Title: “Analyzing Product and Individual Differences in Sensory Discrimination Testing by Thurstonian and Statistical Models”
Friday 26 October, 2018 at 13:00, The Technical University of Denmark, Richard Petersens Plads, Building 324 room 240
Principal supervisor: Professor and Head of Department, Per Bruun Brockhoff
Examiners:
Professor Helle Rootzén, DTU Compute, DTU
Associate Professor Karl Bang Christensen, University of Copenhagen
Vice President John C. Castura, Compusense Inc, Canada
Chairperson at defence:
Associate Professor Line Clemmensen, DTU Compute
Summary:
Sensory discrimination tests are used to get information about products by using the human senses to evaluate the samples. More specifically, a sensory discrimination study is conducted when the aim is to investigate whether products are perceptibly different. This thesis is concerned with the analysis of product and individual differences in sensory discrimination testing.
Sensory discrimination-tests become more and more advanced raising a need for new types of analysis of sensory discrimination data. The analyses of sensory discrimination tests are often using too simplistic methods, ignoring important variables, such as individuals, that affect the results of the analysis. One focus of this project is to propose a way to incorporate such effects in the models when analyzing data from sensory discrimination studies. We denote these models by Thurstonian mixed models, which are embedding Thurstonian modelling into generalized linear mixed models. Considering such models, it becomes possible to gain information about the individuals, the so-called assessors, as well as ensuring that conclusions regarding the products are more proper.
Often discrimination studies involve the evaluation of multiple sensory attributes. The Thurstonian mixed models we are introducing analyze these sensory attributes individually. This thesis is presenting a multivariate analysis to gain knowledge about the product and individual differences across the sensory attributes. This is achieved by analyzing the product and individual differences, on the d-prime scale, by principal component analysis.
Sometimes the aim of sensory discrimination tests is to investigate the performance of sensory panels or to compare different laboratories. Such tests can result in multiple d-prime values. For sensory discrimination tests, which lead to binomially distributed responses, we propose a new test statistic for the comparison of multiple d-prime values. The test statistic we suggest is an improved way of analyzing multiple d-prime values compared to a previous suggested test statistic.
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A copy of the PhD thesis is available for reading at the department
Everyone is welcome.