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Hildegarde Heymann, Professor of Food Sensory Science, Department of Food
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Harry T. Lawless · Hildegarde
Heymann

Sensory Evaluation
of Food
Principles and Practices
Second Edition

123


Harry T. Lawless
Department of Food Science
Cornell University
Stocking Hall, Room 106
14853 Ithaca
NY, USA


Hildegarde Heymann
Department of Viticulture and Enology
University of California – Davis
2003 RMI Sensory Building
Davis 95616
CA, USA


ISSN 1572-0330

ISBN 978-1-4419-6487-8
e-ISBN 978-1-4419-6488-5
DOI 10.1007/978-1-4419-6488-5
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2010932599
© Springer Science+Business Media, LLC 2010
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY
10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection
with any form of information storage and retrieval, electronic adaptation, computer software, or by similar
or dissimilar methodology now known or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are
not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject
to proprietary rights.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)


Preface

The field of sensory science has grown exponentially since the publication of the previous version of this work. Fifteen years ago the journal Food Quality and Preference
was fairly new. Now it holds an eminent position as a venue for research on sensory
test methods (among many other topics). Hundreds of articles relevant to sensory
testing have appeared in that and in other journals such as the Journal of Sensory
Studies. Knowledge of the intricate cellular processes in chemoreception, as well as
their genetic basis, has undergone nothing less than a revolution, culminating in the
award of the Nobel Prize to Buck and Axel in 2004 for their discovery of the olfactory
receptor gene super family. Advances in statistical methodology have accelerated as
well. Sensometrics meetings are now vigorous and well-attended annual events. Ideas
like Thurstonian modeling were not widely embraced 15 years ago, but now seem to

be part of the everyday thought process of many sensory scientists.
And yet, some things stay the same. Sensory testing will always involve human
participants. Humans are tough measuring instruments to work with. They come
with varying degrees of acumen, training, experiences, differing genetic equipment,
sensory capabilities, and of course, different preferences. Human foibles and their
associated error variance will continue to place a limitation on sensory tests and
actionable results. Reducing, controlling, partitioning, and explaining error variance
are all at the heart of good test methods and practices. Understanding the product–
person interface will always be the goal of sensory science. No amount of elaborate
statistical maneuvering will save a bad study or render the results somehow useful
and valid. Although methods continue to evolve, appreciation of the core principles
of the field is the key to effective application of sensory test methods.
The notion that one can write a book that is both comprehensive and suitable as
an introductory text was a daunting challenge for us. Some may say that we missed
the mark on this or that topic, that it was either too superficially treated or too in
depth for their students. Perhaps we have tried to do the impossible. Nonetheless the
demand for a comprehensive text that would serve as a resource for practitioners is
demonstrated by the success of the first edition. Its widespread adoption as a university level text shows that many instructors felt that it could be used appropriately for
a first course in sensory evaluation.
This book has been expanded somewhat to reflect the advances in methodologies, theory, and analysis that have transpired in the last 15 years. The chapters are
now divided into numbered sections. This may be of assistance to educators who
may wish to assign only certain critical sections to beginning students. Much of the
organization of key chapters has been done with this in mind and in some of the
v


vi

opening sections; instructors will find suggestions about which sections are key for
fundamental understanding of that topic or method. In many chapters we have gone

out on a limb and specified a “recommended procedure.” In cases where there are
multiple options for procedure or analysis, we usually chose a simple solution over
one that is more complex. Because we are educators, this seemed the appropriate
path.
Note that there are two kinds of appendices in this book. The major statistical
methods are introduced with worked examples in Appendices A–E, as in the previous edition. Some main chapters also have appended materials that we felt were not
critical to understanding the main topic, but might be of interest to advanced students,
statisticians, or experienced practitioners. We continue to give reference citations at
the end of every chapter, rather than in one big list at the end. Statistical tables have
been added, most notably the discrimination tables that may now be found both in the
Appendix and in Chapter 4 itself.
One may question whether textbooks themselves are an outdated method for
information retrieval. We feel this acutely because we recognize that a textbook is
necessarily retrospective and is only one snapshot in time of a field that may be
evolving rapidly. Students and practitioners alike may find that reference to updated
websites, wikis, and such will provide additional information and new and different
perspectives. We encourage such investigation. Textbooks, like automobiles, have an
element of built-in obsolescence. Also textbooks, like other printed books, are linear in nature, but the mind works by linking ideas. Hyperlinked resources such as
websites and wikis will likely continue to prove useful.
We ask your patience and tolerance for materials and citations that we have left out
that you might feel are important. We recognize that there are legitimate differences of
opinion and philosophy about the entire area of sensory evaluation methods. We have
attempted to provide a balanced and impartial view based on our practical experience.
Any errors of fact, errors typographical, or errors in citation are our own fault. We beg
your understanding and patience and welcome your corrections and comments.
We could not have written this book without the assistance and support of many
people. We would like to thank Kathy Dernoga for providing a pre-publication version of the JAR scale ASTM manual as well as the authors of the ASTM JAR
manual Lori Rothman and Merry Jo Parker. Additionally, Mary Schraidt of Peryam
and Kroll provided updated examples of a consumer test screening questionnaire and
field study questionnaires. Thank you Mary. We thank John Hayes, Jeff Kroll, Tom

Carr, Danny Ennis, and Jian Bi for supplying additional literature, software, and statistical tables. Gernot Hoffmann graciously provided graphics for Chapter 12. Thank
you Dr. Hoffmann. We would like to thank Wendy Parr and James Green for providing some graphics for Chapter 10. Additionally, Greg Hirson provided support with
R-Graphics. Thank you, Greg. Additionally, we want to thank the following people for their willingness to discuss the book in progress and for making very useful
suggestions: Michael Nestrud, Susan Cuppett, Edan Lev-Ari, Armand Cardello, Marj
Albright, David Stevens, Richard Popper, and Greg Hirson. John Horne had also been
very helpful in the previous edition, thank you John. Proofreading and editing suggestions were contributed by Kathy Chapman, Gene Lovelace, Mike Nestrud, and
Marge Lawless.
Although not directly involved with this edition of the book we would also like
to thank our teachers and influential mentors—without them we would be very different scientists, namely Trygg Engen, William S. Cain, Linda Bartoshuk, David

Preface


Preface

vii

Peryam, David Stevens, Herb Meiselman, Elaine Skinner, Howard Schutz, Howard
Moskowitz, Rose Marie Pangborn, Beverley Kroll, W. Frank Shipe, Lawrence E.
Marks, Joseph C. Stevens, Arye Dethmers, Barbara Klein, Ann Noble, Harold
Hedrick, William C Stringer, Roger Boulton, Kay McMath, Joel van Wyk, and Roger
Mitchell.
Ithaca, New York
Davis, California

