Professor Tormod Naes is a Principal Research Scientist based at Matforsk, a government food research laboratory, in Norway. He received his PhD in statistics from University of Oslo in 1984. He is also currently employed as a Professor at the Institute of Mathematics at the University of Oslo. He serves on the editorial boards of Journal of Chemometrics, Journal of Near Infrared Spectroscopy and Food Quality and Preference.
His main area of research is the development and use of multivariate statistical methods in food science. In particular in applications within the areas of sensory analysis, spectroscopy, process optimisation and bioinformatics. He has published 108 refereed papers and co-authored and co-edited 5 books in multivariate analysis and analysis of variance, including the highly cited "Multivariate Calibration" co-authored with Professor Harald Martens (Wiley 1988). He has received the Tomas Hirschfeld award in NIR analysis. (1997), EAS award for achievements in chemometrics (1997), Kowalski award in Chemometrics (J. Wiley and Sons) (2006) and is an Honorary member of the Chemometric Society of Norway (2006).

Preface.
 
Acknowledgements.
 
1 Introduction.
 
1.1 The Distinction between Trained Sensory Panels and Consumer Panels.
 
1.2 The Need for Statistics in Experimental 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 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.
 
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.
 
1

As we move further into the 21st Century, sensory and consumer studies continue to develop, playing an important role in food science and industry. These studies are crucial for understanding the relation between food properties on one side and human liking and buying behaviour on the other.
 
This book by a group of established scientists gives a comprehensive, up-to-date overview of the most common statistical methods for handling data from both trained sensory panels and consumer studies of food.
 
It presents the topic in two distinct sections: problem-orientated (Part I) and method orientated (Part II), making it to appropriate for people at different levels with respect to their statistical skills.
 
This book succesfully:
* Makes a clear distinction between studies using a trained sensory panel and studies using consumers.
* Concentrates on experimental studies with focus on how sensory assessors or consumers perceive and assess various product properties.
* Focuses on relationships between methods and techniques and on considering all of them as special cases of more general statistical methodologies
 
It is assumed that the reader has a basic knowledge of statistics and the most important data collection methods within sensory and consumer science.
 
This text is aimed at food scientists and food engineers working in research and industry, as well as food science students at master and PhD level. In addition, applied statisticians with special interest in food science will also find relevant information within the book.