Highlights•Development of stochastic modeling technique for bacterial inactivation is
reviewed.•Variability in survival cell numbers during inactivation process is described as …

Phenotypic heterogeneity seems to be an important component leading to biological
individuality and is of great importance in the case of microbial inactivation. Bacterial cells …

Kinetic models performing point estimation are effective in predicting the bacterial behavior.
However, the large variation of bacterial behavior appearing in a small number of cells, ie …

A statistical modeling approach was applied for describing and evaluating the individual cell
heterogeneity as variability source in microbial inactivation. The inactivation data (N t vs …

Our ability to predict the lag (λ) prior to growth of foodborne pathogens is limited by our lack
of understanding of the physiological changes taking place in the individual cell during the …

Background The definition of realistic models for microbial risk assessment (MRA) requires
the inclusion of variability as it is part of the microbial response to stress. Most studies are …

The estimation of the concentration of microorganisms in a sample is crucial for food
microbiology. For instance, it is essential for prevalence studies, challenge tests (growth …

Presenting a novel view of the quantitative modeling of microbial growth and inactivation
patterns in food, water, and biosystems, Advanced Quantitative Microbiology for Foods and …

During microbial inactivation by a lethal agent (ignoring damage/injury, recovery from
damage/injury, etc.), an individual spore or cell can be either viable/alive and countable, or …

Despite the development of numerous predictive microbial inactivation models, a model
focusing on the variability in time to inactivation for a bacterial population has not been …