Harry T. Lawless
Hildegarde Heymann




Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1
Introduction and Overview . . . . . . . . . . . . . . . . . . .
1.1.1
Definition . . . . . . . . . . . . . . . . . . . . . . .
1.1.2
Measurement . . . . . . . . . . . . . . . . . . . . .
1.2
Historical Landmarks and the Three Classes
of Test Methods . . . . . . . . . . . . . . . . . . . . . . . . .
1.2.1
Difference Testing . . . . . . . . . . . . . . . . . . .
1.2.2
Descriptive Analyses . . . . . . . . . . . . . . . . .
1.2.3
Affective Testing . . . . . . . . . . . . . . . . . . .
1.2.4
The Central Dogma—Analytic Versus
Hedonic Tests . . . . . . . . . . . . . . . . . . . . .
1.3
Applications: Why Collect Sensory Data? . . . . . . . . . . .
1.3.1
Differences from Marketing Research Methods . . .
1.3.2
Differences from Traditional Product
Grading Systems . . . . . . . . . . . . . . . . . . .
1.4
Summary and Conclusions . . . . . . . . . . . . . . . . . . .

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2 Physiological and Psychological Foundations
of Sensory Function . . . . . . . . . . . . . . . . . . . . . . .
2.1
Introduction . . . . . . . . . . . . . . . . . . . . . . .
2.2
Classical Sensory Testing and Psychophysical Methods
2.2.1
Early Psychophysics . . . . . . . . . . . . .
2.2.2
The Classical Psychophysical Methods . . . .
2.2.3
Scaling and Magnitude Estimation . . . . . .
2.2.4
Critiques of Stevens . . . . . . . . . . . . . .
2.2.5
Empirical Versus Theory-Driven Functions .
2.2.6
Parallels of Psychophysics and Sensory
Evaluation . . . . . . . . . . . . . . . . . . .
2.3
Anatomy and Physiology and Functions of Taste . . . .
2.3.1
Anatomy and Physiology . . . . . . . . . . .
2.3.2
Taste Perception: Qualities . . . . . . . . . .
2.3.3
Taste Perception: Adaptation and Mixture
Interactions . . . . . . . . . . . . . . . . . .
2.3.4

Individual Differences and Taste Genetics . .
2.4
Anatomy and Physiology and Functions of Smell . . . .
2.4.1
Anatomy and Cellular Function . . . . . . . .

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Contents

2.4.2
2.4.3
2.4.4
2.4.5

Retronasal Smell . . . . . . . . . . . . . .
Olfactory Sensitivity and Specific Anosmia
Odor Qualities: Practical Systems . . . . .
Functional Properties: Adaptation, Mixture
Suppression, and Release . . . . . . . . . .
2.5
Chemesthesis . . . . . . . . . . . . . . . . . . . . . .
2.5.1
Qualities of Chemesthetic Experience . . .
2.5.2
Physiological Mechanisms of Chemesthesis
2.5.3
Chemical “Heat” . . . . . . . . . . . . . .
2.5.4
Other Irritative Sensations and Chemical
Cooling . . . . . . . . . . . . . . . . . . .
2.5.5
Astringency . . . . . . . . . . . . . . . . .
2.5.6
Metallic Taste . . . . . . . . . . . . . . . .
2.6
Multi-modal Sensory Interactions . . . . . . . . . . .
2.6.1

Taste and Odor Interactions . . . . . . . . .
2.6.2
Irritation and Flavor . . . . . . . . . . . . .
2.6.3
Color–Flavor Interactions . . . . . . . . . .
2.7
Conclusions . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 Principles of Good Practice . . . . . . . . . . . . .
3.1
Introduction . . . . . . . . . . . . . . . . .
3.2
The Sensory Testing Environment . . . . . .
3.2.1
Evaluation Area . . . . . . . . . .
3.2.2
Climate Control . . . . . . . . . .
3.3
Test Protocol Considerations . . . . . . . .
3.3.1
Sample Serving Procedures . . . .
3.3.2
Sample Size . . . . . . . . . . . .
3.3.3
Sample Serving Temperatures . .
3.3.4
Serving Containers . . . . . . . .
3.3.5
Carriers . . . . . . . . . . . . . .
3.3.6

Palate Cleansing . . . . . . . . . .
3.3.7
Swallowing and Expectoration . .
3.3.8
Instructions to Panelists . . . . . .
3.3.9
Randomization and Blind Labeling
3.4
Experimental Design . . . . . . . . . . . . .
3.4.1
Designing a Study . . . . . . . . .
3.4.2
Design and Treatment Structures .
3.5
Panelist Considerations . . . . . . . . . . .
3.5.1
Incentives . . . . . . . . . . . . .
3.5.2
Use of Human Subjects . . . . . .
3.5.3
Panelist Recruitment . . . . . . .
3.5.4
Panelist Selection and Screening .
3.5.5
Training of Panelists . . . . . . .
3.5.6
Panelist Performance Assessment .
3.6
Tabulation and Analysis . . . . . . . . . . .
3.6.1

Data Entry Systems . . . . . . . .
3.7
Conclusion . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . .

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Contents

xi

4 Discrimination Testing . . . . . . . . . . . . . . . . . . . . . . . . .
4.1
Discrimination Testing . . . . . . . . . . . . . . . . . . . . . .
4.2
Types of Discrimination Tests . . . . . . . . . . . . . . . . . .
4.2.1

Paired Comparison Tests . . . . . . . . . . . . . . .
4.2.2
Triangle Tests . . . . . . . . . . . . . . . . . . . . .
4.2.3
Duo–Trio Tests . . . . . . . . . . . . . . . . . . . .
4.2.4
n-Alternative Forced Choice (n-AFC) Methods . . .
4.2.5
A-Not-A tests . . . . . . . . . . . . . . . . . . . . .
4.2.6
Sorting Methods . . . . . . . . . . . . . . . . . . . .
4.2.7
The ABX Discrimination Task . . . . . . . . . . . .
4.2.8
Dual-Standard Test . . . . . . . . . . . . . . . . . .
4.3
Reputed Strengths and Weaknesses of Discrimination
Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4
Data Analyses . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4.1
Binomial Distributions and Tables . . . . . . . . . .
4.4.2
The Adjusted Chi-Square (χ2 ) Test . . . . . . . . . .
4.4.3
The Normal Distribution and the Z-Test
on Proportion . . . . . . . . . . . . . . . . . . . . .
4.5
Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1

