Use of Bayesian modelling in risk assessment: application to growth of Listeria monocytogenes and food flora in cold-smoked salmon

Abstract

An attempt to use a Bayesian approach to model variability and uncertainty separately in microbial growth in a risk assessment is presented. It was conducted within the framework of a French project aiming at assessing the exposure to Listeria monocytogenes in cold-smoked salmon. The chosen model describes the effect of time and temperature on bacterial growth. A Bayesian approach close to the one proposed by Pouillot et al. [Int. J. Food Microbiol. 81 (2003) 87] is used to estimate the variability and uncertainty of growth parameters from both literature data and data experimentally acquired during the project. Variability between strains and between products is taken into account. The growth of the food flora of cold-smoked salmon is also modelled by the same method. The results obtained for both models are used to predict the simultaneous growth of L. monocytogenes and food flora in cold-smoked salmon with a competitive model, expressing variability and uncertainty through a second-order Monte Carlo simulation.