Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/29578
Título: Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling
Palavras-chave: Thermoprocessed food - Consumer acceptance
Multiple linear regression
Alimentos termoprocessados - Aceitação do consumidor
Regressão linear múltipla
Data do documento: Out-2017
Editor: Wiley
Citação: NUNES, C. A. et al. Prediction of consumer acceptance in some thermoprocessed food by physical measurements and multivariate modeling. Food Processing and Preservation, Westport, v. 41, n. 5, p. 1-7, Oct. 2017.
Resumo: Consumer acceptances for French bread, fish bread, and roasted coffees were calibrated against physical measurements of those products using Multiple Linear Regression. The models obtained were then validated and tested using the widespread used methods of cross‐validation, y‐randomization and external validation. In all cases, multivariate models presented R2 for calibration greater than.9, which was superior to those univariate ones. For the French bread analysis, the multivariate model performed well and the length of the cut on bread surface is the parameter that most strongly influenced this model; on the other hand, a large width of the cut on bread surface would greatly contribute to a lower acceptance. The model for predicting the acceptance of the fish bread also showed a good performance; the bulkier fish breads received a better acceptance. An efficient model was also obtained for the data set of roasted coffee; redder coffees were more accepted.
URI: https://onlinelibrary.wiley.com/doi/abs/10.1111/jfpp.13178
http://repositorio.ufla.br/jspui/handle/1/29578
Aparece nas coleções:DCA - Artigos publicados em periódicos

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