Statistics for Sensory and Consumer Science Book

Contents

Preface

Acknowledgements

Chapter 1. Introduction

Chapter 2. Important data collection techniques for sensory and consumer studies

2.1. Sensory panel methodologies

2.2 Consumer tests

Chapter 3. Quality control of sensory profile data

3.1. General introduction

3.2. Visual inspection of raw data

3.3 Mixed model ANOVA for assessing the importance of the sensory attributes.

3.4 Overall assessment of assessor differences using all variables simultaneously

3.5 Methods for detecting differences in use of the scale

3.6. Comparing the assessors’ ability to detect differences between the products.

3.7. Relations between individual assessor ratings and the panel average

3.8. Individual line plots for detailed inspection of assessors

3.9. Miscellaneous methods

Chapter 4. Correction methods and other remedies for improving sensory profile data.

4.1. Introduction

4.2. Correcting for different use of the scale.

4.3. Computing improved panel averages

4.4 Pre-processing of data for three-way analysis

Chapter 5. Detecting and studying sensory differences and similarities between products.

5.1 Introduction

5.2 Analysing sensory profile data – univariate case

5.3 Analysing sensory profile data – multivariate case

Chapter 6. Relating sensory data to other measurements.

6.2 Estimating relations between consensus profiles and external data

6.3 Estimating relations between individual sensory profiles and external data

Chapter 7. Discrimination and similarity testing

7.1 Introduction

7.2 Analysis of data from basic sensory discrimination tests

7.3 Examples of basic discrimination testing

7.4. Power calculations in discrimination tests.

7.5 Thurstonian modelling – what is it really?

7. 6 Similarity versus difference testing

7.7 Replications – what to do?

7.8 Designed experiments, extended analysis and other test protocols

Chapter 8. Investigating important factors influencing food acceptance and choice (conjoint analysis).

8.1 Introduction.

8.2. Preliminary analysis of consumer data sets (raw data overview).

8.3 Experimental designs for rating based consumer studies

8.4 Analysis of categorical effect variables

8.5. Incorporating additional information about consumers

8.6 Modelling of factors as continuous variables

8.7. Reliability/validity testing for rating based methods.

8.8. Rank based methodology

8.9. Choice based conjoint analysis

8.10 Market share simulation

Chapter 9. Preference mapping for understanding relations between sensory product attributes and consumer acceptance

9.1 Introduction

9.2 External and internal preference mapping

9.3. Examples of linear preference mapping.

9.4 Ideal point preference mapping.

9.5. Selecting samples for preference mapping

9.6. Incorporating additional consumer attributes

9.7 Combining preference mapping with additional information about the samples

Chapter 10. Segmentation of consumer data.

10.1 Introduction

10.2 Segmentation of rating data

10.3. Relating segments to consumer attributes. Chapter 11. Basic Statistics

Chapter 11 Basic Statistics

11.1 Basic concepts and principles.

11.2 Histogram, frequency and probability

11.3. Some basic properties of a distribution(mean, variance and standard deviation)

11.4. Hypothesis testing and confidence intervals for the mean

11.5 Statistical process control

11.6 Relationships between two or more variables

11.7. Simple linear regression.

11.8 Binomial distribution and tests

11.9 Contingency tables and homogeneity testing

Chapter 12. Design of experiments for sensory and consumer data

12. 1. Introduction.

12.2. Important concepts and distinctions.

12.3. Full factorial designs

12.4. Fractional factorial designs – screening designs

12.5. Randomised blocks and incomplete block designs

12.6 Split-plot and nested designs

12.7 Power of experiments

Chapter 13. ANOVA for sensory and consumer data

13.1 Introduction

13.2 One-way ANOVA

13.3 Single replicate two-way ANOVA

13.4 Two-way ANOVA with randomized replications Chapter 13.5 Multi-way ANOVA

13.6. ANOVA for fractional factorial designs.

13.7 Fixed and random effects in ANOVA: Mixed models.

13.8 Nested and split-plot models. Chapter 13.9 Post hoc testing

Chapter 14. Principal Component Analysis

14.1 Interpretation of complex data sets by PCA

14.2 Data structures for the PCA

14.3 PCA – Description of the method

14.4. Projections and linear combinations.

14.5. The scores and loadings plots

14.6. Correlation loadings plot.

14.7 Standardisation

14.8 Calculations and missing values

14.9. Validation

14.10 Outlier diagnostics

14.11 Tucker-1

14.12 The relation between PCA and factor analysis (FA)

Chapter 15. Multiple regression, principal components regression and partial least squares regression.

15.1 Introduction.

15.2. Multivariate linear regression

15.3. The relation between ANOVA and regression analysis

15.4 Linear regression used for estimating polynomial models

15.5 Combining continuous and categorical variables.

15.6. Variable selection for multiple linear regression

15.7. Principal components regression (PCR)

15.8. Partial Least Squares (PLS) regression

15.9. Model validation – prediction performance

15.10. Model diagnostics and outlier detection

15.11 Discriminant analysis

15.12 Generalised linear models, logistic regression and multinomial regression

Chapter 16. Cluster analysis – unsupervised classification

16.1 Introduction

16.2 Hierarchical clustering

16.3. Partitioning methods.

16.4. Cluster analysis for matrices.

17. Miscellaneous methodologies

17.1. Three-way analysis of sensory data

17.2. Relating three-way data to two-way data

17.3. Path modelling

17.4. MDS-multidimensional scaling Chapter 17.5 Analysing rank data

17.6. The L-PLS method

17.7. Missing value estimation

Nomenclature, symbols and abbreviations

Index