on quality. In general, selection is conditioned by the need to establish a compromise between two extreme alternatives: either consider a large number of food attributes, leading to very complete specifications but which are difficult to apply in practice, or else select only those characteristics of higher incidence on quality, which makes it simpler to decide whether the food fulfils the requisites of a certain degree of quality. The second relies on the capacity of each method to measure the variations in each characteristic that influences product quality with sufficient precision (Costell, 2002). Implementation of food sensory quality control systems presents additional problems, mainly because sensory quality is not linked only to food properties or characteristics but to the result of the interaction between the food and the consumer (Costell and Durán, 1981). It is not an easy task to establish the relationship between the composition and characteristics of fresh produce and the physiological human reaction and between the latter and the sensation experienced by people upon consuming. It is difficult to make predictions as to the possible perceptible differences between products differing in composition or structure as a result of genetic manipulation, preharvest, harvest or post-harvest factors (Bartoszewski et al., 2003; Edelenbos et al., 2006). It is even more difficult to predict the degree of consumer acceptance. Analysis of the relationships between the attributes variability and the variability in consumer acceptability will tell us which attributes most influence consumer acceptance. It must be accepted that variability in the intensity of certain attributes may not affect acceptability. Furthermore the degree of variability in an attribute is not necessarily related to how much it affects acceptability. In the particular case of fruit and vegetables, sensory analysis is needed to reveal to what extent changes in composition or structure affect the perceived sensory quality. Another interesting aspect is the possibility of analysing the relationship between the sensorially perceived changes and consumer response.

14.2 The role of sensory analysis in quality control of fruit and vegetables Sensory quality is an apparently clear but elusive concept (Molnar, 1995; Cardello, 1995) and sensory analysis constitutes an indispensable tool to obtain information on those aspects of food quality to which no other

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analytical technique can be applied. As stated by Cardello (1997) ‘establishing the relationship between sensory responses and the pleasure associated with food is one of the most important and practical contributions that sensory science can make to the study of food’. In this context, sensory analysis plays an important role in the quality control of foods (Muñoz et al., 1992). Depending on the specific objective of the considered action, the use of different sensory techniques and approaches are required to develop standards, establish specifications and control food quality in order to gather information on the degree of compliance with specifications (Costell, 2002). In fact for fruit and vegetables, sensory analysis provides a suitable methodology for use as a selection tool (Rousseff et al., 1994; Hampson et al., 2000; Causse et al., 2001; Jaeger and Harker, 2005) or to investigate, for example, how different tomato genotypes (Sinesio et al., 2007), blueberry cultivars (Saftner et al., 2008) or apple varieties affect flavour or texture (Gómez et al., 1998) or how sensory quality varies depending on genetic modifications (Causse et al., 2001; Bartoszewski et al., 2003), on breeding or storage conditions (Auerswald et al., 1999; Maul et al., 2000) or on ripeness degree at the time of harvesting (Cascales et al., 2005). 14.2.1 Sensory methods in quality control It should be noted here that the application of sensory methods to evaluate the sensory quality of fruit and vegetables is not straightforward. It requires ample knowledge of different experimental methods and possible statistical treatments in order to choose the most suitable ones. An interesting point to consider is that not all methods used in quality control are equivalent. Each of them has a specific objective and should be carried out in a particular way, and the information provided may be different from that of other methods (Muñoz et al., 1992). Currently, a great deal of good information is available on sensory methodology and the experimental conditions under which to carry out the different sensory tests (Lawless and Heymann, 1998; Meilgaard et al., 1999; Stone and Sidel, 2004) as well as on experimental designs and statistical treatment applicable to the different types of data (O’Mahony, 1985; Gacula, 1993; MacFie and Thomson, 1994; Meullenet et al., 2007). Besides, the International Organization for Standardization (ISO) has edited various standards, many of which are methodological in nature, on sensory analysis, which can be obtained from www.iso.org under section 67.240. All this information constitutes the foundation on which to develop and set up efficient systems to measure and control the sensory quality of fruit and vegetables as required, according to the particular characteristics of a certain product (Muñoz et al., 1992; Hampson et al., 2000). 14.2.2 Problems associated with sensory analysis of fresh products When dealing with foodstuffs, especially with fruit and vegetables and with sensory quality, it is difficult, and often practically impossible, to obtain a

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product or a series of products showing the same unaltered sensory characteristics for long enough to permit their use as reference items in subsequent comparisons. Fortunately, some attributes, such as colour or appearance, and quality standards (photographs or reproductions of the food in materials such as plastics or tiles) have been successfully used when the product itself cannot be used, generally because of the loss of freshness or reasons of spoilage. For other attributes, mainly those related to flavour and texture, the problem has traditionally been solved by developing written standards, which commonly include a description of the main attributes. In this context the descriptive method is one of the most frequently used in quality control. In this method, quality is basically evaluated in terms of the intensity of each attribute by a trained panel using descriptive profiling, and then the person responsible for quality control makes the final decision based on previously established sensory specifications. The two main advantages of this approach are the absence of any subjectivity in the evaluation and the quality of the data obtained. The main disadvantages are the time and cost necessary to train and calibrate the panel and the time necessary to perform the test and analyse the data. In general, this method is considered unsuitable for solving certain problems that require an immediate decision. In this case, one possibility is to perform a reduced version of the profile. A small group of judges may be selected to evaluate the most important attributes. This simplification may allow its use in daily quality control. Experimental sensory analysis of fruit and vegetables may present also some inconvenient especially in two situations: when the samples to be evaluated are not integrated by homogeneous material and when the number of samples to be evaluated is high. Product variability is not always evident externally and so cannot be controlled by visual classification. Real variation within a given genotype will make differences among genotypes more difficult to detect (Hampson et al., 2000). In this respect, Harker et al. (2005) have analysed the problems posed by the natural heterogeneity of fruits and vegetables when trying to establish whether there are significant differences between two products using the triangle test. The authors concluded that the biological variability associated with some fruit and vegetables will often overwhelm attempts to identify statistically significant differences. To overcome this problem, in certain cases flavour is evaluated in fruits and vegetables after submitting them to treatments, like grinding or extraction, to improve sample homogeneity. However, it is important to know to what extent, both qualitatively and quantitatively, the information obtained in a liquid matrix equates to that obtained from the process of mastication and deglutition of a solid matrix. In the event that there are too many samples to compare, preliminary information on the possible detection of perceptible differences can be obtained using other sensory methods such as similarity analysis (Schiffman and Beeker, 1986). With this method, the assessors evaluate the total perceptible difference in each pair of samples. Differences among a great number of samples can be detected

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simultaneously in several directions and an approximation to their relative magnitude can be obtained (Vélez et al., 1993).

14.3

A case study: Influence of storage temperature on the sensory quality of apples

14.3.1 Starting point and objectives The sensory quality of apples depends on several factors such as variety, prevailing climatic conditions during growth and fruit development, stage of ripeness at harvest and storage conditions. The longer the time between harvest and eating, the greater is the loss of sensory quality. Thus, it is very important to identify optimal post-harvest conditions. During storage, the appearance, flavour and texture of apples can deteriorate, leading to a loss in the characteristic flavour and becoming soft or having a soft and mealy texture. These quality changes depend not only on storage time but also on storage temperature. The main objective of this work was to study the effect of three storage temperatures (2, 5 and 20 °C) during a five week storage period on the sensory quality of two apple varieties (Top Red and Golden) and to select the sensory attributes that most influence stored apple quality, thereby making it possible to decide whether an apple fulfils consumer requisites. In order to do this, the following specific objectives have been established: (a) evaluation of the perceptible differences between samples using a trained panel; (b) evaluation of samples acceptability by a large consumer panel; (c) analysis of the relationship between the sensory attributes variability and the variability in consumer acceptability; and (d) selection of those sensory attributes that drive consumer acceptability.

14.3.2 Samples Apples of different varieties were obtained from a local market (Valencia, Spain) and other different cultivars were delivered directly from Lleida (Spain). In order to obtain samples differing in sensory quality, apples were stored at different temperatures during different storage periods. Apple varieties and storage conditions used in each sensory assay will be described later.

14.3.3 Chemical and instrumental analysis Soluble solids content was determined in a digital refractometer Atago RX-100 and expressed as degrees Brix. Acidity was determined by titration with NaOH and expressed as g/l of malic acid. Both measurements were taken in duplicate for each sample. Penetration tests were performed with a TA-XT2 Texture Analyser (Stable Micro System) using a 11 mm diameter plunger, a cross-head speed

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of 20 mm/s and a depth of 8 mm. Break load values in newtons (N) were registered.

14.3.4 Sensory analysis Four sensory assays were performed. All of them were carried out in a standardised test room (ISO, 2007) in morning sessions. Samples were coded with random three-digit numbers and mineral water was provided for mouth-rinsing. Data acquisition was performed using Compusense® five release 4.6 software (Compusense Inc., Guelph, Ontario, Canada). Generation and selection of descriptors With the aim of identifying the most suitable descriptors to describe the changes in the sensory quality of apples due to storage temperature, an initial assay was performed. Four apple varieties were used: Starking, Golden, Red Chief and Granny Smith, stored for 14 days at two temperatures (2 and 20 °C) to increase variations in sensory properties. The ISO Standard No. 11035 (1994) was followed to generate and select the descriptors. Initially, a literature search was performed and yielded a list of 88 descriptors used in apple sensory evaluation. A group of 14 assessors, with previous experience (more than two years) in sensory evaluation of different foods, was asked to evaluate the suitability of these descriptors to describe the sensory characteristics of the eight above-mentioned apple samples and was also asked to propose new terms. After a discussion among panel members, the initial list was reduced to 77 descriptors by rejection of hedonic, irrelevant and less frequent terms. Then, the assessors scored the intensity of those 77 attributes in the eight apple samples using a structured scale (0, not perceived, and 5, high intensity). Both frequency of perception (scores greater than 0) and attribute intensity (from 1 to 5) were computed according to the cited ISO Standard (1994) and used to select a reduced list with 42 terms. Finally, a PCA was performed with the panel scores for the selected 42 terms. Considering the variability explained by each PCA component and the correlation between each of them and the intensity of the attributes, a final list of 20 terms was selected. These selected descriptors and their definitions are given in Table 14.1. The techniques used to evaluate each sensory attribute were established by consensus among panel members. To evaluate appearance, five unpeeled fruits per sample were presented and placed in plastic trays in a Colour Viewing Chamber equipped with illuminant D65 (ICSTexicon España SA, Barcelona, Spain). For odour, taste and texture evaluations, half an apple per sample was served to each assessor on a white plate just after cutting with the cut surface in contact with the plate. Selection and training of assessors Six apple samples (three varieties, Golden, Granny Smith and Starking, each stored for one month at two temperatures, 5 and 20 °C) obtained from

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Table 14.1 Sensory descriptors used for sensory profiling of apple samples; definitions and corresponding unstructured scale anchor terms Descriptor

Definition

Skin turgidity Colour intensity Shine

The superficial appearance of the skin The typical colour intensity for each variety Attribute of glossy surface showing bright reflection Overripe app. Describes the visual sensation of aged fruit Dark Quantifies the presence and size of dark blemishes blemishes on the skin Bruises Quantifies the bruises presence, dark or white, on the surface of the apple Odour Describes the impact of odour quantity, intensity regardless of its characteristics Typical odour Refers to the odour intensity like apple Fruity odour Describes the typical odour from fresh fruit Taste intensity Refers to the impact of taste quantity which is perceived when chewing, regardless of its characteristics Sweetness Describes the taste represented by sucrose Acidity Describes the taste produced for instance by citric acid Astringency Describes the mouth sensation accompanied by shrinking of the mucosal surface in the mouth, produced by diluted solutions of various substances such as tannins Hardness Mechanical textural attribute relating to the force required to achieve a given deformation or penetration of a product Crispy Mechanical textural attribute related to cohesiveness and to the force necessary to break a product into crumbs or pieces Juiciness Surface attribute which describes the mouth sensation related with the amount of water absorbed by or released from a product Chewiness Mechanical textural attribute related to cohesiveness and to the length of time or the number of chews required to masticate a solid product into a state ready for swallowing Granularity Geometrical textural attribute relating to the perception of the size and shape of particles in a product Flouriness Related to the cohesiveness of a tender product. It is related to the effort required to disintegrate the product Denseness Describes the sensation produced by a tissue with compact cells

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Low end High end Ridged Weak

Smooth Intense

Dull

Shiny

Weak None

Intense Many

None

Many

Weak

Intense

Weak Weak

Intense Intense

Bland

Intense

Weak Weak

Intense Intense

None

Intense

Soft

Hard

None

Very crispy

Dry

Juicy

Weak

Intense

Weak

Intense

Weak

Intense

Weak

Intense

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a local market were used in training sessions. Two previous sessions were held with the panel leader and with all the 14 assessors (10 women and 4 men) to participate in generating and selecting descriptors, to discuss the descriptors selected and their definitions and to establish the definitive score card. Finally, in separate booths each assessor evaluated the intensity of the 20 previously selected attributes, on six samples per session over three sessions. The samples were presented to the assessors in the same order. Attribute intensities were rated on unstructured scales (100 mm) with the extremes marked as described in Table 14.1. At the end of each session the panel leader and assessors discussed the individual results obtained in order to establish a consensus criteria for evaluation. One of the assessors was unavailable to complete the three training sessions. Hence, the final panel comprised 13 assessors. Sensory profile Six apple samples (two varieties, Top Red and Golden, each stored for five weeks at three temperatures, 2, 5 and 20 °C) (Table 14.2) were profiled by the trained panel. Each of the assessors evaluated the intensity of the 20 attributes for the apple samples. Four samples per session were evaluated over three sessions to provide two replicate assessments of each apple sample. Evaluation was performed according to the previously described experimental conditions, except in the order in which samples were served to each assessor. In this case, sample presentation order was randomised across sessions but balanced across assessors within each session. Acceptability by consumers Overall acceptability of the six apple samples previously profiled by the trained panel (Table 14.2) was also evaluated by 99 consumers, using a 9 point hedonic scale ranging from 1 (‘dislike extremely’) to 9 (‘like extremely’). Consumers were recruited by a local consumer association

Table 14.2 Soluble solids, acidity and hardness of apple samples evaluated by sensory profile Sample

Variety

1 2 3 4 5 6

Top Red Top Red Top Red Golden Golden Golden

a

Storage temperature (°C)

Soluble solids (°Brix)

Acidity (g/l)a

Hardness (N)

2 5 20 2 5 20

12.2 14.2 11.2 14.5 14.5 14.6

2.68 2.45 2.14 3.06 3.14 3.21

51.39 39.73 29.50 42.94 43.85 38.82

Expressed as malic acid.

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through a short questionnaire sent by mail. The participants were selected according to the following criteria: age (more than 50 years, 35; from 30 to 50 years, 31, and less than 30 years, 33), sex (68 women and 31 men) and habitual consumers of apples (minimum intake of two apples a week). They were given a brief overview of how the sensory test would be conducted. Each consumer evaluated the six samples in one session under the same conditions as the profile test. The order of sample presentation was fixed across consumers according to a Williams design for six samples in balanced blocks of six consumers (MacFie et al., 1989).

14.3.5 Data analysis Panel performance was evaluated by assessor and by group from data obtained from the training panel sessions (13 assessors, 20 attributes, 6 samples, 3 replications). Two-way ANOVA (samples and sessions) for each assessor and for each attribute was performed to obtain information about individual discrimination ability and repeatability. A three-way ANOVA (assessors, samples and sessions) with interactions for each attribute was applied to obtain information about panel performance and about variation among assessors. Two-way ANOVA (variety and storage temperature) with interaction was applied to the sample scores obtained by sensory profile (13 assessors, 20 attributes, 2 replications) and to the acceptability scores from the consumer panel. Relationships between the sensory attributes and consumer acceptability were established by Partial Least Square (PLS) regression. All these analyses were performed using Xlstat Pro Software v. 2007 (Addinsoft, France).

14.3.6 Results and discussion Evaluation of panel performance Results obtained by the individual two-way ANOVA for each attribute showed that eight of the assessors had a high discriminant capacity. The values of Fsamples were significant (p ≤ 0.50) at least for 19 of 20 attributes evaluated. A second group, formed by four assessors, also showed a good discriminant capacity. Their corresponding Fsample values were significant at least for 18 attributes. Only one of the assessors showed Fsample values that were not significant for 4 of the 20 attributes. The Fsession values were not significant (p ≤ 0.05) for at least 18 attributes for ten assessors. For the other three assessors, the Fsession values were not significant for 16 attributes. According to these results all 13 assessors can be considered to have good discriminant capacity and good repeatability. ANOVA results for the sensory attribute scores across the six apple samples for the 13 assessors are given in Table 14.3. A significant (p ≤ 0.05) sample main effect was observed for all sensory attributes while no significant session main effect was observed except for four of the attributes (colour

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Table 14.3 Three-way (sample, session and assessor) analysis of variance of sensory attributes scores of apple samples; F-ratio values Attribute

Sample

Session

Assessor

Sample × Assessor

Skin turgidity Colour intensity Shine Overripe appearance Dark blemishes Bruises Odour intensity Typical odour Fruity odour Taste intensity Sweetness Acidity Astringency Hardness Crispiness Juiciness Chewiness Granularity Flouriness Denseness

57.82* 46.73* 123.57* 36.53* 39.42* 23.51* 14.03* 18.56* 3.36* 11.52* 40.31* 153.73* 29.57* 83.50* 57.28* 25.22* 5.89* 7.87* 25.72* 36.75*

0.30 10.0* 1.86 2.67 2.39 0.62 1.61 1.98 0.06 5.46* 5.40* 0.20 1.36 0.56 1.91* 1.05 0.11 2.00 1.13 2.86

24.35* 5.56* 4.36* 8.32* 6.93* 6.94* 8.18* 14.51* 19.54* 8.15* 4.65* 4.18* 18.58* 13.24* 9.39* 4.50* 17.67* 19.00* 9.20* 8.36*

2.98* 1.64* 1.26 2.60* 1.51* 1.19 2.25* 2.66* 1.85* 2.31* 1.65* 2.13* 1.49* 2.05* 1.42 1.75* 3.69* 2.93* 2.29* 1.66*

* Significant effect at p ≤ 0.05.

intensity, taste intensity, sweetness and crispiness). However, assessors were also a significant source of variation in all cases. Individual differences among assessors in descriptive panels are always present, even when assessors have been trained, therefore some variations among individual scores are considered acceptable in sensory profile analysis (Damasio and Costell, 1991; Naes, 1991; Carlucci and Monteleone, 2001). In practice, the significance of the assessor effect does not affect profile description, and thus has no influence on the detection of differences among samples, but can give information about assessors’ behaviour. The important thing to be aware of is whether these assessors’ variations may have consequences on the estimation of sample differences. Sample × assessor interaction provides information about this point. In this case it was significant (p ≤ 0.05) for all the 20 attributes except for shine, bruises and crispiness. In spite of this, the main sample effect for the rest of them, except for fruity aroma and chewiness, remained significant when a mixed ANOVA was applied and the sample effect was tested against the assessor × sample interaction term. According to these results, it can be considered that when the influence of individual differences among assessors was eliminated, sample differences in most of the attributes evaluated could be detected by the trained panel.

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Table 14.4 Influence of variety and storage temperature on sensory attributes scores of apples; two-way analysis of variance with interaction; F-ratios and p-values Variety

Temperature

Interaction

Attribute Skin turgidity Colour intensity Shine Overripe appearance Dark blemishes Bruises Odour intensity Typical odour Fruity odour Taste intensity Sweetness Acidity Astringency Hardness Crispiness Juiciness Chewiness Granularity Flouriness Denseness

F

p

F

p

F

p

58.559 54.400 62.282 78.539 63.177 49.029 5.625 4.466 3.439 21.064 6.022 43.858 8.060 23.279 24.296 41.761 13.810 23.714 50.720 25.003

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.019 0.036 0.066 <0.0001 0.015 <0.0001 0.005 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 <0.0001 <0.0001

2.589 0.192 1.230 4.894 3.732 3.289 1.854 1.054 1.164 6.056 1.344 12.908 2.696 30.143 34.295 44.155 10.257 8.252 37.837 24.727

0.078 0.826 0.295 0.009 0.026 0.040 0.160 0.351 0.315 0.003 0.264 <0.0001 0.071 <0.0001 <0.0001 <0.0001 <0.0001 0.0001 <0.0001 <0.0001

8.741 3.614 8.806 13.378 18.539 5.033 1.406 1.524 1.826 5.780 7.592 2.950 1.158 0.579 0.755 4.421 0.183 0.330 3.225 0.688

0.0003 0.029 0.0002 <0.0001 <0.0001 0.008 0.248 0.221 0.165 0.004 0.001 0.055 0.317 0.562 0.472 0.014 0.833 0.719 0.043 0.504

Influence of storage temperature on sensory characteristics and on acceptability of apples ANOVA results for the sensory attribute scores across the six apple samples for 13 assessors are given in Table 14.4. Differences perceived in four attributes (odour intensity, typical odour, fruity odour and astringency) only depended on apple variety. Variation of temperature storage did not have a significant effect on their sensory ratings (p ≤ 0.05). For six of the attributes, mainly those related to texture (hardness, crispiness, chewiness, granularity and denseness) and acidity, variations in storage temperature showed significant effects (p ≤ 0.05) and these effects followed the same trend for the two apple varieties. For both varieties, when storage temperature increased, the intensity of these attributes, except granularity, decreased (Fig. 14.1). For the other attributes evaluated, the effect of the interaction between variety and storage temperature was significant (p ≤ 0.05) which means that the effect of temperature storage on each attribute intensity differed for each variety. In general, variation of intensity in appearance attributes (skin turgidity, colour intensity, shine, overripe appearance, dark blemishes and bruises) was higher for Golden than for Top Red apples (Fig. 14.2). Moreover, variations in taste intensity, juiciness and flouriness due to storage

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(b)

(a)

Hardness

Hardness

5

5 Acidity

Crispiness

Acidity

Crispiness

Denseness

Chewiness

Denseness

Chewiness

Granularity

Granularity

Fig. 14.1 Variation with storage temperature (2 °C —; 5 °C ----; 20 °C 䉭) of sensory attribute scores that varied with the same trend for the two apple varieties: (a) Top Red and (b) Golden.

(a)

(b)

Skin turgidity

Skin turgidity

10

10 Colour intensity

Bruises

Dark blemishes

Shine

Colour intensity

Bruises

Dark blemishes

Overripe appearance

Shine

Overripe appearance

Fig. 14.2 Variation with storage temperature (2 °C —; 5 °C ----; 20 °C 䉭) of sensory attribute scores that varied with different trend for the two apple varieties: (a) Top Red and (b) Golden.

temperature were higher for Top Red samples. For example, variation in taste intensity with storage temperature for both Golden and Top Red apples is given in Fig. 14.3a. Finally, when storage temperature increased, sweetness of Top Red apples decreased while sweetness of Golden apples increased (Fig. 14.3b). ANOVA of acceptability data for the six apple samples obtained from consumers showed that differences in acceptability were mainly attributed to temperature storage (p < 0.001) but also to apple variety (p < 0.001) and that the interaction between these factors was also significant (p < 0.001). There were no significant differences in acceptability of apple samples of both varieties stored at 2 or 5 °C for five weeks. Their mean acceptability scores ranged from 6.4 to 6.8. At the higher storage temperature (20 °C) acceptability of samples decreased significantly although the Golden sample remained acceptable (average acceptability score = 5.8) while consumers

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Sensory analysis for food and beverage quality control (b) 8 Sweetness

Taste intensity

(a) 6 4 2

8 6 4 2 0

0 2

5

2

20

5

20

Storage temperature (°C)

Storage temperature (°C)

Fig. 14.3 Variation with storage temperature of (a) taste intensity and (b) sweetness for apple samples of Top Red (—) and Golden (----) stored at 2, 5 and 20 °C for five weeks.

