Descriptive Analysis in Sensory Evaluation E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 1359

Veröffentlichungsjahr: 2018

Beschreibung

A comprehensive review of the techniques and applications of descriptive analysis

Sensory evaluation is a scientific discipline used to evoke, measure, analyse and interpret responses to products perceived through the senses of sight, smell, touch, taste and hearing. It is used to reveal insights into the ways in which sensory properties drive consumer acceptance and behaviour, and to design products that best deliver what the consumer wants. 

Descriptive analysis is one of the most sophisticated, flexible and widely used tools in the field of sensory analysis. It enables objective description of the nature and magnitude of sensory characteristics for use in consumer-driven product design, manufacture and communication.

Descriptive Analysis in Sensory Evaluation provides a comprehensive overview of a wide range of traditional and recently-developed descriptive techniques, including history, theory, practical considerations, statistical analysis, applications, case studies and future directions.  This important reference, written by academic and industrial sensory scientist, traces the evolution of descriptive analysis, and addresses general considerations, including panel set-up, training, monitoring and performance; psychological factors relevant to assessment; and statistical analysis.

Descriptive Analysis in Sensory Evaluation is a valuable resource for sensory professionals working in academia and industry, including sensory scientists, practitioners, trainers and students, and industry-based researchers in quality assurance, research and development, and marketing.

Leseprobe

Table of Contents

Cover

Title Page

Editor Biographies

List of Contributors

Preface to the Series

References

Preface

References

SECTION 1: Introduction

CHAPTER 1: Introduction to Descriptive Analysis

1.1 Introduction

1.2 Development of Descriptive Analysis

1.3 Descriptive Analysis as a Technique in Sensory Evaluation

1.4 Application of Descriptive Analysis

1.5 Contributions of Descriptive Analysis

1.6 Summary

1.7 Future Developments

1.8 Overview of Book

References

CHAPTER 2: General Considerations

2.1 General Introduction

2.2 Aims of Descriptive Analysis

2.3 Choices of Methodology

2.4 Generic Procedure for Descriptive Analysis

2.5 Factors Affecting Results in Descriptive Analysis

2.6 Data Analysis

2.7 Reporting Results

2.8 Summary

2.9 Future Developments

References

CHAPTER 3: Setting Up and Training a Descriptive Analysis Panel

3.1 Introduction: Descriptive Analysis

3.2 Types of Panel Resource

3.3 Panel Leader

3.4 Recruitment and Screening Programme

3.5 Initial Screening

3.6 Main Screening and Selection of Assessors

3.7 Panel Training

3.8 Panel Motivation

3.9 Panel Attrition

3.10 Summary

3.11 The Future

References

CHAPTER 4: Panel Quality Management: Performance, Monitoring and Proficiency

4.1 Introduction to Panel Quality Management

4.2 Panel Performance

4.3 Panel Monitoring

4.4 Proficiency Testing

4.5 Further Panel Quality Management Concerns

4.6 Standards

4.7 Statistics and Data Collection and Visualization

4.8 Summary

4.9 Future Developments

References

CHAPTER 5: Statistical Analysis of Descriptive Data

5.1 Introduction

5.2 Analysis of Variance

5.3 Multivariate Data Display

5.4 Modelling Relationships between Variables

5.5 Summary

5.6 Future Developments

References

Appendix 5.1 Mixed Models

SECTION 2: Techniques

CHAPTER 6: Consensus Methods for Descriptive Analysis

6.1 Introduction

6.2 Method Overview

6.3 Applications

6.4 Case Study

6.5 Advantages

6.6 Disadvantages

6.7 Future Developments

6.8 Summary

References

CHAPTER 7: Original Flavor and Texture Profile and Modified/Derivative Profile Descriptive Methods

