title: Predictive microbiology models vs. modeling microbial growth within Listeria monocytogenes risk assessment: what parameters matter and why.
abstract: Predictive microbiology models are essential tools to model bacterial growth in quantitative microbial risk assessments. Various predictive microbiology models and sets of parameters are available: it is of interest to understand the consequences of the choice of the growth model on the risk assessment outputs. Thus, an exercise was conducted to explore the impact of the use of several published models to predict Listeria monocytogenes growth during food storage in a product that permits growth. Results underline a gap between the most studied factors in predictive microbiology modeling (lag, growth rate) and the most influential parameters on the estimated risk of listeriosis in this scenario (maximum population density, bacterial competition). The mathematical properties of an exponential dose-response model for Listeria accounts for the fact that the mean number of bacteria per serving and, as a consequence, the highest achievable concentrations in the product under study, has a strong influence on the estimated expected number of listeriosis cases in this context.
authors.affiliation: Center for Food Safety and Applied Nutrition, Food and Drug Administration, College Park, MD 20740, USA. Regis.Pouillot@fda.hhs.gov
authors.forename: Régis
authors.initials: R
authors.lastname: Pouillot
authors.forename: Meryl B
authors.initials: MB
authors.lastname: Lubran
mesh_terms: D000818:Animals; D015169:Colony Count, Microbial; D003198:Computer Simulation; D005396:Fish Products / Q000382:microbiology*; D005516:Food Microbiology / Q000379:methods*; D008089:Listeria monocytogenes / Q000254:growth & development*; D008088:Listeriosis / Q000517:prevention & control; D008954:Models, Biological; D009010:Monte Carlo Method; D018570:Risk Assessment / Q000379:methods