Despite effective inactivation procedures, small numbers of bacterial cells may still remain in
food samples. The risk that bacteria will survive these procedures has not been estimated …

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 …

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 …

We investigated a bacterial sample preparation procedure for single-cell studies. In the
present study, we examined whether single bacterial cells obtained via 10-fold dilution …

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 …

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

The traditional log-linear inactivation kinetics model considers microbial inactivation as a
process that follows first-order kinetics. A basic concept of log reduction is decimal reduction …

Decimal reduction time (D-value) based on the first-order survival kinetics model is not
sufficient for reliable estimation of the bacterial survivors of inactivation treatment because …

The long-term survival of pathogenic microorganisms was evaluated and modeled in
simulated fermented and dried, uncooked sausages, such as salami and pepperoni. Listeria …

Stochastic models take into account the uncertainty and variability of predictions in
quantitative microbial risk assessment. However, a model that considers thermal inactivation …