Severe/potentially severe reactions, allergic comorbidities, and low EDs in real life are frequent in peanut-allergic patients. Asthma, teenage/adulthood and reaction to inhalation are associated with severe symptoms. PAL and criteria guiding dietary advice need to be improved.

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This guidance document provides harmonised and flexible methodologies to apply scientific criteria and prioritisation methods for grouping chemicals into assessment groups for human risk assessment of combined exposure to multiple chemicals. In the context of EFSA’s risk assessments, the problem formulation step defines the chemicals to be assessed in the terms of reference usually through regulatory criteria often set by risk managers based on legislative requirements. Scientific criteria such as hazard‐driven criteria can be used to group these chemicals into assessment groups. In this guidance document, a framework is proposed to apply hazard‐driven criteria for grouping of chemicals into assessment groups using mechanistic information on toxicity as the gold standard where available (i.e. common mode of action or adverse outcome pathway) through a structured weight of evidence approach. However, when such mechanistic data are not available, grouping may be performed using a common adverse outcome. Toxicokinetic data can also be useful for grouping, particularly when metabolism information is available for a class of compounds and common toxicologically relevant metabolites are shared. In addition, prioritisation methods provide means to identify low‐priority chemicals and reduce the number of chemicals in an assessment group. Prioritisation methods include combined risk‐based approaches, risk‐based approaches for single chemicals and exposure‐driven approaches. Case studies have been provided to illustrate the practical application of hazard‐driven criteria and the use of prioritisation methods for grouping of chemicals in assessment groups. Recommendations for future work are discussed.

Identification and characterisation of dietary patterns are needed to define public health policies to promote better food behaviours. The aim of this study was to identify the major dietary patterns in the French adult population and to determine their main demographic, socio-economic, nutritional and environmental characteristics. Dietary patterns were defined from food consumption data collected in the second French national cross-sectional dietary survey (2006–2007). Non-negative-matrix factorisation method, followed by a cluster analysis, was implemented to derive the dietary patterns. Logistic regressions were then used to determine their main demographic and socio-economic characteristics. Finally, nutritional profiles and contaminant exposure levels of dietary patterns were compared using ANOVA. Seven dietary patterns, with specific food consumption behaviours, were identified: ‘Small eater’, ‘Health conscious’, ‘Mediterranean’, ‘Sweet and processed’, ‘Traditional’, ‘Snacker’ and ‘Basic consumer’. For instance, the Health-conscious pattern was characterised by a high consumption of low-fat and light products. Individuals belonging to this pattern were likely to be older and to have a better nutritional profile than the overall population, but were more exposed to many contaminants. Conversely, individuals of Snacker pattern were likely to be younger, consumed more highly processed foods, had a nutrient-poor profile but were exposed to a limited number of food contaminants. The study identified main dietary patterns in the French adult population with distinct food behaviours and specific demographic, socio-economic, nutritional and environmental features. Paradoxically, for better dietary patterns, potential health risks cannot be ruled out. Therefore, this study demonstrated the need to conduct a risk–benefit analysis to define efficient public health policies regarding diet.

A normal distribution and a mixture model of two normal distributions in a Bayesian approach using prevalence and concentration data were used to establish the distribution of contamination of the food-borne pathogenic bacteria Listeria monocytogenes in unprocessed and minimally processed fresh vegetables. A total of 165 prevalence studies, including 15 studies with concentration data, were taken from the scientific literature and from technical reports and used for statistical analysis. The predicted mean of the normal distribution of the logarithms of viable L. monocytogenes per gram of fresh vegetables was ؊2.63 log viable L. monocytogenes organisms/g, and its standard deviation was 1.48 log viable L. monocytogenes organisms/g. These values were determined by considering one contaminated sample in prevalence studies in which samples are in fact negative. This deliberate overestimation is necessary to complete calculations. With the mixture model, the predicted mean of the distribution of the logarithm of viable L. monocytogenes per gram of fresh vegetables was ؊3.38 log viable L. monocytogenes organisms/g and its standard deviation was 1.46 log viable L. monocytogenes organisms/g. The probabilities of fresh unprocessed and minimally processed vegetables being contaminated with concentrations higher than 1, 2, and 3 log viable L. monocytogenes organisms/g were 1.44, 0.63, and 0.17%, respectively. Introducing a sensitivity rate of 80 or 95% in the mixture model had a small effect on the estimation of the contamination. In contrast, introducing a low sensitivity rate (40%) resulted in marked differences, especially for high percentiles. There was a significantly lower estimation of contamination in the papers and reports of 2000 to 2005 than in those of 1988 to 1999 and a lower estimation of contamination of leafy salads than that of sprouts and other vegetables. The interest of the mixture model for the estimation of microbial contamination is discussed.Quantitative microbial risk assessment (QMRA) is in rapid development in the area of food safety. To obtain a quantitative exposure assessment or a quantitative risk characterization for a given food and a given food-borne pathogen, statistical distributions of microbial concentrations are used as input values (such as bacterial concentrations in raw food materials subjected to further process) and/or as output values (predicted contamination in foods after processing and/or storage) (35,75). Microbial contaminations in foods are expressed in two forms: (i) prevalence data (percentage of positive samples in a given study, i.e., growth/no growth of a target pathogen after enrichment in an appropriate broth of an aliquot of a food sample) and (ii) concentration data expressed as CFU per gram or CFU per milliliter. Data on the concentration of pathogenic bacteria in foods, while scarce, are essential to QMRA.Previous works on QMRA did not consider low levels of concentrations, i.e., concentrations below the threshold of detection of microbiological methods (6,15,44...