Analyzing data from sensory discrimination tests: Parameter estimates and confidence regions
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 provide simple, actionable outcomes, whereas replicated tests, which are often used to compensate for insufficient resources, allow for estimation of heterogeneity and discriminable sensory distance parameters. Same-different test data can be used to estimate response bias, as well as discriminable sensory distance, and contribute towards an understanding what constitutes a consumer-relevant sensory difference. Usually two-parameter models are interpreted by considering one parameter at a time. This talk shows the potential for jointly investigating multiple parameters via confidence regions. Bivariate analyses can support visualization and communication of results and also provide analytic approaches to enable decision making.
Castura, J.C. (2019). Analyzing data from sensory discrimination tests: Parameter estimates and confidence regions. Korean Society of Food Science and Technology International Symposium and Annual Meeting. 24-26 June. Incheon, Korea. (Invited Oral).