Abstract: 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.

Key words: food, microorganism, interaction, model, review