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BP NEURAL NETWORK FOR EVALUATING SENSORY TEXTURE PROPERTIES OF COOKED SAUSAGE
Author(s) -
DONG QINGLI
Publication year - 2009
Publication title -
journal of sensory studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.61
H-Index - 53
eISSN - 1745-459X
pISSN - 0887-8250
DOI - 10.1111/j.1745-459x.2009.00240.x
Subject(s) - artificial neural network , sensory system , texture (cosmology) , food science , mathematics , pattern recognition (psychology) , moisture , artificial intelligence , computer science , chemistry , psychology , organic chemistry , image (mathematics) , cognitive psychology
In order to replace sensory evaluation by instrumental measurement with more accuracy for texture properties of cooked sausage, correlation analysis between sensory and instrumental texture was established by multiple regression and back propagation (BP) neural network, respectively. Effect of different fat, salt, moisture and starch addition on the texture of cooked sausage was also investigated in this paper. It indicated that the accuracy and goodness of fit of predicting sensory hardness, cohesiveness and juiciness by BP neural network were more significant than those by multiple regressions with lower root mean square error and standard error of prediction. Although both accuracy and bias factors of two models were in acceptable range, BP neural network provides an accurate and selective method for predicting sensory texture evaluation in similar meat products.PRACTICAL APPLICATIONS The effect of different fat, salt, moisture and starch addition on textural properties of cooked sausage could be valuable to the meat industry in order to select the appropriate components for improving the texture of sausage. Artificial neural network technology used in this study can be useful for the fast, on‐time and convenient detection of texture measurement by instrumental instead of sensory evaluation.