7.1 Introduction/Historical Perspective

7.2 Fundamental and Philosophical Principles of Profile/Technical Descriptive Methodology

7.3 Methodology – The Profile Methods

7.4 Advantages and Disadvantages of Profile Methods

7.5 Applications: Profile Descriptive Analysis

7.6 General Recommendations: Profile Methods

7.7 Modifications of Original Profile Methods

7.8 Statistical Analysis

7.9 Case Study: Use of Profile Descriptive Analysis in Optimization Research Guidance

7.10 Summary

7.11 Future Directions

References

CHAPTER 8: Quantitative Descriptive Analysis

8.1 Introduction

8.2 Background

8.3 Method Description

8.4 Practical Considerations

8.5 Statistical Analysis of Tragon QDA Data

8.6 Advantages

8.7 Case Studies

8.8 Additional Applications

8.9 Summary

8.10 Future Development

References

CHAPTER 9: Spectrum™ Method

9.1 Introduction

9.2 Theory: History Based on Flavor and Texture Profile Methods

9.3 Traditional Methodology for the Spectrum Method

9.4 New Developments: Combining Spectrum with Other Methods

9.5 Statistical Analysis

9.6 Applications with Case Studies

9.7 Practical Hints/Tips

References

CHAPTER 10: Quantitative Flavour Profiling

10.1 Introduction

10.2 Theory

10.3 From Traditional to Modern Quantitative Flavour Profiling

10.4 Statistical Analysis

10.5 Applications of QFP

10.6 Practical Considerations

10.7 Case Studies

10.8 Summary

10.9 Future Development

References

CHAPTER 11: A

5

daptive Profile Method®

11.1 Introduction and Fundamental Principles

11.2 Methodology

11.3 Advantages and Disadvantages of the Adaptive Profile Method

11.4 Applications of the Adaptive Profile Method

11.5 Practical Considerations

11.6 Statistical Analysis of Adaptive Profile Data

11.7 Case Studies

11.8 Summary

11.9 Future Directions

References

CHAPTER 12: Ranking and Rank‐Rating

12.1 Introduction

12.2 History and Background

12.3 Methodology

12.4 Experimental Design

12.5 Analysis of Ranking Data

12.6 Analysis of Rank‐Rating Data

12.7 Applications

12.8 Case Studies

12.9 Summary

12.10 Future Directions

Appendix 12.1: Critical Values for the Friedman Test

References

CHAPTER 13: Free Choice Profiling

13.1 Introduction

13.2 Methodology of Free Choice Profiling

13.3 Generalized Procrustes Analysis

13.4 Case Study

13.5 Options/Practical Considerations

13.6 Summary

13.7 Future Considerations

References

CHAPTER 14: Flash Profile Method

14.1 Introduction

14.2 Theoretical Framework

14.3 Overview of the Flash Profile Method

14.4 Case Studies

14.5 Summary

14.6 Future Developments

References

CHAPTER 15: Projective Mapping & Sorting Tasks

15.1 Introduction

15.2 Projective Mapping

15.3 Sorting Task

15.4 Pros and Cons of Projective Mapping and the Sorting Task

15.5 Summary

15.6 Future Developments

References

CHAPTER 16: Polarized Sensory Positioning

16.1 Introduction

16.2 Description of Polarized Sensory Positioning

16.3 Practical Considerations for the Implementation of Polarized Sensory Positioning

16.4 Data Analysis

16.5 Applications of Polarized Sensory Positioning

16.6 Advantages, Disadvantages and Limitations

16.7 Case Study

16.8 Extensions of Polarized Sensory Positioning

16.9 Summary

16.10 Future Developments

Acknowledgements

References

CHAPTER 17: Check‐All‐That‐Apply and Free Choice Description

17.1 Introduction

17.2 Rationale for Use

17.3 CATA Methodology

17.4 Open‐Ended Questioning

17.5 Practical Considerations

17.6 Advantages and Disadvantages

17.7 Applications

17.8 Case Studies

17.9 Summary

17.10 Future Developments

References

SECTION 3: Applications

CHAPTER 18: Application of Descriptive Sensory Analysis to Food and Drink Products

18.1 Introduction

18.2 General Principles of Descriptive Sensory Analysis Methods as Applied to Food and Drink Products

18.3 Descriptive Sensory Analysis Application in Food and Drink Product Development

18.4 Application of Descriptive Sensory Analysis in Food and Drink Quality Control and Quality Assurance

18.5 Considerations Relating to Specific Food and Drink Products

18.6 Case Studies

18.7 Summary

18.8 Future Developments

References

CHAPTER 19: Application of Descriptive Analysis to Non‐Food Products

19.1 Introduction

19.2 Why is Descriptive Analysis of Non‐Food Products Important?

19.3 General Considerations in Applying Descriptive Analysis to Non‐Food Products

19.4 Development of Descriptive Language

19.5 Considerations Relevant to Specific Non‐Food Products

19.6 Summary

19.7 Future Developments

References

SECTION 4: Summary

CHAPTER 20: Comparison of Descriptive Analysis Methods

20.1 Introduction

20.2 Relevant Comments on the Presentation of the Methods’ Key Characteristics and Their Comparison

20.3 Overall Comparison of Descriptive Methods

20.4 Comparison of Methods Based on Key Characteristics

20.5 Summary

20.6 Future Developments

References

Index

End User License Agreement

List of Tables

Chapter 02

Table 2.1 Recommended number of panellists for a descriptive panel according to the sensory dimension.

Table 2.2 Example of a Flash table summarizing results obtained with 14 panellists who evaluated eight products and rated 14 attributes on a continuous linear scale (panellists’ responses were converted into intensity scores from 0 to 10). Values in bold and preceded by + are significantly higher than the grand mean (gmean), values in italics and preceded by (−) are significantly lower than the grand mean.

Chapter 04

Table 4.1 Example panellist and panel performance parameters.

Table 4.2 Raw data for crispiness attribute (FIZZ).

Table 4.3 Raw data for intensity of golden colour attribute (FIZZ).

Table 4.4 Comparison of means from two panels for the red colour attribute.

Table 4.5 Typical multivariate statistical techniques useful in profiling panel performance measurement.

Chapter 05

Table 5.1 Raw data for four assessors scoring juicy for the same sample in three blind replicates.

Table 5.2 Analysis of variance of the attribute juicy.

Table 5.3 Mean scores for the attribute juicy with LSD (least significant difference) (5%) pairwise comparisons.

Table 5.4 Mean scores for the attribute juicy with Tukey honestly significantly different (HSD) (5%) pairwise comparisons.

Table 5.5 Mean scores for flavour attribute – unripe apple.

Table 5.6 Panel mean scores for texture attributes.

Table 5.7 Covariance matrix of apple texture variables.

Table 5.8 Correlation matrix of apple texture variables.

Table 5.9 Eigenvalues principal component analysis of apple texture.

Table 5.10 Cluster means for apple texture.

Table 5.11 Consumer average liking and texture/flavour sensory mean scores for apples.

Table 5.12 Parameters of fitted model liking versus acidic.

Table 5.13 Analysis of variance table regression of liking on acidic.

Table 5.14 Residuals for regression of liking on acidic.

Table 5.15 Fitted model stepwise selection of liking on sensory descriptors.

Table 5.16 Fitted model prediction of liking from crisp and acidic.

Table 5.17 Principal component analysis variance explained.

Table 5.18 Stepwise selection principal component regression.

Table 5.19 Partial least squares regression equation parameters.

Chapter 06

Table 6.1 Tabular consensus scores for six production time periods.

Table 6.2 Flavour profile of the same chip: gold standard versus current.

Table 6.3 Attributes, with intensities, noted in test samples that were not found in the control sample.

1,2

Table 6.4 Comparison of sensory scores obtained by consensus or by individual scoring for pizzas.

1

Table 6.5 Comparison of sensory scores obtained by consensus or by individual scoring for bananas.

1

Chapter 07

Table 7.1 Comparison of original and modified texture scales (example: brittleness/fracturability).

Table 7.2 Comparison of original and modified/derivative profile methods’ main characteristics.

Table 7.3 Panel screening tests for original and modified/derivative profile methods.

Table 7.4 Comparison of the original and modified/derivative profile panel training approaches. Example illustrates a texture training approach (original texture profile method) and a skinfeel training approach (modified/derivative profile method).

Table 7.5 Terminology/lexicon development – the original flavor profile and a modified profile method (A

5

daptive Profile Method). Example: cheese/herb crackers.

Table 7.6a Case study. Original flavor profile results of the current cheese cracker.

Table 7.6b Case study. The A

5

daptive Profile Method flavour results of the current cheese cracker.

Table 7.7 Case study. The A

5

daptive Profile Method focused evaluation results of two prototypes identified in the optimization of cheese blends.

Table 7.8a Case study. The A

5

daptive Profile Method flavour evaluation results of the current product and two prototypes (F and B).

Table 7.8b Case study. The A

5

daptive Profile Method texture evaluation results of the current product and two prototypes (F and B).

