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When developing quantitative risk assessment models, a fundamental consideration for risk assessors is to decide whether to evaluate changes in bacterial levels in terms of concentrations or in terms of bacterial numbers. Although modeling bacteria in terms of integer numbers may be regarded as a more intuitive and rigorous choice, modeling bacterial concentrations is more popular as it is generally less mathematically complex. We tested three different modeling approaches in a simulation study. The first approach considered bacterial concentrations; the second considered the number of bacteria in contaminated units, and the third considered the expected number of bacteria in contaminated units. Simulation results indicate that modeling concentrations tends to overestimate risk compared to modeling the number of bacteria. A sensitivity analysis using a regression tree suggests that processes which include drastic scenarios consisting of combinations of large bacterial inactivation followed by large bacterial growth frequently lead to a >10-fold overestimation of the average risk when modeling concentrations as opposed to bacterial numbers. Alternatively, the approach of modeling the expected number of bacteria in positive units generates results similar to the second method and is easier to use, thus potentially representing a promising compromise., (Published by Elsevier Ltd.)

In response to increased concerns about spice safety, the United States Food and Drug Administration (FDA) initiated research to characterize the prevalence and levels of Salmonella in imported spices. 299 imported dried capsicum shipments and 233 imported sesame seed shipments offered for entry to the United States were sampled. Observed Salmonella shipment prevalence was 3.3% (1500 g examined; 95% CI 1.6-6.1%) for capsicum and 9.9% (1500 g; 95% Confidence Interval (CI) 6.3-14%) for sesame seed. Within shipment contamination was not inconsistent with a Poisson distribution. Shipment mean Salmonella level estimates among contaminated shipments ranged from 6 × 10(-4) to 0.09 (capsicum) or 6 × 10(-4) to 0.04 (sesame seed) MPN/g. A gamma-Poisson model provided the best fit to observed data for both imported shipments of capsicum and imported shipments of sesame seed sampled in this study among the six parametric models considered. Shipment mean levels of Salmonella vary widely between shipments; many contaminated shipments contain low levels of contamination. Examination of sampling plan efficacy for identifying contaminated spice shipments from these distributions indicates that sample size of spice examined is critical. Sampling protocols examining 25 g samples are predicted to be able to identify a small fraction of contaminated shipments of imported capsicum or sesame seeds., (Published by Elsevier Ltd.)

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., (Published by Elsevier Ltd.)