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Development of GLM regression models to predict the consumer acceptability of cooked ham based on analytical parameters
Journal Contribution - Journal Article
Due to concerns about meat quality, health, sustainability and animal welfare, the typical Belgian meat products such as cooked ham are being threatened by a negative reputation. To address these concerns, an objective quality assessment tool was developed that could predict the consumer acceptability for a range of sensorial descriptors based on analytical parameters. A total of 28 commercial cooked hams were evaluated by a sensorial panel of consumers while simultaneously, a broad range of analytical tests were conducted on the same hams. Per sensorial descriptor, the analytical results and consumer acceptability for all cooked hams were processed by Generalized Linear Modelling (GLM). This holistic approach makes it possible to predict the consumer acceptability of a sensorial descriptor with great reliability and robustness by only using objective analytical parameters. An efficient R&D tool was developed to optimize the sensorial and analytical quality of the cooked ham that meets consumer demands.
Journal: Meat Science
Keywords:Consumer acceptability, Cooked ham, Generalized linear modelling, Meat product quality, Predictive modelling, Product development