Chapter 08

Table 8.1 Segment relationships with sensory attributes. The positive sensory attributes describe the styles of EVOO that consumers preferred in each of the segments. The negative sensory attributes are disliked by the segments.

Chapter 09

Table 9.1 Example lexicon – skinfeel attributes with references for body lotion.

Table 9.2 Universal scale references for aromatics and basic tastes.

Table 9.3 Example skinfeel texture intensity reference scales.

Table 9.4 Tiers and nuances example for spirits.

Table 9.5 Example of Spectrum descriptive method used with DOD rating.

Table 9.6 Example of quality ratings used with Spectrum descriptive method.

Table 9.7 Example of malodour intensity and fidelity using Spectrum methodology.

Table 9.8 Lexicon for tactile evaluation of paper dinner napkins using Spectrum methodology.

Chapter 10

Table 10.1 Example of orange lexicon.

Table 10.2 Effects and interactions included in ANOVA model.

Table 10.3 Examples of three butter descriptors and their references.

Table 10.4 Product codes and types of fat used in QFP.

Table 10.5 Cookies and product codes used in QFP.

Table 10.6 Example of three Sense It vanilla descriptors.

Table 10.7 Sensory characteristics of the 12 samples chosen.

Chapter 11

Table 11.1 A

5

daptive Profile Assessment – Existing programme (audit and improvement).

*

Table 11.2 A

5

daptive Profile Method’s pre‐project activities.

Table 11.3 Examples of flavour descriptive lexicons prior to the A

5

daptive Profile Method’s audit (case study II).

Table 11.4 Improved flavour lexicons based on the A

5

daptive Profile Method’s audit (case study II).

Table 11.5 Total impression measures in flavour lexicons, following the A

5

daptive Profile Method’s philosophy (case study II).

Table 11.6 Texture descriptive results of a pair evaluation prior to the A

5

daptive Profile Method’s audit (case study II).

Table 11.7 Customized/modified quantitative texture scales, following the A

5

daptive Profile Method’s philosophy (case study II).

Chapter 12

Table 12.1 Example of design balanced for order effect and carry‐over.

Table 12.2 Example of a balanced incomplete block design.

Table 12.3 Sample set of ranking data (4 = high).

Table 12.4 Rank totals for each product and significance test between products.

Table 12.5 Sample set of data reformatted by ranking position.

Table 12.6 Pairwise R

JB

values and statistical significance.

Table 12.7 Ranking distribution for each product.

Table 12.8 Pairwise R

MAT

values and statistical significance.

Table 12.9 Data format for PHREG analysis.

Table 12.10 Pairwise probabilities that product (row) is ranked higher than product (column).

Table 12.11 Case Study 1: Data for complete block example (1 = high).

Table 12.12 Case Study 1: Individual and mean rankings for assessor F276.

Table 12.13 Case Study 1: Measures of performance for each assessor.

Table 12.14 Case Study 1: Friedman Analysis Multiple comparison test using the Nemenyi procedure.

Table 12.15 Case Study 1: R

JB

Analysis Pairwise R‐Index R

JB

values.

Table 12.16 Case Study 1: R

MAT

Analysis Pooled data across all assessors.

Table 12.17 Case Study 1: R

MAT

Analysis R‐Index R

MAT

values calculated separately for each assessor.

Table 12.18 Case Study 1: Parametric Analysis Pairwise probabilities that product (row) is ranked higher than product (column).

Table 12.19 Case Study 2: Incomplete block data (1 = high).

Table 12.20 Case Study 2: R‐Index R

JB

for each pair of products.

Table 12.21 Case Study 2: Parametric Analysis Pairwise probabilities that product (row) is ranked higher than product (column).

Table 12.22 Case Study 3: Ranking and ratings.

Table 12.23 Case Study 3: Mean scores and pairwise multiple comparisons.

Chapter 13

Table 13.1 Attribute lists for the six assessors.

Table 13.2 PANOVA results per dimension (%).

Table 13.3 Explained variance by product and dimension.

Table 13.4 Explained variance by assessor and dimension.

Chapter 14

Table 14.1 Different ways in which the sensory vocabulary of a panel can relate to the distinctions between the sensory stimuli.

Table 14.2 Selected samples for the flash profile of smoked fresh cheese.

Table 14.3 Samples used in the flash profile of a model wine system.

Chapter 15

Table 15.1 Information about the 12 orange juices used in the sorting task (the names of orange juices with pulp are printed in italics).

Chapter 16

Table 16.1 Formulation of the yoghurt samples considered in the study.

Chapter 18

Table 18.1 Palate cleansers.

Table 18.2 Vocabulary.

Table 18.3 Drivers for liking – ChocBloc case study.

Table 18.4 Drivers for liking, summary all shoppers and Foods‐to‐go shoppers – ChocBloc case study.

Chapter 19

Table 19.1 Consumer attributes used to describe textural attributes of skin creams.

Table 19.2 Examples of consumer ‘definitions’ of descriptive vocabulary.

Chapter 20

Table 20.1 Overall comparison of key features of traditional descriptive analysis methods.

Table 20.2 Overview comparison of key features of new/rapid methods.

Table 20.3 Comparison of methods based on philosophies.

Table 20.4 Comparison of methods based on their technical nature.

Table 20.5 Comparison of methods based on type of results/output.

Table 20.6 Comparison of methods based on overall needs for programme implementation.

Table 20.7 Comparison of methods based on type of assessors and necessary screening.

Table 20.8 Comparison of methods based on number of assessors.

Table 20.9 Comparison of methods based on panel leader.

Table 20.10 Comparison of methods based on whether training is required.

Table 20.11 Comparison of methods based on nature of training.

Table 20.12 Comparison of methods based on generation of sensory terminology.

Table 20.13 Comparison of methods based on type of sensory terminology.

Table 20.14 Comparison of methods based on complexity of attributes.

Table 20.15 Comparison of methods based on use of qualitative references.

Table 20.16 Comparison of methods based on use of quantitative references.

Table 20.17 Comparison of methods based on type of data collection/scoring.

Table 20.18 Comparison of methods based on data analyses.

Table 20.19 Comparison of methods based on advantages/strengths and disadvantages/weaknesses.

Table 20.20 Comparison of methods based on applications.

List of Illustrations

Chapter 02

Figure 2.1 Examples of experimental designs with intra‐ or interbatch replicates.

