Front Matter
Introduction
Important Data Collection Techniques for Sensory and Consumer Studies
Problem Driven. Quality Control of Sensory Profile Data
Correction Methods and Other Remedies for Improving Sensory Profile Data
Detecting and Studying Sensory Differences and Similarities between Products
Relating Sensory Data to Other Measurements
Discrimination and Similarity Testing
Investigating Important Factors Influencing Food Acceptance and Choice (Conjoint Analysis)
Preference Mapping for Understanding Relations between Sensory Product Attributes and Consumer Acceptance
Segmentation of Consumer Data
Method Oriented. Basic Statistics
Design of Experiments for Sensory and Consumer Data
ANOVA for Sensory and Consumer Data
Principal Component Analysis
Multiple Regression, Principal Components Regression and Partial Least Squares Regression
Cluster Analysis: Unsupervised Classification
Miscellaneous Methodologies
Nomenclature, Symbols and Abbreviations
Index.
Machine generated contents note: 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
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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?
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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
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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 probability11.3. 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
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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
4.3 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
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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
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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.
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