冷鲜肉主要致腐微生物及构建微生物预测模型研究进展

Research Progress in Main Spoilage Microorganisms and Predictive Microbiological Modeling of Chilled Meat

冷鲜肉营养丰富,极易受到致腐微生物的污染,其在屠宰加工、贮藏、运销等过程中不可避免被微生物污染,由微生物引发的腐败变质已成为食品安全关注的重点。通过数学模型描述多种环境因素下食品中微生物的生长规律,构建微生物预测模型,判断食品中主要致腐微生物的生长或存活状态,可以对肉类的品质安全进行评价和预测。此外,人工神经网络是一种复杂的非线性模型,具有大规模并行处理、分布式存储、适应性强、容错性高等优点。本文综述冷鲜肉中主要致腐微生物以及传统微生物预测模型和人工神经网络模型在肉品中的应用,旨在为构建食品中微生物预测模型提供思路。

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.