The approach used to obtain European Union-wide data on the usage and concentration of substances in different food packaging materials is described. Statistics were collected on pack sizes and market shares for the different materials used to package different food groups. The packaging materials covered were plastics (both flexible and rigid), metal containers, light metal packaging, paper and board, as well as the adhesives and inks used on them. An explanation as to how these data are linked in various ways in the FACET exposure modelling tool is given as well as an overview of the software along with examples of the intermediate tables of data. The example of bisphenol A (BPA), used in resins that may be incorporated into some coatings for canned foodstuffs, is used to illustrate how the data in FACET are combined to produce concentration distributions. Such concentration distributions are then linked probabilistically to the amounts of each food item consumed, as recorded in national food consumption survey diaries, in order to estimate exposure to packaging migrants. Estimates of exposure are at the level of the individual consumer and thus can be expressed for various percentiles of different populations and subpopulations covered by the national dietary surveys.
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.
The choice of suitable normal foods is limited for individuals with particular medical conditions, e.g., inborn errors of metabolism (phenylketonuria - PKU) or severe cow's milk protein allergy (CMPA). Patients may have dietary restrictions and exclusive or partial replacement of specific food groups with specially formulated products to meet particular nutrition requirements. Artificial sweeteners are used to improve the appearance and palatability of such food products to avoid food refusal and ensure dietary adherence. Young children have a higher risk of exceeding acceptable daily intakes for additives than adults due to higher food intakes kg-1 body weight. The Budget Method and EFSA's Food Additives Intake Model (FAIM) are not equipped to assess partial dietary replacement with special formulations as they are built on data from dietary surveys of consumers without special medical requirements impacting the diet. The aim of this study was to explore dietary exposure modelling as a means of estimating the intake of artificial sweeteners by young PKU and CMPA patients aged 1-3 years. An adapted validated probabilistic model (FACET) was used to assess patients' exposure to artificial sweeteners. Food consumption data were derived from the food consumption survey data of healthy young children in Ireland from the National Preschool and Nutrition Survey (NPNS, 2010-11). Specially formulated foods for special medical purposes were included in the exposure model to replace restricted foods. Inclusion was based on recommendations for adequate protein intake and dietary adherence data. Exposure assessment results indicated that young children with PKU and CMPA have higher relative average intakes of artificial sweeteners than healthy young children. The reliability and robustness of the model in the estimation of patient additive exposures was further investigated and provides the first exposure estimates for these special populations.
The feasibility of using a retailer fidelity card scheme to estimate food additive intake was investigated in an earlier study. Fidelity card survey information was combined with information provided by the retailer on levels of the food colour Sunset Yellow (E110) in the foods to estimate a daily exposure to the additive in the Swiss population. As with any dietary exposure method the fidelity card scheme is subject to uncertainties and in this paper the impact of uncertainties associated with input variables including the amounts of food purchased, the levels of E110 in food, the proportion of food purchased at the retailer, the rate of fidelity card usage, the proportion of foods consumed outside of the home and bodyweights and with systematic uncertainties was assessed using a qualitative, deterministic and probabilistic approach. An analysis of the sensitivity of the results to each of the probabilistic inputs was also undertaken. The analysis identified the key factors responsible for uncertainty within the model and demonstrated how the application of some simple probabilistic approaches can be used quantitatively to assess uncertainty.
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