A chemical structure-based approach for estimating the added levels of flavourings to foods for the purpose of assessing consumer intake. Issue 1 (2nd January 2021) - Bastaki, Maria; Aubanel, Michel; Bialk, Heidi; Choi, Christopher; Demyttenaere, Jan; Diop, Malick Maodo; Etter, Sylvain; Han, Xing; Koyanagi, Mihoko; Krammer, Gerhard; Schnabel, Jürgen; Wilson, Cody L.; et al.
ABSTRACT: Intake assessment and hazard profile of chemical substances are the two critical inputs in a safety assessment. Human intake assessment presents challenges that stem either from the absence of data or from numerous sources of variability and uncertainty, which have led regulators to adopt conservative approaches that inevitably overestimate intake. Refinements of intake assessments produce more realistic estimates and help prioritise areas of concern and better direct investment of resources. However, use levels (ULs), which represent the usual added amount of flavourings to food products, are the starting point for refined intake assessments, are data-intensive, and data availability is often a limitation. The work presented here was undertaken to investigate the use level patterns of substances used as flavourings in foods and to develop a systematic tool for data extrapolation based on chemical structure. The available dataset consists of use levels reported through eight industry surveys and hence are representative of industry uses rather than regulatory limits, which are higher by design and not realistic. A systematic statistical analysis was undertaken to determine whether the industry-reported UL data can be used to estimate use levels of flavouring substances belonging to the same chemical group for which such data are not available. Predictive modelling approaches were explored to evaluate relationships in the data and utilised additional variables relevant to technological considerations, such as volatility losses upon heat treatment, and Tanimoto index-based pair-wise structural similarity scores to determine whether more granular similarity information can reduce the within-group variability. The analyses indicated that the use levels of flavouring substances can reasonably be estimated based on the available data using chemical group classifications stratified by food category. Source of uncertainty and limitations are discussed.