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

• Published in: Food chemistry Vol. 344; p. 128586
• Main Authors: Zhu, Ning, Wang, Kai, Zhang, Shun-liang, Zhao, Bing, Yang, Jun-na, Wang, Shou-wei
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15.05.2021
• Subjects: Amino Acids - analysis; Animals; Artificial neural network; Chromatography, Ion Exchange; Color; Desiccation; Dry-cured ham; Multiple quality; Neural Networks, Computer; Pork Meat - analysis; Protein degradation; Proteolysis; Swine; Protein degradation; Multiple quality; Artificial neural network; Dry-cured ham
• ISSN: 0308-8146; 1873-7072
• DOI: 10.1016/j.foodchem.2020.128586

•Proteins degradation parameters had significant changes throughout the processing.•Electronic bionic methodologies were used to observe the quality properties.•A BP model was developed to predict the multi-quality 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.