9 Acceptability

a

a

a

a b

c

5

1 T2

T5

T20

G2

G5

G20

Samples

Fig. 14.4 Average consumer acceptability scores (n = 99) of Top Red (T) and Golden (G) apple samples stored at 2, 5 and 20 °C for five weeks. Different letters on the top of bars indicate significant differences at p ≤ 0.05.

slightly disliked the Top Red sample (average acceptability score = 4.0) (Fig. 14.4). Relationships between sensory characteristics and acceptability; selection of sensory attributes that drive consumer acceptability Considering the perceptible differences between the samples analysed in this study and the differences in their acceptability, information can be obtained concerning the influence exerted by storage temperature on the variation in the different sensorial attributes. Also, we can know up to what point this variation influences the acceptability of the apples analysed. Out of the 20 attributes under assessment, only 16 were modified by the differences in storage temperature tested. In six of these, the effect of storage temperature proved to follow a similar trend in both varieties (Fig. 14.1) and one could expect that a variation in these attributes influences the acceptability of the apples. Of the remaining ten traits, the variation in the six appearance attributes due to storage temperature is higher for Golden samples (Fig. 14.2). This would indicate that the storage conditions used here had little influence on the variation in acceptability of the apples,

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given that the decrease in acceptability with storage temperature was lower in the Golden variety than in Top Red (Fig. 14.4). Finally, the extent to which storage temperature influenced the intensity of the attributes, taste intensity (Fig. 14.3a), flouriness and juiciness in both varieties and the different effect that temperature had on sweetness intensity (Fig. 14.3b) would suggest that the intensity of these four attributes must have a direct influence on the acceptability of both apple varieties. A partial least squares (PLS) regression was performed to establish the relationships between sensory attributes and acceptability of apple samples. The two first dimensions explained 91.67% of variability. The PLS plot (Fig. 14.5) showed that acceptability seemed to be related mainly to textural traits. Acceptability was positively correlated with hardness, juiciness, crispiness, denseness and chewiness and negatively correlated with flouriness and granularity. Besides these textural traits, taste intensity and acidity also

1

Shine Skin turgidity

0.75 Colour 0.5 Second component (17.96 % of variability)

T5

T2

intensity

Acceptability Sweetness

0.25 G2

Granularity

Hardness Juiciness Crispiness Denseness Chewiness

0 Taste intensity

Flouriness

Acidity

−0.25

G5

T20

−0.5 Blemishes −0.75

−1

G20

−1

−0.75

−0.5

−0.25

0

Overripe

0.25

0.5

Bruises

0.75

1

First component (74.71 % of variability)

Fig. 14.5 PLS regression analysis of sensory attributes and acceptability of Top Red (T) and Golden (G) apple samples stored at 2, 5 and 20 °C for five weeks.

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proved to be positively correlated with acceptability. The good correlations found for these nine attributes indicate that they can be involved in the acceptance decision and can be considered good predictors of acceptability. The remaining attributes did not prove to be correlated with the changes in acceptability due to storage temperature and were more closely related with the apple variety.

14.3.7 Conclusion Among the changes detected in the intensity of the 20 sensory attributes in the two apple varieties evaluated, only the variation in 9 of them was related with differences in acceptability due to storage temperature. Thus, according to these results, a simplified descriptive method can be used to control changes in the sensory quality of apples due to temperature during storage. Furthermore, a reduced version of the sensory profile with only nine attributes can be used in quality control.

14.4 Acknowledgements To MICINN of Spain for financial support (Project AGL2007-63444). To Fondo Social Europeo for financing the contract of author S. Bayarri in the program I3P from CSIC.

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damasio m.h., costell e. (1991). Análisis sensorial descriptivo: Generación de descriptores y selección de catadores. Rev. Agroquím. Tecnol. Alim., 31, 165–178. edelenbos m., thybo a.k., christensen l.p. (2006). Flavour quality of organic tomatoes grown in different systems. In W.L.P. Bredie and M.A. Petersen eds. Flavour Science: Recent Advances and Trends. Elsevier. Amsterdam. p. 301–304. gacula m.c. (1993). Design and Analysis of Sensory Optimization. Food & Nutrition Press, Inc. Connecticut. gómez c., fiorenza f., izquierdo l., costell e. (1998). Perception of mealiness in apples: A comparison of consumers and trained assessors. Z. Lebens. Unters. Forsch. A., 207, 304–310. hampson c.r., quamme h.a., hall j.w., macdonald r.a., king m.c., cliff m.a. (2000). Sensory evaluation as a selection tool in apple breeding. Euphytica, 111, 79–90. harker f.r., gumson f.a., jaeger s.r. (2003). The case for fruit quality: an interpretative review of consumer attitudes and preferences from apples. Post. Biol. Technol., 28, 333–347. harker f.r., norquay c., amos r., jackman r., gunson a., williams m. (2005). The use and misuse of discrimination test for assessing the sensory properties of fruit and vegetables. Post. Biol. Technol., 38, 195–201. iso (1994). Identification and selection of descriptor for establishing a sensory profile by a multidimensional approach. Standard No 11035. International Organization for Standardization, Geneva, Switzerland. iso (2007). General guidance for the design of test room. Standard No 8589. International Organization for Standardization, Geneva, Switzerland. jaeger s.r., harker f.r. (2005). Consumer evaluation of novel kiwifruit: willingnessto-pay. J. Sci. Food Agric., 85, 2519–2526. jaeger s.r., rossiter k.l., wismer w.v., harker f.r. (2003). Consumer driven product development in the kiwi industry. Food Qual. Pref., 14, 187–198. kader a.a. (2008). Perspective: flavor quality of fruits and vegetables. J. Sci. Food Agric., 88, 1863–1868. lawless h.t., heymann h. (1998). Sensory Evaluation of foods: Principles and Practices. Chapman and Hall, New York. macfie h.j.h., thomson d.m.h. (1994). Measurement of Food Preferences. Blakie Academic & Professional, London. macfie h.j.h., bratchell n., greenhoff k., vallis l.v. (1989). Designs to balance the effect of order of presentation and first-order carry-over effects in hall test. J. Sensory Stud., 4, 129–148. maul f., sargent s.a., sims c.a, baldwin e.a., balaban m.o., huber d.j. (2000). Tomato flavor and aroma quality as affected by storage temperature. J. Food Sci., 65, 1228–1237. meilgaard m., civille g.v., carr b.t. (1999). Sensory Evaluation Techniques (3rd edition). CRC Press, Boca Raton, Florida. meullenet j.f., xiong r., findlay c.j. (2007). Multivariate and Probabilistic Analyses of Sensory Science Problems. Blackwell Academic and Professional. Ames. Iowa. molnar p.j. (1995). A model for overall description of food quality. Food Qual. Pref., 6, 185–190. muñoz a.m., civille g.v., carr b.t. (1992). Sensory Evaluation in Quality Control. Van Nostrand Reinhold, New York. naes t. (1991). Handling individual differences between assessors in sensory profiling. Food Qual. Pref., 2, 187–199. o’mahony m. (1985). Sensory Evaluation of Food. Statistical Methods and Procedures. Marcel and Dekker, Inc., New York. rouseff r., gemitter f., grosser j. (1994). Citrus breeding and flavour. In J.R. Piggott and A. Paterson eds. Understanding Natural Flavours. Blackie Academic & Professional, Chapman & Hall, London. p. 113–127.

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saftner r., polashock j., ehlenfeldt m., vinyard b. (2008). Intrumental and sensory quality characteristics of blueberry fruit from twelve cultivars. Post. Biol. Technol., 49, 19–26. schiffman s.s., beeker t.g. (1986). Multidimensional scaling and its interpretation. In J.R. Piggott ed. Statistical Procedures in Food Research, Elsevier Applied Science, London, p. 255–292. sinesio f., moneta e., peparaio m. (2007). Sensory characteristics of traditional field grown tomato genotypes in Souththern Italy. J. Food Qual., 30, 878–895. stone h., sidel j.l. (2004). Sensory Evaluation Practices. (3nd edition). Academic Press, Inc., New York. vélez c., costell e., orlando m.l., nadal m.i., sendra j.m., izquierdo l. (1993). Multidimensional scaling as a method to correlate sensory and instrumental data of orange juice aromas. J. Sci. Food Agric., 61, 41–46. wismer w.v., harker f.r., gunson f.a., rossiter k.l., lau k., seal a.g., lowe r.g., beatson r. (2005). Identifying flavour targets for fruit breeding: a kiwifruit example. Euphytica, 141, 93–104.

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15 Sensory quality management of fish E. Martinsdóttir, Matís – Icelandic Food Research, Iceland

Abstract: This chapter discusses how to perform sensory evaluation of fish, training of panellists and facilities. Different methods for sensory evaluation of whole fresh fish, fish fillets and cooked samples are described – EU scheme, quality index method and the Torry scale. It is shown how to develop a quality index and the use of the quality index in storage management and production planning is demonstrated. Key words: sensory evaluation, quality index method, fish freshness, Torry scale, fish storage.

15.1 Introduction: quality indices for fish Quality and production management of fish as raw material on storage is of great importance in the fish sector. Fish is a very perishable product. Supplies of fish are unstable and fresh fish can be stored only for a short time. Freshness is one of the most important aspects of fish and fish products. For all kinds of fish and fishery products freshness makes a major contribution to the quality of fish and fishery products (Olafsdóttir et al., 1997). From the moment the fish is caught the deterioration process starts and the quality of the fish for use as a food product is affected. Changes occur in composition and structure because of (bio)chemical, physical, enzymatic and bacterial influence. Sensory changes are perceived with the human senses, i.e. appearance, odour, texture and taste. Sensory methods have great advantages. They can be very fast, reliable, non-destructive on raw fish and no expensive instruments are needed. However, panellists need training and retraining under the supervision of experienced panel leaders using fish samples of known freshness stage. Sensory evaluation of raw whole fish in fish auctions and landing sites and during storage of fish in ice before processing is done by assessing

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appearance, texture and odour. Most of the scoring systems are based upon changes taking place during storage in melting ice. Characteristic changes, however, vary depending on the storage method. Knowledge of time–temperature history and handling of the fish gives important information about the quality. A characteristic pattern of the deterioration of fish stored in ice can be detected by sensory evaluation and divided into four phases (Huss, 1988). In the first phase, the fish is described as fresh with delicate taste, but in phase two, it loses its characteristic odour and taste. In phase three signs of spoilage occur but in phase four, the fish can be characterised as spoiled and putrid. Textural changes are very evident after catch at onset of rigor. Muscles become hard and stiff, rigor resolves and the muscle relaxes and becomes limp and not so elastic as before. Utilisation and interest in sensory analysis in the fish sector are growing (York and Sereda, 1993). Sensory analysis will continue to be essential, even if more cost-effective instrumental methods are developed. Sensory evaluation is used as a tool for grading according to product standards and for studying specific properties of fish species in connection with evaluation of quality, shelf-life, storage conditions and product development (Nielsen, 1997). Sensory methods performed in a proper way are rapid and accurate, and give unique information about food. They give direct measurement of perceived attributes and provide information assisting in better understanding of consumer responses. Methods to verify freshness are needed at different transaction points in the fishery chain from catch to consumer, at landing site and the auction and, at all levels of trade from auctioning via wholesale to retail (Oehlenshläger, 1997). In-house evaluation of raw materials, during producing and of products is carried out regularly in fish processing plants, in quality control for utilisation of incoming raw material, and during processing and of products for compliance with given specification. Detection of bruising, bones, scales, parasites, blood-spots, etc. is part of the inspection. The sensory evaluation is mostly performed by a single expert or to a lesser extent by a small group. In fish processing plants small sized panels often test cooked samples according to specification of buyers and retailers. Bremner and Sakaguchi (2000) put forward as an approach to the overall idea of freshness the totality of characteristics of a recently harvested product that bear on its ability to meet stated or implied requirements. The product is undamaged and shows no signs of spoilage. Freshness evaluated by senses not only describes freshness sensory properties of the fish but also include factors such as bleeding and storage, which may normally be considered as workmanship. In this chapter quality indices for fish will be dealt with, which are mainly freshness and handling or storage indices, criteria related to the specific definitions and sensory methods for determining the criteria.

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15.2 Guidelines for sensory evaluation of fish The sampling system, methods and procedures for sensory evaluation must be very well defined for sensory evaluation to serve its purpose in quality management. The sampling plan for pre-packaged food as described in the Codex sampling plans for pre-packaged foods (Codex XPT 13-1969) might be used as a basis for sampling plans. Lots and batches have to be defined before deciding the number of samples taken from each batch. Sensory evaluation can be practised at different levels in fish processing. Sensory evaluation of whole fish is generally carried out by trained assessors in the reception or processing halls of fish factories or at the auction site. However, sensory evaluation of cooked fillets must be carried out in rooms with special facilities. The Codex guidelines for the sensory evaluation of fish and shellfish in laboratories (Codex 1999) describe facilities, procedures and training of assessors and can be used as a basis for practising sensory evaluation in the fish sector. The guidelines are written with the Codex requirements in mind but can be used where sensory evaluation is used in testing fishery products for conformity requirements. The guidelines also cover a reference list of reference documents. In quality assurance systems it is most common to use a few types of sensory tests and a small number of highly trained inspectors. Martinsdóttir et al. (2009a) give guidelines on sensory evaluation of fish. 15.2.1 Facilities for sensory evaluation In sensory evaluation within the fish sector a sensory panel of trained inspectors perform sensory analysis on the daily production. To avoid errors in the sensory evaluation in daily quality control, it is necessary to follow well-defined grading systems or guidelines and standards. To get good results with sensory evaluation, assessors must be trained and have clear and descriptive guidelines. Textbooks on sensory evaluation of foods often describe the facilities required for sensory evaluation. There are international and national standards and guidelines for the design and construction of sensory assessment rooms (ISO 8589, 1988; Meilgaard et al., 1999). The recommendations in these publications are intended for establishments or situations where sensory evaluation is a major activity, e.g. R&D laboratories of food companies and research institutes. Sensory evaluation for quality control purposes must be carried out no less accurately and conscientiously than in R&D laboratories, but the requirements need not be as elaborate. They are intended to be used by specialists who need to apply sensory methods when using criteria based on sensory attributes on the products (Botta, 1995). The effects of the surroundings should be reduced as much as possible; lighting and ventilation is very important, noise level, foreign odours and distracting elements should be minimised, etc.

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15.2.2 Training of panellists The screening, selection and training of panellists are extensively described in international guidelines and standards (Codex CAC-GL31-1999; ISO8586-1, 1993). Panellists should be able to perceive odours and basic tastes and describe these in a consistent manner. They should have normal colour vision and be able to learn terminology, etc. The participants are screened for perception of odour, colour and texture if needed. The Codex guidelines also suggest a syllabus for training courses for assessors in the sensory assessment of fish and fishery products. For general guidelines for panels ISO-8586-2 is recommended for the panels of R&D work. Companies using sensory evaluation in their quality control use in-house panels. Personal characteristics such as conscientiousness and accuracy are very important when selecting people for sensory work. People should be interested in sensory evaluation and food in general. Depending on their regular duties, individuals should be readily available and healthy. Training is an expensive job and the panellists are needed on a regular basis. Highly specialised evaluators and larger panels using attributes assessments are also described by Botta (1995).

15.3 Sensory evaluation of fish During the last 50 years many schemes have been developed for sensory evaluation of raw fish. The Torry Research Station (Shewan et al., 1953) developed the first modern and detailed method. In the article score sheets for sensory evaluation of white raw fish are described and the sensory factors are classified. The general appearance and odour, texture of the fish and the flesh, including belly flaps are described. The odour, flavour and texture of cooked fish are also described. Descriptive testing can be used for quality determination and shelf-life studies. In structured scaling the panellists are presented an actual scale showing degrees of intensity. A few detailed attributes are chosen, often based on work from trained panels. Descriptive words are selected and panellists trained so that they agree on the terms and objective terms used instead of subjective (ISO 11035, 1994; Nielsen, 1995). Martinsdóttir et al. (2009b) give an overview of how to select a sensory method for the different purposes of the sensory evaluation.

15.3.1 Evaluation of whole fish: EU scheme In Europe today, the method most used and recommended for quality assessment of raw fish in the industry and the inspection service is the EU scheme, according to the Council Regulation (EC) No. 2406/96 of 26 November 1996 (Anon., 1996). In this scheme, three grades of freshness are established: E, A and B, corresponding to various stages of spoilage. E

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(Extra) is the highest possible quality, while below B is the level where fish is considered unfit for human consumption. This method gives rather limited information about the condition of the fish, as it is not species-related and does not therefore take into account the differences between species. The EU scheme is commonly accepted at auction levels; however, its use has been disputed. The EU scheme does not give results that can provide information to predict shelf-life or remaining shelf-life. The degradation is described in steps that are not continuous. A grading system like the EU scheme has several serious drawbacks. Before the fish can be categorised into grades, different quality criteria are evaluated. Whenever the sensory characteristics do not agree with all of the subscriptions of any specific grade, the grader becomes confused. This increases the time to decide the grade and decreases the objectivity of the evaluation (Botta, 1995). Statistical analysis of data from grading scale has been disputed by Land and Sheperding (1984). Treating scales as interval scales is to be discouraged. Data treatment of quality scores can be very problematic. Unidimensional scales should be used when possible. However, quality scales are often multidimensional even for single defects such as oxidation (Lawless, 1994).

15.3.2 Evaluation of whole fish: quality index method It is critical for a sensory system used in quality management that it reflects the different quality levels in a simple and documented way. Therefore, new and improved seafood freshness and quality grading systems that are both rapid and objective have been under development for various species. The quality index method (QIM) is one such system and has several unique characteristics. It is thoroughly described by Hyldig et al. (2007). QIM is based upon a scheme originally developed by the Tasmanian Food Research Unit (TFRU) (Bremner, 1985). The QIM has to be adapted to each fish species. QIM schemes have been published for following fish species: cod (Gadus morhua) (Larsen et al., 1992), herring (Clupea harengus) (Jónsdóttir, 1992; Nielsen and Hyldig, 2004), Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus) (Andrade et al., 1997; Inácio et al., 2003), European sardine (Sardina pilchardus) (Andrade et al., 1997), redfish (Sebastes mentella/marinus), deep water shrimp, fjord shrimp and peeled shrimp (Pandalus borealis), plaice (Pleuronectes platessa), brill (Rhombus laevis), dab (Limanda limanda), haddock (Melanogrammus aeglefinus), pollock (Pollachius virens), sole (Solea vulgaris) and turbot (Scophthalmus maximus) (Martinsdóttir et al., 2001, 2004), gilthead seabream (Sparus aurata) (Huidobro et al., 2000), farmed Atlantic salmon (Salmo salar) (Sveinsdóttir et al., 2002, 2003), frozen hake (Merluccius capensis and M. paradoxus) (Herrero et al., 2003), Mediterranean hake (Merluccius merluccius) (Baixas-Nogueras et al., 2003), octopus (Octopus vulgaris) (Barbosa and Vaz-Pires, 2004), flounder (Paralichthys patagonicus) (Massa et al.,

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2005), Mediterranean anchovies (Engraulis encrasicholus) (Pons-SánchezCascado et al., 2006), cuttlefish (Sepia officinalis) and broadtail shortfin squid (Illex coindetii) (Vaz-Pires and Seixas, 2006), farmed Atlantic halibut (Hippoglossus hippoglossus L.) (Guillerm-Regost et al., 2006), tub gunard (Chelidonichthys lucernus) (Bekaert, 2006) and Arctic charr (Salvelinus alpinya) (Ogombe et al., 2008). Additionally, schemes similar to QIM, based on the originally developed TFRU scheme have been developed for other species, such as cultured sea bream (Sparus aurata) (Alasalvar et al., 2001) and cultured and wild sea bass (Dicentrarchus labrax) (Alasalvar et al., 2002). There are some concerns that the total QI may be influenced and reduced by washing the fish. This has been studied by Huidobro et al. (2001), who showed that the QI was significantly reduced at later storage stages by washing gilthead seabream (Sparus aurata). In contrast, Inácio et al. (2003) found no such effects by washing of horse mackerel (Trachurus trachurus). Warm et al. (1998) described development of the QIM for frozen cod. Jensen and Jørgensen (1997) used QIM on thawed whole cod and concluded that the method was well suited for industrial quality grading of frozen raw material. Bonilla et al. (2007) published a QIM scheme for fresh cod fillets. Lyhs and Schelvis-Smit (2005) have published schemes for other products like air and modified atmosphere (MA) packed maatjes herring (Clupea harengus). QIM has several advantages, including estimation of past and remaining storage time in ice (Botta 1995; Hyldig and Nielsen, 1997; Luten and Martinsdóttir, 1997; Martinsdóttir et al., 2001, 2004; Barbosa and Bremner, 2002). The method is based on characteristic changes that occur in raw fish. QIM is based on significant, well-defined characteristic changes of outer appearance attributes (eyes, skin, gills, smell) for raw fish and a score system from 0 to 3 demerit (index) points. The scores for all of the characteristics are summarised to give an overall sensory score, the so-called quality index. In Table 15.1 an example of a QIM scheme is given. The scientific development of QIM for various species aims at having the quality index increase linearly with the storage time in ice (Nielsen, 1995). The descriptions of each score for each parameter are listed in the QIM scheme. The assessor must evaluate all the parameters involved in the scheme. As the quality index increases linearly with storage time in ice, the information is well suited to use in production management. The QIM is well suited to teach inexperienced people to evaluate fish, train panellists and monitor performance of panellists. When applying the QIM schemes, the outer appearance of the fish, eyes, gills and texture are evaluated. Figs 15.1 and 15.2 show the eyes and gills of a very fresh newly caught ocean perch and Fig 15.3 and Fig 15.4 show the eyes and gills of caught ocean perch near the end of shelf-life. The odour of gills is evaluated, and for some species the odour and mucus of the skin is also evaluated. The colour of blood and fillets (or the cut surface at the

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Table 15.1 The QIM scheme for farmed salmon (Sveinsdottir et al., 2003) Quality parameters

Description

Skin

Pearl-shiny all over the skin The skin is less pearl-shiny The fish is yellowish, mainly near the abdomen Clear, not clotted Milky, clotted Yellow and clotted Fresh seaweed, neutral Cucumber, metal, hay Sour, dish cloth Rotten In rigor Fingermark disappears rapidly Finger leaves mark over 3s

0 1 2 0 1 2 3 0 1 2

Clear and black, metal shiny Dark grey Matt, grey Convex Flat Sunken

0 1 2 0 1 2

Red/dark brown Pale red, pink/light brown Grey-brown, brown, grey, green Transparent Milky, clotted Brown, clotted Fresh, seaweed Metal, cucumber Sour, mouldy Rotten

0 1 2 0 1 2 0 1 2 3

Colour/appearance

Mucus Door

Texture

Eyes

Pupils Form

Gills*

Colour/appearance Mucus Odour

Abdomen

Blood in abdomen Odour

Blood red/not present Blood more brown, yellowish Neutral Cucumber, melon Sour, fermenting Rotten/rotten cabbage

Maximum sum (quality index):

Points 0 1 2

0 1 0 1 2 3 24

* Examine the side where the gills have not been cut through.

flaps) is evaluated in gutted fish. For some fish species that are not gutted, such as redfish, dissolution of viscera is evaluated as well. The QIM has been reported as a rapid method (Larsen et al., 1992). For use of QIM and other measurements of quality in a quality assurance system a sample has to be taken from a lot. A homogeneous lot has to be defined. It could be a

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Fig. 15.1 Eyes of a newly caught ocean perch.

Fig. 15.2 Gills of a newly caught ocean perch.

catching day but a catch from one catching day could consist of different batches if the boat is fishing from different fishing grounds within a day. Individual fish spoil at different rates. Some conclusions can be drawn on sample sizes from controlled storage experiments. In such experiments wild

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Fig. 15.3 Eyes of ocean perch near the end of shelf-life.

Fig. 15.4 Gills of ocean perch near the end of shelf-life.

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fish might be taken from the same haul of a trawler or farmed fish slaughtered from different days from the same processor for the whole set-up. Sveinsdóttir and others (2002, 2003) reported that assessing three fish per lot storage time of salmon might be predicted within ± 2.0 days at the 95% significance level, but examining greater number of salmon per lot might increase the precision. By using five fish per lot the storage time was predicted within ± 1.4 days. Larsen et al. (1992) reported that when using an average of the assessor’s score it is possible to predict the remaining storage life of the fish to ± one day. The method was very fast (approximately five minutes to assess ten fish resulting in an average demerit point). In a reference manual (Martinsdóttir et al., 2001) guidelines are given for freshness assessment of whole fish by the QIM. A minimum of three fish to maximum of ten fish should be included in the assessment of each lot of fish to cover some of the biological difference in spoilage rate of fish.

15.3.3 Evaluation of raw fillets Grading schemes for fillets for quality need to describe different sensory attributes such as appearance, odour and textures. The main sensory attributes are odour, gaping, blood veins in belly flaps, bloodstains, bruises, colour of muscle, number of areas damaged by guts and visible cod-worms. Some are related to freshness and some to handling. Defects such as bruises and blood-spots can be measured and counted and worms can be counted and compared with standards. A grading scale for fillets was reported by Learson and Ronisvalli (1969). Scores were given from 5 to 0 and for each score odour and appearance are described. A freshness quality grading scheme for cod fillets was suggested by Martinsdóttir and Stefansson (1984). The results of the QIM system for evaluating fillets has been reported to correlate with the length of chilled storage (Bremner et al., 1987). A QIM scheme for fillets from thawed cod has been developed (Warm et al., 1998) and a similar schemes were developed for fillets of cod (Bonilla et al., 2007).