The Power of the Statistical Test . . . . . . . . . . .
4.5.2
Replications . . . . . . . . . . . . . . . . . . . . . .
4.5.3
Warm-Up Effects . . . . . . . . . . . . . . . . . . .
4.5.4
Common Mistakes Made in the Interpretation
of Discrimination Tests . . . . . . . . . . . . . . . .
Appendix: A Simple Approach to Handling the A, Not-A,
and Same/Different Tests . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Similarity, Equivalence Testing, and Discrimination Theory . . . .
5.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2
Common Sense Approaches to Equivalence . . . . . . . . . .
5.3

Estimation of Sample Size and Test Power . . . . . . . . . . .
5.4
How Big of a Difference Is Important?
Discriminator Theory . . . . . . . . . . . . . . . . . . . . . .
5.5
Tests for Significant Similarity . . . . . . . . . . . . . . . . .
5.6
The Two One-Sided Test Approach (TOST)
and Interval Testing . . . . . . . . . . . . . . . . . . . . . . .
5.7
Claim Substantiation . . . . . . . . . . . . . . . . . . . . . . .
5.8
Models for Discrimination: Signal Detection Theory . . . . . .
5.8.1
The Problem . . . . . . . . . . . . . . . . . . . . . .
5.8.2
Experimental Setup . . . . . . . . . . . . . . . . . .
5.8.3
Assumptions and Theory . . . . . . . . . . . . . . .
5.8.4
An Example . . . . . . . . . . . . . . . . . . . . . .
5.8.5
A Connection to Paired Comparisons Results
Through the ROC Curve . . . . . . . . . . . . . . .
5.9
Thurstonian Scaling . . . . . . . . . . . . . . . . . . . . . . .
5.9.1
The Theory and Formulae . . . . . . . . . . . . . . .
5.9.2
Extending Thurstone’s Model to Other

Choice Tests . . . . . . . . . . . . . . . . . . . . . .

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xii

Contents

5.10

Extensions of the Thurstonian Methods, R-Index . . .
5.10.1 Short Cut Signal Detection Methods . . . .
5.10.2 An Example . . . . . . . . . . . . . . . . .
5.11
Conclusions . . . . . . . . . . . . . . . . . . . . . .
Appendix: Non-Central t-Test for Equivalence of Scaled Data
References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6 Measurement of Sensory Thresholds . . . . . . . . . . . . . . . . .
6.1
Introduction: The Threshold Concept . . . . . . . . . . . . . .
6.2
Types of Thresholds: Definitions . . . . . . . . . . . . . . . .
6.3
Practical Methods: Ascending Forced Choice . . . . . . . . . .
6.4
Suggested Method for Taste/Odor/Flavor
Detection Thresholds . . . . . . . . . . . . . . . . . . . . . .
6.4.1
Ascending Forced-Choice Method of Limits . . . . .
6.4.2
Purpose of the Test . . . . . . . . . . . . . . . . . .
6.4.3
Preliminary Steps . . . . . . . . . . . . . . . . . . .
6.4.4
Procedure . . . . . . . . . . . . . . . . . . . . . . .
6.4.5
Data Analysis . . . . . . . . . . . . . . . . . . . . .
6.4.6
Alternative Graphical Solution . . . . . . . . . . . .
6.4.7
Procedural Choices . . . . . . . . . . . . . . . . . .
6.5
Case Study/Worked Example . . . . . . . . . . . . . . . . . .
6.6
Other Forced Choice Methods . . . . . . . . . . . . . . . . . .
6.7

Probit Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .
6.8
Sensory Adaptation, Sequential Effects, and Variability . . . .
6.9
Alternative Methods: Rated Difference, Adaptive
Procedures, Scaling . . . . . . . . . . . . . . . . . . . . . . .
6.9.1
Rated Difference from Control . . . . . . . . . . . .
6.9.2
Adaptive Procedures . . . . . . . . . . . . . . . . .
6.9.3
Scaling as an Alternative Measure of Sensitivity . . .
6.10
Dilution to Threshold Measures . . . . . . . . . . . . . . . . .
6.10.1 Odor Units and Gas-Chromatography
Olfactometry (GCO) . . . . . . . . . . . . . . . . .
6.10.2 Scoville Units . . . . . . . . . . . . . . . . . . . . .
6.11
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix: MTBE Threshold Data for Worked Example . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
7.2
Some Theory . . . . . . . . . . . . . . . . . . . . . . . . .
7.3
Common Methods of Scaling . . . . . . . . . . . . . . . .
7.3.1
Category Scales . . . . . . . . . . . . . . . . . .
7.3.2
Line Scaling . . . . . . . . . . . . . . . . . . . .
7.3.3
Magnitude Estimation . . . . . . . . . . . . . . .
7.4
Recommended Practice and Practical Guidelines . . . . . .
7.4.1
Rule 1: Provide Sufficient Alternatives . . . . . .
7.4.2
Rule 2: The Attribute Must Be Understood . . . .
7.4.3
Rule 3: The Anchor Words Should Make Sense .
7.4.4
To Calibrate or Not to Calibrate . . . . . . . . . .
7.4.5
A Warning: Grading and Scoring are Not Scaling

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7.5

Variations—Other Scaling Techniques . . . . . . . . . . . . .
7.5.1
Cross-Modal Matches and Variations on
Magnitude Estimation . . . . . . . . . . . . . . . . .
7.5.2
Category–Ratio (Labeled Magnitude) Scales . . . . .
7.5.3
Adjustable Rating Techniques: Relative Scaling . . .
7.5.4
Ranking . . . . . . . . . . . . . . . . . . . . . . . .
7.5.5
Indirect Scales . . . . . . . . . . . . . . . . . . . . .
7.6
Comparing Methods: What is a Good Scale? . . . . . . . . . .
7.7
Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.7.1
“Do People Make Relative Judgments”
Should They See Their Previous Ratings? . . . . . .
7.7.2
Should Category Rating Scales Be Assigned
Integer Numbers in Data Tabulation? Are
They Interval Scales? . . . . . . . . . . . . . . . . .
7.7.3

Is Magnitude Estimation a Ratio Scale or
Simply a Scale with Ratio Instructions? . . . . . . .
7.7.4
What is a “Valid” Scale? . . . . . . . . . . . . . . .
7.8
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix 1: Derivation of Thurstonian-Scale Values
for the 9-Point Scale . . . . . . . . . . . . . . . . . . . . . . .
Appendix 2: Construction of Labeled Magnitude Scales . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8 Time–Intensity Methods . . . . . . . . . . . . . . . . . . . . . .
8.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . .
8.2
A Brief History . . . . . . . . . . . . . . . . . . . . . . .
8.3
Variations on the Method . . . . . . . . . . . . . . . . . .
8.3.1
Discrete or Discontinuous Sampling . . . . . . .
8.3.2
“Continuous” Tracking . . . . . . . . . . . . . .
8.3.3
Temporal Dominance Techniques . . . . . . . . .
8.4
Recommended Procedures . . . . . . . . . . . . . . . . . .
8.4.1
Steps in Conducting a Time–intensity Study . . .
8.4.2
Procedures . . . . . . . . . . . . . . . . . . . . .
8.4.3