Chapter 03

Figure 3.1 Flow chart showing the generic stages of panel selection and training for descriptive analysis.

Figure 3.2 Alphabet sugar letters.

Figure 3.3 Scoring scheme for the oral shape recognition test. No points are allocated to any other letter selected. Points allocated to each of the five letters are added to give the total score (minimum score = 0, maximum score = 15).

Figure 3.4 A basic format for creating a screening mark scheme in Excel.

Figure 3.5 An extract from a screening mark scheme in Excel showing total scores for individual tests as well as the grand total score achieved by each candidate.

Figure 3.6 A summary of the key facets of the training process.

Figure 3.7 A line scale with indented anchor points and descriptive labels. The vertical line towards the top end of the scale marks the point at which the intensity of the attribute has been rated.

Figure 3.8 ‘Absorption rate’ performance interaction plot showing four panellists’ intensity ratings for five products.

Figure 3.9 ‘Shininess’ performance interaction plot showing four panellists’ intensity ratings for five products.

Figure 3.10 An example of a panel performance summary chart produced using SENPAQ, licensed data analysis software supplied by Qi Statistics Ltd, UK. This type of chart gives a top‐level synopsis of each panellist’s performance across the set of attributes used for a specific study.

Chapter 04

Figure 4.1 Schematic representing panel quality management.

Figure 4.2 Overall process of panel performance measurement.

Figure 4.3 Distribution graphs for crispiness (FIZZ).

Figure 4.4 Extract from overall summary table (SenPAQ).

Figure 4.5 Simple graphical illustration of panellist repeatability within a project; the panel mean is represented by a black line (FIZZ).

Figure 4.6 Comparison of panellist MSE values for intensity of golden colour and crispiness (PanelCheck).

Figure 4.7 Extract from panellist performance table (SenPAQ).

Figure 4.8 ANOVA output for intensity of golden colour (SenPAQ).

Figure 4.9 Intensity of golden colour

post hoc

analysis. Samples with the same letters were not found to be statistically significantly different at P < 0.05.

Figure 4.10 P‐values for panellist discrimination (SenPAQ).

Figure 4.11 Profile plot for intensity of golden colour (PanelCheck).

Figure 4.12 ANOVA output for darkness of edges (SenPAQ).

Figure 4.13

Post hoc

analysis for darkness of edges (SenPAQ).

Figure 4.14 Profile or interaction plot for darkness of edges (PanelCheck).

Figure 4.15 Extract of panellist contribution to overall interaction table (SenPAQ).

Figure 4.16 p‐MSE plots by panellist for all attributes with selected attributes highlighted (PanelCheck).

Figure 4.17 p‐MSE plots for intensity of golden colour and crispiness with panellist 5 results highlighted (PanelCheck).

Figure 4.18 Judge performance graph for all panellists for intensity of golden colour and darkness of edges attributes (FIZZ).

Figure 4.19 Panellist performance summary table and graph (SenPAQ).

Figure 4.20 Scatterplot example (Compusense).

Figure 4.21 Schematic for ongoing project‐based panel monitoring system.

Figure 4.22 Example of simple ongoing project‐based panel monitoring database architecture.

Figure 4.23 Schematic for panel monitoring system based on scheduled diagnostic checks.

Figure 4.24 Example of hypothetical control charts for panellist consistency as measured in scheduled diagnostic checks.

Figure 4.25 Strategy for comparing panel performance in various contexts.

Figure 4.26 GPA analysis showing the position of each of the products A–L for both panels and the consensus (XLSTAT).

Figure 4.27 GPA analysis showing the position of each attribute for both panels (XLSTAT).

Figure 4.28 CVA/discriminant analysis plot from a data set of nine samples and 12 panellists (XLSTAT).

Figure 4.29 Confusion matrix from CVA/discriminant analysis (XLSTAT).

Figure 4.30 Overall judge performance plot incorporating information from three attributes (FIZZ).

Chapter 05

Figure 5.1 Assessor by sample interaction plot for juicy.

Figure 5.2 Star diagram of apple texture attributes.

Figure 5.3 Tape plot of apple texture attributes with Tukey HSD (5%). Tapes are centred on attribute mean scores with tape width ±1/2 HSD from centre.

Figure 5.4 Scatter plots of attributes.

Figure 5.5 Graphical representation of principal components.

Figure 5.6 Apples texture samples plotted on first two principal components.

Figure 5.7 Apples texture principal components correlation plot PC1 versus PC2.

Figure 5.8 Apples texture principal components correlation plot PC1 versus PC3.

Figure 5.9 Apples texture samples plotted on first and third principal component.

Figure 5.10 (a) Canonical variates apple texture first and second component. (b) Canonical variates loadings plot first and second component.

Figure 5.11 Dendrogram cluster analysis for apples.

Figure 5.12 Clustering of attributes using correlation coefficient.

Figure 5.13 Clustering of attributes using absolute value of correlation coefficient.

Figure 5.14 Anscombe’s Quartet.

Figure 5.15 Linear regression model

Figure 5.16 Visualization of R

2

.

Figure 5.17 Fitted regression with 95% confidence bands.

Figure 5.18 Fitted regression liking versus acidic with 95% confidence bands.

Figure 5.19 Residual plot regression of liking on acidic.

Figure 5.20 Liking versus sweetness.

Figure 5.21 Geometric illustration of partial least squares.

Figure 5.22 Correlation plot of first two PLS components.

Figure 5.23 Standardized coefficients PLS regression.

Figure 5.24 Residual plot partial least squares regression.

Figure 5.25 Hypothetical path model for purchase intent.

Chapter 06

Figure 6.1 Consensus descriptive panel training flowchart.

Figure 6.2 Radar chart of three pizza samples.

Figure 6.3 Tree diagram indicating similarities/differences among various types of cheeses.

Figure 6.4 Two‐dimensional map of consensus sensory scores and products.

Chapter 07

Figure 7.1 Advantages and disadvantages of the original and modified/derivative profile methods.

Chapter 08

Figure 8.1 Appearances (Ap) and aromas (Ar): laboratory and home testing (scale 0–45).

Figure 8.2 Mint candy shelf‐life (Fl = flavour, Mf = mouthfeel, At = aftertaste) (scale 0–60).

Figure 8.3 Mint candy shelf‐life of spearmint aftertaste.

Figure 8.4 Extra virgin olive oils.