15.3.4

Evaluation of cooked fillets: Torry scale, quantitative descriptive analysis (QDA) method For sensory evaluation of fish fillets, it is common to cook the fillets and evaluate their odour and flavour. The Torry scale is the most widely used scale for evaluating the freshness of cooked fish (Martinsdóttir, 1997), but sensory profiling is also used in research laboratories in Europe (Hyldig and Nielsen, 1997). The Torry scale is also used in the fish industries of some countries and by buyers of fish products. It is a descriptive 10 point scale developed at the Torry Research Station for lean, medium fat and fat fish species. Scores are

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given from 10 (very fresh in taste and odour) to 3 (spoiled). It is considered unnecessary to have descriptions below 3, as the fish is then not fit for human consumption. The maximum storage time of fish can be determined by sensory evaluation of cooked samples. The average score of 5.5 has been used as the limit for consumption (Martinsdóttir et al., 2001, 2004). At that stage members of the sensory panel detect evident spoilage characteristics, such as sour taste and hints of ‘off’-flavours. In storage studies in the EU project ‘Development and Implementation of a Computerised Sensory System (QIM) for Fish Freshness’ a linear relationship was found between QI of raw material and Torry score of cooked fillets (haddock and cod from two seasons (Martinsdóttir et al., 2001, 2004). This indicates that using the QIM on whole raw fish could replace sensory evaluation of cooked samples. The QIM is more rapid and is performed earlier in the production chain. The Torry scale provides limited information about how the individual characteristics of the cooked fish change through the storage time, but by using quantitative descriptive analysis (QDA), much more detailed information can be gained. QDA is a sensory method (Stone and Sidel, 1992, 1998), which may be used for a detailed description of the sensory profile for a product (Green-Petersen et al., 2009; Ogombe et al., 2008) and in addition the determination of maximum shelf-life (Sveinsdóttir et al., 2002; Bonilla et al., 2007; Wang et al., 2008). With the QDA, all detectable aspects of a product are described and listed by a trained panel under guidance of a panel leader. The panellists make a list of concepts/words describing the product. The list is then used to evaluate the product and the panellists quantify the sensory aspects of the product using an unstructured scale. The panellists are trained in using an unstructured scale for each of the concepts, before participating in the sensory analysis of the product. The words used to describe the odour and flavour of the fish can be grouped into ‘positive sensory parameters’ and ‘negative sensory parameters’, depending on whether they described fresh fish or fish at the end of the storage period (Sveinsdóttir et al., 2002, 2003). The fish had reached marginal acceptability when the negative attributes dominated.

15.4

Developing a quality index

Information or detailed guidelines on how to develop a quality index are scarce in the literature. Hyldig and Nielsen (1997) and Hyldig et al. (2007) described test procedures for development of QIM schemes. Whenever QIM is used for new species, storage studies must be conducted to ensure that appropriate criteria and their corresponding defined characteristic are included in QIM schemes. There are mainly three steps in the development of a new QIM scheme (Sveinsdóttir et al., 2003). Firstly a preobservation is conducted. Two or three experts in sensory evaluation of fish

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observe fish that have been stored for different periods in ice. All changes occurring in appearance, odour and texture during storage are listed in a preliminary scheme (ISO 11053, 1994). The next step is development of the QIM scheme and training of a QIM panel. Prior to a shelf-life study several sensory sessions should be undertaken for the development of the scheme and training of the QIM panel. In each session, three or four different groups of fish that have been stored for different periods of time in ice are observed. During the first sessions the preliminary scheme is explained to the panellists while they evaluate fish identified as being stored for different periods. Special attention is given if the evaluation of some quality parameters is destructive to the fish. Suggestions of changes should be taken into consideration. The panellists are trained during the next sessions by evaluating the samples without knowledge of the storage period prior to the sessions. During the development of the scheme some parameters might be removed from the scheme if they are destructive to the sample when evaluated. Changes in the selection of words to describe the changes more precisely might be made. In the third step QIM assessment is used in a full-scale shelf-life study where throughout the storage trial the fish must not be handled so each time a new sample is taken. In parallel with this a trained sensory panel working in recommended facilities (ISO 8589, 1988) should conduct a sensory evaluation of cooked samples to estimate the reasonable maximum shelf-life that can be obtained in this way. The QIM scheme should be applied to evaluate the fish at least every third day during the shelf-life of the fish. Preferably five fish should be evaluated each of the storage days, but more may be included if the fish are small. During storage experiments chemical and microbiological indices might also be measured to follow the spoilage pattern and to use for comparison (Sveinsdóttir et al., 2002, 2003). The shelf-life study should be repeated to observe if the same slope was found between the quality index and storage time in ice. Data analysis is an important part of the development. O’Mahony (1986) describes statistical methods and procedures for handling data from sensory analysis. The results from the shelf-life studies should be fitted into a linear relationship and studied. The linearity of the quality index should be checked. The changes of all attributes during the storage should be studied and the weight of scores might be changed to obtain a quality index with higher correlation to storage time. To obtain a better understanding of how the different quality parameters of fish change with storage time the results could be analysed with multivariate statistical methods (i.e. principal component analysis, PCA) (Martens and Jørgensen, 1997). The accuracy of the quality index to predict storage life could be analysed using partial least square (PLS) regression. PCA might also be used to decrease the numbers of attributes needed. However, the attributes have to be of a sufficient number to give a possible score of reasonable magnitude (Bremner et al., 1987).

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15.5 Using quality indices in storage management and production planning 15.5.1 Shelf-life QIM for whole fish can be used to predict storage time in ice, remaining shelf-life and Torry scores of cooked fillets in storage management and production planning. The end of shelf-life is defined as the number of days that whole, fresh (gutted) fish can be stored in ice until it becomes unfit for human consumption. The shelf-life of fish is thus the whole period of time in which it is regarded as being fit for human consumption. Spoilage due to microbial activity is the main limitation of the shelf-life. Another cause of spoilage may be rancidity, especially in fat fish species. The estimated storage time in ice is defined as the number of days that the fish has been stored in ice. From these results, a prediction can be calculated for the remaining shelf-life (= shelf-life-estimated storage time). It is emphasised that remaining shelf-life should be used with some precaution due to the uncertainty in the estimation. Various factors can affect the remaining shelf-life; it depends on the handling of the fish. Rapid cooling after the catch and an uninterrupted cold storage, different fishing gear, bleeding and gutting methods are important and the season and catching ground can also have an effect. Martinsdóttir et al. (2001, 2004) give an estimated shelf-life of 12 fish species, assuming optimal storage conditions, i.e. storage in ice without fluctuations in temperature. The shelf-life and the estimated storage time in ice are based upon the outcome of very wellcontrolled storage experiments with whole, fresh (gutted) fish stored in ice under good manufacturing conditions on board the vessel, which implies proper gutting, washing and use of fish/ice ratio. The end of storage time is defined when a trained sensory panel detects spoilage flavour in cooked samples of the fish. Estimated shelf-life for most species are from 13 to 18 days in ice but may be shorter (8 days for herring and 6 days for shrimp) but longer for salmon (20 days). A linear relationship between the quality index and storage time in ice has been found and the best fit of the regression lines calculated for each species are also shown in the manual. The regression lines are used to predict storage time in ice after evaluation of the quality index and to find the remaining shelf-life. This information can be used in quality and product management of stored fish in ice.

15.5.2 Quality control charts and statistical analysis Whenever the results of freshness grading is conducted as a part of a quality control program, analysis of the data might consist of comparing the results with a lower limit and upper limit or to both lower and upper limit, which have previously been established by the management and the buyer. When QIM results are used these limits refer to the total of demerit points or the

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quality index. When control charts are used to monitor seafood freshness of a specific seafood over time they permit immediate detection of trends and out of control conditions, allowing appropriate handling and processing procedures to be corrected and the variability to be reduced. The preparation of bar graphs of the QIM scores of a particular group of samples such as a specific batch of samples or the samples from an entire production lot reveal the variability and central tendency of the quality index scores. Control charts are used to monitor the output of a process to determine if the process is in control. Users of control charts have statistical criteria to distinguish between random variability and assignable causes. When sensory panel data are used in a statistical process control program it is important to distinguish between panel mean for an individual sample of product and the mean of a small group of production samples. If only a single sample of product is collected during each of the periodic quality control samplings the sensory panel yields only one piece of raw data (panel mean) about the state of the process at that point of time regardless of the number of individuals on the panel. A third source of variability of particular importance to sensory data is measurement variability. The instrument is the panel, which can be sensitive to a variety of factors that can influence the evaluations. Quality standards can be used for data from a sensory panel as for any other analytical data. There are two components of variability: within session variability and between session variability. Within session variability can be reduced by increasing the number of panellists who participate in the session; assuming they are well trained and calibrated. Between sessions variability might arise from changes in testing environment, general shifts in the calibration of the panellists, etc. Good analytical test controls must be exercised to keep those sources of variability to a minimum. The maintenance of the panel is very important (Muñoz et al., 1992). Using analysis of variance to analyse results of some types of result from grading systems can be problematic. The results may not meet the underlying assumptions necessary to conduct analysis of variance (O’Mahony, 1982). Analysis of quality index scores is based on the total demerit points not on individual assigned grades for each attribute. These ranges are much larger than the range normally used for grading and some assumptions when statistically analysing results of quality index scores may be used (Botta, 1995). The sum of scores may be normally distributed even though the individual scores are not.

15.6 Keeping fish under different storage conditions Bremner et al. (1987) described estimation of time–temperature effects by the QIM. Two major determinants of deterioration in chilled seafood, namely microbial spoilage and nucleotide degradation follow similar temperature functions. There is also evidence that the sum total of all effects

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of all the degradative processes also follow a similar pattern up to 15 °C when other factors may intrude. Whatever method of calculating change in the stored seafood is used it must respond to temperature in the same way as the seafood itself. If the method does not have this property it can only be useful at or close to particular temperature. The use of sensory methods as integrators of time and temperature has rarely been considered. Most experiments are done at one temperature – generally in melting ice. Furthermore some scoring systems do not allow for continuous assessment or sufficient differentiation between samples. In the Torry scale the fish undergoes a series of discrete changes during storage and the fish is given an appropriate score according to its description. Descriptive scales are usually constructed to give a linear progression. Bremner (1985) outlined an alternative approach to have one common measurement for the quality of fish with different temperature histories. The system should be used with fish in any form such a whole, gutted or in modified forms for fillets. Once the pattern of spoilage for a particular species has been established the demerit point score can be related directly to a suitable reference temperature such as days in ice. The remaining shelflife or time elapsed post mortem can be readily calculated (Branch and Vail, 1985). The fact that the demerit points of different categories appear sequentially does not affect the system because the relative rate spoilage curve is based on the relative time at different temperatures to reach specified spoilage levels. Most degradation phenomena follow similar patterns. Research has shown that relative rates of increase in K value and the demerit points were similar for two fish species kept in ice and at 24–26 °C. Most emphasis has been on chilled storage but the system should be applicable to frozen seafood. The system is suited for in-plant control and any commodity that alters with time and temperature. Further research is needed to evaluate the applicability of the QIM for fish stored under different conditions such as frozen – thawed fish, storage in ice slurry, temperature abuse during storage, etc. This is very important because in reality fish is not always kept under best storage conditions in ice and furthermore new packaging techniques to extend the shelf-life of fish alter the spoilage pattern and this has to be taken into account when using the QIM evaluation. The ideas of Bremner should be studied further about time–temperature integration and building models using the sensory method based on QIM for prediction of shelf-life at different storage time and temperature.

15.7 Future trends SeafoodSense Kolbrún: Research effort during the past years funded by the EU has focused on developing more objective sensory methods for each fish species. The need for quality measurements, monitoring and labelling

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of fish has also been discussed. One of the outcomes of the EU-funded project ‘Evaluation Fish Freshness’ (EU AIR3 CT942283) at the end of 1997 was that the QIM was considered by the European fish research institutes as a scientifically valid objective sensory method for determining fish freshness (Olafsdóttir et al., 1997). Results from a finalised EU-funded project, ‘Development and implementation of a computerised sensory system (QimIT) for evaluating fish freshness’ (Craft Fair FA-S2-9063), have shown that QIM is a rapid and reliable method to assess freshness in practical circumstances of auctions and processing sites (Luten, 2000a). The number of QIM schemes developed at research level is growing steadily. The results from the on going EU Concerted Action ‘Fish Quality Labelling and Monitoring’ (Fair PL98-41) shows that the level of acceptance of QIM by the partners within the fishery chain is growing (Luten, 1999, 2000b). However, a critical phase of a further and broader implementation of QIM in the fishery chain has now been reached. The establishment of QIM Eurofish in April 2001 is therefore a step forward to implement the QIM method in the fishery chain (www.qim-eurofish.com). The web-page is updated with information on popular and scientific publications.The mission of QIM Eurofish is to stimulate the use of the QIM as a versatile tool within the fishery distribution and production chain in Europe. The last 25 years there has been an increasing interest in improved quality assurance procedures in the fish processing industry (Howgate, 1987; Luten and Martinsdóttir, 1997). It is very important that all partners in the fishery chain – fishermen, fish auctions (ports of landing), fish processors and retailers – agree on using the same standardised method for evaluating the quality (freshness) of fish. To speed up and shorten the time between catch and processing, it is now common to sell fish via computerised fish auctions and in some cases fish is even sold before landing. However, buying fish unseen can be difficult, as it requires reliable information on fish quality. Using the same method for quality/freshness evaluation of whole fish will facilitate trade on fish. Commerce via the Internet will increase and computerised information on the freshness of fish will be a necessity. Fish auctions and the fish processing industry are interested in having valid methods to facilitate grading of the raw material and to ensure its quality. Information systems are used in the fish industry to ensure traceability in the inner control system of companies as required by the EC directives. Modern quality assurance systems require monitoring, controlling and recording of important quality parameters and parameters that might be critical throughout the production chain. Information about temperature and time from catch are of course of major importance. To verify this information sensory methods like QIM are very useful tools. It is foreseen that the quality index will be useful to give feedback to crew members of fishing vessels about the quality of their catch, which may influence better handling onboard. Fish processing plants using raw material from their own vessels

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have records of time/temperature but they also have to rely on supplies from auctions or other sources and would like to have information on how fresh these catches are. Buyers of fish often use different sensory methods and sensory schemes often demand that the processors use the same method. It would minimise cost and effort if the buyers and sellers used the same method. For tracing and tracking of fish throughout the whole fishery chain it is also recommended to apply accepted standardised methodologies for the determination of quality (freshness). The quality attributes of a batch can be defined at each stage and labelled. To ensure traceability and labelling, methods are needed to measure and verify the quality at certain stages. An EU-funded project EU Traceability of Fish products (Concerted Action QLK1-2000-00164) dealt with traceability of fish and fish products. Information is needed about properties of fish products and the inevitable changes that occur in the production processes (http://www.tracefish.org). This facilitation of full-chain traceability for fish products will in the end aid the consumer in guaranteeing safe and healthy products with well-documented characteristics. Consumers are demanding more information about the quality of fish and fish products and consumer acceptance of fish is related to its freshness. Consumers might not be able to detect all the freshness/spoilage stages of fish preparing their meal but the sulphuric and nitrogenous compounds formed during storage are usually not appealing to consumers. In some countries consumers more often have their meals of fish in restaurants than at home. The demand for freshness or knowledge of the freshness stage will be from retailers. Retailers play a significant role in influencing consumer perception of quality (Bisogny et al., 1987). Most of the fish is now sold at supermarkets instead of from a fishmonger. The supermarkets are already demanding information on freshness of fish and fish products from their suppliers even though this information is not stated on the packages in the supermarket. How consumers perceive edible fish products, what sensory attributes they use to describe and discriminate between species and what sensory factors contribute most to consumption have been studied by several authors. Consumer awareness of quality factors related to fish has also been studied. High correlation was found between descriptions of trained panels and the opinions of consumers even though the trained panel used a wider range of intensity scale (Sawyer et al., 1988; Bech et al., 1997; Sveinsdóttir et al., 2009) showed it is possible to establish a link between consumers demand for taste quality and attributes from sensory profiling. Food choice is under a wide range of influences beyond sensory considerations (Marshall, 1988). However, perception of taste determines a major part of overall attitudes of buying fish (Bredal and Grunert, 1997). Perceived taste has been found to be significant in determining the frequently of consumption (Nauman et al., 1995; Myrland et al., 2000).

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Objective, standardised sensory methods would make information on fish quality more reliable and readily accessible and would facilitate and enhance quality and process management in the fish industry. Moreover, a standardised sensory method would facilitate communication between buyers and sellers of fish and fulfil the demands of inspection authorities and regulations for tracking and tracing information about the quality of fish. In processing and distribution of seafood the focus should be on maintaining the freshness of the products as needed for consumers to receive high-quality product. Unique information on the quality of the products at each stage in the chain from catch to consumer can be obtained by using sensory methods to evaluate the quality giving useful for information for product and quality management. The seafood producer should know the consumer attitudes to, motives for and barriers to fish consumption to be able to choose market strategies. Although consumer liking and attitudes differ somewhat between countries, different segments of consumers can be found within each country and are comparable to segments found in other countries (Martinsdóttir et al., 2008).

15.8 Acknowledgements Special thanks to my colleagues and co-authors of Sensory Evaluation of Fish Freshness. Reference Manual for the Fish Sector: Kolbrún Sveinsdóttir, Matís, Iceland, Joop Luten, NOFIMA, Norway, Rian Schelvis-Smit, Wageningen IMARES The Netherlands and Grethe Hyldig, DTU AQUA, Denmark. I also wish to acknowledge other authors of published sources I have used information from.

15.9 References alasalvar c, taylor kda, öksüz a, garthwaite t, alexis mn and grigorakis k (2001). Freshness assessment of cultured sea bream (Sparus aurata) by chemical, physical and sensory methods. Food Chem. 72: 33–40. alasalvar c, taylor kda, öksüz a, shahidi f and alexis m (2002). Comparison of freshness quality of cultured and wild sea bass (Dicentrarchus labrax). J. Food Sci., 67 (9): 3220–3226. andrade a, nunes ml and batista i (1997). Freshness quality grading of small pelagic species by sensory analysis, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action ‘Evaluation of Fish Freshness’ AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 333–338. anon. (1996). Council regulation (EC) No. 2406/96 of 26. November 1996 laying down common marketing standards for certain fishery products, Official Journal of the European Communities, No. L334, 1–14.

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baixas-nogueras s, bover-cid s, veciana-nogués t, nunes ml and vidal-carou mc (2003). Development of a quality index method to evaluate freshness in mediterranean hake (Merluccius merluccius). J. Food Sci. 3: 1067–1071. barbosa a and bremner a (2002). The meaning of shelf-life, in Bremner HA (ed.). Safety and Quality Issues in Fish Processing. Woodhead Publishing, Cambridge, 173–190. barbosa a and vaz-pires p (2004). Quality index method (QIM): development of a sensorial scheme for common octopus (Octopus vulgaris). Food Control 15: 161–168. bech ac, kristensen k, juhla hj and poulsen cs (1997). Development of farmed smoked eel in accordance with consumer demands, in Luten J, Børresen T and Oehlenschläger J, Seafood from Producer to Consumer, Integrated Approach to Quality, Proceedings of the International Seafood Conference on the occasion of the 25th anniversary of the WEFTA, held in Noordwijkerhout, The Netherlands, 13–16 November 1995, Elsevier Science BV, Amsterdam, 21–30. bekaert k (2006). Development of quality index method scheme to evaluate freshenss of tub gunard (Chelidonichthys lucernus), in Luten JB, Jacobsem C, Bekaert K, Sæbö A and Oehlenschläger J (eds) Seafood from Fish to Dish. Wageningen Academic Publishers, the Netherlands, 289–296. bisogny ca, ryan j and regenstein jm (1987). What is fish quality? Can we incorporate consumer perceptions, in Kramer DE and Liston J, Seafood Quality Determination, Proceedings of the International Symposium on Seafood Quality Determination, coordinated by the University of Alaska Sea Grant College Program, Anchorage, Alaska, USA, 10–14 November 1986, Elsevier Science BV, New York, 547–573. bonilla ac, sveinsdottir k and martinsdottir e (2007). Development of quality index method (QIM) scheme for fresh cod (Gadus morhua) fillets and application in shelf life study. Food Control 18: 352–358. botta jr (1995). Evaluation of Seafood Freshness Quality, VCH Publishers Inc., New York. branch ac and vail ama (1985). Bringing fish into the computer age. Food Technol. Aust., 37, 352–355. bredahl l and grunert kg (1997). Determinants of the consumption of fish and shellfish in Denmark: an application of the theory of planned behaviour, in Luten J, Børresen T and Oehlenschläger J, Seafood from Producer to Consumer, Integrated Approch to Quality, Proceedings of the International Seafood Conference on the occasion of the 25th anniversary of the WEFTA, held in Noordwijkerhout, The Netherlands, 13–16 November 1995, Elsevier Science BV, Amsterdam, 3–19. bremner ha (1985). A convenient easy to use system for estimating the quality of chilled seafood. Fish Processing Bull., 7: 59–703. bremner a and sakaguchi m (2000). A critical look at whether ‘freshness’ can be determined. J Aquatic Food Prod Technol. 9: 5–24. bremner ha, olley a and vail amv (1987). Estimating time–temperature effect by a rapid sensory method, in Kramer D E and Liston J, Seafood Quality Determination, Proceedings of the International Symposium on Seafood Quality Determination, coordinated by the University of Alaska Sea Grant College Program, Anchorage, Alaska, USA, 10–14 November 1986, Elsevier Science BV, New York, 413–436. codex (1969) Codex standards for methods of analysis and sampling, Sampling Plans for Prepackaged Foods (AQL 6.5), XOT 13-1969, FAO/WHO Codex Alimentarius, Rome. codex (1999) Codex standards for fish and fishery product, Guidelines for the sensory evaluation of fish and shellfish in laboratories, CAC-GL31-1999, FAO/ WHO Codex Alimentarius, Rome.