Recommended Analysis . . . . . . . . . . . . . .
8.5
Data Analysis Options . . . . . . . . . . . . . . . . . . . .
8.5.1
General Approaches . . . . . . . . . . . . . . . .
8.5.2
Methods to Construct or Describe Average Curves
8.5.3
Case Study: Simple Geometric Description . . .
8.5.4
Analysis by Principal Components . . . . . . . .
8.6
Examples and Applications . . . . . . . . . . . . . . . . .
8.6.1
Taste and Flavor Sensation Tracking . . . . . . .
8.6.2
Trigeminal and Chemical/Tactile Sensations . . .
8.6.3
Taste and Odor Adaptation . . . . . . . . . . . .
8.6.4
Texture and Phase Change . . . . . . . . . . . .
8.6.5
Flavor Release . . . . . . . . . . . . . . . . . . .
8.6.6
Temporal Aspects of Hedonics . . . . . . . . . .
8.7
Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8.8
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .


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9 Context Effects and Biases in Sensory Judgment . . . . . . .
9.1
Introduction: The Relative Nature of Human Judgment .
9.2
Simple Contrast Effects . . . . . . . . . . . . . . . . .
9.2.1
A Little Theory: Adaptation Level . . . . . .
9.2.2
Intensity Shifts . . . . . . . . . . . . . . . .
9.2.3
Quality Shifts . . . . . . . . . . . . . . . . .
9.2.4
Hedonic Shifts . . . . . . . . . . . . . . . . .
9.2.5
Explanations for Contrast . . . . . . . . . . .
9.3

Range and Frequency Effects . . . . . . . . . . . . . .
9.3.1
A Little More Theory: Parducci’s Range
and Frequency Principles . . . . . . . . . . .
9.3.2
Range Effects . . . . . . . . . . . . . . . . .
9.3.3
Frequency Effects . . . . . . . . . . . . . . .
9.4
Biases . . . . . . . . . . . . . . . . . . . . . . . . . .
9.4.1
Idiosyncratic Scale Usage and Number Bias .
9.4.2
Poulton’s Classifications . . . . . . . . . . .
9.4.3
Response Range Effects . . . . . . . . . . . .
9.4.4
The Centering Bias . . . . . . . . . . . . . .
9.5
Response Correlation and Response Restriction . . . .
9.5.1
Response Correlation . . . . . . . . . . . . .
9.5.2
“Dumping” Effects: Inflation Due
to Response Restriction in Profiling . . . . .
9.5.3
Over-Partitioning . . . . . . . . . . . . . . .
9.6
Classical Psychological Errors and Other Biases . . . .
9.6.1

Errors in Structured Sequences: Anticipation
and Habituation . . . . . . . . . . . . . . . .
9.6.2
The Stimulus Error . . . . . . . . . . . . . .
9.6.3
Positional or Order Bias . . . . . . . . . . . .
9.7
Antidotes . . . . . . . . . . . . . . . . . . . . . . . . .
9.7.1
Avoid or Minimize . . . . . . . . . . . . . .
9.7.2
Randomization and Counterbalancing . . . .
9.7.3
Stabilization and Calibration . . . . . . . . .
9.7.4
Interpretation . . . . . . . . . . . . . . . . .
9.8
Conclusions . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10 Descriptive Analysis . . . . . . . . . . . . . . . . . . . . .
10.1
Introduction . . . . . . . . . . . . . . . . . . . . .
10.2
Uses of Descriptive Analyses . . . . . . . . . . . .
10.3
Language and Descriptive Analysis . . . . . . . . .
10.4
Descriptive Analysis Techniques . . . . . . . . . .
10.4.1 Flavor Profile R . . . . . . . . . . . . . .
10.4.2 Quantitative Descriptive Analysis R . . .

10.4.3 Texture Profile R . . . . . . . . . . . . .
10.4.4 Sensory Spectrum R . . . . . . . . . . . .
10.5
Generic Descriptive Analysis . . . . . . . . . . . .
10.5.1 How to Do Descriptive Analysis in Three
Easy Steps . . . . . . . . . . . . . . . . .

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10.5.2

Studies Comparing Different Conventional
Descriptive Analysis Techniques . . . . . .
10.6 Variations on the Theme . . . . . . . . . . . . . . . .
10.6.1
Using Attribute Citation Frequencies Instead
of Attribute Intensities . . . . . . . . . . .
10.6.2

Deviation from Reference Method . . . . .
10.6.3
Intensity Variation Descriptive Method . . .
10.6.4
Combination of Descriptive Analysis
and Time-Related Intensity Methods . . . .
10.6.5
Free Choice Profiling . . . . . . . . . . . .
10.6.6
Flash Profiling . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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253

Texture Evaluation . . . . . . . . . . . . . . . . . . . . . . .
11.1 Texture Defined . . . . . . . . . . . . . . . . . . . . .
11.2 Visual, Auditory, and Tactile Texture . . . . . . . . . .
11.2.1

Visual Texture . . . . . . . . . . . . . . . . .
11.2.2
Auditory Texture . . . . . . . . . . . . . . .
11.2.3
Tactile Texture . . . . . . . . . . . . . . . . .
11.2.4
Tactile Hand Feel . . . . . . . . . . . . . . .
11.3 Sensory Texture Measurements . . . . . . . . . . . . .
11.3.1
Texture Profile Method . . . . . . . . . . . .
11.3.2
Other Sensory Texture Evaluation Techniques
11.3.3
Instrumental Texture Measurements
and Sensory Correlations . . . . . . . . . . .
11.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12

Color and Appearance . . . . . . . . . . . . . . . . . .
12.1 Color and Appearance . . . . . . . . . . . . . . .
12.2 What Is Color? . . . . . . . . . . . . . . . . . . .
12.3 Vision . . . . . . . . . . . . . . . . . . . . . . .
12.3.1
Normal Human Color Vision Variations
12.3.2

Human Color Blindness . . . . . . . . .
12.4 Measurement of Appearance and Color Attributes
12.4.1
Appearance . . . . . . . . . . . . . . .
12.4.2
Visual Color Measurement . . . . . . .
12.5 Instrumental Color Measurement . . . . . . . . .
12.5.1
Munsell Color Solid . . . . . . . . . . .
12.5.2
Mathematical Color Systems . . . . . .
12.6 Conclusions . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . .

