Abstract：Chilled meat is rich in nutrients but highly susceptible to contamination by spoilage microorganisms during slaughter, processing, storage and transportation. Microbial spoilage in foods has become a major concern for food safety. Predictive models for microbial growth in foods can judge the growth or survival of major spoilage microorganisms in foods, thereby allowing the evaluation and prediction of the quality and safety of meat. The artificial neural network (ANN), a complex nonlinear model, has the advantages of massively parallel processing, distributed storage, high adaptability, and fault tolerance. This paper reviews the main spoilage microorganisms in chilled meat and the application of traditional microbial prediction models and ANN models in meat to provide ideas for constructing microbial growth prediction models in foods.
王 丽，林 颖，谭 旭，邝金艳，李宗军，王远亮. 冷鲜肉主要致腐微生物及构建微生物预测模型研究进展[J]. 肉类研究, 2023, 37(10): 42-48.
WANG Li, LIN Ying, TAN Xu, KUANG Jinyan, LI Zongjun, WANG Yuanliang. Research Progress in Main Spoilage Microorganisms and Predictive Microbiological Modeling of Chilled Meat. Meat Research, 2023, 37(10): 42-48.
2023, Vol. 37 