Figure 8.5 Selected set of extra virgin olive oils.

Figure 8.6 Key sensory attributes of EVOOs (scale 0–45).

Figure 8.7 Preference segments of EVOOs. The preference segments liked different styles of EVOO than the total population. Preference segment 1 liked Greece‐2, segment 2 liked California‐2 and segment 3 preferred Turkey‐1.

Chapter 09

Figure 9.1 Overview of the steps to develop a lexicon.

Figure 9.2 Summary plot of panel performance. Panellist 7 is an outlier.

Figure 9.3 Green tea lexicon.

Figure 9.4 Attribute intensity range plot with control overlay.

Figure 9.5 Liking drivers plot.

Figure 9.6 Example of a sequence map template for describing an event in bathing.

Figure 9.7 Photo of eyes showing mascara evaluation attributes.

Chapter 10

Figure 10.1 Representation of the various language used to describe the same flavour concept.

Figure 10.2 Visual analogue scales used in QFP.

Figure 10.3 PCA on butter and other fat analysed using QFP (PC1, PC2).

Figure 10.4 Quantitative flavour profiles of two samples tested.

Figure 10.5 PCA on cookies made with butter and other fat analysed using QFP (PC1, PC2).

Figure 10.6 Representation of sensory profiles for a vanilla ice cream.

Figure 10.7 PCA map on the 20 vanilla ice cream samples.

Figure 10.8 Average liking scores (1–9) for French segment 1.

Figure 10.9 External preference mapping, France, consumer segment 1.

Figure 10.10 Two‐dimensional representation of preference mapping, France, consumer segment 1.

Figure 10.11 Representation of the optimal score for a descriptor in order to maximize liking.

Figure 10.12 Estimated optimal sensory profile maximizing liking for consumer segment 1, France.

Figure 10.13 Sensitivity card, consumer segment 1, France (

x

‐axis = intensity of the descriptors,

y

‐axis = estimated percentage of consumers liking the product).

Chapter 11

Figure 11.1 Schematic overview of three different A

5

daptive Profile Method’s programmes.

Figure 11.2 Advantages of the A

5

daptive Profile Method.

Figure 11.3a Do’s and don’ts – Preliminary activities (prior to training). The A

5

daptive Profile Method.

Figure 11.3b Do’s and don’ts – Training. The A

5

daptive Profile Method.

Figure 11.3c Do’s and don’ts – Project work and panel maintenance. The A

5

daptive Profile Method.

Figure 11.4 Flowchart of the A

5

daptive Profile Assessment process (case study I).

Chapter 12

Figure 12.1 Shape stimuli for training.

Figure 12.2 Weight stimuli for training.

Figure 12.3 Model solutions for training.

Figure 12.4 Example questionnaire for ranking task.

Figure 12.5 Example of rank‐rating intensity scale.

Figure 12.6 Rank‐rating intensity scale with positioning of products.

Figure 12.7 Stacked bar chart of ranks for each product.

Figure 12.8 Summary of analysis from parametric model.

Figure 12.9 Decision chart for choice of methodology.

Figure 12.10 Case Study 1: Biplot representation of mean rankings for each assessor.

Figure 12.11 Case Study 1: Results from parametric analysis.

Figure 12.12 Case Study 2: Results from parametric analysis.

Chapter 13

Figure 13.1 Data set from classic QDA profiling.

Figure 13.2 Data set from free choice profiling.

Figure 13.3 The Procrustes transformation in action.

Figure 13.4 FCP data matrix for four consumers with different number of attributes.

Figure 13.5 Zero padding of data matrix to make them of equal size.

Figure 13.6 Configurations for the individual consumers.

Figure 13.7 Creating the averaged consensus space on the basis of the individual configurations.

Figure 13.8 Schematic representation of the PANOVA.

Figure 13.9 Consensus space (dimension 1 accounts for 32.8% and dimension 2 for 18.2% of the variance).

Figure 13.10 Individual weights.

Figure 13.11 Consensus space with the attributes of assessor 1 (ss1).

Figure 13.12 Consensus space with the attributes of assessor 5 (ss5).

Figure 13.13 Consensus space with the attribute ‘fruity’ for different assessors.

Figure 13.14 Consensus space with the attribute ‘musk’ for different assessors.

Chapter 14

Figure 14.1 Schematic overview of the steps in the original flash profile method.

Figure 14.2 Schematic overview of the steps in an extended flash profile method for acoustic panels proposed by Lorho (2010).

Figure 14.3 Example of a flash profile ranking sheet and data coding (only three attributes are represented).

Figure 14.4 Example of a data table with rank numbers from a flash profile corresponding to the ranking sheet in Figure 14.3. The rank numbers are presented here for only one replicate.

Figure 14.5 Example of a Spearman’s test result when evaluating the panellists’ reproducibility between two replicates. The Spearman rank correlation coefficient is: r = 1 – 6 × Sum (V

2

) / (N × (N

2

– 1)). V is the differences between the ranks of both variables: V = Rank (R1) – Rank (R2); N is the number of products. In this example, r = 0.9375.

Figure 14.6 GPA consensus product map of the flash profile analysis of smoked fresh cheese. Sample codes: B, beech wood smoke; C, common alder smoke; H, hay smoke; P, orange peel smoke additive; 1, 1 min smoking; 3, 3 min smoking; 5, 5 min smoking; codes 3‐1 and 3‐2 signify batch replicates for smoking at 3 min.

Figure 14.7 GPA consensus product map of the flash profile analysis of wine samples. Benzaldehyde, isopentyl acetate and 2‐phenylethanol are abbreviated to Benz, Iso and 2‐Phen, respectively. Benz High and 2‐Phen Low were each presented twice in each flash profile replicate. The line connecting these sample pairs gives an indication of the reliability of the evaluation of the weak (Phen low) versus the stronger (Benz) attribute intensity.

Chapter 15

Figure 15.1 Example of score sheets obtained in the projective mapping test.

Figure 15.2 Example of data obtained in the projective mapping test.

Figure 15.3 Map of the between‐judges similarity obtained from analysis of the

R

V

coefficient table computed between the judges.

Figure 15.4 Compromise map for MFA. The data tables of judges 2 and 7 were not used to position the products.

