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.

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).