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13

Preference Testing . . . . . . . . . . . . . . . . . .
13.1 Introduction—Consumer Sensory Evaluation
13.2 Preference Tests: Overview . . . . . . . . .
13.2.1
The Basic Comparison . . . . . .
13.2.2
Variations . . . . . . . . . . . . .
13.2.3
Some Cautions . . . . . . . . . .
13.3 Simple Paired Preference Testing . . . . . .

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Contents

13.3.1 Recommended Procedure . . . . . . . . . . . . . . .
13.3.2 Statistical Basis . . . . . . . . . . . . . . . . . . . .
13.3.3 Worked Example . . . . . . . . . . . . . . . . . . .
13.3.4 Useful Statistical Approximations . . . . . . . . . .
13.3.5 The Special Case of Equivalence Testing . . . . . . .
13.4
Non-forced Preference . . . . . . . . . . . . . . . . . . . . . .
13.5
Replicated Preference Tests . . . . . . . . . . . . . . . . . . .
13.6
Replicated Non-forced Preference . . . . . . . . . . . . . . . .
13.7
Other Related Methods . . . . . . . . . . . . . . . . . . . . .
13.7.1 Ranking . . . . . . . . . . . . . . . . . . . . . . . .
13.7.2
Analysis of Ranked Data . . . . . . . . . . . . . . .
13.7.3
Best–Worst Scaling . . . . . . . . . . . . . . . . . .
13.7.4 Rated Degree of Preference and Other Options . . . .
13.8
Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix 1: Worked Example of the Ferris k-Visit Repeated
Preference Test Including the No-Preference Option . . . . . .
Appendix 2: The “Placebo” Preference Test . . . . . . . . . . . . . . .
Appendix 3: Worked Example of Multinomial Approach
to Analyzing Data with the No-Preference Option . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14 Acceptance Testing . . . . . . . . . . . . . . . . . . . . . .

14.1
Introduction: Scaled Liking Versus Choice . . . . . .
14.2
Hedonic Scaling: Quantification of Acceptability . . .
14.3
Recommended Procedure . . . . . . . . . . . . . . .
14.3.1 Steps . . . . . . . . . . . . . . . . . . . . .
14.3.2 Analysis . . . . . . . . . . . . . . . . . . .
14.3.3 Replication . . . . . . . . . . . . . . . . .
14.4
Other Acceptance Scales . . . . . . . . . . . . . . . .
14.4.1 Line Scales . . . . . . . . . . . . . . . . .
14.4.2 Magnitude Estimation . . . . . . . . . . . .
14.4.3 Labeled Magnitude Scales . . . . . . . . .
14.4.4 Pictorial Scales and Testing with Children .
14.4.5 Adjustable Scales . . . . . . . . . . . . . .
14.5
Just-About-Right Scales . . . . . . . . . . . . . . . .
14.5.1 Description . . . . . . . . . . . . . . . . .
14.5.2 Limitations . . . . . . . . . . . . . . . . .
14.5.3 Variations on Relative-to-Ideal Scaling . . .
14.5.4
Analysis of JAR Data . . . . . . . . . . . .
14.5.5 Penalty Analysis or “Mean Drop” . . . . .
14.5.6 Other Problems and Issues with JAR Scales
14.6
Behavioral and Context-Related Approaches . . . . .
14.6.1 Food Action Rating Scale (FACT) . . . . .
14.6.2 Appropriateness Scales . . . . . . . . . . .
14.6.3 Acceptor Set Size . . . . . . . . . . . . . .

14.6.4 Barter Scales . . . . . . . . . . . . . . . .
14.7
Conclusions . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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15

16

Consumer Field Tests and Questionnaire Design . . . . . . . . . . .
15.1 Sensory Testing Versus Concept Testing . . . . . . . . . . . .
15.2 Testing Scenarios: Central Location, Home Use . . . . . . . .
15.2.1
Purpose of the Tests . . . . . . . . . . . . . . . . . .
15.2.2
Consumer Models . . . . . . . . . . . . . . . . . . .
15.2.3
Central Location Tests . . . . . . . . . . . . . . . .
15.2.4

Home Use Tests (HUT) . . . . . . . . . . . . . . . .
15.3 Practical Matters in Conducting Consumer Field Tests . . . . .
15.3.1
Tasks and Test Design . . . . . . . . . . . . . . . . .
15.3.2
Sample Size and Stratification . . . . . . . . . . . .
15.3.3
Test Designs . . . . . . . . . . . . . . . . . . . . . .
15.4 Interacting with Field Services . . . . . . . . . . . . . . . . .
15.4.1
Choosing Agencies, Communication,
and Test Specifications . . . . . . . . . . . . . . . .
15.4.2
Incidence, Cost, and Recruitment . . . . . . . . . . .
15.4.3
Some Tips: Do’s and Don’ts . . . . . . . . . . . . .
15.4.4
Steps in Testing with Research Suppliers . . . . . . .
15.5 Questionnaire Design . . . . . . . . . . . . . . . . . . . . . .
15.5.1
Types of Interviews . . . . . . . . . . . . . . . . . .
15.5.2
Questionnaire Flow: Order of Questions . . . . . . .
15.5.3
Interviewing . . . . . . . . . . . . . . . . . . . . . .
15.6 Rules of Thumb for Constructing Questions . . . . . . . . . .
15.6.1
General Principles . . . . . . . . . . . . . . . . . . .
15.6.2
Brevity . . . . . . . . . . . . . . . . . . . . . . . . .

15.6.3
Use Plain Language . . . . . . . . . . . . . . . . . .
15.6.4
Accessibility of the Information . . . . . . . . . . .
15.6.5
Avoid Vague Questions . . . . . . . . . . . . . . . .
15.6.6
Check for Overlap and Completeness . . . . . . . . .
15.6.7
Do Not Lead the Respondent . . . . . . . . . . . . .
15.6.8
Avoid Ambiguity and Double Questions . . . . . . .
15.6.9
Be Careful in Wording: Present Both Alternatives . .
15.6.10 Beware of Halos and Horns . . . . . . . . . . . . . .
15.6.11 Pre-test . . . . . . . . . . . . . . . . . . . . . . . .
15.7 Other Useful Questions: Satisfaction, Agreement,
and Open-Ended Questions . . . . . . . . . . . . . . . . . . .
15.7.1
Satisfaction . . . . . . . . . . . . . . . . . . . . . .
15.7.2
Likert (Agree–Disagree) Scales . . . . . . . . . . . .
15.7.3
Open-Ended Questions . . . . . . . . . . . . . . . .
15.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix 1: Sample Test Specification Sheet . . . . . . . . . . . . . .
Appendix 2: Sample Screening Questionnaire . . . . . . . . . . . . . .
Appendix 3: Sample Product Questionnaire . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Qualitative Consumer Research Methods . . . . . . .

