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Clostridium perfringens is a pathogen that mainly causes food poisoning outbreaks when large quantities of food are prepared. Therefore, a model was developed to predict the effect of different cooling procedures on the growth of this pathogen during cooling of food: Dutch pea soup. First, a growth rate model based on interpretable parameters was used to predict growth during linear cooling of pea soup. Second, a temperature model for cooling pea soup was constructed by fitting the model to experimental data published earlier. This cooling model was used to estimate the effect of various cooling environments on average cooling times, taking into account the effect of stirring and product volume. The growth model systematically overestimated growth of C. perfringens during cooling in air, but this effect was limited to less than 0.5 log N/ml and this was considered to be acceptable for practical purposes. It was demonstrated that the growth model for C. perfringens combined with the cooling model for pea soup could be used to sufficiently predict growth of C. perfringens in different volume sizes of pea soup during cooling in air as well as the effect of stirring, different cooling temperatures, and various cooling environments on the growth of C. perfringens in pea soup. Although fine‐tuning may be needed to eliminate inaccuracies, it was concluded that the combined model could be a useful tool for designing good manufacturing practices (GMP) procedures.

International audience ; A methodology based on the combination of two complementary approaches to rank microbiological risks in foods is presented. In the forward approach data on the pathogenicity of hazards and their behaviour in food during processing and following steps, up to consumption, are used in decision trees to qualitatively estimate the risk associated with foods. In the backward approach risks are evaluated based on the analysis of data available on the past occurrence of hazards and food-borne outbreaks. The categorisation of foods using the forward approach should prevail, and whenever it leads to a likely risk for a given food, the risk can be further qualified with the results from the backward approach. The methodology developed was applied to rank the public health risk posed by certain composite products, which contain both processed products of animal origin and products of plant origin (e.g., bread, cakes, chocolate). Despite limitations in the data available for these foods, valuable results were obtained. The method is therefore considered suitable for application with success to other types of food, and is proposed as a tool for risk managers to rank foods based on their potential food safety risks. Introduction In the World Trade Organization (WTO) Agreement on the Application of Sanitary and Phytosanitary (SPS) Measures it is stated that sanitary measures should be based on an assessment of the risks to humans, taking into account risk assessment techniques. In the European Union (EU), the general principles governing food and feed safety are laid down in Regulation (EC) No 178/2002 (General

International audience ; A methodology based on the combination of two complementary approaches to rank microbiological risks in foods is presented. In the forward approach data on the pathogenicity of hazards and their behaviour in food during processing and following steps, up to consumption, are used in decision trees to qualitatively estimate the risk associated with foods. In the backward approach risks are evaluated based on the analysis of data available on the past occurrence of hazards and food-borne outbreaks. The categorisation of foods using the forward approach should prevail, and whenever it leads to a likely risk for a given food, the risk can be further qualified with the results from the backward approach. The methodology developed was applied to rank the public health risk posed by certain composite products, which contain both processed products of animal origin and products of plant origin (e.g., bread, cakes, chocolate). Despite limitations in the data available for these foods, valuable results were obtained. The method is therefore considered suitable for application with success to other types of food, and is proposed as a tool for risk managers to rank foods based on their potential food safety risks. Introduction In the World Trade Organization (WTO) Agreement on the Application of Sanitary and Phytosanitary (SPS) Measures it is stated that sanitary measures should be based on an assessment of the risks to humans, taking into account risk assessment techniques. In the European Union (EU), the general principles governing food and feed safety are laid down in Regulation (EC) No 178/2002 (General

International audience ; A methodology based on the combination of two complementary approaches to rank microbiological risks in foods is presented. In the forward approach data on the pathogenicity of hazards and their behaviour in food during processing and following steps, up to consumption, are used in decision trees to qualitatively estimate the risk associated with foods. In the backward approach risks are evaluated based on the analysis of data available on the past occurrence of hazards and food-borne outbreaks. The categorisation of foods using the forward approach should prevail, and whenever it leads to a likely risk for a given food, the risk can be further qualified with the results from the backward approach. The methodology developed was applied to rank the public health risk posed by certain composite products, which contain both processed products of animal origin and products of plant origin (e.g., bread, cakes, chocolate). Despite limitations in the data available for these foods, valuable results were obtained. The method is therefore considered suitable for application with success to other types of food, and is proposed as a tool for risk managers to rank foods based on their potential food safety risks. Introduction In the World Trade Organization (WTO) Agreement on the Application of Sanitary and Phytosanitary (SPS) Measures it is stated that sanitary measures should be based on an assessment of the risks to humans, taking into account risk assessment techniques. In the European Union (EU), the general principles governing food and feed safety are laid down in Regulation (EC) No 178/2002 (General

International audience ; According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of huard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus ...

According to the World Health Organization estimates in 2015, 600 million people fall ill every year from contaminated food and 420,000 die. Microbial risk assessment (MRA) was developed as a tool to reduce and prevent risks presented by pathogens and/or their toxins. MRA is organized in four steps to analyse information and assist in both designing appropriate control options and implementation of regulatory decisions and programs. Among the four steps, hazard characterisation is performed to establish the probability and severity of a disease outcome, which is determined as function of the dose of toxin and/or pathogen ingested. This dose-response relationship is subject to both variability and uncertainty. The purpose of this review/opinion article is to discuss how Next Generation Omics can impact hazard characterisation and, more precisely, how it can improve our understanding of variability and limit the uncertainty in the dose-response relation. The expansion of omics tools (e.g. genomics, transcriptomics, proteomics and metabolomics) allows for a better understanding of pathogenicity mechanisms and virulence levels of bacterial strains. Detection and identification of virulence genes, comparative genomics, analyses of mRNA and protein levels and the development of biomarkers can help in building a mechanistic dose-response model to predict disease severity. In this respect, systems biology can help to identify critical system characteristics that confer virulence and explain variability between strains. Despite challenges in the integration of omics into risk assessment, some omics methods have already been used by regulatory agencies for hazard identification. Standardized methods, reproducibility and datasets obtained from realistic conditions remain a challenge, and are needed to improve accuracy of huard characterisation. When these improvements are realized, they will allow the health authorities and government policy makers to prioritize hazards more accurately and thus refine surveillance programs with the collaboration of all stakeholders of the food chain.