Infodoc : Sciences, Techniques et Culture

1.1 The Distinction between Trained Sensory Panels and Consumer
Panels
1.2 The Need for Statistics in Expenmental Planning and Analysis
1.3 Scales and Data Types
1.4 Organisation of the Book
2 Important Data Collection Techniques for Sensory and Consumer Studies
2.1 Sensory Panel Methodologies
2.2 Consumer Tests
PART I PROBLEM DRIVEN
3 Quality Control of Sensory Profile Data
3.1 General Introduction
3.2 Visual Insp[...]

1.1 The Distinction between Trained Sensory Panels and Consumer
Panels
1.2 The Need for Statistics in Expenmental Planning and Analysis
1.3 Scales and Data Types
1.4 Organisation of the Book
2 Important Data Collection Techniques for Sensory and Consumer Studies
2.1 Sensory Panel Methodologies
2.2 Consumer Tests
PART I PROBLEM DRIVEN
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
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
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
6 Relating Sensory Data to Other Measurements
6.1 Introduction
6.2 Estimating Relations between Consensus Profiles and External Data
6.3 Estimating Relations between Individual Sensory Profiles and External Data
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
8 Investigating Important Factors Influencing Food Acceptance and Choice
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
9 Preference Mapping for Understanding Relations between Sensory Product Attributes and Consumer Acceptance
9.1 Introduction
9.2 External and InternaI 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
10 Segmentation of Consumer Data
10.1 Introduction
10.2 Segmentation of Rating Data
10.3 Relating Segments to Consumer Attributes
PART II METHOD ORIENTED
11 Basic Statistics
1l.1 Basic Concepts and Principles
11.2 Histogram, Frequency and Probability
1l.3 Some Basic Properties of a Distribution (Mean, Variance and Standard Deviation)
11.4 Hypothesis Testing and Confidence Intervals for the Mean µ
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
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
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 Randomised Replications
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
13.9 Post Hoc Testing
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-l
14.12 The Relation between PCA and Factor Analysis (FA)
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
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
17.5 Analysing Rank Data
17.6 The L-PLS Method
17.7 Missing Value Estimation