16.1 Introduction . . . . . . . . . . . . . . . . . . .
16.1.1
Resources, Definitions, and Objectives
16.1.2
Styles of Qualitative Research . . . .
16.1.3
Other Qualitative Techniques . . . . .

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16.2

Characteristics of Focus Groups . . . . . . . . . . . . . . . . . 383
16.2.1 Advantages . . . . . . . . . . . . . . . . . . . . . . 383
16.2.2 Key Requirements . . . . . . . . . . . . . . . . . . . 384
16.2.3 Reliability and Validity . . . . . . . . . . . . . . . . 384
16.3
Using Focus Groups in Sensory Evaluation . . . . . . . . . . . 385
16.4
Examples, Case Studies . . . . . . . . . . . . . . . . . . . . . 386
16.4.1 Case Study 1: Qualitative Research Before
Conjoint Measurement in New Product Development 387

16.4.2 Case Study 2: Nutritional and Health Beliefs About Salt 387
16.5
Conducting Focus Group Studies . . . . . . . . . . . . . . . . 388
16.5.1 A Quick Overview . . . . . . . . . . . . . . . . . . 388
16.5.2 A Key Requirement: Developing Good Questions . . 389
16.5.3 The Discussion Guide and Phases
of the Group Interview . . . . . . . . . . . . . . . . 390
16.5.4 Participant Requirements, Timing, Recording . . . . 391
16.6
Issues in Moderating . . . . . . . . . . . . . . . . . . . . . . . 392
16.6.1 Moderating Skills . . . . . . . . . . . . . . . . . . . 392
16.6.2 Basic Principles: Nondirection, Full
Participation, and Coverage of Issues . . . . . . . . . 393
16.6.3 Assistant Moderators and Co-moderators . . . . . . . 394
16.6.4 Debriefing: Avoiding Selective Listening
and Premature Conclusions . . . . . . . . . . . . . . 395
16.7
Analysis and Reporting . . . . . . . . . . . . . . . . . . . . . 395
16.7.1 General Principles . . . . . . . . . . . . . . . . . . . 395
16.7.2 Suggested Method (“Sorting/Clustering
Approach”), also Called Classical Transcript
Analysis . . . . . . . . . . . . . . . . . . . . . . . . 396
16.7.3 Report Format . . . . . . . . . . . . . . . . . . . . . 397
16.8
Alternative Procedures and Variations of the Group
Interview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 398
16.8.1 Groups of Children, Telephone Interviews,
Internet-Based Groups . . . . . . . . . . . . . . . . 398
16.8.2 Alternatives to Traditional Questioning . . . . . . . . 399
16.9

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . 400
Appendix: Sample Report Group Report . . . . . . . . . . . . . . . . . 402
Boil-in-bag Pasta Project Followup Groups . . . . . . . . . . . . . . . 402
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404
17 Quality Control and Shelf-Life (Stability) Testing .
17.1
Introduction: Objectives and Challenges . . .
17.2
A Quick Look at Traditional Quality Control .
17.3
Methods for Sensory QC . . . . . . . . . . .
17.3.1 Cuttings: A Bad Example . . . . . .
17.3.2 In–Out (Pass/Fail) System . . . . .
17.3.3 Difference from Control Ratings . .
17.3.4 Quality Ratings with Diagnostics . .
17.3.5 Descriptive Analysis . . . . . . . .
17.3.6 A Hybrid Approach: Quality Ratings
with Diagnostics . . . . . . . . . . .

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17.3.7
The Multiple Standards Difference Test . . . . . . .
Recommended Procedure: Difference Scoring with
Key Attribute Scales . . . . . . . . . . . . . . . . . . . . . . .
17.5 The Importance of Good Practice . . . . . . . . . . . . . . . .
17.6 Historical Footnote: Expert Judges and Quality Scoring . . . .
17.6.1
Standardized Commodities . . . . . . . . . . . . . .
17.6.2
Example 1: Dairy Product Judging . . . . . . . . . .
17.6.3
Example 2: Wine Scoring . . . . . . . . . . . . . . .
17.7 Program Requirements and Program Development . . . . . . .
17.7.1
Desired Features of a Sensory QC System . . . . . .
17.7.2
Program Development and Management Issues . . .
17.7.3
The Problem of Low Incidence . . . . . . . . . . . .
17.8
Shelf-Life Testing . . . . . . . . . . . . . . . . . . . . . . . .
17.8.1

Basic Considerations . . . . . . . . . . . . . . . . .
17.8.2
Cutoff Point . . . . . . . . . . . . . . . . . . . . . .
17.8.3
Test Designs . . . . . . . . . . . . . . . . . . . . . .
17.8.4
Survival Analysis and Hazard Functions . . . . . . .
17.8.5
Accelerated Storage . . . . . . . . . . . . . . . . . .
17.9 Summary and Conclusions . . . . . . . . . . . . . . . . . . .
Appendix 1: Sample Screening Tests for Sensory Quality Judges . . . .
Appendix 2: Survival/Failure Estimates from a Series
of Batches with Known Failure Times . . . . . . . . . . . . . .
Appendix 3: Arrhenius Equation and Q10 Modeling . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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17.4

18

19

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428
429
429
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431

Data Relationships and Multivariate Applications . . . . . . . . . .
18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . .
18.2 Overview of Multivariate Statistical Techniques . . . . . . . .
18.2.1
Principal Component Analysis . . . . . . . . . . . .
18.2.2
Multivariate Analysis of Variance . . . . . . . . . . .
18.2.3
Discriminant Analysis (Also Known
as Canonical Variate Analysis) . . . . . . . . . . . .
18.2.4
Generalized Procrustes Analysis . . . . . . . . . . .
18.3 Relating Consumer and Descriptive Data Through
Preference Mapping . . . . . . . . . . . . . . . . . . . . . . .

18.3.1
Internal Preference Mapping . . . . . . . . . . . . .
18.3.2
External Preference Mapping . . . . . . . . . . . . .
18.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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446

Strategic Research . . . . . . . . . . . . . . .
19.1 Introduction . . . . . . . . . . . . . . .
19.1.1
Avenues for Strategic Research
19.1.2
Consumer Contact . . . . . . .
19.2 Competitive Surveillance . . . . . . . .
19.2.1
The Category Review . . . . .
19.2.2
Perceptual Mapping . . . . . .
19.2.3
Multivariate Methods: PCA . .
19.2.4
Multi-dimensional Scaling . .

