Stochastic simulation for death probability of bacterial population considering variability in individual cell inactivation time and initial number of cells.

Abstract 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 model does not consider inactivation curvature. However, even though doubt exists in the calculation of D -value, it is still widely used for risk assessment and sterilisation time estimation. This paper proposes an approach for estimating the time-to-inactivation and death probability of bacterial population that considers individual cell heterogeneity and initial number of cells via computer simulation. In the proposed approach, Weibull and Poisson distributions are respectively used to provide individual cell inactivation time variability and initial number of cells variability. Our simulation results show that the time-to-inactivation significantly depends on kinetics curvature and initial number of cells. For example, with increases in the initial number of cells, the respective variance of the time-to-inactivation of log-linear, concave downward curve, and concave upward curve remains constant, decreases, and increases, respectively. The death probability contour plot was successfully generated via our computer simulation approach without using D -value estimation. Further, the death probability calculated using our stochastic approach was virtually the same as that obtained using inactivation kinetics. We validated the simulation by using literature data for acid inactivation of Salmonella population. The results of this study indicate that inactivation curvature can replace D -value extrapolation to estimate the death probability of bacterial population. Further, our computer simulation facilitates realistic estimation of the time-to-inactivation of bacterial population. The R code used for the above stochastic calculation is outlined. Highlights • Bacterial time-to-inactivation and death probability estimation approach is proposed • Proposed approach considers individual cell heterogeneity and initial number of cells • Decimal reduction time extrapolation is replaced with stochastics estimation • The variance of the time-to-inactivation depends on kinetics curvature and initial number of cells. [ABSTRACT FROM AUTHOR]