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green-petersen d, hyldig g, sveinsdóttir k, schelvis r and martinsdóttir e (2009). Consumer preference and description of salmon in four Northern Atlantic countries and association with sensory characteristics. J. Aquatic Food Technol. 18 (2) 120–143. guillerm-regost c, haugen t, nortvedt r, carlehög m, lunestad bt, kiessling a and rora amb (2006). Quality characterization of farmed Atlantic halibut during ice storage. J. Food. Sci. 71 (2): 83–90. herrero am, huidobro a and careche m (2003). Development of a quality index method for frozen hake (M. capensis and M. paradoxus). J. Food Sci. 68 (3): 1086–1092. howgate p (1987), Fish inspection and quality control in Europe, in Kramer DE and Liston J, Seafood Quality Determination, Proceedings of the International Symposium on Seafood Quality Determination, Coordinated by the University of Alaska Sea Grant College Program, Anchorage, Alaska, USA, 10–14 November 1986, Elsevier Science BV, New York, 605–613. huidobro a, pastor a and tejada m (2000). Quality index method developed for raw gilthead seabream (Sparus aurata). J. Food Sci. 65 (7): 1202–1205. huidobro a, pastor a, lopez-caballero me and tejada m (2001). Washing effect on the quality index method (QIM) developed for raw gilthead seabream (Sparus aurata). European Food Res. Technol. 212 (4): 408–412. huss hh (1988). Fresh Fish Quality and Quality Changes, FAO Fisheries Series 29, Italy, 27–61. hyldig g and nielsen j (1997). A rapid sensory method for quality management, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action ‘Evaluation of Fish Freshness’ AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 297–306. hyldig g, bremner a, martinsdóttir e and schelvis r (2007). Quality index methods, in Nollet LML (ed.) Handbook of Meat, Poultry & Seafood Quality, Blackwell Publishing, Ames, Iowa, 529–561. inácio p, bernardo f and vaz-pires p (2003). Effect of washing with tap and treated seawater on the quality of whole scad (Trachurus trachurus). European Food Res. Technol. 217 (5): 406–411. iso 8586-1 (1993). Sensory analysis–general guidance for the selection, training and monitoring of assessors. Part 1: Selected Assessors, The International Organization for Standardization, Geneva, Switzerland. iso 8586-2 (1994), Sensory analysis–general guidance for the selection, training and monitoring of assessors. Part 2: Experts, The International Organization for Standardization, Geneva, Switzerland. iso 8589 (1988), Sensory analysis–general guidance for the design of test rooms, The International Organization for Standardization, Geneva, Switzerland. iso 11035 (1994). Sensory analysis – Identification and selection of descriptors for establishing a sensory profile by a multidimensional approach, The International Organization for Standardization, Geneva, Switzerland. jensen hs and jørgensen bm (1997). A sensometric approach to cod-quality measurement. Food Quality and Preferences 8 (5/6): 404–407. jonsdóttir s (1992). Quality index method and TQM system, in Ólafsson R and Ingthorsson AH, Quality Issues in the Fish Industry, the Research Liaison Office, University of Iceland, Reykjavik, Iceland, 81–94. land d and sheperding r (1984). Scaling and ranking methods, in Piggott JR, Sensory Analysis of Foods, Elsevier, New York, 141–149. larsen e, heldbo j, jespersen cm and nielsen j (1992). Development of a method for quality assessment of fish for human consumption based on sensory evaluation, in Huss HHM and Liston J Quality Assurance in the Fish Industry, Proceed-

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ings of an International Conference, Copenhagen, Denmark, 26–30 August 1991, Elsevier Science Publishers BV, Amsterdam, 351–358. lawless ht (1994). Getting results you can trust from sensory evaluation. Cereal Foods World 39 (11): 809–814. learson rj and ronisvalli lj (1969). A new approach for evaluating the quality of fish products. Fishery Industry Res. 4 (7), 249–259. luten jb (1999), Annual Reports EU Concerted Action PL98-4174 Fish Quality Labelling and Monitoring, November 1999, Wageningen, The Netherlands, RIVO The Netherlands Institute for Fisheries Research, 22. luten jb (2000a). Development and implementation of a computerised sensory system (QIM) for evaluating fish freshness. CRAFT FAIR CT97 9063. Final Report for the period from 01-01-98 to 31-03-00, RIVO, The Netherlands Institute for Fisheries Research, Wageningen, The Netherlands, 18. luten jb (2000b). Annual Reports EU Concerted Action PL98-4174 Fish Quality Labelling and Monitoring, December, 23. luten jb and martinsdóttir e (1997). QIM – a European tool for fish freshness evaluation in the fishery chain, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action ‘Evaluation of Fish Freshness’ AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 287–296. lyhs u and schelvis-smit r (2005). Development of a quality index method (QIM) for Maatjes herring stored in air and under modified atmosphere. J Aquatic Food Prod. Technol. 14 (2): 63–76. marshall pw (1988). Behavioural variables influencing the consumption of fish and fish products, in Thompson DMH, Food Acceptability, Elsevier Applied Science, London, 219–231. martens m and jørgensen bm (1997). Multivariate data analysis used for investigation of the sensory quality of fish, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action ‘Evaluation of Fish Freshness’ AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 325–332. martinsdóttir e (1997). Sensory evaluation in research of fish freshness, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and industry. Proceedings of the Final Meeting of the Concerted Action Evaluation of Fish Freshness AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 306–312. martinsdóttir e and stefansson g (1984). Development of a new grading system for fresh fish, in Möller A, Fifty Years of Fisheries Research in Iceland, Icelandic Fisheries Laboratories, Reykjavik, Iceland, 23–31. martinsdóttir e, sveinsdóttir k, luten j, schelvis-smit r and hyldig g (2001). Sensory Evaluation of Fish Freshness. Reference Manual for the Fish Sector, QIMEurofish, IJmuiden, The Netherlands. martinsdóttir e, sveinsdóttir k, luten j, schelvis-smit r and hyldig g (2004). Reference Manual for the Fish Sector: Sensory evaluation of fish freshness. QIMEurofish, The Netherlands. martinsdóttir e, sveinsdóttir k, green-petersen d, hyldig g and schelvis r (2008). Improved eating quality of seafood: the link between sensory characteristics, consumer likings and attitudes, in Börresen T (ed.), Improving Seafood Products for the Consumer, Wodhead Publishing Ltd, Cambridge, 40–58. martinsdóttir e, schelvis r, hyldig g and sveinsdóttir k (2009a). Sensory evaluation of seafood – guidelines, in Rehbein H and Oehlenschläger J (eds), Fishery Products: Quality, Safety and Authenticity, Chichester, Wiley, Blackwell Publishing, 411–424.

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martinsdóttir e, schelvis r, hyldig g and sveinsdóttir k (2009b). Sensory evaluation of seafood – methods, in Rehbein H and Oehlenschläger J (eds), Fishery Products: Quality, Safety and Authenticity, Chichester, Wiley-Blackwell Publishing, 425–443. massa ae, palacios dl, paredi me and crupkin m (2005). Post-mortem changes in quality indices of ice-stored flounder (Paralichthys patagonicus). J. Fd. Biochemistry 29: 570–590. meilgaard g, civille v and carr bt (1999). Sensory Evaluation Techniques, 3rd ed., CRC Press, New York, 23–36. muñoz am, civille gv and carr bt (1992). Sensory Evaluation in Quality Control, Van Nostrand Reinhold, New York. myrland o, trondsen t, johnston rs and lund e (2000). Determinants of seafood consumption in Norway: lifestyle, revealed preferences and barriers to consumption. Food Quality and Preferences 11 (3): 169–188. nauman fa, gempesaw cm and bacon jr (1995). Consumer choice for fresh fish: factors affecting purchase decisions. Marine Resource Economics 10: 117–142. nielsen j (1995). Sensory methods, in Huss H H, Quality and Quality Changes in Fresh Fish. FAO Fisheries Technical Paper, No 348, Rome, 130–139. nielsen j (1997). Sensory analysis of fish, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action Evaluation of Fish Freshness AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 279–286. nielsen d and hyldig g (2004). Influence of handling procedures and biological factors on the QIM evaluation of whole herring (Clupea harengus L.). Food Res. Int. 37: 975–983. oehlenschläger j (1997). Sensory evaluation in inspection, in Olafsdóttir G et al., Methods to Determine the Freshness of Fish in Research and Industry. Proceedings of the Final Meeting of the Concerted Action Evaluation of Fish Freshness AIR3CT942283, Nantes Conference, 12–14 Nov, International Institute of Refrigeration, Paris, 339–344. ogombe c, sveinsdóttir k, magnússon h and martinsdóttir e (2008). Application of quality index method (QIM) scheme and effects of short time temperature abuse in shelf life study of fresh water arctic charr (Salvelinus alpinya). J. Aquatic Food Product Technol. 17 (3): 303–321. ólafsdóttir g, martinsdóttir e, oehlenschläger j, dalgaard p, jensen b, undeland i, mackie im, henehan g, nielsen j and nilsen h (1997). Methods to evaluate fish freshness in research and industry. Trends Food Sci. Technol. 258–265. o’mahony m (1982). Some assumptions and difficulties with common statistics for sensory analysis. Food. Technol. 36 (11): 75–82. o’mahony m (1986). Sensory Evaluation of Food. Statistical methods and procedures, Marcel Dekker Inc, New York. pons-sánchez-cascado s, vidal-carou mc, nunes ml and veciana-nogués mt (2006). Sensory analysis to assess the freshness of Mediterranean anchovies (Engraulis encrasicholus) stored in ice. Food Control 17: 564–569. sawyer fm, cardello av and prell pa (1988). Consumer evaluation of sensory properties of fish. J. Food Sci. 53 (1): 12–24. shewan jm, macintosh rg, tucker cg and ehrenberg asc (1953). The development of a numerical scoring system for the sensory assessment of the spoilage of wet white fish stored in ice. J. Sci. Food Agric. 4: 283–298. stone h and sidel jl (1992). Sensory Evaluation Practices, Academic Press, Inc., Orlando, Florida. stone h and sidel jl (1998). Quantitative descriptive analysis: developments, applications and the future. Food Technol. 52 (8): 48–52.

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sveinsdóttir k, martinsdóttir e, hyldig g, jorgensen b and kristbergsson k (2002). Application of quality index method (QIM) scheme in shelf-life study of farmed Atlantic salmon (Salmo salar). J. Food Sci. 67 (4): 1570–1579. sveinsdóttir k, hyldig g, martinsdóttir e, jorgensen b and kristbergsson k (2003). Quality index method (QIM) scheme developed for farmed Atlandic salmon (Salmo salar). Food Qual. Preference 14: 237–245. sveinsdóttir k, martinsdóttir e, green-petersen d, hyldig g, schelvis r and delahunty c (2009). Sensory characteristics of different cod products related to consumer preferences and attitudes. Food Quality and Preference 20 (2): 120–132. vaz-pires p and seixas p (2006). Development of new quality index method (QIM) schemes for cuttlefish (Sepia officinalis) and broadtail shortfin squid (Illex coindetii). Food Control 17 (12): 942–949. wang t, sveinsdóttir k, magnússon h and martinsdóttir e (2008). Combined application of modified atmosphere packaging and superchilled storage to extend the shelf-life of fresh cod (Gadus morhua) loins. J. Food Sci. 73 (1): 11–19. warm k, bøknæs n and nielsen j (1998). Development of quality index methods for evaluation of frozen cod (Gadus morhua) and cod fillets. J. Aquat. Food Prod. Tech. 7 (1): 45–59. york rk and sereda lm (1993). Sensory assessment of quality on fish and seafood, in Shahidi F and Botta JR, Seafood: Chemistry, Processing, Technology and Quality, Blackie Academic and Professional, New York, 233–262.

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16 Sensory quality control in foodservice P. G. Creed, formerly of Bournemouth University, UK

Abstract: Sensory evaluation techniques arose from the need to produce manufactured foods of consistent quality and to develop new foods for the growing consumer market. However in foodservice, these methods are often only applicable to prepared foods: the rapid service needed for satisfying restaurant customers leaves only time for a cursory, mainly visual assessment of the sensory qualities to act as a form of quality control. This relies on the expertise of the foodservice expert – the highly trained chef. This chapter reviews the differences between the panel-based methodology for food product manufacture and the more informal methods which have to be used in the customer-driven foodservice environment. It outlines how the sensory qualities of meals produced in restaurants and other foodservice outlets form only part of many factors influencing the consumer’s perception of the whole meal. Key words: catering, experts, meals, restaurants.

16.1 Introduction Using sensory analysis techniques in food manufacturing as part of the product development and quality management processes has long been part of a logical and methodical approach to ensuring the quality of processed food products (Amerine et al., 1965; Lawless and Heymann, 1998). In contrast, in the foodservice sector, where an almost immediate response is required to provide meals to consumers, these sensory techniques are usually not practicable except where foodservice operators use prepared ingredients. Indeed, textbooks specialising in the application of sensory evaluation in quality control focus on the manufacturing operation and do not put forward any ideas on its application to the foodservice situation (Muñoz et al., 1992). More recent articles by Costell (2002) and Muñoz (2002a,b) in the same area mostly limit themselves to the use of sensory quality control for product manufacture and marketing except for King et al. (2002) who included foodservice applications in their description of SQS (sensory quality system).

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This chapter reviews the importance of the consumption of food from the foodservice sector compared with the consumption of food in the home by preparation from raw or processed ingredients. The focus for sensory assessment of food products in the taste panel environment is then contrasted with the situation in foodservice establishments which can range from fast-food outlets to fine dining restaurants. This difference in the situation where food is eaten can mean that the perception of sensory quality by the consumer in the restaurant can be manipulated and modified. When food is cooked to order, informal methods relying on the sometimes intangible expertise of the chef, feedback from customers and the views of experts and critics are discussed. The chapter concludes with a case study, ideas on future trends and sources of useful information.

16.2 Aspects of sensory analysis in foodservice 16.2.1

Importance of the foodservice sector compared with the food product sector It is worthwhile comparing the amount of money spent by consumers in foodservice outlets compared with that spent on raw and processed food eaten at home. In the UK commercial foodservice sector, the market size for eating out is forecast to remain static at around £32 bn from 2009 to 2014 at 2009 prices with innovation being put forward as the key to attracting consumers during the present economic climate (Mintel, 2009a). Other market research data forecast that the amount spent on food for consumption in the home will stay at £65 bn up to 2010; about twice the amount spent on eating out (Mintel, 2005). The foodservice sector in UK has strong competition from chilled and frozen ready meals to eat at home which are often food retailers’ versions of restaurant dishes. However sales of these ready meals are forecast to decline by 4% from £1891 m in 2009 to £1821 m in 2014 at 2009 prices (Mintel, 2009b). These ready meals in turn have been thought to contain too much salt, fat and sugar, so ready-to-cook meals, which allow the consumer to cook raw ingredients with prepared sauces and marinades, are perceived as ‘healthier’ than ready meals (Mintel, 2008). The market for these ready-to-cook meals is forecast to rise from £543 m to £670 m at 2008 prices between 2008 and 2013. Overall of the £97 bn total spent in UK on food approximately 33% is provided by foodservice outlets where conventional sensory analysis methods are difficult to apply as a quality control method. On a global basis, consumer spending (at 2008 prices) on foodservice has risen from $1311 bn in 2003 to $1969 bn in 2008, an increase of 50% (Euromonitor, 2009). These figures hide large variations by region; the lowest increase of 33% in North America, to 54% in Western Europe up to the highest increase of 192% in Eastern Europe. These figures exclude hospital

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feeding. Thus it can be seen that consumer spending in the foodservice sector is substantial and growing, and therefore so is the need for foodservice operators to assure and control the sensory quality of meals to keep their customers satisfied and to stay competitive.

16.2.2

Difference in focus for assessing foods in taste panels and in the restaurant The focus for assessing foods in taste panels necessarily means eliminating as many external influences as possible through controlling and standardising lighting, utensils, seating, procedures, etc., as discussed in this book, and through the use of international standards. In contrast, in a commercial restaurant especially, the foodservice operator is deliberately varying these external factors for business marketing reasons relating to the type of customer, the location, trends in food styles and many other reasons. Both situations, the taste panel room and the restaurant, are thus artificial but for different and opposite reasons. Predictions of consumer acceptability of a food product using information collected in the controlled environment of the taste panel room would need to be modified to predict acceptability in the home. The wide variation in consumers’ behaviour in how they prepare food in the home thus makes any prediction of consumer acceptability using taste panel data very difficult. Extending this idea to predict the acceptability of meals in the foodservice environment would also be problematic. Taste panels focus on simple single component foods such as orange juice or cooked beef but in foodservice, where complete meals are served, the large number of possible interactions between meal components make the taste panel approach much more complex and difficult for any panellists. This is reflected in the limited application of sensory science applied to meals compared with individual products (Meiselman, 2008). Furthermore, if this same approach is used in a restaurant, it would have a disruptive effect on what the customer expects to happen normally while eating out. As discussed later, many more external factors have an effect on customer’s attitudes towards the overall experience other than the ‘simple’ sensory qualities of the meal components. Logistical problems make the use of customers to assess the sensory quality of their meals in a real restaurant eating environment very difficult: for example, the customer cannot be expected to make notes or fill in forms while eating the meal, or between courses, so often the only method is to ask the customer for information after the main courses have been served, perhaps while they are waiting for coffee to be served (Creed, 1997). King et al. (2004) used different times during a meal in a simulated restaurant environment and a real restaurant to present a questionnaire; at the start of the test, 15 minutes after the start, after the meal for the simulated restaurant and with the bill after the meal in the real restaurant, concluding that the respondents’ expectations and

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consequent evaluation of the meal may have been affected by the questionnaire information.

16.2.3

Comparison of the effects of the environment and sensory quality on the consumer’s assessment of food quality The wide use of manipulation of the physical environment by restaurateurs through decor, layout, style of service and type of information, etc. has received relatively little attention compared to studies on the sensory quality of foods (Meiselman, 1992). Some studies have changed restaurant decor to fit in with an Italian theme which increased perceived ethnicity but not food palatability (Bell et al., 1994). Increasing the effort required to purchase a snack item reduced customers’ normal selection rate and increased the numbers of those selecting an alternative menu item (Meiselman, 1994). Using pre-prepared foods tended to show higher hedonic and appropriateness ratings in a restaurant than in a student refectory, with laboratory testing showing intermediate results (Meiselman, 1996a; Meiselman et al., 2000). Meal items priced individually compared with a set meal price increased the selection of vegetables for the set meal although hedonic ratings did not change (Meiselman, 1996b). Attributes related to the restaurant table such as topography, neatness, quantity, harmony and emphasis can adversely affect the acceptability of the meal if not appropriate, whatever the sensory quality of single meal components (Eckstein, 1980). The acceptability of one food could be partially determined by which foods were also eaten, particularly important for prepared packaged meals (Meiselman, 1996a). In the free-choice environment, the main dish showed the largest effect on overall meal acceptability (Hedderley and Meiselman, 1995). The social environment also has effects: meal size and duration of the meal increased as numbers increased; meals eaten alone were smaller than those eaten with others, food acceptance was correlated with food intake when meals were eaten with others; and opinions of authority figures, e.g. army sergeants, had a significant influence on how much their subordinates ate and liked the food (Meiselman, 1996a). The type of meal occasion whether more planned and formal such as a special birthday meal, or less formal such as a last-minute decision to eat out will alter the relative importance of those groups of factors which consumers perceive as affecting their enjoyment of the occasion (Pierson et al., 2000). For the more formal occasion, factors connected with the restaurant procedures (paying, waiting times, tipping, etc.) were most important but for a less formal event, these factors became less important to be replaced by mood and comfort factors (decor, ambience, seating, etc.). The pace of the meal which affects the numbers of customers a restaurant can serve also has an effect on customer satisfaction (Noone et al., 2007): fine dining customers were adversely affected by a more rapid pace compared with those in more

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casual restaurants but they appreciated a rapid pace during the final payment procedure. King et al. (2004) performed five tests in a simulated restaurant environment and one in a real restaurant, adding to the context sequentially by changing the portion size of the meal, the order of meal presentation, the seating plan, allowing conversation or not, the type of dinnerware used, the decor and server attire, allowing freedom of choice for meal components (two flavour variations of pizza, salad and iced tea) and how and when the questionnaire was presented to the respondents. For tea and salad, adding the context variables made acceptability scores more similar to the final ‘real life’ situation but not for pizza. Thus it was concluded that context did not affect meal components consistently. This work was followed up by three test situations; a simulated environment, a restaurant and a large national restaurant survey (King et al., 2007). The results confirmed the previous work (King et al., 2004) using a wider range of meal components (lasagne, cannelloni, salad, breadsticks and iced tea). However, to understand the effect of all these factors on consumer acceptability in a real restaurant is probably more complicated than the research outlined above can achieve by examining the different factors alone or in combination. The natural focus of the chef is to ensure that the prepared meal components emerging from the kitchen are of the appropriate sensory quality for the type of foodservice outlet and to ensure that a final check before serving on the visual appearance of the meal will highlight any imperfections which would disappoint the expectations of the consumer. Many ways have been suggested for classifying the factors which affect the experience of consumer satisfaction during a meal. One way is to classify them into those relevant to the food, the situation and the individual (Meiselman, 1996a). Another recent method – the Five Aspect Meal Model (FAMM) (Fig. 16.1) – consists of the room, meeting (between consumer and staff), product (food and drink), management control system (organisation behind the scene) and entirety – expressing an atmosphere (Gustafsson et al., 2006). Jönsson and Knutsson (2009) have discussed extending the management control aspect of the FAMM concept but concentrated on various frameworks for performance targets rather than specific suggestions on controlling the sensory quality of meals. Meiselman (2008), using the phrase ‘dimensions of the meal’, highlights its multi-disciplinary aspects in 13 areas relevant to meal research: history, product development, foodservice, art and design, sensory experience, biology, physiology, nutrition/ dietetics, anthropology, sociology, psychology, marketing and abnormal psychology/health. This emphasises the complexity of understanding meals and how the sensory quality of the food itself is just one aspect. Thus the use of taste panels where all external influences are minimised as far as possible is suitable for studying the sensory properties of a food product in an objective manner but all the factors affecting the consumer’s

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Atmosphere

Meeting Interactions: staff/customers, customers/customers

Room Decor, lighting, etc.

Product Sensory, menu, meal type, etc.

Management control systems Ordering, billing, etc. Atmosphere

Fig. 16.1

FAMM (Five Aspect Meal Model) (adapted from Gustafsson et al., 2006, and Edwards and Gustafsson, 2008).

perception of eating food in a foodservice outlet would make the same approach almost impossible for a real eating situation.

16.2.4 Contrast between assessing a meal and a meal component Even a simple main meal can be composed of several components, so how can all the possible combinations of these components be assessed and balanced to provide some sort of overall sensory quality? The generally held belief is that the main protein source needs to be acceptable while the other components, vegetables and starch in the form of rice or potato, have less of an effect on the consumer’s perception of acceptability (Hedderley and Meiselman, 1995). The skill of the chef is to produce all the meal components and assemble them into a meal at the point where all the components will have a high level of acceptability through their optimal sensory qualities. One method to obtain an overall assessment is to convert the scores from the attributes of all the components assessed into desirability values ranging from 0 (undesirable) to 1 (totally desirable) and then to take the geometric mean (Harrington, 1965). If any one attribute causes the product to be undesirable with a zero score, this will make the overall desirable score zero (Creed, 1997). Other approaches taking the mean value of a range of attri-

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bute scores would allow any low scoring attribute to be balanced by a high scoring attribute which would not take account of a product’s particular failings. Kambe et al. (2006) used Fourier transforms of photographs of meals before and after eating and correlated the data with consumers’ assessment of mood, number of people eating, time and place of the meal using cluster and factor analysis. They concluded that the fluctuation data from the analysis of the photographs correlated well with consumers’ assessment of the mood and situation. Watz (2008) takes the concept of the meal further than just the perceptions of the consumer and marketers to look at the design of the meal, its visual aspects, its ‘mental vision’, and how the room and atmosphere interact. Reisfelt et al. (2009) also used photos of meals composed in different ways and found that consumers’ preferences were affected by demographic variables, educational level and geographical location, supporting the importance of visual cues on consumer preferences.

16.3 Formal methods applicable to foodservice 16.3.1 Information available in textbooks For institutional foodservice settings such as hospitals, care homes, schools, prisons, etc., manufactured meals are often used. These meals may be bought in or produced centrally in large-scale production kitchens using techniques such as cook–chill, cook–freeze or sous vide (Creed and Reeve, 1998; Creed, 2001, 2006). This type of food therefore offers the opportunity to use conventional sensory analysis methods to check beforehand that it will be suitable for the particular type of consumer when reheated and served. However, examining textbooks for students and practitioners studying foodservice reveals that sensory evaluation is rarely mentioned: it seems almost taken for granted that the trainee chef will build up knowledge on how many common sensory problems such as curdling sauces, drying out, etc. can be avoided or rectified through informal knowledge gained from the experienced chef-instructor or through personal experience. This is understandable as much of this knowledge can be very difficult to put into words. These textbooks tend to concentrate on cooking techniques, properties of ingredients and management of the kitchen, although individual recipes may contain guidance on the suitability of different cooking methods for different foods, systems for checking the sensory quality of food are not mentioned (Ceserani et al., 2000; Donovan, 1997). One text (Cracknell et al., 2000) recommends the use of ISO 9000 (ISO, 2005) for systems for inspecting delivery of goods, keeping samples of cooked foods to test for safety and quality with proper record keeping. Quality control is mentioned only with reference to warm-holding for cooked foods, preferably at

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65–70 °C and using customer feedback to help with overall operational objectives. On the other hand, examining the most prominent textbooks on sensory evaluation, reveals that foodservice applications are rarely mentioned, with quality control only discussed in terms of product manufacture (Carpenter et al., 2000; Gacula, 1997; Lawless and Heymann, 1998; Meilgaard et al., 2007; Moskowitz, 1988; Stone and Sidel, 2004). Other chapters in this book will provide comprehensive outlines of all these techniques.

16.3.2 Use of systems for sensory quality control in foodservice King et al. (2002) included foodservice applications in their description of SQS. This system relies on panels of trained assessors who compare products to a control and can reliably determine if a sample deviation is large enough to put the sample into the unacceptable or reject categories or small enough to match the acceptable or matching control categories. For product development applications, ballot forms using a scale from 0 (reject) to 10 (match to control) plus a relative-to-ideal scale for a number of product attributes were used. For a foodservice application where an alternative supplier was required, the relative-to-ideal scale was replaced by descriptive terms for the control and those applying to lower-quality products. In the same way that systems for assuring food safety such as HACCP (Hazard Analysis Critical Control Point) are being required in foodservice operations, Hering et al. (2006) proposed that the nutritional and sensory quality during meal production could be incorporated into a similar system using critical control points. They used this approach for the manufacture of meat-based menu components, suggesting control methods with corresponding actions for operations involved in the pre-preparation, preparation, assembly and distribution stages, many of which linked in with the control of hygiene.

