Ordinal Analysis of Variation of Sensory Responses in Combination with Multinomial Ordered Logistic Regression vs. Chemical Composition: A Case Study of the Quality of a Sausage from Different Producers. (13th December 2022)

• Record Type: Journal Article
• Authors: Gadrich, Tamar; Pennecchi, Francesca R.; Kuselman, Ilya; Hibbert, D. Brynn; Semenova, Anastasia A.; Cheow, Pui Sze
• Other Names: Smaoui Slim Academic Editor.
• Is Part Of: Journal of food quality. Volume 2022(2022)
• Publication Date: 2022-12-13
• Subjects: Food industry and trade -- Quality control -- Periodicals; Food industry and trade -- Standards -- Periodicals; Food -- Periodicals; 664.07
• DOI: 10.1155/2022/4181460
• Languages: English
• ISSNs: 0146-9428

Abstract

Abstract : The newly developed statistical technique of two-way ordinal analysis of variation (ORDANOVA) was applied for the first time to sensory responses in combination with multinomial ordered logistic regression of a response category vs. chemical composition. A corresponding tutorial is provided. As a case study, samples of a sausage from different producers, purchased at the same time from a market, were compared based on sensory responses of experienced experts. A decomposition of total variation of the ordinal data and simulation of the multinomial distribution of the relative frequencies of the responses in different categories showed a statistically significant difference between the producers' samples, and an insignificant difference between the experts' responses related to the same sample. The capabilities of experts were also evaluated. The influence of chemical composition of a sausage sample on the probability of a response category was modeled using multinomial ordered logistic regression of the response on mass fractions of the main sausage components. This statistical technique can be helpful for understanding sources of variation of sensory responses on food quality properties. It is also promising for a revision of specification limits for chemical composition, as well as for the prediction of sensory properties when the chemical composition of the product is subject to quality control.