Prediction of time to growth of Listeria monocytogenes using Monte Carlo simulation or regression analysis, influenced by sublethal heat and recovery conditions

Abstract

Stochastic models, including the variability in extent and probability of microbial growth, are useful for estimating the risk of foodborne illness (i.e. Nauta, 2000). Risk assessment typically has to embrace all sources of variability. In this paper, a stochastic approach to evaluate growth of heat damaged Listeria monocytogenes cells influenced by different stresses (pH and presence of eugenol) was performed, using an individual-based approach of growth through OD measurements. Both the lag phase duration and the "work to be done" (h(0) parameter) were derived from the growth curves obtained. From results obtained histograms of the lag phase were generated and distributions were fitted. Histograms showed a shift to longer lag phases and an increase in variability with high stress levels. Using the distributions fitted, predictions of time to unacceptable growth (10(2) cfu/g) of L. monocytogenes were established by Monte Carlo simulation and they were compared with results from statistical methods. It was evidenced that both methods (Monte Carlo and regression analysis) gave a good indication of the probability of a certain level of growth other than the average. Tornado plots were obtained to establish a sensitivity analysis of the influence of the conditions tested (heat, pH, eugenol) applied to the microorganism and their combinations.