Incorporating strain variability in the design of heat treatments: A stochastic approach and a kinetic approach.

Abstract

For the design of thermal processes, the decimal reduction times (D-values) of target organisms can be used. However, many factors influence the D-value, like inherent organism s characteristics (strain variability), the effect of the history of the cells, as well as product factors and process factors. Strain variability is a very large contributor to the overall variation of the D-value. Hence, the overall reduction of microbial contaminants by a heat treatment is a combination of the occurrence of a strain with a certain heat resistance and its reduction given the prevailing conditions. This reduction can be determined using two approaches: a kinetic analysis based on integral equations or a stochastic approach based on Monte Carlo analysis. In this article, these two approaches are compared using as case studies the inactivation of two microorganisms: Listeria monocytogenes in a pasteurization process and the sporeformer Geobacillus stearothermophilus in a UHT process. Both approaches resulted in similar conclusions, highlighting that the strains with the highest heat resistance are determinant for the overall inactivation, even if the probability of cells having such extreme heat resistance is very low.