食品中微生物间交互模型的研究进展

Microbial Interaction Modeling in Foods: A Review

数学模型是食品预测微生物学研究的核心,基于数学模型得到的腐败菌生长情况可用于预测食品的货架期,得到的食源性致病菌生长数据是微生物暴露评估不可缺少的部分。近年来,预测微生物学发展的主要方向之一是研究微生物在实际食品环境中的生长动态。本文首先简要介绍了传统的预测微生物数学模型;然后阐述了实际食品中微生物与环境之间的交互作用现象,在此基础之上,介绍了描述性模型和机械性模型两类微生物间交互模型,并分析了两类模型的推导过程,最后对食品中微生物间交互模型的应用前景进行了展望,为预测微生物学这一学科的进步提供了理论基础。

Mathematical models are at the core of research in predictive food microbiology(PFM). The growth of spoilage microorganisms predicted by mathematical models can be used to predict the shelf life of foods. Growth data of pathogens predicted by mathematical models are indispensible in microbial exposure assessment. In recent years, the growth dynamics of bacteria in actual food environments has been one of the major advances in PFM. First, this article presents a brief description of the traditional mathematical models used in PFM. Next, it interprets the interaction between microbes and the environment in actual food samples. Furthermore, two microbial interaction models, known as descriptive and mechanistic models, are described along with analysis of how they are deduced. Finally, future prospects for the application of these models in PFM are discussed. This review could provide useful data for the development of PFM.