Analyzing data using the chance-corrected beta-binomial model: Parameter estimates and their confidence regions

John Castura/ August 6, 2019/ Poster/ 0 comments

Data arising from replicated sensory discrimination test methods with a correct response are often modelled using the chance-corrected beta-binomial distribution. The model can provide maximum likelihood estimates of the mean proportion of correct responses in the population (and discriminal distances under Thurstonian assumptions) and of the assessor heterogeneity (overdispersion). Both of these parameters are estimated with uncertainty. Previously uncertainty associated

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Does the τ estimate from same-different test data represent a relevant sensory effect size for determining sensory equivalency?

John Castura/ July 1, 2019/ Peer-reviewed Paper/ 0 comments

Analysis of data arising from the same-different test method can be submitted to Thurstonian-derived modelling with the goal of estimating the sensory distance between two products (the discriminal distance δ) and the response bias for responding “same” (τ). Previously it has been proposed that it is possible to use τ estimates from same-different test data to represent the consumer-relevant effect

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Analyzing data from sensory discrimination tests: Parameter estimates and confidence regions

John Castura/ June 30, 2019/ Oral Presentation/ 0 comments

The sensory profile of a food product is an important contributor to its identity, and is linked to brand value. Sensory testing provide an objective way to ensure that the product’s target sensory profile is achieved, e.g., after making production changes. Historical data, including business outcomes, can provide context to assist decision making. Unreplicated sensory discrimination tests without response bias

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