Quantitative risk assessment of Listeria monocytogenes in French cold-smoked salmon: I. Quantitative exposure assessment.

A quantitative assessment of the exposure to Listeria monocytogenes from cold-smoked salmon (CSS) consumption in France is developed. The general framework is a second-order (or two-dimensional) Monte Carlo simulation, which characterizes the uncertainty and variability of the exposure estimate. The model takes into account the competitive bacterial growth between L.

Effect of temperature, water-phase salt and phenolic contents on Listeria monocytogenes growth rates on cold-smoked salmon and evaluation of secondary models.

Salting and smoking are ancient processes for fish preservation. The effects of salt and phenolic smoke compounds on the growth rate of L. monocytogenes in cold-smoked salmon were investigated through physico-chemical analyses, challenge tests on surface of cold-smoked salmon at 4 degrees C and 8 degrees C, and a survey of the literature.

Reflections on the use of robust and least-squares non-linear regression to model challenge tests conducted in/on food products.

In this research, we question the straight-forward use of the classical sum of squared error criterion for identifying the typical parameters of a primary model (like growth rate mumax and lag time lambda) when applied to growth curves obtained in and on food products. Firstly, we base our reflections on 62 Listeria monocytogenes laboratory challenge tests collected in various environments (broth, crushed cold-smoked salmon, and surface of cold-smoked salmon slices). Whereas growth data in broth resulted in residual values consistent with a Gaussian distribution, growth data in the crushed product and even more on the surface of slices appeared different.

Uncertainty distribution associated with estimating a proportion in microbial risk assessment.

The uncertainty associated with estimates should be taken into account in quantitative risk assessment. Each input's uncertainty can be characterized through a probabilistic distribution for use under Monte Carlo simulations. In this study, the sampling uncertainty associated with estimating a low proportion on the basis of a small sample size was considered.