Figure 15.5 An example of the analysis results of a sorting task with products and descriptors. Here, seven beers (letters A–G) were sorted by 11 assessors who also described the groups of beers that they provided. The words have been lemmatized and projected as barycenters of the beers to which they have been associated. The analysis was performed with DISTATIS (for details and original data, see Abdi & Valentin 2007a).

Figure 15.6 Example of score sheets obtained in a sorting task.

Figure 15.7 Example of data obtained in sorting task.

Figure 15.8 Two‐dimensional metric MDS map. Squares represent pure juices, circles represent juices from concentrate, full symbols represent pasteurized juices, empty symbols represent sterilized juices and the labels printed in italics indicate the juices.

Figure 15.9 Two‐dimensional DISTATIS map showing the products with their 95% confidence ellipsoids. When the confidence ellipsoids of two products do not intersect, these two products are perceived as significantly different by the judges. The configuration of the ellipsoids suggests that there are three groups of products.

Figure 15.10 Two‐dimensional DISTATIS map of the judges. The map suggests that the judges are rather homogeneous because no judge is far from the other judges.

Chapter 16

Figure 16.1 Example of the evaluation sheet used in polarized sensory positioning with continuous scales to compare samples with three reference products or poles (R1, R2 and R3).

Figure 16.2 Example of the evaluation sheet used in triadic polarized sensory positioning to evaluate samples.

Figure 16.3 Example of the data matrix used for analysing average PSP data using principal component analysis or multidimensional scaling unfolding. Each column (R1, R2, R3) represents the average degree of difference between a sample and one of the reference products or poles (R1, R2 and R3 respectively), across all assessors.

Figure 16.4 Example of the data matrix used for analysing PSP data using multiple factor analysis, STATIS or generalized Procrustes analysis. Each group of columns (R1, R2, R3) contains the degree of difference between a sample and one of the reference products or poles (R1, R2 and R3 respectively), for one of the assessors.

Figure 16.5 Example of the data matrix used for analysing data from triadic polarized sensory positioning data using correspondence analysis. Each column contains the number of assessors that regarded a pole as most similar to (+) and the most different from (−) each of the samples.

Figure 16.6 Example of the data matrix used for analysing data from triadic polarized sensory positioning data using multiple factor analysis. Each pair of columns (+ and ‐) contains the pole (R1, R2 or R3) that one of the assessors regarded as most similar to (+) and most different from (‐) each of the samples.

Figure 16.7 Representation of eight yoghurt samples in the first two dimensions of the multiple factor analysis performed on polarized sensory positioning data from a consumer study with 50 participants.

Figure 16.8 Average RV coefficient of sample configurations with respect to the reference configuration of samples as a function of the number of consumers considered in the resampled virtual panels for polarized sensory positioning data obtained in a consumer study in which 50 participants evaluated eight yoghurt samples. Vertical bars correspond to standard deviations.

Figure 16.9 Example of the evaluation sheet used in a polarized projective mapping task.

Figure 16.10 Example of the evaluation sheet used in the pivot profile.

Chapter 17

Figure 17.1 Example of a continuous line scale for ‘aroma strength’ with the assessors score marked towards the stronger end of the scale.

Figure 17.2 Example of a CATA grid and instructions.

Figure 17.3 Overall acceptability of aged salted snack samples (samples that do not differ significantly (P > 0.05) are boxed together).

Figure 17.4 Freshness rating of aged salted snack samples (samples that do not differ significantly (P > 0.05) are boxed together).

Figure 17.5 CATA endorsements of ‘Freshness’ of aged salted snack samples (samples that do not differ significantly (P > 0.05) are boxed together).

Figure 17.6 GPA consensus plot of British cheddar and territorial cheeses showing associations with free choice descriptions.

Figure 17.7 Case study 3: small selection of the input data for three attributes of the six meat‐based snack products (where: A = Dev’t. 3 ; B = Dev’t. 1; C = Comp. 1; D = Current; E = Dev’t. 2 and F = Comp. 2).

Figure 17.8 Case Study 3: summary statistics for one selected attribute – ‘salty’.

Figure 17.9 Case Study 3: Cochran’s Q test for significance for one selected attribute – ‘salty’.

Figure 17.10 Case study 3: correspondence analysis biplot of CATA sensory citations for six meat‐based snacks.

Figure 17.11 Case study 3: correspondence analysis biplot of CATA sensory citations for six meat‐based snacks with ‘emotional’ CATA citations plotted as passive points.

Chapter 18

Figure 18.1 Process flow sensory analysis.

Figure 18.2 Spider graph of different mint products.

Figure 18.3 Example specification for a fruit drink with examples of flavour/aftertaste glossary terms.

Figure 18.4 Example data and specification for fully quantitative descriptive specifications.

Figure 18.5 Internal preference mapping of breads.

Figure 18.6 Spider chart showing statistically significant attributes for ChocBloc case study.

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A series of books on selected topics in the field of Sensory Evaluation

The first book in the Sensory Evaluation series is Sensory Evaluation: A Practical Handbook, published in May 2009. It focuses on the practical aspects of sensory testing, presented in a simple, ‘how to’ style for use by industry and academia as a step‐by‐step guide to carrying out a basic range of sensory tests. In‐depth coverage was deliberately kept to a minimum. Subsequent books in the series cover selected topics in sensory evaluation. They are intended to give theoretical background, more complex techniques and in‐depth discussion on application of sensory evaluation that were not covered in the Practical Handbook. However, they will seek to maintain the practical approach of the handbook and chapters will include a clear case study with sufficient detail to enable practitioners to carry out the techniques presented.

Descriptive Analysis in Sensory Evaluation

EDITED BY

Sarah E. Kemp

Consultant and formerly Head of Global Sensory and Consumer Guidance, Cadbury Schweppes, UK

Joanne Hort

Massey Institute of Food Science and TechnologyMassey UniversityNew Zealand

Tracey Hollowood

Sensory Dimensions Ltd.Nottingham, UK

This edition first published 2018© 2018 John Wiley & Sons Ltd

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The right of Sarah E. Kemp, Joanne Hort and Tracey Hollowood to be identified as authors of the editorial material in this work has been asserted in accordance with law.

