Quantitative microbial risk assessment for Listeria monocytogenes in cold smoked salmon

Abstract

A wide variety of foodstuffs could be contaminated with Listeria monocytogenes (Lm), but in the majority of cases listeriosis is predominately related to ready-to-eat (RTE) food. A stochastic model for the growth of Lm with the inhibiting effect of Lactic Acid bacteria (LAB) in cold smoked salmon (CSS) was developed. An existing model describing the inhibiting effect of LAB on the growth of Lm in CSS was extended using the Barany and Roberts model lag phase and stochastic models for growth rate and initial concentration. A deterministic model for the growth of Lm was adapted by adding the Winner stochastic process in order to simulate the growth of Lm. The Poisson distribution is used to represent the initial count (occurrence) of Lm. A deterministic model for the growth of LAB is used and the inhibiting effects of Lm and LAB on each other are taken into account. In order to estimate and predict the risk of illness from the stochastic mathematical models for growth of Lm, environmental conditions and dose response are used. The second order Monte Carlo (MC) simulation is used to obtain the probability density function (PDF) for the concentration of Lm at the moment of consumption of CSS. The Milstein algorithm has been used to solve the stochastic differential equation. The PDF for Lm obtained from the MC simulation is used in the dose response model to obtain the risk of illness at the moment of consumption. The Beta-Poisson model is used for the dose response. © 2010 WIT Press.