16.3.3 Use of experts for specific foods linked to foodservice Many commodities important in foodservice are evaluated by experts. These experts have the power to determine what is bought by a large foodservice, hospitality or retail organisation. In contrast to the use of taste panels for assessing the sensory quality of foods, the use of experts with special knowledge and sensitive abilities enable organisations to assess the qualities of special, often high-value food products such as wine, coffee and tea, known to possess a wide range of subtle differences in colour, flavour, aroma and mouth-feel. These foods are all important sectors in the foodservice industry either as specialist outlets or in restaurants. For less special food products, experts may not be needed: Moskowitz (1996) compared the assessment of 37 sauce products by experts and consumers and concluded that for the majority of attributes, the high correlation coefficients showed

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that the notion that consumers were not capable of reliable sensory assessment was not proved. However, for wine, coffee and tea experts still have an important part to play in monitoring the qualities for these sensorially complex products. Wine Methods to help consumers choose wines often rely on wine experts to provide opinions for selecting the product range for large food retailers, wine merchants and hospitality organisations. They are trained to assess wine, leading to qualifications such as Master of Wine (Institute of Masters of Wine, 2009). This entails studies on viticulture, wine-making, the business of wine and contemporary issues, practical examinations to determine the grape variety, origin, wine-making method, quality and style of several wines and a dissertation. The language of wine experts differs from that used in normal sensory evaluation practice which aims to use objective terms often linked to reference materials. Terms such as ‘long’, ‘exuberant’, ‘managing an exclusive spirit’ appear in wine experts’ notes analysed lexically by Brochet and Dubourdieu (2001). They concluded that these terms were based on prototypes and not on analytical terms, that idealistic and hedonic terms were used in addition to more objective sensory terms and that experts developed an individual set of terms, not necessarily shared by other wine experts. Hughson and Boakes (2002) found that experts could recall wine-related words more easily than novices by making use of their greater knowledge of varietal types. If free language is used, as for example by sommeliers advising restaurant customers, the descriptions used by experts are not consistent enough to be recognised by consumers (Sauvageot et al., 2006). Therefore it was concluded that this method needs more work to become a useful tool in the food industry. In an Australian study (Gawel, 1997), trained experts were found to use concrete terms less often than untrained experts and to be more consistent assessing red wine compared with white (Gawel and Godden, 2008) while in France, Perrin and Pagès (2009) found consumers and experts agreed on judging the typicality of red wines but not white wines. The award of medals from wine exhibitions is often used as a quality cue by consumers: Schiefer and Fischer (2008) concluded that these cues can be useful for a proportion of consumers but a more consumer-oriented system of labelling is required. Etaio et al. (2010) have investigated how experts can be better trained and monitored using validated methods for food certification of products with PDO (Protected Designation of Origin) for example. Wine buyers are often experts but a study by Oberfeld et al. (2009) has shown that the ambient colour of a room can influence their perception of the wine’s flavour; blue and red ambient colours made Riesling wine taste better compared with a room using green and white ambient colours.

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The pairing of cheese with wine is another area where experts’ views are used by consumers. Bastian et al. (2009) studied experts’ and consumers’ opinions on how eight pairings should ideally be made. In six of the cases there was agreement between experts and consumers with support for anecdotal evidence that red wine goes better with cheese than white wine. One wine expert with a very strong influence on consumers’ perception of wine and hence the wine’s reputation for quality is Robert Parker (Langewiesche, 2000) who awards marks between 50 and 100 to the 10,000 wines he samples each year. His short comments on the wines accompany these ‘Parker points’, where a score in the 70s means ‘a poor wine’, in the 80s means ‘adequate’ and 90s means ‘really good’. Coffee The term for an expert at tasting coffee is a ‘cupper’ (Feria-Morales, 2002) but as there is no internationally agreed system for quality control, this widely traded commodity is graded on the basis of the experts’ personal opinions of sensory quality, its aroma, taste and mouth-feel. Feria-Morales (2002) discussed the problems caused by the weaknesses in the objectivity of experts and concluded that to improve the situation sensory evaluation by trained assessors should be compulsory, tasting should be blind, key attributes should be identified rather than imposed by traders, the range of faults should be more comprehensive and sensory quality should be much more important in the grading procedure. Willis (2008) provides practical advice on cupping based on experience of tasting Jamaican coffee. Dzung et al. (2005) compared coffee sourced from Europe and Vietnam and found significant differences only in sour, bitter and colour attributes. Tea Another commodity with a wide variety of sensory characteristics is tea, which is traded worldwide. Like coffee, expert tea tasters, often at the auction stage, are part of the chain from the grower on the plantation to the consumer in the kitchen or in the restaurant. Owuor et al. (2006) provided correlations between an important flavour component, theaflavin digallate equivalent relevant to an indicator of tea quality – brightness, and the two experts’ scores and auction price valuation. The correlation coefficients of 0.799 and 0.584 for the two experts and 0.788 for valuation were significant for teas sourced from Kenya, Central and Southern Africa.

16.3.4 Use of standards for the expert assessment For the industrial application of sensory methods, ISO standards allow harmonisation of the methods used for performing, for example, a triangle test as defined in EN ISO 4120:2004 (ISO, 2004) or paired comparison test as defined in EN ISO 5495:2007 (ISO, 2007). Standards are also available

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for expert assessors as defined in EN ISO 8586–2:2008 (ISO, 2008a) and how they can be selected, trained and monitored. This would apply notably for wine coffee and tea tasters. An expert sensory assessor is defined as a ‘selected assessor with a demonstrated sensory sensitivity and with considerable training and experience in sensory testing, who is able to make consistent and repeatable sensory assessments of various products’ while a selected assessor is defined simply as an ‘assessor chosen for his/her ability to perform a sensory test’. A prerequisite of becoming an expert sensory assessor is to possess the knowledge required to be a selected assessor (ISO, 2008b). The EN ISO 8586–2:2008 standard assumes that experts will work as a panel, an assumption that seems to be rarely complied with in most applications where assessments from just one or two expert assessors are common. The standard also requires them to have an ‘above-average’ longterm sensory memory, to undergo training to build up a thesaurus of sensory descriptors, to be able to deal with a large number of samples at one time, to have their performance monitored and to be willing to be retrained. Thus training experts according to an ISO standard can go a long way towards increasing confidence that experts are working objectively but cannot tie down the procedures in the same way as, for example, triangle and paired comparison tests.

16.4 Informal methods applicable to foodservice Systems used in restaurant kitchens to control the sensory quality of meals vary according to the type of foodservice outlet and therefore the effects of operational restrictions will also vary.

16.4.1 The chef as expert The chef has many subtle methods to ensure that the sensory quality of meals is at the level required by the restaurant and his or her own standards; knowing how to balance flavours of meal ingredients, correct seasoning, adjusting cooking methods, suggesting combinations of courses in a meal, presentation, garnishing, suggesting wine to go with dishes, etc. A normal method in a restaurant is for the plated meal to be checked visually before it is put on the hot table before the waiting staff take it to the customer’s table. In restaurants at a lower market level where meals are not cooked to order, foodservice staff can reduce deterioration in the sensory quality of hot foods by minimising warm-holding times. For example, many fast-food companies have timing systems to ensure that products stored for too long are discarded; for McDonald’s 10 minutes for burgers and 5 minutes for French fries are allowed after reaching serving temperature before they are discarded (Anon, 2009a).

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16.4.2

Use of consumer feedback to improve control of sensory quality of meals In the institutional setting, the use of questionnaires for hospital patients or care home residents is a common way of gathering data for improving the customers’ perceptions of the food and meal quality. In a rehabilitation hospital, Donini et al. (2008) relied on objective measurements, some equivalent to similar situations in a restaurant; how long the consumer had to wait; how long to deliver the food from the kitchen to the ward; the amount of waste, etc. and more subjective personal ratings from patient questionnaires; satisfaction ratings of menu range, portion size, food temperature and food quality. Through making improvements based on the results, they found that positive opinions increased from 18% to 48.3% over four years. Wright et al. (2006) and Porter and Cant (2009) studied hospital foodservice systems using a validated questionnaire for acute-care hospital patients. The question areas provided feedback on food quality, staff/service issues, meal service quality and the physical environment which could be used to monitor patient satisfaction and to allow a valid comparison with the performance of meal provision at other hospitals. Use of another validated questionnaire by Crogan et al. (2004) focused on obtaining feedback from older people in care homes and showed that it could highlight possible organisational factors for improvement and that four of the five areas (enjoying food, standards of cooking perceived, positive/negative views on the service provided but not exercising choice) correlated significantly with a measure of depression. Delahunty (2004) has highlighted the need to take account of how changes in sensory physiology in older people can influence their food liking and food intake. These changes were a decreasing sensitivity with age in identification of odour, more difficulty with chewing and mouth-feel abilities but fewer effects on taste and astringency, but effects varied with the individual. In contrast, in the foodservice industry, especially fine dining establishments, the use of formal questionnaires would probably impinge on the atmosphere deliberately created by the restaurateur. Indeed, one reason why customers may patronise a particular restaurant would be their faith that the sensory quality of the food will be almost guaranteed to be of the highest level because of the chef or the rating from a guide such as Michelin or the rating given by a particular critic. Many commercial foodservice outlets offer feedback forms but these are often very brief, to be used perhaps by those who do not wish to complain directly.

16.4.3 Use of tasting menus for developing new products This type of menu is one way for a customer to enjoy a wide range of a restaurant’s specialities without the risk that they may not enjoy a full-sized portion of a particular dish; it could also be thought to be a way of a restaurant trying out new ideas or taking advantage of seasonal produce

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(Wiseman, 2008). From the foodservice operator’s point of view, it could be considered as a taste panel. Wine tasting is another way of using customers as a taste panel to increase the uptake of wine. One study by Wansink et al. (2006) found that offering low-price tasting portions, a list of recommended wines or pairings of wine and food all led to increased sales, by 48%, 12% and 7.6% respectively.

16.4.4

The use of restaurant reviews as a method of sensory quality control Another informal method of sensory quality control can be thought of as the process where potential customers investigate the likely quality of a foodservice operation through reading reviews from restaurant critics. These critical reviews are most likely to assess the performance of restaurants at the higher end of the market if published in the ‘quality’ newspapers and magazines or to look at more local foodservice establishments if published in local and regional newspapers and magazines. In the UK, all the ‘quality’ newspapers have a range of restaurant reviews especially in the weekend editions. However, extracting information from reviews relevant to the sensory qualities of the meals served can often be problematic. Reviewers such as Winner (2009) offer a diary of culinary experiences with views designed to provoke a response interspersed with entertaining anecdotes and photographs of the author; Gill (2009) offers scatological humour alongside views on the food and restaurant; Coren (2009) also relies heavily on humour. Some reviews are in purely narrative form (Joseph, 2009), others attempt to give an overall rating, e.g., marks out of 10 (Norman, 2009) or marks out of 20 (Markwell, 2009), others split up their score into components reflecting aspects of the restaurant such as the food itself, ambience and service to give an overall average (Walsh, 2009). Others attach punning labels from one to five stars which link up to the humour of the review’s content or to the name of the restaurant (Gill, 2009). In the USA, the New York Times use zero stars for ‘satisfactory’, one star ‘good’, two stars ‘very good’, three stars ‘excellent’ and four stars ‘extraordinary’ although one wonders how many stars would be given for an unsatisfactory experience (Bruni, 2009). The system focuses almost completely on the food and surroundings and goes much further than UK reviews in providing useful information for the customer on atmosphere, sound level, recommended dishes, the wine list, price range, opening hours, reservations, credit cards and wheelchair access, which are many of the factors discussed earlier as having an effect on consumer satisfaction. The Los Angeles Times also uses four stars with half stars: no star meaning ‘poor to satisfactory’, one star ‘good’, two stars ‘very good’, three stars ‘excellent’ and four stars rarely used indicating ‘outstanding on every level’ (Virbila, 2009). Again, the review focuses on the food and surroundings and provides

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information on location, ambience, service, price, best dishes, wine list, best table, special features and other details. The Washington Post also uses stars for some critics but also allows feedback from readers (Sietsema, 2009). Overall, these US reviews appear to be more objective than their UK counterparts resisting the temptation to overwhelm the text with sometimes self-indulgent trivia. In some cases, newspapers can be sued for unflattering reviews: for example, in Ireland a critic described a pizza restaurant as having a ‘joyless, smoky atmosphere’ (Sharrock 2008). The case was eventually overturned on appeal.

16.5 Sensory quality control in foodservice – a case study When the same restaurant is reviewed by different critics, the results may not always be consistent: for example, Lutyens, a London restaurant opened in 2009 by Conran (Anon., 2009b), was reviewed by several critics at around the same time. In the first of these four reviews, Gill (2009) gave it three out of five stars, only half of the review focusing on the restaurant itself (the other half being journalistic whimsy) and of that, only a third described the food; highlighting flaws such as soup being ‘slightly too watery’ and potato ‘a touch grainy’ but smoked salmon was ‘unimpeachably excellent’. Rayner (2009) focused on the Lutyens as a French brasserie, using enthusiastic language to describe coquille Saint Jacques, lobster mousse and snails but admitting a love of the restaurant’s location in his narrative review. Walsh (2009) used a five star system for food, ambience and service, awarding three, four and five stars respectively. His review made much of the building and how the restaurant had been styled but though he considered the menu old-fashioned, the food actually served was ‘a lot better’ but was not one of the things to love about the restaurant. The last review by Joseph (2009) took the narrative approach and concentrated much more on the food and the restaurant environment with only a small amount of journalistic filler. Impressions of the food ranged from fish soup being ‘pretty good’ with ‘good’ veal cordon bleu. Reading these reviews together would probably provide useful information for the potential customer but nothing very objective, so leaving them to make decisions based on whose opinion they found most appealing.

16.6 Future trends The use of new technology may be a driving force to help overcome the difficulties in using sensory quality control methods in foodservice applications. Instrumentation such as the electronic nose aims to mimic the human nose and to detect volatile components of foods which can be used to evaluate the odour element of food quality (Zhang, 2003).

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The systems rely on sensors for specific compounds of interest combined with sophisticated data acquisition and analysis software often linked into artificial intelligence. They require ‘training’ with known odour samples to allow the results to be evaluated. Whether similar instrumentation of a practical size can be developed for checking texture, taste and colour is a question for the future but the electronic nose might certainly be a candidate to take over from experts in assessing the odours of wine, coffee and tea in view of the often subjective results. Imaging technology is another area being developed for use in the industrial production of food items such as potato chips (Pedreschi et al., 2006; Romani et al., 2009) but whether it could ever be adapted on the foodservice scale is another question. The growth of interest in ‘molecular’ gastronomy is based on focusing on the sensory quality of foods as often highlighted by well-known chefs such as Heston Blumenthal (2009), Hervé This (2008, 2009) and Thomas Keller (2008), who are popularising the concept. This new area of foodservice involving the use of unusual foods and processing techniques and equipment relies on surprise and expectations to produce novel sensory sensations (Mielby and Frøst, 2010), reflecting the need for foodservice to become more innovative (Mintel, 2009a). However, innovation and novelty have long been an objective of the enterprising chef. One example, arising in the 1970s, has been sous vide cooking (Creed and Reeve, 1998) relying on scientific studies to provide a strong base for the method in the form of reliable safety procedures and sophisticated packaging materials and cooking techniques. This has now been extended by using scientific knowledge to produce and underpin innovative cuisine (van der Linden et al., 2008; Vega and Ubbink, 2008). The products of ‘molecular’ gastronomy often involve unusual combinations of flavour and use of materials. Personal experience of new food product development teaching at undergraduate level over 20 years has revealed that there is no lack of imagination in trying out new ideas in food. Indeed, education in this area has led to courses in ‘Culinology’ where these new ideas are taught on a formal basis especially in the United States (Schnepf and O’Malley, 2005). Overall, new technology whether applied to develop equipment suitable for use in monitoring sensory quality in the restaurant or to provide new sensations in novel foods is bound to surprise the foodservice operators and their customers in the future.

16.7 Sources of further information and advice Barham P (2000), The Science of Cooking, Berlin, Germany, SpringerVerlag. Blumenthal H (2009), Total Perfection: In Search of Total Perfection, London, UK, Bloomsbury Publishing.

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Keller T (2008), Under Pressure: Cooking Sous Vide, New York, USA, Artisan Division of Workman Publishing. McGee H (1984), On Food and Cooking: The Science and Lore of the Kitchen, Upper Saddle River, NJ, USA, Prentice Hall and IBD. McGee H (2004), McGee on Food and Cooking: An Encyclopedia of Kitchen Science, History and Culture, London, UK, Hodder & Stoughton. Meiselman HL (2000), Dimensions of the meal: The Science, Culture, Business and Art of Eating, Gaithersburg, MD, USA, Aspen Publishers. Meiselman HL (2009), Meals in Science and Practice: Interdisciplinary Research and Business, Cambridge, UK, Woodhead Publishing. This H (2008), Molecular Gastronomy Exploring the Science of Flavor, New York, USA, Columbia University Press. This H (2009), The Science of the Oven, New York, USA, Columbia University Press. Food Service Research Institute, Chicago, USA – specialist marketing information – http://foodserviceresearchinstitute.com/ Research Chefs Association, USA – culinary and technical information/ networking – http://www.culinology.com/ School of Hospitality, Culinary Arts & Meal Science at Örebro University, Grythyttan, Sweden – undergraduate courses and postgraduate research – www.oru.se

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17 Sensory quality control of consumer goods other than food A. Giboreau, Institut Paul Bocuse, France

Abstract: This chapter discusses how sensory evaluation is used in various non-food sectors. The relative importance of each sense to perception in different circumstances is examined, and general guidelines regarding the set-up of tests, including environment and method, are described. Examples based on industrial case studies or international standards for different product categories are included, focusing on the critical senses involved. Key words: sensory methodology, cosmetics, cars, textiles, spectacles/glasses, environment.

17.1 Introduction 17.1.1 Sensory evaluation of non-food products The senses are stimulated at all stages of consumer behaviour, from purchase to use. Sensory analysis, based on accurate studies of sensations, is one of the most powerful tools for driving product development and is now widely used outside the food industry. Cosmetics companies have been using sensory methods for several decades, and more recently car manufacturers have followed suit. Nowadays, all consumption goods are studied through the senses. The main methodology is the use of expert (trained) panels to measure sensory properties precisely, for instance to compare new variants versus a reference, or to evaluate a new supplier or an innovative process. Experts may also be required to give a detailed picture of the competitors in the market, or to help identify new product opportunities defined by their sensory characteristics (preference mapping). 17.1.2 Non-food products and the five senses For many products today, consumers expect far more than the technical characteristics of the product would suggest. Consumers expect products to

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provide pleasure, feelings of comfort, a sense of well-being and more, and most competition between products today is about these intangible effects. Marketers and product developers therefore need new sources of innovation other than technical improvements, and manufacturers need alternative means of product quality control. This is why the five senses are now so important for all companies. The sense of sight is certainly the most important. It is the primary source of information for most instinctive actions, such as escaping danger, but it also drives all other perceptions. The visual sense is linked to internal psychological consistency. For instance, whether a fragrance is mint or eucalyptus, if the product is pink or orange the consumer will immediately think of fruit: such a product would never be linked to perceptions of freshness. In addition, appearance is directly linked to symbolic interpretations, as discussed by Bloch et al. (2003). Thus, colour and visual characteristics have to be controlled, and are most often analysed through human panels as the eye is more sensitive than many instruments. The sense of hearing is also crucial as it is the main channel for oral communication, an essential human activity. The sound environment is central in human well-being and sound sources come under demanding international regulations. Standards on noise or sound quality in transportation, for instance, are mainly based on instrumental measures, although specific descriptive approaches are now being developed (Civille and Seltsam, 2003; Bech and Zacharov, 2006). As pointed out by Dubois (2000), cognitive psychology suggests that acoustic stimuli are perceived according to their source (car engine), their function (warning) or a specific causal event (closing a door). For cosmetic products, the main acoustic issue is the sound produced when opening and closing the pack. Olfaction is the third distal sense. Odours are hardly recognised by consumers but are implicitly memorised together with simultaneous available information such as other sensations, object name and usage, symbolic value, etc. (e.g. Köster et al., 2002). They are known to be highly linked to emotion, and to strongly influence mood and individual judgement. Olfaction plays a large role in cosmetic products and perfume may reinforce the likeability of a given product or a given brand. In some cases – luxury brands or personal care products – their symbolic impact could be as high as any visual property, and linked to very deep individual memory, as so nicely described in Proust’s 1913 masterpiece. As for food packaging, smell is principally concerned with identifying defects (BS 3755, 1964; ASTM E462, 1984a; ASTM E619, 1984b; DIN 10955, 2004). The level of nondesirable odours has also been evaluated for cars (solvent, glue, material). In cosmetics, quality control is mainly concerned with fine fragrances and in ensuring they have a reliable, constant profile. Touch is a proximal sense, needing contact, and covers several dimensions that can be specifically analysed according to the product type. Tact – from tactile receptors – mostly informs on material surfaces and gentle superficial gestures on skin, and is often taken into account when assessing © Woodhead Publishing Limited, 2010

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the quality of cosmetics and materials such as textiles and plastics (Hollins et al., 1993; Picard et al., 2003). Kinesthesia involves sensations that occur while moving a body part. It originates in muscles and bones under conditions of changing pressure, and is studied in transportation (Dairou et al., 2003). Thermal sensations come from heat transfer between a material and the body surface in contact with it. Sensory approaches to thermal perception have been developed in the transportation, clothing and housing sectors (Petit et al., 2005). However, quality control still relies more often on physical modelling than sensory expert work. Pain sensations from the trigeminal nerve are important for assessing skin irritation in cosmetics, and are studied together with olfaction and taste. Somesthesia encompasses all these sensations, and is thus a major sense for cosmetic products, cars, telephones, pencils and so on, as well as for packaging. It is important to note that all haptic properties are linked to using the product: packaging handling, skin–hair–body contact, hand perception, body motion. Haptic sensations are created when the subject manipulates the object, so ergonomics should also be considered important to the final perceived quality, especially as inter-individual differences in size and physical strength have a strong impact on perception. Taste, the intimate sense, specifically concerns in-mouth sensations. It is obviously critical for oral care and lipsticks. Taste buds respond only to water-soluble compounds, such as salts, acids, sugars and peptides (Faurion, 2004). Thus, as for olfaction and volatile compounds, taste is assessed through sensory panels rather than chemical measurements because of the large number of components involved. The increasing demand for affective performance from non-food products means that manufacturers have to ensure a constant level of high sensory quality, so they need to know how to measure and control sensory properties. Although acoustic and thermal properties are mainly measured using instruments, sensory descriptive studies represent the most useful and the most used tool for achieving such control.

17.2 General recommendations Since the early 2000s, the ASTM Committee has published several non-food standards (e.g. for skin-feel ASTM E1490, 2003, deodorants ASTM E1593, 2006, and malodour ASTM E1207, 2009). In France, a ‘Non-Food’ committee has been established within AFNOR, the French standardisation bureau, gathering members from various industries, including automotive, textile, spectacles, food packaging, sportswear, and so on. Several manuals of good practice have been published in France (general guide AFNOR BPX40, 2003, tactile AFNOR BPX41, 2005, visual AFNOR BPX42, 2006), as in other countries. From a general point of view, sensory methodologies have the same objectives and are similar to those used for food. Sensory studies are used © Woodhead Publishing Limited, 2010

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to better understand consumers’ perception, to better understand formulation and processes, and to better control product quality. In non-food sectors, as in food, sensory tests are based on a stimulus–response model in the psychophysical tradition. The composition and manufacturing process for the product is controlled. Instruments are used to measure physical and chemical properties and sensory approaches are used to measure the related sensations. Psychological perceptions linked to personal and cultural memory and meaning of sensations are not considered relevant in the quality control process, although they are in marketing. In order to control the sensory quality of non-food products, most companies use dedicated physical measures. Although sensory approaches can go further in accounting for the complexity and multidimensionality of how non-food products are perceived, they are not systematically used for quality control in most industries. Each type of non-food product has a specific vocabulary and protocols linked to it, but there are some general characteristics that should be taken into account when setting up a sensory laboratory, as given in Table 17.1 and described below.

