Quantitative microbial risk assessment of Salmonella in fresh chicken patties.

Quantitative microbial risk assessment (QMRA) has witnessed rapid development within the context of food safety in recent years. As a means of contributing to these advancements, a QMRA for Salmonella spp. in fresh chicken patties for the general European Union (EU) population was developed. A two-dimensional (Second Order) Monte-Carlo simulation method was used for separating variability and uncertainty of model's parameters. The stages of industrial processing, retail storage, domestic storage, and cooking in the domestic environment were considered in the exposure assessment. For hazard characterization, a dose-response model was developed by combining 8 published dose-response models using a Pert distribution for describing uncertainty. The QMRA model predicted a mean probability of illness of 1.19*10-4 (5.28*10-5 - 3.57*10-4 95 % C.I.), and a mean annual number of illnesses per 100,000 people of 2.13 (0.96 - 6.59 95 % C.I.). Moreover, sensitivity analysis was performed, and variability in cooking preferences was found to be the most influential model parameter (r = -0.39), followed by dose-response related variability (r = 0.22), and variability in the concentration of Salmonella spp. at the time of introduction at the processing facility (r = 0.11). Various mitigation strategy scenarios were tested, from which, "increasing the internal temperature of cooking" and "decreasing shelf life" were estimated to be the most effective in reducing the predicted risk of illness. Salmonella-related illnesses exhibit particularly high severity, making them some of the most prominent zoonotic diseases in the EU. Regular monitoring of this hazard in order to further highlight its related parameters and causes is a necessary procedure. This study not only provides an updated assessment of Salmonella spp. risk associated with chicken patties, but also facilitates the identification of crucial targets for scientific investigation and implementation of real-world intervention strategies.

Quantitative microbiological spoilage risk assessment (QMSRA) of fresh poultry fillets during storage at retail.

A quantitative microbiological spoilage risk assessment model (QMSRA) of aerobically stored fresh poultry fillets was developed based on pseudomonads growth and metabolic activity. Simultaneous microbiological and sensory analyses were performed in poultry fillets to evaluate the relation between pseudomonads concentration and sensory rejection due to spoilage. The analysis showed no organoleptic rejection at pseudomonads concentrations less than 6.08 log CFU/cm2. For higher concentrations, a "spoilage-response" relationship was developed using a beta-Poisson model. The above relationship was combined with a stochastic modeling approach for pseudomonads growth by taking into account both variability and uncertainty of factors affecting spoilage. To enhance the reliability of the developed QMSRA model, uncertainty was quantified and separated from variability using a second order Monte Carlo simulation. For a batch of 10,000 units, the QMSRA model predicted a median number of 11, 80, 295, 733 and 1,389 spoiled units for retail storage times of 6,7, 8, 9 and 10 days, respectively, while no spoiled units were predicted for storage time of up to 5 days at retail. Scenario analysis showed that a reduction of 1 log in the pseudomonads concentration at the time of packaging or 1 °C in retail storage temperature results in up to 90% reduction of the spoiled units while the combination of the above interventions can reduce the risk of spoilage by up to 99%, depending on the storage time. The poultry industry can utilize the QMSRA model as a transparent scientific basis to support food quality management decisions in determining appropriate expiration dates which maximize the utilization of the product's "true" shelf life while minimize the risk of spoilage to an acceptable level. Furthermore, the scenario analysis can provide the necessary components for an effective cost-benefit analysis, enabling the identification and comparison of appropriate strategies for extending the shelf life of fresh poultry products.

The magnitude of heterogeneity in individual-cell growth dynamics is an inherent characteristic of Salmonella enterica ser. Typhimurium strains.

Individual-cell heterogeneity is a major source of variability in biological systems affecting importantly, among others, microbial behavior. Characterization of cell populations of pathogenic bacterial strains in their entirety, ignoring the phenotypic variability of single cells, may result in erroneous safety risk estimates. The objective of the present study was the evaluation and comparison of the heterogeneity in the individual-cell growth dynamics of different strains of Salmonella enterica. The stochasticity in the growth of single cells of five S. enterica ser. Typhimurium strains was quantitatively described using time-lapse microscopy, and the existence of a strain effect was statistically assessed. In total, 831 growing microcolonies originating from single cells were monitored and analyzed, and the growth kinetic parameters of lag time (λ) and maximum specific growth rate (µmax) for each one of them were estimated. An extensive heterogeneity in individual-cell growth kinetics was recorded, while significant inter-strain differences in their heterogeneity were evident based on simultaneous Bonferroni confidence intervals and Levene's tests. The Logistic and LogLogistic probability distribution provided the best fitting for µmax and λ data, respectively for all the tested strains. The strain effect on the above distributions was also demonstrated with pairwise comparisons of the decile differences. The impact of strain-dependent heterogeneity on microbial growth was visualized by comparing stochastic growth curves of different strains using Monte Carlo simulation. In conclusion, the individual-cell growth dynamics of S. enterica are heterogeneous, with the magnitude of the observed heterogeneity appearing to be an inherent characteristic of bacterial strains.