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Contents

19.2.5

Cost-Efficient Methods for Data Collection:
Sorting . . . . . . . . . . . . . . . . . . . . . .
19.2.6
Vector Projection . . . . . . . . . . . . . . . .
19.2.7 Cost-Efficient Methods for Data Collection:
Projective Mapping, aka Napping . . . . . . . .

19.3
Attribute Identification and Classification . . . . . . . . .
19.3.1 Drivers of Liking . . . . . . . . . . . . . . . .
19.3.2 The Kano Model . . . . . . . . . . . . . . . .
19.4
Preference Mapping Revisited . . . . . . . . . . . . . . .
19.4.1 Types of Preference Maps . . . . . . . . . . . .
19.4.2 Preference Models: Vectors Versus Ideal Points
19.5
Consumer Segmentation . . . . . . . . . . . . . . . . . .
19.6
Claim Substantiation Revisited . . . . . . . . . . . . . .
19.7
Conclusions . . . . . . . . . . . . . . . . . . . . . . . .
19.7.1 Blind Testing, New Coke, and the Vienna
Philharmonic . . . . . . . . . . . . . . . . . .
19.7.2 The Sensory Contribution . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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496

Appendix A Basic Statistical Concepts for Sensory Evaluation . .
A.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . .
A.2
Basic Statistical Concepts . . . . . . . . . . . . . . . . .
A.2.1
Data Description . . . . . . . . . . . . . . . .
A.2.2
Population Statistics . . . . . . . . . . . . . . .
A.3
Hypothesis Testing and Statistical Inference . . . . . . .
A.3.1
The Confidence Interval . . . . . . . . . . . . .
A.3.2
Hypothesis Testing . . . . . . . . . . . . . . .
A.3.3
A Worked Example . . . . . . . . . . . . . . .
A.3.4
A Few More Important Concepts . . . . . . . .
A.3.5
Decision Errors . . . . . . . . . . . . . . . . .
A.4
Variations of the t-Test . . . . . . . . . . . . . . . . . . .
A.4.1
The Sensitivity of the Dependent t-Test for
Sensory Data . . . . . . . . . . . . . . . . . .
A.5
Summary: Statistical Hypothesis Testing . . . . . . . . .

A.6
Postscript: What p-Values Signify and What They Do Not
A.7
Statistical Glossary . . . . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix B Nonparametric and Binomial-Based Statistical
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . .
B.1
Introduction to Nonparametric Tests . . . . . . . . .
B.2
Binomial-Based Tests on Proportions . . . . . . . . .
B.3
Chi-Square . . . . . . . . . . . . . . . . . . . . . . .
B.3.1
A Measure of Relatedness of Two Variables
B.3.2
Calculations . . . . . . . . . . . . . . . . .
B.3.3
Related Samples: The McNemar Test . . . .
B.3.4
The Stuart–Maxwell Test . . . . . . . . . .
B.3.5
Beta-Binomial, Chance-Corrected
Beta-Binomial, and Dirichlet
Multinomial Analyses . . . . . . . . . . . .

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B.4

Useful Rank Order Tests . . . . . . . . . . . . . . . .
B.4.1
The Sign Test . . . . . . . . . . . . . . . .
B.4.2
The Mann–Whitney U-Test . . . . . . . . .
B.4.3
Ranked Data with More Than Two Samples,
Friedman and Kramer Tests . . . . . . . . .
B.4.4
Rank Order Correlation . . . . . . . . . . .
B.5
Conclusions . . . . . . . . . . . . . . . . . . . . . .
B.6
Postscript . . . . . . . . . . . . . . . . . . . . . . . .
B.6.1
Proof showing equivalence of binomial
approximation Z-test and χ 2 test for
difference of proportions . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix C Analysis of Variance . . . . . . . . . . . . . . . . . . .
C.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . .
C.1.1

Overview . . . . . . . . . . . . . . . . . . . .
C.1.2
Basic Analysis of Variance . . . . . . . . . . .
C.1.3
Rationale . . . . . . . . . . . . . . . . . . . .
C.1.4
Calculations . . . . . . . . . . . . . . . . . . .
C.1.5
A Worked Example . . . . . . . . . . . . . . .
C.2
Analysis of Variance from Complete Block Designs . . .
C.2.1
Concepts and Partitioning Panelist Variance
from Error . . . . . . . . . . . . . . . . . . . .
C.2.2
The Value of Using Panelists
As Their Own Controls . . . . . . . . . . . . .
C.3
Planned Comparisons Between Means Following ANOVA
C.4
Multiple Factor Analysis of Variance . . . . . . . . . . .
C.4.1
An Example . . . . . . . . . . . . . . . . . . .
C.4.2
Concept: A Linear Model . . . . . . . . . . . .
C.4.3
A Note About Interactions . . . . . . . . . . .
C.5
Panelist by Product by Replicate Designs . . . . . . . . .
C.6

Issues and Concerns . . . . . . . . . . . . . . . . . . . .
C.6.1
Sensory Panelists: Fixed or Random Effects? .
C.6.2
A Note on Blocking . . . . . . . . . . . . . . .
C.6.3
Split-Plot or Between-Groups (Nested) Designs
C.6.4
Statistical Assumptions and the Repeated
Measures ANOVA . . . . . . . . . . . . . . .
C.6.5
Other Options . . . . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix D Correlation, Regression, and Measures of Association
D.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . .
D.2
Correlation . . . . . . . . . . . . . . . . . . . . . . . . .
D.2.1
Pearson’s Correlation Coefficient Example . . .
D.2.2
Coefficient of Determination . . . . . . . . . .
D.3
Linear Regression . . . . . . . . . . . . . . . . . . . . .
D.3.1
Analysis of Variance . . . . . . . . . . . . . .
D.3.2
Analysis of Variance for Linear Regression . .
D.3.3
Prediction of the Regression Line . . . . . . . .

D.3.4
Linear Regression Example . . . . . . . . . . .

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D.4
D.5

Multiple Linear Regression . . . . . . . . . . . . . .
Other Measures of Association . . . . . . . . . . . .
D.5.1
Spearman Rank Correlation . . . . . . . . .
D.5.2
Spearman Correlation Coefficient Example
D.5.3
Cramér’s V Measure . . . . . . . . . . . . .
D.5.4
Cramér Coefficient Example . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix E Statistical Power and Test Sensitivity . . . . . . . .
E.1
Introduction . . . . . . . . . . . . . . . . . . . . . . .
E.2
Factors Affecting the Power of Statistical Tests . . . . .
E.2.1
Sample Size and Alpha Level . . . . . . . . .
E.2.2
Effect Size . . . . . . . . . . . . . . . . . . .
E.2.3
How Alpha, Beta, Effect Size, and N Interact
E.3
Worked Examples . . . . . . . . . . . . . . . . . . . .
E.3.1
The t-Test . . . . . . . . . . . . . . . . . . .
E.3.2
An Equivalence Issue with Scaled Data . . .
E.3.3
Sample Size for a Difference Test . . . . . . .