Table 17.1 Some recommendations for setting up a sensory laboratory for non-food products Visual

Tactile/thermal

Acoustic

Olfactory

Room

Only artificial light

Acoustic walls

Air renewal temperature

Booth

Dark surface (dark materials) Large size (seats/doors) Mirrors (cosmetics) Controlled light Distance Vary lights Temporality

Temperature Hygrometry Air control Holding support (textiles) Curtains (for blind evaluation)

Critical points for protocols

Data

Ergonomics (subject’s size) Gestures (subject’s action) Environment (outside variation)

Headsets Low frequency loudspeakers

Recording of real sounds (acoustic head)

Odours in bag (sampling)

Portable acquisition (out of lab) Free sorting acquisition and analysis software

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17.2.1 Room/environment As for all sensory tests, artificial lighting and temperature control are necessary in order to standardise the environment. Generally speaking, lighting is often better controlled in non-food industries, as appearance is central for object recognition and identification of use. Instead of a simple red or green filter with no specification regarding the light delivered, as is often encountered in food sensory laboratories, the architecture and transport industries in particular use light cabins where the light spectrum is accurately calculated (International Lighting Committee (CIE)). This allows researchers to take into account both daylight, for instance for the perception of car paint quality, and indoor light, such as lighting in salesrooms. In cosmetics, particularly for make-up, such contrasts in lighting are important because they can reproduce the differences between light in the bathroom, indoors and outdoors. Together with systematic temperature control, it is also important to control hygrometry, especially for sensory evaluation of materials such as plastics and textiles, where hand moisture and material state can be affected by humidity. For thermal properties, aeration strength should be taken into account as well as its direction and distance from the subject, as these parameters influence the dynamic responses of thermal sensors. Acoustic quality control measures are conducted in small acoustic labs with protected walls, floor, sealing and doors. Olfactory controls are only performed in rooms with strong air renewal systems, or by using specially designed chambers.

17.2.2 Booths Many non-food materials may be dark in colour (cars, clothes, telephones, etc.) and white testing environments should be avoided as they produce a strong contrast and lower the perception of small variations in the samples. Although light-coloured materials might also be studied, the compromise bench colour is an intermediate grey, with a matte surface to avoid reflection. When large samples are studied, as in the car industry, booths are wider (120–140 cm) and the light dispenser is higher (80–90 cm). Cosmetic products need mirrors for self-evaluation. Direct and indirect lights should be available inside the booth to reproduce the bathroom environment. In order to standardise the way subjects manipulate fabrics or papers, specific holding supports can be used. For tactile measures, evaluation is often performed blind (to remove any visual information). A convenient system is to place a curtain in the booth through which the subject can use his/her hands naturally, but without seeing what they are touching. Acoustic samples can be reproduced using headsets, and low-frequency loudspeakers are useful for engine sounds. Each booth should include a data acquisition system. Portable systems are preferable, as they can be used

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in the laboratory as well as on-site, and many non-food situations are difficult to reproduce in the laboratory.

17.2.3 Protocols Protocols are extremely important for non-food products. Biological differences must be taken into account, as there may be large inter-individual differences between subjects. For instance in cosmetics, differences in skin type and hair type are important; for transportation or clothing, body size, hand size, leg length, weight, physical strength and others will be important. Cultural differences mean that the same product may have a wide diversity of uses. The experimenter will need to reproduce these uses in the evaluation protocols. To do so, it will be necessary to study the differences – in terms of gestures, environmental conditions, sequence of actions – and then to classify them into typical usage sequences accounting for the most significant differences. This is particularly important for tactile and haptic measures, where the sensations are created by the subject when he/she uses the product. Auditory and olfactory sampling is a third difficulty in non-food sensory protocols, most notably for the complexity of the changing environmental conditions that must be considered when sampling.

17.2.4 Scales A common method is to rate a sample against a reference. The reference is usually either a production standard or a controlled sample, such as a butanol solution used to evaluate intensity in olfactory tests. Descriptive scales of absolute intensity are also used for quality control of non-food products. In this case, panels are trained over a long period of time to ensure knowledge of all possible sensations for the specific product space. Semihedonic scales, i.e. those incorporating a value judgement, are still encountered in some professional practices. Free sorting approaches are also being developed for positioning a large number of samples according to quality, mainly for acoustic, olfactory and tactile applications, such as fabrics or other materials (Faye et al., 2004).

17.3 The control of sensory quality of non-food products: cases Few publications have been found in scientific journals but methodologies are reported in national and international standards documents. Some case study examples from published information are described here (see references below).

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17.3.1 Personal care The five senses can be addressed in cosmetics: olfaction is central for emotional response (Bensafi et al., 2002), taste is important for lipsticks, and sound for skin dryness or packaging materials. However, sight and touch are the most commonly addressed senses, and appearance and texture are the most studied characteristics. The cosmetics industry was the first sector to develop sensory approaches (Aust et al., 1987; Civille and Dus, 1991; Giboreau, 2007). Descriptive methods originally come from the food industry and have been applied to cosmetics since the 1980s (Lawless and Heymann, 1999; Meilgaard et al., 2006), with dedicated cosmetics standards (ASTM E1490, 2003). As in food, the main technical points are: • the recruitment, selection and training of the sensory panel; • the use of specialised words – sensory attributes or descriptors; and • the exact definition of these terms to help panel members to identify, recognise and rate each attribute on intensity scales. Application protocols are an important aspect when using sensory approaches to describe personal care products. A limited number of samples can be handled in one session (for instance three to four creams applied on the forearm), and half-face applications are often used for comparison. Another important consideration is how products interact with different skin, lip or hair types, which has to be taken into account by having larger panels than in the food industry. The effect of subjective factors on the results is more important for personal products than for food, and descriptive approaches must take both product and subjective aspects into account. For instance, make-up products and skin care products are described both physically (appearance, etc.) and through the effects or feelings on the skin. Sensory methods are now used extensively in the cosmetics industry for control, development and marketing, with a particular emphasis on touch and skin-feel (e.g. Bacle et al., 1999; Wortel and Wiechers, 2000; Van Reeth, 2006).

17.3.2 Glass products Products made of glass, such as spectacles and watches, and objects made of transparent or translucent plastics, such as food and cosmetics packaging, are considered together here because they rely mainly on visual quality control. While haptic properties, especially weight, contribute to overall consumer perceptions of quality, they are not, as far as we know, studied as quality control criteria. At Essilor, all quality control for lens production is carried out in the factory by professional experts (Fauquier, 2009). They visually examine (vision in transmission) the homogeneity of monomers under a controlled light, checking for transparency defects and image distortion. After

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polymerisation, 100% of the lenses are checked. These raw materials are then covered with specific varnishes to make the lenses more solid and resistant to scratches. After varnishing, experts check for the absence of dripping (waves), deposits (craters), scratches or dust in the varnish. For the final anti-reflective coating, experts examine the lenses for the colour of the anti-reflective varnish, the presence of dust and any heterogeneity. For sunglasses, colorimetric measures are conducted. For corrective sunglasses, both lenses are coloured in the same batch. If one lens has to be produced separately, the colour is adjusted in successive batches to the other lens, with visual control to ensure exact matching. Guerra et al. (2006) developed specific visual control methods for a Swiss watch-making factory. In this very demanding field of luxury products, sensory experts not only have to detect visual defects but they also have to ensure the samples reflect the high quality that the company’s clients expect. Their method is based on standardised training and detailed definitions of each attribute. Descriptors include dot (point), particle (particule), hole (trou) and bubble (bulle). To achieve an accurate level of control, Guerra used several experts to reach a consensus regarding the relevant descriptors and evaluation protocols under direct vision or instrumented vision (using a magnifying glass). After training, accredited judges conduct quality control of the watches (front and back) following the three steps given in Fig. 17.1: perception, quantification and notification of conformity or nonconformity. Using this standardised protocol, the authors succeeded in ensuring manufacturing quality by reducing inter-subject variability among quality control experts.

17.3.3 Transportation Sounds Three types of acoustic signals are important in transportation: (1) warnings, which have to be strong enough to signal an emergency or confirm an action (Patterson, 1982); (2) environmental noise, which has to be kept as low as possible for people living near airports, motorways or in urban zones; and (3)

1

2 Perceive the defect

3 Quantify the defect

Exploration

Level of expertise

Judge the defect

Evaluation Name of defect

Intensity of defect

Criteria of conformity

Fig. 17.1 The three stages of quality control of watches (Guerra et al., 2006).

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the overall perceived sound quality in a cabin, which has to be kept as low as possible, this being done by balancing the technical properties and costs of soundproof materials, weight of the vehicle and levels of sounds produced. All variants of acoustic quality are taken into account. The main quality control procedures rely on the intensity of the sound. Methods are based on psychoacoustic knowledge and practices. The frequency spectrum of each signal is analysed in detail and calculations are made to weight it in relation to theoretical models of ear physiology and filtering processes. For environmental acoustic quality, for example, car sounds are recorded using microphones with all recording conditions being precisely defined (exact position of the microphone, speed of the car, type of microphone, outdoor and indoor parameters, and so on). The recorded sound is then submitted to energy and frequency analysis (ECE 51.020, 2007). An example of parameters reported is given in Fig.17.2, showing the level of detail needed for a car to comply with regulation acoustic constraints. While standard methods are still purely based on decibels and thus the level of sound delivered (dBA), sensory methods are becoming used more and more frequently in the transportation sector. The SAE International Engineering Society for Advancing Mobility has edited guidelines for setting up panels (Otto et al., 1999), and future standards that account for the timbre quality will certainly be developed. Odours Testing the odours of materials is often performed using air chambers to control the temperature, strength and quality of the circulating air, as described by Gunnarsen et al., (2007). In a CLIMPAQ Chamber for Laboratory Investigation of Materials, Pollution and Air Quality, the exhaust air that runs through the chamber is analysed by test panels, as illustrated in Fig. 17.3. Certified sampling methods exist for analysing emitted volatile organic compounds (VOCs) (Henneuse-Boxus and Pacary, 2003). These techniques permit precise chemical analysis and controls. In the automotive industry, the reference method for testing odours is published by Verband Der Automobilindustrie (VDA), the German association of car manufacturers. The VDA 270 (1992) standard evaluates the level of odours emitted by various materials. Samples are taken from different places in the vehicle interior and stored in a closed glass bottle at various temperatures, from 70 to 100 °C, depending on the type of material. A panel then evaluates the strength of the odour (http://product-testing. eurofins.com/industries/automotive.aspx) or uses a descriptive methodology, such as Jaubert’s method (Jaubert et al., 1987) used at Renault (Nesa et al., 2004). Touch Car manufacturers have developed internal standards for controlling the tactile quality of their materials, both textiles and plastics. Renault devel-

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Sensory analysis for food and beverage quality control FORMAT OF REPORT OF TEST OF MOTOR VEHICLE Name of test laboratory (centre) Test report__________ Of tests of motor vehicle with respect to GOST R51616-2000

Object of testing Make or trade name _____________________________________________________ Vehicle type____________________________________________________________ Manufacturer’s name and address __________________________________________ Category of motor vehicle _________________________________________________ Model of body (chassis) __________________________________________________ Year of manufacture _____________________________________________________ Year of start of manufacture of this type _____________________________________ Chassis (body) identification number and engine number ________________________ Technical characteristics of the motor vehicle: Engine (model) _________________________________________________________ Engine type ____________________________________________________________ positive ignition, or diesel, or electric motor

Strokes _______________________________________________________________ two-stroke or four-stroke

Number and arrangement of cylinders _______________________________________ Displacement, litres ______________________________________________________ Maximum or nominal engine power, kW _____________________________________ Engine speed at maximum power, rpm ______________________________________ Additional equipment for passenger compartment heating and ventilation ___________ type, model

Vehicle laden mass (for tractive units – including trailer), kg _____________________ Number of seats, including the driver ________________________________________ Model, designation of tyres and pressure ____________________________________ Type of transmission _____________________________________________________ Number of gears ________________________________________________________ Ratios of the gears ______________________________________________________ Gearbox, transfer case (if any), final drive

Overall gear ratio chosen for conducting the tests______________________________ Vehicle speed attained at 1000 rpm with the chosen overall gear ratio, km/h ________ Conditions of tests conduction Type (model) of the noise meter and microphone ______________________________ Calibration deviation _____________________________________________________ Type (model) of other devices used for testing ________________________________ Test results Gear at which the tests were conducted _____________________________________ Vehicle speed, km/h, and the corresponding engine speed, rpm: initial ____________, final __________________________________________________________________ Noise levels measured when the vehicle was accelerating, dB (A) ________________ Noise levels measured when the vehicle was moving with constant speeds, dB (A) ___ Pemissible noise level, dB (A) _____________________________________________ Noise level measured when the ventilation system was operating, dB (A) ___________ Conclusion The vehicle submitted for certification _______________________________________ motor vehicle type, make, model

______________________________________________________________________ complies (does not comply) with the requirements of GOST R51616-2000 Date Stamp

Head of test laboratory Signature Full name

Fig. 17.2 Example of a report for acoustic compliance of cars (GOST R51616, 2000).

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Fig. 17.3 A CLIMPAQ chamber for olfactory tests in controlled condition (http://www.nordicinnovation.net/nordtestfiler/build482.pdf).

oped a set of reference materials to illustrate various touch criteria (Crochemore and Nesa, 2004), which is a useful tool for demonstrating major tactile differences to panel members. Daillant (2007) describes the way Peugeot Citroen used trained panels to explain consumers’ tactile preferences and write precise guidelines for suppliers from preference mapping results. To our knowledge, there are no national or international regulations published on the subject to date. In the clothing sector, touch control could be extended to thermal comfort. In a similar way to acoustic and olfactory controls, there are standards that define physical models based on physiological laws and instrumental data, in this case to evaluate thermal comfort through perspiration calculations (ISO 11092, 1993). However, sensory approaches to the tactile quality of clothes are being developed, opening the way to integrating sensory expert control (Soufflet et al., 2004).

17.3.4 Environment/architecture Acoustic and olfactory qualities are the most developed sensory topics in the environmental and architectural fields, mainly because of growing concerns regarding pollution and the need to improve consumer protection. Methodologies for quality control of environmental air or sound are rather similar to those used in transportation. They are mostly based on sampling and recording followed by instrumental measurements, using sonometers for ambient noise and VOC analysis for olfactory perturbations. Panels are proposed as a new way of studying environmental quality, either using consumers and gathering their verbal comments or by training volunteers. Rognon and Pourtier (2001) propose training volunteers to

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evaluate the air quality of industrial areas. A ‘trained’ panel among the local population could alert the regional authority when olfactory intensity is too high, too unpleasant or persistent. Sensory descriptors are selected from consumer complaints and are illustrated with olfactory samples taken from the field. Panellists are trained to recognise a set of relevant descriptors and to score their intensity (Pierret, 2008). The objective is to identify the main sources of unpleasant odours and to reduce olfactory annoyance for people living near to large petrochemical installations. For instance, this technique is now used in the Provence region in France (near the industrial zone of Marseilles), where volunteers report their olfactory perceptions. Each volunteer provides information on air quality, and statistics are calculated according to geographical zone. Many industries operate in the area, and the percentages of declared olfactory nuisances are used as an index of air quality. Where volunteers have been trained to recognise several odours, reports can be used to identify possible sources of pollution, as illustrated in Fig. 17.4. Further examples of environmental approaches are given by Evin and Siekerski (2002), who use preference mapping to study the acoustic and thermal performances of various heating systems, and Dreyfuss et al. (2005), who determined the ideal acoustic quality of a railway station by modelling consumer perceptions using parameters devised by an expert panel. The methodology based on training consumers to evaluate change in the quality of the environment is a relatively new approach.

Sewers; 5%

Treatment plants; 1%

Household waste; 6%

Industry; 43%

Others (and not described); 35% Transport; 9%

Fig. 17.4 Origin of odours in the Etang de Berre region as perceived by trained volunteers (http://www.sro-paca.org).

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17.4 Conclusion A high level of expertise is required for human (i.e. non-machine) quality control of appearance (e.g. for lenses, paintings). The use of trained subjects for olfactory quality control is becoming more common (e.g. for environment, materials), although this approach is not systematic and is mainly concerned with intensity scores. Instrumental measures are the primary method for controlling acoustic levels (e.g. in transportation or the environment). Trained sensory panels – in the classical meaning of food sensory evaluation – have now been developed extensively for the analysis of touch (e.g. for textiles, cosmetics), and collecting sensory data is a compulsory step in developing new industrial processes to deliver high quality products. As sensory methods begin to demonstrate success across a range of industries, they will become more widely used in non-food sectors. Two sectors that show particularly strong interest in sensory knowledge are pharmaceuticals and the environment.

17.5 Future trends Although non-food sections are included in conferences more and more often, ‘non-food products’ may not be considered a proper category. Are a car and a shampoo similar in terms of perceptions or behaviour? Comparisons within the non-food category (beauty products, home care products, vehicles, telephones, pencils, clothes, etc.) do not seem particularly relevant, either from the point of view of consumer use or from the related sensations involved. Symmetrically evaluating coffee, for instance, requires very specific protocols that have nothing in common with those involved in evaluating pizzas or different types of chewing gum. However, sensory approaches are in fact rather similar from strategic (production, R&D, marketing) and methodological (discriminative, descriptive, hedonic) points of view, whatever the object under consideration, although technical specificities exist for each type of product. Approaches for developing sensory methodologies could follow two directions. First, methodologies could be based on the physical and chemical properties of the products, together with the formulation and processes specificities. For instance water/oil emulsions could be described and controlled similarly, whether they were for edible or non-edible products, because this type of analysis is object-centred. The specific methodology could focus on the parameters appropriate for strategic or operational objectives, with the development of dedicated tools, working on the efficiency of each technical step, such as selection of panellists, training, definitions, instructions, software, statistics and so on. Second, methodologies could be based on the consumer’s attitude to the product (level of implication, self and social image, psychological and func-

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tional benefits, etc.). For instance, personal technological (telephone, computer, camera) or luxury (pen, lighter, wallet) objects could be approached similarly, combining a sensory approach with the relevant complementary methodology (from ergonomics, psychology, sociology, etc.), selected according to the subject’s state of mind rather than the product’s sensory properties. Using these two global approaches – object-centred or subject-centred – could help sensory scientists from various sectors to work together and exchange information on methodological issues, whatever industry they work in. Independently, they could then develop methods specific to a given product, and develop tools that correspond to the normal use of the product. Thus a third direction for future, product-specific development would be to work in more ecological situations, taking into account the multisensory nature of the sensations, the place, time and duration characteristics of normal use and other parameters linked to each situation (e.g. psychological, social, economical).

17.6 Sources of further information Lexicons and definitions for descriptive panels can be found for various products in methodological manuals such as Meilgaard et al. (2006); Lawless and Heymann (1999) or in dedicated standards as mentioned above. Case studies relating sensory properties to consumer perception are described for shampoo, skin care, spectacles, cars, fabric care and perfumes in Giboreau and Body (2007). Besides publications in specific industrial journals, an increasing number of papers dealing with textile, cars, cosmetics, etc. are now regularly available in Food Quality and Preference and the Journal of Sensory Studies.

17.7 References afnor nf bpx 10-040 (2003), Caractérisation sensorielle des matériaux – Méthodologie générale – Recommandations méthodologiques pour l’analyse sensorielle de la matière première au produit fini, Saint-Denis, AFNOR. afnor nf bpx 10-041 (2005), Caractérisation sensorielle des matériaux – Méthodologie générale – Recommandations méthodologiques pour l’analyse tactile de la matière première au produit fini, Saint-Denis, AFNOR. afnor nf bpx 10-042 (2006), Caractérisation sensorielle des matériaux – Méthodologie générale – Recommandations méthodologiques pour l’analyse visuelle de la matière première au produit fini, Saint-Denis, AFNOR. astm standard e462 (1984a), Standard test method for odor and taste transfer from packaging film, West Conshohocken, PA, ASTM International. astm standard e619 (1984b), Standard practice for evaluating foreign odors in paper packaging, West Conshohocken, PA, ASTM International. astm standard e1490 (2003), Standard practice for descriptive skinfeel analysis of creams and lotions, West Conshohocken, PA, ASTM International.

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astm standard e1593 (2006), Standard guide for assessing the efficacy of air care products in reducing sensory perceived indoor air malodor intensity, West Conshohocken, PA, ASTM International. astm standard e1207 (2009), Standard practice for the sensory evaluation of axillary deodorancy, West Conshohocken, PA, ASTM International. aust, l.b., oddo, p., wild, j.e., mills, o.h. and deupree, j.s. (1987), The descriptive analysis of skin care products by a trained panel of judges. J. Soc. Cosmet. Chem., 38, 443–449. bacle, i., meges, s., lauze, c., macleod, p. and dupuy, p. (1999), Sensory analysis of four medical spa spring waters containing various mineral concentrations. International Journal of Dermatology 38 (10), 784–786. bech, s. and zacharov, n. (2006), Perceptual Audio Evaluation: Theory, Method and Application, Chichester, John Wiley and Sons Inc. bensafi, m., rouby, c., farget, v., bertrand, b., vigouroux, m. and holley, a. (2002), Influence of affective and cognitive judgments on autonomic parameters during inhalation of pleasant and unpleasant odors in humans. Neuroscience Letters, 319 (3), 162–166. bloch, p.h., brunel, f.f. and arnold, t.j. (2003), Individual differences in the centrality of visual product aesthetics: concept and measurement. Journal of Consumer Research, 29, 551–565. bs 3755 (1964), Methods of test for the assessment of odour from packaging materials used for foodstuffs, London, British Standards. civille, g.v. and dus, c.a. (1991), Evaluating tactile properties of skincare products: a descriptive analysis technique. Cosmetic Toiletries, 106, 83–88. civille, g. v. and seltsam, j. (2003), Sensory evaluation methods applied to sound quality. Noise Control Engineering Journal, 51 (4), 262–270. crochemore, s. and nesa, d. (2004), Analyse sensorielle des matériaux de l’habitacle automobile: toucher – vision, Techniques de l’ingénieur AM3292. din 10955 (2004), Sensory analysis – Testing of packaging materials and packages for foodstuffs, Deutsches Institut Fur Normung EV. daillant, b. (2007), PSA, les voitures Peugeot et Citroën in Le marketing sensoriel, Giboreau, A. and Body, L., Paris, Vuibert. dairou, v., priez, a., sieffermann, j. m. and danzart, m. (2003), Modelling of brake feel using design experiments, sensory profile and PLS, 5th Pangborn Sensory Science Symposium, Boston, July 20–24. dreyfuss, l., tardieu, j., nicod, h., guerrand, s. and giboreau, a. (2005), The soundscape of public places: a new field for sensory research, 6th RM Pangborn Symposium, Harrogate, August 5–7. dubois, d. (2000), Categories as acts of meaning: the case of categories in olfaction and audition, Cognitive Science Quarterly, 1, 35–68. ece 51.02 (1982(2007)), Prescriptions uniformes relatives à l’homologation des automobiles ayant au moins quatre roues, en ce qui concerne le bruit, Genève, Réglementation internationale. evin, f. and siekerski, e. (2002), Sensory evaluation of heating and air conditioning systems. Energy and Buildings, 34 (6), 647–651. fauquier, c. (2009), Le contrôle qualité dans les usines Essilor. Personal Communication. faurion, a. (2004), Physiologie sensorielle à l’usage des IAA, Paris, Lavoisier. faye, p., brémaud, d., durand daubin, m., courcoux, p., giboreau, a. and nicod, h. (2004), Perceptive free sorting with naïve subjects: an alternative to descriptive mappings and a tool for sensory segmentation of consumers, Food Quality and Preference, 15, 781–792. giboreau, a. (2007), Cosmetics and the 5 senses: perception and description, SOFW Journal, 133 (5), 2–7.

