Logistic regression in threshold detection in replicated discrimination sensory tests

Abstract

Sensory discrimination tests often consider the possibility that each assessor performs a test more than once, that is, they are performed with replications. However, these experiments are generally analyzed as if there were no repetitions or as if all judgments came from different assessors, which the usual analysis assumes that the probability of success is the same for all of them. It is emphasized the importance of considering possible differences in the perception of the assessors, which is consistent with Thurstone's psychological ideas about perception and decision processes. In this article it is suggested a mixed generalized linear model that considers the random effect of the assessor. Particularly in dose-response experiments it is showed that this model allows estimating detection thresholds for several treatments simultaneously. The results allowed to conclude that the proposed model presents good results and can be used in the analysis of replicated sensory tests. We will assume that the variation in the assessor' responses can be modeled as a random effect in a mixed generalized linear model. This model should be able to produce a better analysis, with the ability to detect differences between treatments with greater power and produce narrower confidence intervals for the estimates.