A Novel Class of Predictive Microbial Grown Models: Implementation in an Individual-Based Framework

Abstract

Abstract In the field of predictive microbiology, mathematical models are developed to describe and predict the behaviour and possible outgrowth of spoilage and/or pathogenic microorganisms in food products. Research has mostly focused on the development of macroscopic models, which have a number of inherent disadvantages. This paper adopts the methodology of individual-based modelling (IbM) as a complement to macroscopic models to overcome some of these issues. In addition, this paper exploits a new bacterial growth model to circumvent shortcomings of established logistic type models. The new model incorporates substrate limitation and metabolite inhibition factors, providing it with a more solid mechanistic base for modelling the stationary phase. A case study is presented implementing the model in an IbM framework and exploratory results are presented