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.

Boobis (2019) Value and limitation of invitro bioassays to support the application of the threshold of toxicological concern to prioritise unidentified chemicals in food contact materials, Food Additives & Contaminants:

A two-dimensional probabilistic model has been developed to estimate the short-term dietary exposure of UK consumers to migrants from food packaging materials. The current EU approach uses a default scenario of assuming that all individuals are 60 kg weight and consume 1 kg of food packaged in the material of interest per day. Using four UK National Dietary and Nutrition Surveys comprising 4-7 day dietary records for different age groups and survey years, a sample representative of the UK population has been obtained consuming around 4200 different food items. Each survey provides records for around 2000 individuals and supplies detailed information on the consumption of food and data on sex, height and socio-economic status which may be used to analyse the exposure of selected groups within the community. As a result we are able to address the variation in consumption of food amongst individuals, and account for actual body weights providing a more accurate representation of the 'true' exposure. The migrants bisphenol A diglycidyl ether (BADGE), di-2-ethylhexyl adipate (DEHA) and styrene were considered as specimen compounds although the methodology employed has the flexibility to adapt to other migrants and packaging types and indeed other food contaminants. Exposure for each individual is estimated by calculating and summing the individual exposure from each item in their diet, and is repeated for all individuals in each survey to produce a distribution of exposures for the population. The packaging type of each food item is assigned by utilizing known packaging types from the database or, by sampling from a distribution based upon market share information. The parameters contributing towards the exposure from a packaged dietary item are migrant concentration and item weight. Distributions are used to represent the inherent variation and uncertainty affecting these parameters. Where data on concentrations for a particular type of food are lacking, expert judgement is used to extrapolate from available data for other food types. The model can also be run using only migration data for food simulants. In this case, concentrations expected for each of the food items are assigned based on the data for the relevant food simulant. The primary outputs of the model are distributions of estimated daily intakes for the selected population. Each distribution gives the variation across the population subject to the uncertain parameters sampled in that iteration of the model. Analysing the ensemble of distributions allows us to obtain the confidence limits around estimates for percentiles due to the uncertainties. The probabilistic approach allows sensitivity analysis to evaluate the relative importance of the input parameters and places confidence bounds on the outputs to show the effect of the uncertainties and the contribution of each food type toward the overall exposure.

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