Application of artificial neural networks to predict multiple quality of dry-cured ham based on protein degradation

This study investigated protein degradation and quality changes during the processing of dry-cured ham, and then established the multiple quality prediction model based on protein degradation. From the raw material to the curing period, proteolysis index of external samples were higher than that of internal samples, however, the difference gradually decreased from the drying period to the maturing period. Protein degradation can be used as indicators for controlling quality of the hams. With protein degradation index as input variables, the back propagation-artificial neural networks (BP-ANN) models were optimized, with training function of trainlm, transfer function of logsig in input-hidden layer and tansig in hidden-output layer, and 20 hidden layer neurons. Furthermore, the relative errors of predictive data and experimental data of 12 samples were approximately 0 with the BP-ANN model. Results indicated that the BP-ANN has great potential in predicting multiple quality of dry-cured ham based on protein degradation.