Comparison of probabilistic and deterministic predictions of time to growth of Listeria monocytogenes as affected by pH and temperature in food.

Stochastic models are useful for estimating the risk of foodborne illness and they can be integrated, besides other sources of variability, into microbial risk assessment. A stochastic approach to evaluate growth of two strains of Listeria monocytogenes influenced by different factors affecting microbial growth (pH and storage temperature) was performed. An individual-based approach of growth through optical density measurements was used. From results obtained, histograms of the lag phase were generated and distributions were fitted. Histograms presented increased variation when the factors applied were suboptimal for L. monocytogenes and they were combined. The extreme value distribution was ranked as the best one in most cases, whereas normal was the poorest fitting distribution. To evaluate the influence of pH and storage temperature on L. monocytogenes CECT 5672 in real food, commercial samples of courgette and carrot soup were inoculated with this pathogen. It was able to grow in both soups at storage temperatures from 4°C to 20°C. Using the distributions adjusted, predictions of time to growth (10² cfu/g) of L. monocytogenes were established by Monte Carlo simulation and they were compared with deterministic predictions and observations in foods.

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

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.