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giboreau, a. and body, l. (2007), Le marketing sensoriel: de la stratégie à la mise œuvre, Paris, Vuibert. gost, r51616 (2000), Internal noise: Permissible levels and test methods, Federal Agency of Technical Regulation and Metrology, National Standard of the Russian Federation. guerra, a.s., pillet, m. and maire, j.l. (2006), Formalisation of subjective knowledge by the sensory analysis. Global Manufacturing Innovation (GMI 2006), Coimbatore, India, July 2006. gunnarsen, l., nielsen, p.a. and wolkoff, p. (2007), Design and characterization of the CLIMPAQ, chamber for laboratory investigations of materials, pollution and air quality. Indoor Air, 4, 56–62. henneuse-boxus, c. and pacary, t. (2003), Emissions from Plastics. Review reports, 14, 5, Rapra Technology Limited. hollins, m., bensmaïa, s., karlof, k., and young, f. (2000), Individual differences in perceptual space for tactile textures: Evidence from multidimensional scaling. Perception and Psychophysics, 62 (8), 1534–1544. http://www.nordicinnovation.net/nordtestfiler/build482.pdf http://www.sro-paca.org iso 11092 (1993)/nf en 31092 (1994), Indice de perméabilité à la vapeur d’eau – Skin Model Saint-Denis, AFNOR. jaubert, j.n., gordon, g. and doré, j.c. (1987), Une organisation du champs des odeurs – II, Parfums, Cosmétiques, Arômes, 78, 71–82. köster, e.p., degel, j. and piper, d. (2002), Proactive and retroactive interference in implicit odor memory. Chemical Senses, 27, 191–206. lawless, h.t. and heymann, h. (1999), Sensory Evaluation of Food: Principles and Practices, Chapman and Hall, New York. meilgaard, m., civille, gv. and carr, b. (2006), Sensory evaluation techniques, CRC Press, New York (4th edition). nesa, d., crochemore, s. and couderc, s. (2004), Analyse sensorielle des matériaux d’habitacle automobile: olfaction, Techniques de l’ingénieur AM 3 291. otto, n., amman, s., eaton, c. and lake, s. (1999), Guidelines for jury evaluations of automotive sounds, SAE Technical paper series 1999-01 – 1822, USA. patterson, r.d. (1982), Guidelines for auditory warning systems in civil aircraft, CAA paper 82017, Civil Aviation Authority, London. petit, c., siekerski, e. and danzart, m. (2005), Thermal perceptions and preferences in indoor environments. Journal of Sensory Studies, 19–5, 395–421. picard, d., dacremont, c., valentin, d. and giboreau, a. (2003), Perceptual dimensions of tactile textures. Acta Psychologica, 114, 165–184. pierret, c. (2008), Les nez bénévoles formés aux odeurs du site de Lavéra, La Provence, Edition du 14/07/2008. proust, m. (1913), Du côté de chez Swann, Paris, Grasset. rognon, c. and pourtier, l. (2001), Mesurer les odeurs, Techniques de l’ingénieur, G2940. soufflet, i., calonnier, m. and dacremont, c. (2004), A comparison between industrial experts’ and novices’ haptic perceptual organization: a tool to identify descriptors of the handle of fabrics, Food quality and preference, 15, 7–8, 689–699. van reeth, i. (2006), Beyond skin feel: innovative methods for developing complex sensory profiles with silicones. Journal of Cosmetic Dermatology, 5 (1), 61–67. vda 270 (1992), Bestimmung des Geruchsverhaltens von Werkstoffen der Kraftfahrzeug-Innenausstattung, Berlin, Verband Der Automobilindustrie. wortel, v.a.l. and wiechers, j.w. (2000), Skin sensory performance of individual personal care ingredients and marketed personal care products. Food Quality and Preference, 11 (1–2), 121–127.

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Appendix: Going forward – Implementing a sensory quality control program M. A. Everitt, ME Consultancy Ltd, UK

Abstract: Suggestions and recommendations are provided on how to implement, consolidate and develop the application of a sensory quality control program to help achieve its full benefit. Key words: trend analysis, quality assurance, continuous improvement, quality advantage.

A.1 Piloting the program As recommended at the start of this guide, it usually proves most effective to pilot a sensory quality control (QC) program first using one product line at one production site. This enables all aspects of the system to be tried, refined and consolidated which in turn ensures it is functioning efficiently prior to roll-out across a business; the value of the program is then more likely to be fully realised. Roll-out on the same product line as used for the pilot is also recommended before other products are included, to facilitate a common understanding of how the sensory program operates.

A.2 Refinement and consolidation Various aspects of the system are likely to be refined, adapted and developed as the system is consolidated. • Initial training regime for panellists. As a standard training format is established with the core elements defined, a ‘train the trainer’ approach is often adopted. Selected personnel from each production site are

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trained usually by the sensory program coordinator or an external sensory consultant to become the official instructor of the ‘course’ for their site. The training can be tailored to suit product and process but to ensure that the core elements are maintained the overall responsibility for the content and delivery of the course should stay with the sensory program coordinator. • Training aids. A database of the most suitable materials, formulations/ products can be developed which aids efficiency and consistency in the preparation and delivery of the initial training. • Standard operating procedures (SOPs) that support the sensory assessment method. For example, sampling frequency may change, some aspect of the product preparation, assessment protocol or specification may need to be modified. Response procedures in particular are likely to receive modification as the system stabilises; the hierarchy of actions and reporting mechanisms becoming more clearly defined.

A.3 Quality assurance (QA) As a sensory QC program becomes established and especially if the assessment method enables focused trouble-shooting of sensory quality issues, the system may progress from one of QC (ensures finished product sensory quality adheres to defined requirements) to that of quality assurance (QA), i.e. ensures that a product during production (before the work is complete) meets specified criteria. The original assessment method may be adapted to enable stages along the process, known to have a significant effect on the sensory quality, i.e. sensory critical control points, to be checked. Steeping, blanching, pre-mixing, blending are a few examples of potential control points plus raw material inspection may also be included. Visual, odour and/or tactile assessment tend to be primarily involved rather than taste at these stages. The focus is on just one or two key sensory criteria, in particular defects which could exacerbate further along the process and off-notes which could signal developing off flavours. As a QA approach develops it should enable the frequency of the final product inspection to be maintained at a practical level.

A.4 The effectiveness of a sensory quality control (QC) program The benefits are not in general fully appreciated initially. It is over time, by trending the data, that a realistic measure of the system’s performance (effectiveness) and full value from the resulting information can be gained. Some or all of the following methods may be suitable to measure performance in relation to sensory QC over a specified period:

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• number and degree of deviation from specified target quality (deviations can be prioritised if a diagnostic assessment method is used); • percentage of sub-standard, re-worked, rejected product; • number of minor consumer/customer complaints; • number of major consumer/customer complaints; • measure of quality margin versus key competitors.

A.4.1 Trend analysis and control charts Control charts as classically used to monitor process variation are also suitable for trending the sensory variation; as they are familiar to production personnel they provide a readily acceptable format for communication. As shown in Fig. A.1 the chart contains the upper and lower limits of the specified target range; the x axis relates to the assessment frequency, the y axis to the rating or grading scale. The mean or consensus score is plotted for each assessment, one chart per product type. Additional limits can also be added to depict grading zones if appropriate. Variation can be tracked within and between shifts and across production sites. In addition to measuring the degree of variation over time, trend analysis also enables the sensory features causing the most frequent deviations from target quality to be identified, which in turn provides a business with additional insight about the process itself. The increased knowledge this information provides about the links between materials, process and a products sensory attributes can further business understanding of how to best manage the process with minimum risk to the final quality. Figure A.1 compares the variation for a product made at two production sites. Site 1 maintains a tighter control of the texture quality to the specified range than site 2. Over time the quality variation should tighten with a

70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0 Assmt1

Assmt2

Product site 1

Assmt3 Product site 2

Assmt4

Assmt5

Lower limit

Fig. A.1 Trend chart for overall texture.

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Appendix

subsequent reduction in major deviations and the associated amount of sub-standard, re-worked or rejected product. Trend charts are also valuable communication aids to use at quality review meetings. A.4.2 Consumer and customer complaints If the system is operating effectively, major issues relating to sensory quality should be identified and dealt with well before product is packed for release, reducing the risk of major complaints to a minimum. Minor complaints should be infrequent particular for the key consumer sensory attributes if the variation is suitably controlled. A.4.3 Quality advantage versus competitors As stated at the start of Chapter 1, gaining and maintaining a quality advantage have become primary competitive issues within the food and drink industry; the measurement of sensory quality now being used by an increasing number of companies as a key indicator of how well their products perform versus the competition. An external consumer acceptance test (central location or home use) provides the most reliable way of gaining this measure. Degree of overall liking along with that for key sensory characteristics is typically investigated; a statistically significant higher level of liking determines which product has the quality advantage. The importance assigned to the sensory characteristics as overall indicators of quality has increased as companies recognise the extent to which appearance, flavour and texture have in winning and retaining consumer loyalty. The true measure of a quality advantage again comes from tracking the results over time to check consistency, rather than from an individual test. Tracking consumers’ liking can also provide a back-check for the specified target quality to see how well it meets or continues to meet consumers’ requirements. Control of the sensory variation, in particular that of the attributes key to consumers’ acceptance, should help maintain an advantage once it has been established.

A.5

Maintaining the effectiveness of a sensory quality control/quality assurance (QC/QA) program

The continued commitment and support from all management levels is crucial for the continued effectiveness and success of a sensory QC/QA system. A.5.1 Staff motivation The positive attitude usually seen in those who are pro-active towards quality control and problem solving tends to help maintain enthusiasm and

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diligence in others. It is beneficial therefore to identify and include these people in the pool of trained panellists and involve them regularly in the routine assessments. Feedback sessions Updates given by the sensory coordinator supported by QC and production management about the performance of the system convey commitment. They also provide opportunity to formally acknowledge specific efforts and to keep staff informed in general about how the information they produce benefits the business. The frequency of these sessions may vary, depending on circumstance. Refresher training This is advisable on a formal basis at least once every 12-month period. In addition to re-aligning and re-validating panellists’ ability to identify and score the specified levels of quality, it encourages diligence and signals continued investment in the process.

A.6 Continuous improvement A.6.1 Internal audits Informal and formal internal audits help keep check on the overall functioning of the system and that all related procedures and records are current. In addition they provide opportunity for suggestions for improvement to be discussed and progressed, and employee initiatives to be encouraged and rewarded. The sensory coordinator is likely to take responsibility for organising the audits supported by quality management and the QC teams local to each site.

A.6.2 Kaizen The philosophy of total quality and continuous improvement has become common practice within many businesses, with the Japanese word ‘kaizen’ meaning ‘improvement’ now being used collectively to refer to activities that continually improve all functions of a business. The philosophy encourages thinking and involvement from all employees to aid improvement. Team working and quality circles (small groups of employees that volunteer to investigate and resolve quality issues) are central to the process. In this culture the sensory QC program should automatically be addressed in the same manner with subsequent benefit to its operation. In commercial practice achieving an acceptable balance between output and quality will always pose a challenge; having a robust sensory QC system in place will provide a business with further means to best manage the dilemma.

© Woodhead Publishing Limited, 2010

Index

A not A method, 65–6, 72 ballot paper, 65 accelerated shelf-life testing, 151–2 Actinomyces, 162 action standard, 52 Advisory Committee on Novel Foods and Processes, 23 2-AFC method see 2-alternative forced choice method AFNOR, 339 AFNOR BPX40, 339 AFNOR BPX41, 339 AFNOR BPX42, 339 Alicyclobacillus acidoterrestris, 159 alpha risk, 121 2-alternative forced choice method, 66, 166 ambient product setting up shelf-life confirmation studies, 148–52 collection, analysis and reporting, 154–5 considerations and preparations, 148–52 developing sensory plan, 152–4 shelf-life considerations and preparations, 148–52 accelerated methods in storing the product, 151–2 changes in attributes, 149–50 data requirement, 150–1

explanations for changing attributes, 150 noticeable product changes, 150 product changes, 149 product stability, 149 products on the shelf, 150 American Dairy Science Association, 71 analysis of variance, 28, 130, 131, 269 Anscombe data sets, 109 apple juice, 42–3 apples average consumer acceptability scores, 288 descriptors for sensory profiling, 282 sensory analysis, 281–4 assessors selection and training, 281, 283 consumer acceptability, 283–4 descriptors generation and selection, 281 sensory profile, 283 sensory attributes scores 2-way ANOVA, 286 3-way ANOVA, 285 PLS regression analysis, 289 soluble solids, acidity and hardness evaluated by sensory profile, 283 storage temperature on sensory quality, 280–90 characteristics and acceptability, 286–8

© Woodhead Publishing Limited, 2010

Index chemical and instrumental analysis, 280–1 data analysis, 284 panel performance evaluation, 284–5 samples, 280 sensory attributes selection, 288–90 starting point and objectives, 280 variation with storage temperature sensory attribute scores varied with different trend, 287 sensory attribute scores varied with same trend, 287 taste intensity and sweetness of samples, 288 applied statistics, 119 Arctic charr see Salvelinus alpinya Arnagnac, 263 artificial neural networks, 114 ASLT see accelerated shelf-life testing assessment area, 11 assessors, 91 proficiency and validation, 137–8 ASTM E462, 338 ASTM E619, 338 ASTM E1207, 339 ASTM E1490, 339, 343 ASTM E1593, 339 ASTM E1301-95, 38 ASTM E-1958-07, 139 ASTM Standard Guide for Sensory Claim Substantiation, 139 Atago RX-100, 280 Atlantic mackerel see Scomber scombrus attributes product sensory specifications critical consumer attributes identification, 80 definitions and ranges, 82–3 score, 126 sensory attribute list for chocolate product, 82 spider graph displaying sensory attributes, 90 weighting, 6 Australian Wine Research Institute, 240 AWRI see Australian Wine Research Institute balanced reference, 72 batch-to-batch variability test, 53

359

Beer Flavour Wheel, 246 belief rule-based models, 114 Beringer Vineyards see Foster’s Wine Estates best before date, 143, 145, 148 best before end, 145, 148 beta risk, 121 Blender, 266–7 blending, 224, 247, 263, 269 blind control, 126, 128 sample scores for a DFC test, 128 blue-sky creativity, 204 blue Stilton cheese crusts, 217 Bordeaux, 252 Bordeaux wines, 239 Bostwick consistometer, 211 bottling, 269 bourbon, 269 brandy aroma wheel, 264 BRB see belief rule-based models Brettanomyces, 256, 258 brill see Rhombus laevis broadtail shortfin squid see Illex coindetii broken-stick model, 108 bromination, 162 bromophenol, 159 Brookfield viscometer, 212, 213 BS 3755, 338 butanol solution, 342 Cabernet-based wines, 251 Cabernet Sauvignon, 247, 248 California Syrah wines, 256 canonical correlation analysis, 112 CCA see canonical correlation analysis Central Valley winery producing low-priced wines, 249–50 producing moderately priced wines, 250 Chardonnay, 241, 250 Chelidonichthys lucernus, 298 chewy, 191 Chianti wine quality, 241 chlorination/bromination system, 162 chlorine, 162 2-chloro-6-methylphenol, 271 6-chloro-o-cresol, 271 chloroanisole, 271 chlorophenols, 162, 163, 271 CIE see International Lighting Committee CLIMPAQ, 345, 347 Clupea harengus, 297, 298

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360

Index

cluster analysis, 112 co-ordinator, 46 see also sensory program co-ordinator cod see Gadus morhua Codex CAC-GL31-1999, 296 Codex XPT 13-1969, 295 coffee, 323, 325 Coffey still, 263 Cognac, 263 cohesiveness, 191 colour, 100, 102–3 measurement, 76, 77 reference charts, 212, 214 Colour Viewing Chamber, 281 colour–flavour–fragrance intensity, 241 Commission nationale de l’informatique et des libertes, 24 comprehensive descriptive method see descriptive specifications method Compusense five release 4.6 software, 281 consensus profiling, 89 constant reference, 72 consumer acceptance test, 356 consumer-targeted approach, 12–14 collaboration between departments, 13–14 consumer information level, 14 key stages in sensory specifications defining process, 13 sensory profiles comparison, 14 consumer testing, 205 control product, 148 correlation analysis, 108–9 Craft Fair FA-S2-9063, 308 critical consumer attributes, 80 crumblinesss, 191 Culinology, 330 cupper, 325 customer brief, 204–5 cut-off value, 126 cuttlefish see Sepia officinalis cuvée, 250 Cyanobacteria, 162 dab see Limanda limanda Davis 20-point scorecard, 242–4 sample, 243 dechar rechar, 269 decibels, 345 degree-of-difference, 31, 32, 126, 128–9 delay procedure, 228

descriptive specifications method, 55–60, 131–4, 279 ballot paper, 58–9 data and specifications, 57 fruit drink specifications, 56 descriptive statistics, 119 descriptive test, 88–90, 167–8 despatch, 231–2 detection threshold, 165 Development and Implementation of Computerised Sensory System for Fish Freshness, 303 Dewar’s number, 268 DFC method see difference from control method Dicentrarchus labrax, 298 difference from control method, 62–4, 87–8, 126, 128–9, 167 ballot paper, 63 blind control sample scores, 128 Friedman’s test on ranking averages, 129 samples mean scores, 128 difference testing, 205, 217 DIN 10955, 338 directional form test, 166 discrimination tests, 166–7 DFC method, 167 duo-trio test, 166 paired comparison test, 166 R-index test, 167 triangle test, 167 distillation, 268 distilled beverages current industry practices, 266–70 Scotch Whisky Research Institute’s Flavour Wheel, 266 sensory assessment introduction, 266–7 sensory integrity, 267–70 blending and bottling, 269 critical sensory points in distilling process, 267–8 distillery operator sensory training/calibration, 268 maturation, 269 mature spirit sensory training, 270 sensory quality control, 262–72 origins, 263–4 procedures and precautions, 264–6 taints and off-flavours, 270–2 causes and symptoms, 271 investigative techniques, 272

© Woodhead Publishing Limited, 2010

Index distribution depot, 232 do-it-yourself method, 73–4 DoD see degree-of-difference Dolavs, 218 draff, 267 DS method see descriptive specifications method duo–trio test, 66, 72–3, 166, 251, 263 ballot paper, 73 formats, 72 EA-4/09, 38 EC No. 2406/96, 296 ECE 51.020, 345 elasticity, 191 electromyography, 113 electronic nose, 34, 104, 114, 138, 329 electronic tongue, 34, 104, 114 emulsifiers, 189 EN ISO 4120:2004, 325 EN ISO 5495:2007, 325 EN ISO 8586–2:2008, 326 EN ISO/IEC 17025, 38 end-of-life assessment, 232 Engineering Society for Advancing Mobility, 345 Engraulis encrasicholus, 298 Enologix, 241 enzymes, 189 equivalence testing, 139 equivalency, 139 Essilor, 343 Ethical and Professional Practices for the Sensory Analysis of Foods, 22–3 EU AIR3 CT942283, 308 EU Concerted Action, 308 EU scheme, 296–7 European sardine see Sardina pilchardus European standards, 238–9 Evaluation Fish Freshness, 308 everyday wines, 248 expert sensory assessor, 326 F-test, 130, 131 factor analysis, 111–12 Fair PL98-41, 308 FAMM see Five Aspect Meal Model farmed Atlantic halibut see Hippoglossus hippoglossus L. farmed Atlantic salmon see Salmo salar FCP see free choice profiling feints, 268

361

fish ocean perch eyes of near the end of shelf-life, 301 eyes of newly caught, 300 gills of near the end of shelf-life, 301 gills of newly caught, 300 QIM scheme for farmed salmon, 299 quality indices in storage management and production planning, 305–6 quality control charts and statistical analysis, 305–6 shelf-life, 305 sensory evaluation, 296–303 cooked fillets evaluation, 302–3 raw fillets evaluation, 302 whole fish evaluation (EU scheme), 296–7 whole fish evaluation (quality index method), 297–302 sensory evaluation guidelines, 295–6 facilities for sensory evaluation, 295 panellists training, 296 sensory quality management, 293–310 different storage conditions, 306–7 future trends, 307–10 quality index development, 303–4 quality indices, 293–4 see also specific fish Fish Quality Labelling and Monitoring, 308 Fisher’s LSD test, 128 Five Aspect Meal Model, 320, 321 FlavorActiv, 270 flavour, 5, 100, 104 measurement, 104 profile method, 89, 98 wheel, 263, 265 flavoured spirits, 263 flounder see Paralichthys patagonicus food grade, 223 food ingredients definition, 187 good quality in consumer-oriented approach, 186–8 preference assessment, 188 sensory evaluation, 187–8 sensory testing specificity, 186–7 sensory quality definition, 186–201 soft products texture profiling, 190

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Index

texture, 188–92 bread under pressure with TPA, 191–2 ingredients for texture improvement in soft products, 189 soft bakery goods evaluation, 189 soft products sensorial analysis by expert panel, 189–91 toast bread for Chinese consumers, 193–201 colour, texture and flavour levels combinations, 194 consumers clustering, 198, 201 HAC of consumers in Beijing, 200 hedonic evaluation, 194, 196 methodology to define good quality, 193 preference mapping, 198 sensory characterisation, 194 toast breads average ratings for consumers, 197 toast breads preference mappings, 199–200 toast breads product space definition, 193 toast breads GPA sensory mapping consensus product and attributes positioning, 196 product positioning for all judges and consensus, 195 Food Labelling Directive, 143 food quality control combining instrumental and sensory methods, 97–115 food quality perceptual basis, 97–8 role of instrumental measurement, 98–9 future trends, 113–15 data analysis, 114–15 electronic noses and tongues, 114 in vivo measurements, 113 non-destructive testing, 114 instrumental measurements analysis and validation, 105–13 adhesive and cohesive failure, 107 correlation analysis, 108–9 data inspection, 106–8 multivariate methods, 111–13 Pearson product moment correlations, 110 penetration test probe geometry on gel breakage patterns, 107

regression analysis, 110–11 scatter plots, 109 quality factors instrumental measurement, 101–5 appearance measurement, 102–3 flavour measurement, 104 general principles, 101–2 methods selection, 105 other measurements, 105 texture measurement, 103–4 sensory analysis, 99–101 human senses, 99–100 key sensory test methods, 101 sensory test procedures, 100–1 sensory testing procedures classification, 101 foodservice applicable formal methods, 322–6 experts for specific foods linked to foodservice, 323–5 information in textbooks, 322–3 standards for expert assessment, 325–6 systems for sensory quality, 323 applicable informal methods, 326–9 chef as expert, 326 consumer feedback for meals sensory control improvement, 327 restaurant reviews, 328–9 tasting menus for new products development, 327–8 aspects of sensory analysis, 317–22 contrast between assessing a meal and a meal component, 321–2 environment and sensory quality effects on consumer’s assessment, 319–21 focus for foods in taste panels vs restaurant, 318–19 foodservice sector vs food product sector, 317–18 FAMM, 321 sensory quality control, 316–30 case study, 329 future trends, 329–30 Foster’s Wine Estates, 251–2 free choice profiling, 89 freezer burn, 220, 230 freezing, 230–1 French cuvée system, 250 French Data Production Authority, 24

© Woodhead Publishing Limited, 2010

Index fresh produce sensory analysis in fruit and vegetables quality control, 277–80 associated problems, 278–80 sensory methods, 278 sensory quality control, 276–90 storage temperature on sensory quality of apples, 280–90 chemical and instrumental analysis, 280–1 data analysis, 284 panel performance evaluation, 284–5 samples, 280 sensory analysis, 281–4 sensory characteristics and acceptability relationships, 288–90 storage temperature on apples sensory characteristics and acceptability, 286–8 freshness, 191, 293 Friedman’s test, 128 non-parametric rank sum test, 131 rank sum test, 131 ranking of averages in DFC test, 129 frozen hake see Merluccius capensis G-G assay, 241 Gadus morhua, 297 gas-chromatography-mass spectrometry, 145 GC-olfactometry, 145–6 General Food Texturometer, 113 generalised procustes analysis, 190, 195, 196 geosmin, 162, 271 Gewürztraminer, 250 gilthead seabream see Sparus aurata gin, 263 Global R&D Sensory Panel, 31, 33 gold standard, 83–4, 88, 124 sensory properties within a product space, 125 Golden apple, 280–90 goût de terroir, 250 GPA see generalised procustes analysis grading, 10–11 method, 87 Granny Smith apple, 281 grist, 267 guaiacol, 159

363

HAC see hierarchical ascendant clustering HACCP see Hazard Analysis Critical Control Points haddock see Melanogrammus aeglefinus haloanisoles, 159 halogenated phenolic compounds, 271 halophenols, 159 haptic sensations, 339 hard cheese, 43 hardness, 191 Hazard Analysis Critical Control Points, 267, 323 9-point hedonic scale, 283 herring see Clupea harengus hierarchical ascendant clustering, 198 high-risk chilled food sectors post-development product scale-up sensory QA, 206–9 product sensory description/ check-sheet, 210 recipe development process sensory QA, 204–6 sensory QA after product despatch, 232–3 conflicts of interest, 233 distribution depot and in-store inspection, 232 end-of-life assessment/review, 232 sensory QA in production process, 209–32 cooling/chilling phases and end product sensory evaluation, 229 delays/holding times upon batch completion, 227 despatch, 231–2 freezing, 230–1 ingredient shelf-life extension, 220–1 ingredient supply changes, cost improvement initiatives and substitutions, 216–18 ingredients, 214–16 mixing and cooking operations, 223–6 packaging, 218–19 product assessment techniques and useful equipment, 209–14 product organoleptic quality standard definition, 209 product packing, 227–9 recipe preparation phase, 221–3 storage, 219–20, 231

