Evolution and limitations of primary mathematical models in predictive microbiology

Abstract

Purpose Traditional microbiological methods to monitor the growth or survival of microbes are very labour‐intensive and rather expensive and the knowledge acquired is not cumulative. Predictive microbiology as an alternative approach has been developed utilizing mathematical models to predict the microbial inactivation, survival or growth during food processing. The purpose of this paper is to review the evolutions and limitations of primary mathematical models in predictive microbiology. Design/methodology/approach Primary models deal with the variation of microbial populations against time under particular environmental and cultural conditions. According to the behaviour of micro organisms during food processing and storage, primary models can be divided into inactivation/survival models and growth models. Literature is reviewed to assess the performance of these mathematical models. Findings In order to predict microbial survival or growth curves, some empirical mathematical models have been used. Most of them have no or little microbiological or physiological basis, which make the interpretation of some model parameters difficult and their performances do not match observed microbiological outcomes. To produce a more accurate mathematical model, more mechanisms are necessary to interpret model parameters with a biological basis. Originality/value The paper reviews the evolution and limitations of primary mathematical models, which may help future model development.