1. Establishment of a Predictive Model for the Quality Assessment of Chilled Meat Using a Moth-Flame Optimization BP Neural Network
- Author
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Li WANG, Zikang YAN, Jin DU, and Yuanliang WANG
- Subjects
chilled meat ,staphylococcus sciuri ,prediction model ,bp neural network ,moth-flame optimization bp neural network ,Food processing and manufacture ,TP368-456 - Abstract
To quickly and accurately evaluate and predict the quality and safety of food products, in this study, storage temperatures (0, 4 and 25 ℃) were used to investigate the effects on the total number of colonies, TVB-N, pH, moisture content, color, and biogenic amine content of chilled meat. It aimed to accurately predict the pattern of change in the quality of chilled meat in storage, as well as its quality and safety, and determined the characteristic quality indexes of chilled meat. Based on Backpropagation (BP) neural network and Moth-Flame Optimization (MFO) BP neural network, the quality prediction model of chilled meat under different storage temperatures was constructed using characteristic indexes as the training data to quickly and accurately evaluate and predict the quality and safety of food. The results showed that the total number of colonies, pH, TVB-N, color and biogenic amine content of chilled meat under different storage temperatures showed an increasing trend with the extension of storage time (P
- Published
- 2024
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