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Library of Congress Cataloging‐in‐Publication Data

Names: Kemp, Sarah E., editor. | Hort, Joanne, editor. | Hollowood, Tracey, editor.Title: Descriptive analysis in sensory evaluation / [edited] by Sarah E. Kemp, Joanne Hort, Tracey Hollowood.Description: Hoboken, NJ : John Wiley & Sons, 2018. | Includes bibliographical references and index. |Identifiers: LCCN 2017028435 (print) | LCCN 2017043923 (ebook) | ISBN 9781118991671 (pdf) | ISBN 9781118991664 (epub) | ISBN 9780470671399 (cloth)Subjects: LCSH: Sensory evaluation.Classification: LCC TA418.5 (ebook) | LCC TA418.5 .D47 2018 (print) | DDC 660.072–dc23LC record available at https://lccn.loc.gov/2017028435

Cover Design: WileyCover Image: © nepstock/Gettyimages

To George, Elizabeth, George and William

To Mike, Holly and Socks

To Campbell, Emma and Lara

In memory of Pieter Punter

Editor Biographies

Sarah E. Kemp, BSc (Hons), PhD, CSci, FIFST, RSensSci, is a chartered sensory and consumer science professional with more than 30 years of experience in academia and industry. Dr Kemp gained a BSc in Food Technology in 1986 and a PhD in Taste Chemistry in 1989 from the Food Science and Technology Department at the University of Reading, UK. In 1990, she did a postdoctoral research fellowship in sensory science at the Monell Chemical Senses Center in Philadelphia, USA. Dr Kemp has held many positions in industry, including Manager of Sensory Psychology (US) and Director of European Consumer and Marketing Research (France) in the Fragrance Division at Givaudan, Product Area Leader and Sensory Science Leader in Foods Consumer Science at Unilever Research, Colworth, UK, Head of Global Sensory and Consumer Guidance at Cadbury Schweppes, UK, and Director of Sensory and Consumer Services at Reading Scientific Services Limited, UK. Dr Kemp has also set up and run her own consultancy service and catering company. She has written numerous scientific articles in the field of sensory evaluation, has provided sensory training courses, including lecturing on the European Masters Course in Food Science, and has worked on bodies developing standards in sensory evaluation, including the British Standards Institution and ASTM International. She is a fellow of the Institute of Food Science and Technology and a founder member, past Chair and examiner for the IFST’s Sensory Science Group, as well as being a member of other professional sensory societies. Her other activities include Governor of East Kent College, UK.

Tracey Hollowood, BSc (Hons), PhD, MIFST, is currently Managing Director of Sensory and Consumer Research for Sensory Dimensions (Nottingham) Ltd in the UK. She has over 25 years’ experience in academia and industry; she worked at Nottingham University for 10 years during which time she achieved her doctorate investigating perceptual taste‐texture‐aroma interactions. She established the UK’s first Postgraduate Certificate in Sensory Science and designed and managed the University’s prestigious Sensory Science Centre. Her research focused on psychophysical studies, interactions in sensory modalities and fundamental method development. She has over 30 peer‐reviewed publications, has run numerous workshops and delivered oral presentations to many international audiences including at the Pangborn Sensory Science Symposia 2015 in Gothenburg. She has participated in the organization of seven international symposia, including the International Symposium of Taste 2000 and Pangborn 2005 in Harrogate.

Tracey is a previous chair of the Institute of Food Science and Technology (IFST) Midland branch and the Professional Food Sensory Group (PFSG), now the Sensory Science Group (SSG).

Joanne Hort, BEd (Hons), PhD, CSci, FIFST, RSensSci, is the Fonterra‐Riddet Chair of Consumer and Sensory Science at Massey University in New Zealand following on from her various academic roles, latterly SABMiller Chair of Sensory Science at the University of Nottingham. Initially, Professor Hort studied food technology and began her career in teaching. However, she returned to university to receive her doctorate concerning the modelling of the sensory attributes of cheese from analytical and instrumental measures in 1998. As a lecturer at Sheffield Hallam University, she carried out sensory consultancy for local industry, developed a sensory programme at undergraduate level and oversaw the installation of new sensory facilities before being appointed as Lecturer in Sensory Science at the University of Nottingham in 2002. There she established the University of Nottingham Sensory Science Centre, which is internationally renowned for both its sensory training and research into flavour perception. She obtained her Chair in 2013 and her multidisciplinary approach combining analytical, brain imaging and sensory techniques provides rich insight into multisensory interactions, individual variation and temporal changes in flavour perception, and the emotional response to sensory properties, leading to over 90 publications. Joanne sits on the editorial board for Food Quality and Preference and Chemosensory Perception. She is a Fellow of the Institute of Food Science and Technology. She is a founder member and past Chair of the European Sensory Science Society and a founder member, past Chair and examiner for the IFST’s Sensory Science Group.

List of Contributors

Hervé AbdiSchool of Brain and Behavioral Sciences, The University of Texas at Dallas, Richardson, TX, USA

Christel AdamGivaudan, Argenteuil, France

Lucía AntúnezSensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Pando, Uruguay

Gastón AresSensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Pando, Uruguay

Cindy BeerenLondon, UK

Rebecca N. BleibaumDragonfly SCI, Inc., Santa Rosa, CA, USA

Wender L.P. BredieDepartment of Food Science, University of Copenhagen, Frederiksberg, Denmark

Dominic BuckSensometrica, London, UK

Edgar Chambers IVCenter for Sensory Analysis and Consumer Behavior, Kansas State University, Manhattan, KS, USA

Sylvie CholletInstitut Charles Viollette, Laboratoire régional de recherche en agroalimentaire et biotechnologies, Equipe Qualité et Sécurité des Aliments – QSA, Lille, France

Anne ChurchillGivaudan UK Ltd, Ashford, UK

Gail Vance CivilleSensory Spectrum, New Providence, NJ, USA

Graham Cleaver (Retired)Unilever Research & Development, Wirral, UK

Sophie DavodeauGivaudan, Naarden,The Netherlands

Christian DehlholmDepartment of Food Science, University of Copenhagen, Frederiksberg, Denmark

Luis de SaldamandoSensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Pando, Uruguay

Clare DusSensory Spectrum, New Providence, NJ, USA

Margaret A. Everitt (Retired)Margaret Everitt Ltd, Cheltenham, UK

Ana GiménezSensometrics & Consumer Science, Instituto Polo Tecnológico de Pando, Facultad de Química, Universidad de la República, Pando, Uruguay

Ruth GreenawaySensory Dimensions, Nottingham, UK

Anne HastedQi Statistics Ltd, Reading, UK

Hildegarde HeymannDepartment of Viticulture and Enology, University of California, Davis, CA, USA

Tracey HollowoodSensory Dimensions, Nottingham, UK

Joanne HortMassey Institute of Food Science and Technology, Massey University, Palmerston North, New Zealand

Sylvie IssanchouCentre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRA, Université de Bourgogne Franche‐Comté, Dijon, France.

