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Temperature abuse during commercial transport and retail sale of leafy greens negatively impacts both microbial safety and product quality. Consequently, the effect of fluctuating temperatures on Escherichia coli O157:H7 and Listeria monocytogenes growth in commercially-bagged salad greens was assessed during transport, retail storage, and display. Over a 16-month period, a series of time-temperature profiles for bagged salads were obtained from five transportation routes covering four geographic regions (432 profiles), as well as during retail storage (4,867 profiles) and display (3,799 profiles). Five different time-temperature profiles collected during 2 to 3 days of transport, 1 and 3 days of retail storage, and 3 days of retail display were then duplicated in a programmable incubator to assess E. coli O157:H7 and L. monocytogenes growth in commercial bags of romaine lettuce mix. Microbial growth predictions using the Koseki-Isobe and McKellar-Delaquis models were validated by comparing the root mean square error (RMSE), bias, and the acceptable prediction zone between the laboratory growth data and model predictions. Monte Carlo simulations were performed to calculate the probability distribution of microbial growth from 8,122,127,472 scenarios during transport, cold room storage, and retail display. Using inoculated bags of retail salad, E. coli O157:H7 and L. monocytogenes populations increased a maximum of 3.1 and 3.0 log CFU/g at retail storage. Both models yielded acceptable RMSEs and biases within the acceptable prediction zone for E. coli O157:H7. Based on the simulation, both pathogens generally increased <2 log CFU/g during transport, storage, and display. However, retail storage duration can significantly impact pathogen growth. This large-scale U.S. study-the first using commercial time/temperature profiles to assess the microbial risk of leafy greens-should be useful in filling some of the data gaps in current risk assessments for leafy greens.

Most of the foodborne microbial diseases are linked to foods of animal origin such as milk, meat, and poultry. Nowadays, the presence of multi-drug resistant (MDR) pathogens in foods is becoming an increasingly public health concern worldwide due to the overuse of antimicrobial drugs in animal feed. MDR pathogens can enter the food chain by posing a significant risk to both animals and consumers. MDR pathogens causing infections are untreatable due to their resistance to various antibiotics, primarily cephalosporin and carbapenems and to their extended-spectrum beta-lactamase (ESBL)-producing capability. In addition, foods of animal origin and food-related environments can be likely vehicles for spreading of multi-drug resistance genes, which accelerates the thriving of global antibiotic resistance. This paper reviews the role of foods of animal origin as a vehicle for MDR pathogens, stressing the contribution of food processes, environments, and storage conditions in dissemination and reduction of antimicrobial resistances (AMRs). Controlling the growth of MDR microorganisms and limiting the transmission/expression of AMR genes in food ecosystems could be an effective mitigation strategy, putting the focus on food processes as a part of the solution for AMR in foods. Bioprotective cultures are also a promising and environmentally friendly technology to reduce the incidence of MDR pathogens, though caution is taken as microbial starters and probiotics can also carry AMR. Finally, applying Whole Genome Sequencing (WGS) and predictive microbiology, within a Risk Assessment framework, is key to get insight into those mechanisms and conditions along the food chain favoring or reducing AMR.

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A quantitative risk characterization of Listeria monocytogenes in various ready-to-eat (RTE) food categories (heat-treated meat; smoked and gravad fish; and soft and semi-soft cheeses) in the European Union (EU) was performed; starting from the retail stage. For prevalence and concentration, data from the EU-wide baseline survey was complemented with EU monitoring data and data from other sources. Food serving size and the number of servings per year were estimated from the European food consumption database. Demographical data from Eurostat were also used. Growth of L. monocytogenes considering interaction with lactic acid bacteria was modelled from retail to consumption using temperature-time profiles during transport and storage. This information was combined with the Pouillot dose-response models to estimate the number of listeriosis cases per 10 6 servings as well as the annual number of listeriosis cases in the EU associated with the consumption of the RTE foods. The total number of cases was estimated as 2,318 (95 confidence interval (CI): 1,450-3,612). Cooked meat and sausage presented most cases (median of 863 and 541, respectively). Sliced pâté packaged in normal atmosphere presented the highest listeriosis risk per million servings. With respect to the estimation of the total number of cases per population group, considering each food subcategory separately, the higher risk population group corresponded to elderly, followed, in most cases, by pregnant and healthy, with the exceptions of cooked meat and hot smoked fish in which pregnant presented higher risk than elderly. In the light of results, it seems necessary that educative programs and specific recommendations are specially oriented the most susceptible population groups so as to mitigate the risk. Uncertainty sources for some variables such as initial MAY prevalence should be further elucidated as well as variability in Listeria growth when types of product and populations are compared.

In food safety and public health risk evaluations, microbiological exposure assessment plays a central role as it provides an estimation of both the likelihood and the level of the microbial hazard in a specified consumer portion of food and takes microbial behaviour into account. While until now mostly phenotypic data have been used in exposure assessment, mechanistic cellular information, obtained using omics techniques, will enable the fine tuning of exposure assessments to move towards the next generation of microbiological risk assessment. In particular, metagenomics can help in characterizing the food and factory environment microbiota (endogenous microbiota and potentially pathogens) and the changes over time under the environmental conditions associated with processing, preservation and storage. The difficulty lies in moving up to a quantitative exposure assessment, because the development of models that enable the prediction of dynamics of pathogens in a complex food ecosystem is still in its infancy in the food safety domain. In addition, collecting and storing the environmental data (metadata) required to inform the models has not yet been organised at a large scale. In contrast, progress in biomarker identification and characterization has already opened the possibility of making qualitative or even quantitative connection between process and formulation conditions and microbial responses at the strain level. In term of modelling approaches, without changing radically the usual model structure, changes in model inputs are expected: instead of (or as well as) building models upon phenotypic characteristics such as for example minimal temperature where growth is expected, exposure assessment models could use biomarker response intensity as inputs. These new generations of strain-level models will bring an added value in predicting the variability in pathogen behaviour. Altogether, these insights based upon omics techniques will increase our (quantitative) knowledge on pathogenic strains and consequently will reduce our uncertainty; the exposure assessment of a specific combination of pathogen and food will be then more accurate. This progress will benefit the whole community of safety assessors and research scientists from academia, regulatory agencies and industry.