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Index

taste panel, 229–30 work in progress storage, 223 sensory quality assurance, 203–34 Hippoglossus hippoglossus L., 298 horse mackerel see Trachurus trachurus Hunter Lab system, 103 hydrogen sulphide, 164, 271 hygrometry, 341 hypochlorite bleach, 271 hypothesis testing, 120–1 ICS-Texicon España, 281 ILAC-G13:2000, 38 Illex coindetii, 298 illuminant D65, 281 improvers, 189 in-house method, 73–4 in/out method, 60–2, 87, 125–6 ballot paper, 61 in-process correction, 226 in-store inspection, 232 inferential statistics, 119 ingredients high-risk chilled food sector production process sensory QA, 214–16 shelf-life extension, 220–1 supply changes, cost improvement initiatives and substitutions, 216–18 see also food ingredients Institute of Food Science and Technology, 144 instrumental measurement analysis and validation, 105–13 correlation analysis, 108–9 data inspection, 106–8 multivariate methods, 111–13 regression analysis, 110–11 future trends, 113–15 data analysis, 114–15 electronic noses and tongues, 114 in vivo measurements, 113 non-destructive testing, 114 outlined steps, 102 quality factors, 101–5 appearance measurement, 102–3 flavour measurement, 104 general principles, 101–2 methods selection, 105 other measurements, 105 texture measurement, 103–4 sensory methods in food quality control, 97–115

perceptual basis of food quality, 97–8 quality sensory analysis, 99–101 role, 98–9 instrumental measurements, 76–7 instrumental methods, 138 International Lighting Committee, 341 International Organisation for Standardisation, 19, 100, 278 Ishihara test technique, 211 ISO 3972, 40 ISO 4120:2004, 70 ISO 4121, 40 ISO 5492, 107, 156, 165 ISO 5496, 39 ISO 6564, 40 ISO 6658, 39 ISO 8587, 40 ISO 8589, 39, 91, 295, 304 ISO 9000, 322 ISO 11035, 281, 296 ISO 11092, 347 ISO 17025, 38 ISO 8586-1, 25, 39, 86, 168, 296 ISO 8586-2, 39, 296 ISO/CD 13300 – 1, 39 ISO Guide 35:2006, 38 ISO/IEC 17025:2005, 38 ISO/IEC Guide 43-1, 38 ISO/IEC Guide 43-2, 38 ISO standard sensory facilities, 265–6 ISO wine tasting glasses, 264 Jamaican coffee, 325 Kaizen, 357 key performance indicator, 6, 225 key quality criteria, 222 key sensory points, 205 kinesthesia, 339 kinesthesis, 103 KPI see key performance indicator KSP see key sensory points L a b system, 76, 103 laboratory information management system, 12 lignin, 159 Limanda limanda, 297 LIMS see laboratory information management system line plot, 45 linear regression, 110 Los Angeles Times, 328

© Woodhead Publishing Limited, 2010

Index Lutyens, 329 luxury wines, 248 magnitude estimation method, 72–3 Maillard reactions, 224 malted barley, 267 Manhattan plot, 45 MAP see modified atmosphere packaging marker chemicals, 145–6 Marks & Spencer, 162 Codes of Practice, 178 mashing process, 267 Master of Wine, 324 matching tests, 25 maturation, 269 mature spirit sensory training, 270 MDS see multidimensional scaling me too/copycat approach, 205 Mediterranean anchovis see Engraulis encrasicholus Mediterranean hake see Merluccius merluccius Melanogrammus aeglefinus, 297 melting, 191 memory tests, 85 Merluccius capensis, 297 Merluccius merluccius, 297 Merluccius paradoxus, 297 mesityl oxide, 164, 271 Methode l’Ancienne, 250 2-methoxyphenol, 159 methyl bromide, 164 2-methyl-iso-borneol, 162, 271 4-methyl-pent-3-en-2-one, 271 mixed model ANOVA, 45 modified atmosphere packaging, 220 moist, 191 molecular gastronomy, 330 motivation maintenance program, 29–30 mouldy off-note, 268 mouth-feel characteristics, 5 MSE plot, 45 multidimensional scaling, 112 multiple linear regression, 111 multiple regression analysis, 112 Muscat, 250 muscat distillates, 264 Napa winery, 251 near-infrared spectroscopy, 241 new product development, 204 approach that help deliver what the customer wants

365

customer panels, 206 old product development, 206 typical scenarios, 204–5 blue-sky creativity, 204 customer brief, 204–5 me too/copycat approach, 205 utilised techniques in process, 205–6 difference testing, 205 preference testing, 205 New York Times, 328 NMKL Procedure No. 6, 39 non-destructive testing, 114 non-food products, 342 acoustic compliance report for cars, 346 cases of sensory quality control, 342–8 environment/architecture, 347–8 glass products, 343–4 personal care, 343 CLIMPAQ chamber, 347 five senses, 337–9 olfaction, 338 sense of hearing, 338 sense of sight, 338 taste, 339 touch, 338–9 future trends, 349–50 general recommendations, 339–42 booths, 341–2 protocols, 342 room/environment, 341 scales, 342 odours origin in Etand de Berre region, 348 sensory evaluation, 337 sensory laboratory setting up recommendations, 340 sensory quality control, 337–50 stages of quality control of watches, 344 transportation, 344–7 odours, 345 sounds, 344–5 touch, 345, 347 nordtest, 347 NPD see new product development ocean perch eyes near the end of shelf-life, 301 newly caught, 300 gills near the end of shelf-life, 301 newly caught, 300

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366

Index

octopus see Octopus vulgaris Octopus vulgaris, 297 odour, 34, 104 off-aroma compounds, 251 off-flavours, 81, 156, 163, 270–2 causes and symptoms, 271 investigative techniques, 272 orthonasal route, 104 overall product profile approach, 146 p-value, 121, 131 pain sensations, 339 pair tests, 251 paired comparison methods, 66–7, 85, 166, 325 2-AFC method, 66 ballot paper, 67 simple difference test, 66–7 Pandalus borealis, 297 panel leader, 19 panel monitoring techniques, 62 panel supervisor, 19 PanelCheck, 45, 137 panellists, 10, 20 aspects of candidates worth investigating, 23–4 ability to communicate, 24 availability, 23 character and personality, 23 dislikes and attitudes to foods, 24 health, 23 knowledge and aptitude, 24 motivation, willingness and interest, 23 general scheme for recruitment and training, 169 maintenance, 28–30 follow-up, 33–4 motivation, 29–30 performance, 28–9 size, 30 performance evaluation, 284–5 pre-screening and recruitment, 21–4 number of candidates, 22 process, 22–4 suggested area, 21 screening, 24–5 selection, 27–8 selection and training, 168–9 training, 25–7, 296 building expertise, 27 improving ability to detect, recognise and describe sensory stimuli, 26–7

sensory test procedures familiarisation, 25–6 Pantone, 214 Paralichthys patagonicus, 297 partial least square regression, 112, 284, 289, 304 pass/fail method, 60–2, 125–6 PCA see principal component analysis PCR see principal component regression PDO see Protected Designation of Origin Pearson product moment correlation coefficients, 108, 110 Peugeot Citroen, 347 Pinot gris, 250 Pinot noir, 250 plaice see Pleuronectes platessa Pleuronectes platessa, 297 PLS see partial least square regression p*MSE plot, 45 Pollachius virens, 297 pollock see Pollachius virens power, 121 preference mapping, 198 preference testing, 217 premium wines, 248 prepared shelf-life, 223 principal component analysis, 113, 252, 281, 304 combined data sets, 112 principal component regression, 112 producer-focused approach, 14–15 sales data and consumer complaint information, 15 product-based approach, 3 product packing, 227–9 product quality, 3–4 manufacturing-based operations management approach, 4 product-based approach, 3 transcendent approach, 3 user-based approach, 4 value-based operations management approach, 4 Product Quality Tool, 94–5 results, 95 set up, 94 usage, 94–5 assessors, 95 protocols, 95 product sensory specifications, 75–95 benefits, 78 case study, 94–5

© Woodhead Publishing Limited, 2010

Index results, 95 set up, 94 using product quality tool, 94–5 defining, 78–83 creation, 80–3 identification critical consumer attributes, 80 quality control, 83 sensory attribute list – chocolate product, 82 target product, 79–80 implementation, 84–93 category scale example, 89 grading – visualisation over time period, 88, 89 line scales, 89 raw fish grades – Atlantic ground fish, 88 sensory assessors, 85–6 sensory test methods, 86–90 sensory test protocols, 90–3 spider graph displaying sensory attributes, 90 instrumental measurements, 76–7 maintenance and follow-up, 93 tracking data by sensory methodology, 94 rationale, 78 reference samples, 83–4 competitive material samples, 84 daily products as references, 84 gold standard, 83–4 rejected or manipulated samples, 84 sensorial measurements, 77–8 proficiency testing, 46 definition, 37–8 design and implementation, 38–43 different steps, 40 material, 41–3 methods, 40–1 item, 39, 40, 41, 42, 44, 46 round, 44, 46 scheme, 46 sensory panels, 37–46 analysis of data/validation of results, 44–5 glossary, 46 panel performance, 45–6 profile plot, 45 proprioception, 103 Protected Designation of Origin, 324

367

QDA see quantitative descriptive analysis QIM Eurofish, 308 quality index method, 131, 133–4, 297–302 seafood evaluation, 131 scoring method, 70–2, 87 winemaker definition, 258 quality circles, 357 quality control, 119 quality ratings, 129–30 quality safety-net, 232 quantitative descriptive analysis, 89, 303 R-index test, 167 rancidity, 305 ranking test, 25, 42–3, 69–70, 86, 251, 270 ballot paper, 69 ready meal, 203–34 recognition test, 270 recognition threshold, 165 Red Chief apple, 281 red wine, 42 redfish see Sebastes marinus; Sebastes mentella reference material, 46 reference test, 270 refresher training, 357 regression analysis, 110–11 rejuvenation, 269 Renault, 345, 347 resiliency, 191 restaurant reviews, 328–9 retronasal route, 104 Rhombus laevis, 297 Riesling, 250 Riesling wine, 324 right first time approach, 215, 225 ring trial, 43–4, 46 round robin test, 29 Royal Horticultural Society, 214 S500 improver, 189 Salmo salar, 297 Salvelinus alpinya, 298 Sardina pilchardus, 297 sauce, 203–34 scales, 126 scaling method, 67–8 ballot paper, 68 Scomber scombrus, 297 Scophthalmus maximus, 297

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368

Index

Scotch whisky, 265, 269 sensory quality control, 262–72 Scotch Whisky Research Institute’s Flavour Wheel, 266 sea bass see Dicentrarchus labrax sea bream see Sparus aurata SeafoodSense Kolbrún, 307–8 Seagram company, 264 Sebastes marinus, 297 Sebastes mentella, 297 selected assessor, 326 selected ion monitoring, 173 semi-quantitative descriptive specifications method, 55–7 ballot paper, 58 sensorial measurements, 77–8 sensory analysis, 99–101 human senses, 99–100 test procedures, 100–1 sensory assessors, 85–6 screening potential, 85–6 training potential, 86 sensory attributes, 5–6 sensory environment, 90–1 sensory instrumental correlations, 138 sensory methods ballot paper A not A method, 65 DFC method, 63 duo-trio test method, 73 fully quantitative DS method, 58–9 in/out or pass/fail method, 61 paired comparison methods, 67 quality score method, 71 ranking method, 69 scaling method, 68 semi-quantitative DS method, 58 TDFC method, 64 triangle test method, 70 instrumental methods in food quality control, 97–115 classification of testing procedures, 101 future trends, 113–15 key test methods, 101 quality sensory analysis, 99–101 paired comparison methods, 66–7 2-AFC method, 66 simple difference test, 66–7 quality control, 51–74 A not A method, 65–6 descriptive specifications, 55–60 DFC method, 62–4 fruit drink specification, 56

in-house and do-it-yourself methods, 73–4 in/out method, 60–2 magnitude estimation and duo-trio methods, 72–3 overview, 54 quality scoring/grading/rating method, 70–2 ranking test, 69–70 recommended number of panellists, 55 scaling method including targeted scaling, 67–8 SPC, 60 triangle test, 70 reasons for determining shelf-life, 145–6 sensory panels, 164 proficiency testing, 37–46 analysis of data/validation of results, 44–5 design and implementation, 38–43 glossary, 46 panel performance, 45–6 sensory plan, 152–4 sensory profiling tests, 40, 42 sensory program co-ordinator, 8–9 sensory quality control, 4 approaches to define sensory targets, 12–15 consumer-targeted approach, 12–14 key stages, 13 producer-focused approach, 14–15 sensory profiles comparison, 14 case study: staff selection and management, 31–4 panel maintenance and follow-up, 33–4 production samples, 33 qualitative/quantitative training, 32 qualitative training, 32 quantitative training, 32 spiked samples, 33 training results, 33 company culture and quality commitment, 6–7 consumer goods other than food, 337–50 cases of non-food products sensory quality control, 342–8 future trends, 349–50 general recommendations, 339–42 non-food products and five senses, 337–9

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Index non-food products sensory evaluation, 337 control program design, 3–16 establishment, 7–8 external support and consultancy, 15–16 principle and objective, 4 distilled beverages, 262–72 current industry practice, 266–70 origins, 263–4 procedures and precautions, 264–6 taints and off-flavours, 270–2 failings in defining the sensory specification, 4–6 attribute weighting, 6 flavour, texture and mouth-feel characteristics, 5 relevance and priority of sensory attributes, 5–6 visual characteristics, 4–5 fish management, 293–310 different storage conditions, 306–7 future trends, 307–10 guidelines for sensory evaluation, 295–6 quality index development, 303–4 quality indices, 293–4 sensory evaluation, 296–303 storage management and production planning quality indices, 305–6 food ingredients definition, 186–201 good quality ingredients in consumer-oriented approach, 186–8 texture, 188–92 toast bread for Chinese consumers, 193–201 foodservice, 316–30 aspects of sensory analysis, 317–22 case study, 329 formal methods, 322–6 future trends, 329–30 informal methods, 326–9 fresh produce, 276–90 fruit and vegetables quality control, 277–80 storage temperature of apples, 280–90 maintaining panel, 28–30 motivation, 29–30 performance monitoring, 28–9 size, 30

369

managing risk change in p-value, 123 false alarms, 122 respondent judgments on p-value curve, 124 statistical significance vs commercial importance, 122 measurement methods and practical examples, 125–34 ANOVA for appearance, 130 blind control sample scores for a DFC test, 128 descriptive analysis, 131–4 ideal profile value and attribute weighting factors, 133 in/out or pass/fail, 125–6 mean scores of five samples in a DFC test, 128 multiple attributes, 130 quality index calculation for rice product, 133 quality ratings, 129–30 rating systems, 126, 128–9 results from one week of pass/fail test, 127 rice product quality index, 134 ten attributes analysed using Tukey’s HSD test, 132 methods, 51–74 panel pre-screening and recruitment, 21–4 number of candidates, 22 process, 22–4 where to look, 21 practical considerations, 134–7 confidence, 135 confidence vs cost, 135 cost, 134–5 diagnostic strength vs power of test, 137 power, 135–6 speed, 135 speed of test vs cost, 136 speed of test vs power of test, 136 product knowledge, 122–5 action standards and consumer validation, 123–5 establishing the gold standard, 125 gold standard sensory properties, 125 product matching, 138–9 equivalency, 139 new suppliers, processes and ingredient replacement, 139

© Woodhead Publishing Limited, 2010

370

Index

quality assurance in ready meal, soup and sauce sectors, 203–34 after product despatch, 232–3 post-development product scale-up, 206–9 production process, 209–32 recipe development, 204–6 quality program key elements, 8–12 assessment area, 11 data capture and dissemination, 11–12 procedures and protocols, 12 selecting panellists, 10 sensory assessment method, 10–11 sensory program co-ordinator, 8–9 sensory specification, 8 training program, 9 training samples, 9–10 required personnel, 18–20 other resources, 20 panellists, 20 sensory responsible, 19 support staff, 19–20 selling the benefits, 6–7 setting up a panel, 20–8 screening, 24–5 selection, 27–8 training, 25–7 staff selection and management, 17–34 future trends, 34 possible issues, 30–1 statistical approaches, 118–40 assessor proficiency and validation, 137–8 sensory instrumental correlations, 138 statistics defined, 119–21 alpha risk, beta risk and power, 121 function, 119 hypothesis testing, 120–1 p-value notes, 121 Type I and Type II error, 120 taint prevention, 156–83 case studies, 181–4 chemistry of taint, 159–60 detection and analysis, 164–5 diagnostic taint testing, 173–4 ethical aspects, 179, 181 future trends, 183 preventive test methods standardisation, 177–8

role, 178–9 sensory testing procedures, 165–73 sources of taints, 160–4 transfer testing, 175–7 wine industry, 236–58 approaches to wine quality determination, 248–9 current sensory quality control practices in winemaking, 249–56 European standards, 238–9 factors affecting wine quality, 246–7 future of sensory evaluation, 257–8 historical perspective, 237–8 quality evaluation standardisation, 242–5 sensory evaluation development, 245–6 wine quality concept, 239–42 wine quality levels, 248 winning management support, 6–7 sensory quality control panel maintaining performance, motivation and size, 28–30 panel motivation, 29–30 panel size, 29–30 performance monitoring, 28–9 set-up, 20–8 pre-screening and recruitment, 21–4 screening, 24–5 selection, 27–8 training, 25–7 sensory quality control program continuous improvement internal audits, 357 Kaizen, 357 implementation, 353–7 piloting the program, 353 quality assurance, 354 maintaining the effectiveness of QC/QA program, 356–7 feedback sessions, 357 refresher training, 357 staff motivation, 356–7 program effectiveness, 354–6 consumer and customer complaints, 356 quality advantage vs competitors, 356 trend analysis and control charts, 355–6 trend chart for overall texture, 355

© Woodhead Publishing Limited, 2010

Index refinement and consolidation, 353–4 initial training regime for panellists, 353–4 SOPs that support the sensory assessment method, 354 training aids, 354 sensory quality system, 316, 323 sensory responsible, 19 sensory specification, 8 common failings in defining, 4–6 attribute weighting, 6 flavour, texture and mouth-feel characteristics, 5 relevance and priority of sensory attributes, 5–6 visual characteristics, 4–5 sensory targets, 12–15 consumer-targeted approach, 12–14 producer-focused approach, 14–15 sensory test methods, 86–90 descriptive testing, 88–90 difference from control, 87–8 grading method, 87 in/out method, 87 quality rating method, 87 protocols, 90–3 assessors, 91 samples, 91–2 sensory environment, 90–1 test methodology and protocol, 92–3 selecting and operating, 169–70 Sepia officinalis, 298 shelf-life assessment, 207–9 using sensory techniques, 143–55 definition, 144–5 determination using sensory methods, 145–6 end of shelf-life determination, 146 fish, 305 ingredient shelf-life extension, 220–1 monitoring over shelf-life, 231 setting or confirming, 147–8 additional considerations for setting, 147–8 reasons for confirming during production, 147 setting up confirmation studies for an ambient product, 148–55

371

collection, analysis and reporting, 154–5 considerations and preparations, 148–52 developing sensory plan, 152–4 sherry, 269 shrimp see Pandalus borealis SIM see selected ion monitoring simple difference test, 66–7 sodium hypochlorite, 163 sodium stearoil lactilare, 189 softness, 191 sole see Solea vulgaris Solea vulgaris, 297 somesthesia, 339 somethesis, 103 sonometers, 347 soup, 203–34 Sparus aurata, 297, 298 SPC see statistical process control spectrum descriptive analysis, 89 spoilage, 305 SQS see sensory quality system SSL see sodium stearoil lactilare Stable Micro System, 280 Starking apple, 281 statistical process control, 57, 60 statistics approaches to sensory quality control, 118–40 assessor proficiency and validation, 137–8 managing risk, 122–5 measurement methods and practical examples, 125–34 practical considerations, 134–7 product matching, 138–9 sensory instrumental correlations, 138 defined, 119–21 alpha risk, beta risk and power, 121 hypothesis testing, 120–1 notes on the p-value, 121 function descriptive, 119 inferential, 119 measurement, 119 significance vs commercial importance, 122 still house, 268 storage, 219–20, 231 sweaty aroma, 268 Syrah wines, 257

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372

Index

TA-XT2 Texture Analyser, 280 taint, 81, 270–2 case studies, 181–3 flooring, 181 ingredients, 182–3 water treatment, 181–2 wood preservatives, 182 causes and symptoms, 271 chemical sources and descriptions, 160 chemistry, 159–60 taste threshold of halophenols and haloanisoles, 159 definition, 156–7 detection and analysis, 164–5 perception, 164 thresholds, 164–5 diagnostic testing, 173–4 investigative techniques, 272 prevention, 175–8 ethical aspects, 179, 181 foods/food stimulants for taint transfer testing, 177 future trends, 183 role, 178–9 sensory quality control, 156–83 simple model system for taint transfer testing, 176 standardisation of preventive test methods, 177–8 steps in taint transfer testing, 175 taint transfer testing, 175–7 problem areas in business, 157–8 insurance claims, 158 litigation, 158 lost consumer confidence, 158 lost stock, 157–8 product recall costs, 158 production disruption, 158 sensory testing procedures, 165–73 chemical analysis, 171–3 data analysis, 170–1 descriptive test, 167–8 discrimination tests, 166–7 panel selection and training, 168–9 panelist recruitment and training scheme, 169 selecting and operating sensory tests, 169–70 sources, 160–4, 161 aerial transfer, 162–3 direct contact, 161–2

internal chemical reaction, 163–4 water supply contamination, 162 transfer testing, 175–7 foods/food stimulants, 177 screening procedure questionnaire, 180 simple model system for suspect materials, 176 steps schematic diagram, 175 target product, 79–80 targeted difference from control method, 64 targeted scaling method, 67–8 Tasmanian Food Research Unit, 297 taste, 34 taste buds, 339 taste panel, 229–30 taste recognition test, 85 taste the vineyard, 250 taste thresholds, 159 tasting, 247 tasting menus, 327–8 tea, 323, 325 textural evaluation, 138 texture, 5, 100, 189 measurement, 103–4 empirical methods, 103 fundamental methods, 104 imitative methods, 103–4 perception, 103 texture profile analysis, 191–2 texture profiling method, 89, 98 TFRU see Tasmanian Food Research Unit The Washington Post, 329 theaflavin digallate, 325 thermal sensations, 339 thresholds, 164–5 time–intensity methods, 89 toast breads Chinese consumers, 193–201 average ratings from consumers, 197 clustering, 198, 201 colour, texture and flavour levels combinations, 194 HAC in Beijing, 200 hedonic evaluation, 194, 196 methodology to define good quality, 193 preference mapping, 198 preference mappings, 199–200 product space definition, 193 sensory characterisation, 194

© Woodhead Publishing Limited, 2010

Index GPA sensory mapping consensus product and attributes positioning, 196 product positioning for all judges and consensus, 195 top five organoleptic quality points, 222 Top Red apple, 280–90 Torry Research Station, 296, 302–3 Torry scale, 302–3, 305, 307 TPA see texture profile analysis Trachurus trachurus, 297, 298 train the trainer approach, 353 trained sensory panels, 349 training program, 9 training samples, 9–10 triangle tests, 25, 66, 72, 85, 167, 187, 263, 279, 325 ballot paper, 70 tribromoanisole, 271 2,4,6-tribromophenol, 271 2,4,6-trichloroanisole, 179, 251, 271 trichloroanisoles, 85 trichlorophenols, 85 trigeminal nerve, 339 true value, 37, 46 tub gunard see Chelidonichthys lucernus Tucker-1 plot, 45 Tukey’s HSD test, 132 tulip-shaped glasses, 264 turbot see Scophthalmus maximus user-based approach, 4 UV spectrometry, 241 vacuum packaging, 220 vanillin, 159 vanillin sugar, 84 VDA see Verband Der Automobilindustrie VDA 270, 345 Verband Der Automobilindustrie, 345 visual flavour, 100 visual texture, 103 vodka, 263 volatile organic compounds, 345, 347

wine, 323, 324–5 Wine Aroma Wheel, 245 wine industry and sensory evaluation development as science, 245–6 approaches to wine quality determination, 248–9 case studies, 249–56 Foster’s Wine Estates, 251–2 large California Central Valley winery, 249–50 mid-sized California winery, 250 Napa winery, 251 Syrah wines principal component analysis, 257 wineries which have out-sourced sensory services, 252–6 current sensory quality control practices in winemaking, 249–56 descriptive analysis scorecard preselected attributes for red wines, 254–5 preselected attributes for white wines, 253–4 European standards, 238–9 factors affecting wine quality, 246–7 future of sensory evaluation, 257–8 historical perspective, 237–8 quality evaluation standardisation, 242–5 Davis 20-point scorecard, 242–4 Davis 20-point scorecard sample, 243 other wine quality scales, 244–5 sensory quality control, 236–58 wine quality concept, 239–42 wine quality levels, 248 wine quality concept, 239–42 definition, 241 evaluation standardisation, 242–5 levels, 248 scales, 244–5 work in progress, 223 wort, 267 Xlstat Pro Software v. 2007, 284

wash safe, 268 wheel for Pisco, 264 whisky see Scotch whisky Williams design, 284

373

yeast, 267 z-score, 44

© Woodhead Publishing Limited, 2010