E.4
Power in Simple Difference and Preference Tests . . . .
E.5
Summary and Conclusions . . . . . . . . . . . . . . .
References . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Appendix F Statistical Tables . . . . . . . . . . . . . . . . . . . . . . . . .
Table F.A Cumulative probabilities of the standard normal
distribution. Entry area 1–α under the standard
normal curve from −∞ to z(1–α) . . . . . . . . . . . . .
Table F.B Table of critical values for the t-distribution . . . . . . . .
Table F.C
Table of critical values of the chi-square (χ 2 )
distribution . . . . . . . . . . . . . . . . . . . . . . . . .
Table F.D1 Critical values of the F-distribution at α = 0.05 . . . . . .
Table F.D2 Critical values of the F-distribution at α = 0.01 . . . . . .
Table F.E
Critical values of U for a one-tailed alpha at 0.025
or a two-tailed alpha at 0.05 . . . . . . . . . . . . . . . .
Table F.F1 Table of critical values of ρ (Spearman Rank

correlation coefficient) . . . . . . . . . . . . . . . . . . .
Table F.F2 Table of critical values of r (Pearson’s correlation
coefficient) . . . . . . . . . . . . . . . . . . . . . . . . .
Table F.G Critical values for Duncan’s multiple range test
(p, df, α = 0.05) . . . . . . . . . . . . . . . . . . . . . . .
Table F.H1 Critical values of the triangle test for similarity
(maximum number correct as a function of the
number of observations (N), beta, and proportion
discriminating) . . . . . . . . . . . . . . . . . . . . . . .
Table F.H2 Critical values of the duo–trio and paired
comparison tests for similarity (maximum number
correct as a function of the number of observations
(N), beta, and proportion discriminating) . . . . . . . . . .
Table F.I
Table of probabilities for values as small as
observed values of x associated with the binomial
test (p=0.50) . . . . . . . . . . . . . . . . . . . . . . . .

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Contents

xxiii

Table F.J

Critical values for the differences between rank
sums (α = 0.05) . . . . . . . . . . . . . . . . . . . . . . .
Critical values of the beta binomial distribution . . . . . .
Minimum numbers of correct judgments
to establish significance at probability levels of 5
and 1% for paired difference and duo–trio
tests (one tailed, p = 1/2) and the triangle
test (one tailed, p = 1/3) . . . . . . . . . . . . . . . . . .
Minimum numbers of correct judgments
to establish significance at probability levels of 5
and 1% for paired preference test (two tailed,
p = 1/2) . . . . . . . . . . . . . . . . . . . . . . . . . . .
Minimum number of responses (n) and correct
responses (x) to obtain a level of Type I
and Type II risks in the triangle test. Pd is
the chance-adjusted percent correct or proportion
of discriminators . . . . . . . . . . . . . . . . . . . . . .

Minimum number of responses (n) and correct
responses (x) to obtain a level of Type I
and Type II risks in the duo–trio test. Pc is the
chance-adjusted percent correct or proportion
of discriminators . . . . . . . . . . . . . . . . . . . . . .
d and B (variance factor) values for the duo–trio
and 2-AFC (paired comparison) difference tests . . . . . .
d and B (variance factor) values for the triangle
and 3-AFC difference tests . . . . . . . . . . . . . . . . .
Random permutations of nine . . . . . . . . . . . . . . .
Random numbers . . . . . . . . . . . . . . . . . . . . . .

569
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572

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

573

Subject Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

587

Table F.K
Table F.L

Table F.M

Table F.N1


Table F.N2

Table F.O1
Table F.O2
Table F.P
Table F.Q

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Chapter 1

Introduction

Abstract In this chapter we carefully parse the definition for sensory evaluation,
briefly discuss validity of the data collected before outlining the early history of
the field. We then describe the three main methods used in sensory evaluation
(discrimination tests, descriptive analysis, and hedonic testing) before discussing the
differences between analytical and consumer testing. We then briefly discuss why one

may want to collect sensory data. In the final sections we highlight the differences and
similarities between sensory evaluation and marketing research and between sensory
evaluation and commodity grading as used in, for example, the dairy industry.

Sensory evaluation is a child of industry. It was spawned in the late 40’s by the rapid growth of the
consumer product companies, mainly food companies. . . . Future development in sensory
evaluation will depend upon several factors, one of the most important being the people and their
preparation and training.
— Elaine Skinner (1989)

Contents
Introduction and Overview . . . . . . . . .
1.1.1 Definition . . . . . . . . . . . . . .
1.1.2 Measurement . . . . . . . . . . . .
1.2 Historical Landmarks and the Three Classes
of Test Methods . . . . . . . . . . . . . .
1.2.1 Difference Testing . . . . . . . . . .
1.2.2 Descriptive Analyses . . . . . . . . .
1.2.3 Affective Testing . . . . . . . . . .
1.2.4 The Central Dogma—Analytic Versus
Hedonic Tests . . . . . . . . . . . .
1.3 Applications: Why Collect Sensory Data? . .
1.3.1 Differences from Marketing Research
Methods . . . . . . . . . . . . . .
1.3.2 Differences from Traditional Product
Grading Systems . . . . . . . . . .
1.4 Summary and Conclusions . . . . . . . . .
References . . . . . . . . . . . . . . . . . . .
1.1


1.1 Introduction and Overview
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1.1.1 Definition
The field of sensory evaluation grew rapidly in the second half of the twentieth century, along with the expansion of the processed food and consumer products
industries. Sensory evaluation comprises a set of techniques for accurate measurement of human responses
to foods and minimizes the potentially biasing effects
of brand identity and other information influences on
consumer perception. As such, it attempts to isolate
the sensory properties of foods themselves and provides important and useful information to product
developers, food scientists, and managers about the
sensory characteristics of their products. The field was
comprehensively reviewed by Amerine, Pangborn, and
Roessler in 1965, and more recent texts have been published by Moskowitz et al. (2006), Stone and Sidel
(2004), and Meilgaard et al. (2006). These three later
sources are practical works aimed at sensory specialists

H.T. Lawless, H. Heymann, Sensory Evaluation of Food, Food Science Text Series,
DOI 10.1007/978-1-4419-6488-5_1, © Springer Science+Business Media, LLC 2010

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