Recent advances in predictive microbiology: theory and application of conversion from population dynamics to individual cell heterogeneity during inactivation process

Stochastic approaches for describing microbial growth and/or death responses in foods have gained increased attention in the field of predictive microbiology and quantitative microbial risk assessment. Particularly, recent studies have focused on describing variability and uncertainty of microbial responses. Although variability due to individual cell heterogeneity is inherent in a small number of bacteria during the inactivation process, an appropriate theoretical stochastic description for the variability in individual cell heterogeneity has not been achieved. This article reviews recent advances in the development of models for describing the variability in individual cell heterogeneity from the perspective of bacterial inactivation as a stochastic process of bacterial cell numbers. Large numbers of replicated experiments, computer simulations, and mathematical formulations have led to novel techniques for describing variability in individual cell heterogeneity during the microbial survival process. The proposed stochastic theory will enable reinterpretation of conventional bacterial behavior based on the average bacterial numbers.

Automated summary

Stochastic approaches are increasingly used in describing microbial growth and/or death responses in foods, with a focus on variability and uncertainty. Although variability in individual cell heterogeneity during the inactivation process is inherent, there is still a lack of appropriate theoretical stochastic description.