Patricia A. Keane (Retired Principal)Arthur D. Little, Inc., Cambridge, MA, USA

Sarah E. KempConsultant and formerly Head of Global Sensory and Consumer Guidance, Cadbury Schweppes, UK

Annlyse Retiveau KrogmannSensory Spectrum, New Providence, NJ, USA

Jing LiuDepartment of Food Science, University of Copenhagen, Frederiksberg, Denmark

Alejandra M. MuñozIRIS: International Resources for Insights and Solutions, LLC, Mountainside, NJ, USA

Michael NestrudOcean Spray Cranberries, Lakeville‐Middleboro, MA, USA

May NgPepsiCo R&D, Leicester, UK

Pieter H. Punter (Deceased)OP&P Product Research, Utrecht, The Netherlands

Carol RaithathaCarol Raithatha Ltd, Norwich, UK

Lauren RogersConsultant, Stoke‐on‐Trent, UK

Joel L. SidelJoel L. Sidel Consulting, Los Altos, CA, USA

Lee StapletonSensory Spectrum, New Providence, NJ, USA

K.W. Clara TaoQRS‐Tragon LLC, Redwood City, CA, USA

Amy TrailSensory Spectrum, New Providence, NJ, USA

Dominique ValentinAgroSup Dijon, Université de Bourgogne, Dijon, France

Preface to the Series

Sensory evaluation is a scientific discipline used to evoke, measure, analyse and interpret responses to products perceived through the senses of sight, smell, touch, taste and hearing (Anonymous, 1975). It is used to reveal insights into the way in which sensory properties drive consumer acceptance and behaviour, and to design products that best deliver what consumers want. It is also used at a more fundamental level to provide a wider understanding of the mechanisms involved in sensory perception and consumer behaviour.

Sensory evaluation emerged as a field in the 1940s. It began as simple ‘taste testing’ typically used in the food industry for judging the quality of products such as tea, cheese, beer, and so on. From the 1950s to the 1970s, it evolved into a series of techniques to objectively and reliably measure sensory properties of products, and was typically used to service quality assurance and product development. Through the 1980s and 1990s, the use of computers for data collection and statistical analysis increased the speed and sophistication of the field, so that sensory, consumer and physicochemical data could be combined to design products that delivered to consumer needs.

Today, sensory evaluation is a sophisticated, decision‐making tool that is used in partnership with marketing, research and development and quality assessment and control throughout the product lifecycle to enable consumer‐led product design and decision making. Its application has spread from the food industry to many others, such as personal care, household care, cosmetic, flavours, fragrances and even the automotive industry. Although it is already widely used by major companies in the developed market, its use continues to grow in emerging markets, smaller companies and new product categories, as sensory evaluation is increasingly recognised as a necessary tool for competitive advantage.

The field of sensory evaluation will continue to evolve and it is expected that faster, more flexible and more sophisticated techniques will be developed. Social networking tools are transforming the way research is undertaken, enabling direct and real‐time engagement with consumers. The use of sensory evaluation by marketing departments will continue to grow, particularly in leveraging the link between product sensory properties and emotional benefits for use in branding and advertising. Advances in other fields, such as genomics, brain imaging, and instrumental analysis, will be coupled with sensory evaluation to provide a greater understanding of perception.

Owing to the rapid growth and sophistication of the field of sensory evaluation in recent years, it is no longer possible to give anything but a brief overview of individual topics in a single general sensory science textbook. The trend is towards more specialised sensory books that focus on one specific topic, and to date, these have been produced in an ad‐hoc fashion by different authors/editors. Many areas remain uncovered.

We, the editors, wanted to share our passion for sensory evaluation by producing a comprehensive series of detailed books on individual topics in sensory evaluation. We are enthusiastic devotees of sensory evaluation, who are excited to act as editors to promote sensory science. Between us, we have over 70 years of industrial and academic experience in sensory science, covering food, household and personal care products in manufacturing, food service, consultancy and provision of sensory analysis services at local, regional and global levels. We have published and presented widely in the field; taught workshops, short courses and lecture series; and acted as reviewers, research supervisors, thesis advisors, project managers and examiners. We have been active in many sensory‐related professional bodies, including the Institute of Food Science and Technology Sensory Science Group, of which we are all past Chairs, the European Sensory Science Society, of which one of us is a past Chair, the Institute of Food Technologists, the British Standards Institute and ASTM International, to name but a few. As such, we are well placed to have a broad perspective of sensory evaluation, and pleased to be able to call on our network of sensory evaluation colleagues to collaborate with us.

The book series Sensory Evaluation covers the field of sensory evaluation at an advanced level and aims to:

be a comprehensive, in‐depth series on sensory evaluation

cover traditional and cutting‐edge techniques and applications in sensory evaluation using the world’s foremost experts

reach a broad audience of sensory scientists, practitioners and students by balancing theory, methodology and practical application

reach industry practitioners by illustrating how sensory can be applied throughout the product life cycle, including development, manufacture, supply chain and marketing

cover a broad range of product applications, including food, beverages, personal care and household products.

Our philosophy is to include cutting‐edge theory and methodology, as well as illustrating the practical application of sensory evaluation. As sensory practitioners, we are always interested in how methods are actually carried out in the laboratory. Often, key details of the practicalities are omitted in journal papers and other scientific texts. We have encouraged authors to include such details in the hope that readers will be able to replicate methods themselves. The focus of sensory texts often tends to be food and beverage products assessed using olfaction and taste. We have asked authors to take a broad perspective to include non‐food products and all the senses.

The book series is aimed at sensory professionals working in academia and industry, including sensory scientists, practitioners, trainers and students; and industry‐based professionals in marketing, research and development and quality assurance/control, who need to understand sensory evaluation and how it can benefit them. The series is suitable as:

reference texts for sensory scientists, from industry to academia

teaching aids for senior staff with responsibility for training in an academic or industrial setting

course books, some of which to be personally owned by students undertaking academic study or industrial training

reference texts suitable across a broad range of industries; for example, food, beverages, personal care products, household products, flavours, fragrances.

The first book in the series, Sensory Evaluation: A Practical Handbook