A regression-based model to predict chemical migration from packaging to food.

Packaging materials can be a source of chemical contaminants in food. Process-based migration models (PMM) predict the chemical fraction transferred from packaging materials to food (F _C ) for application in prioritisation tools for human exposure. These models, however, have a relatively limited applicability domain and their predictive performance is typically low. To overcome these limitations, we developed a linear mixed-effects model (LMM) to statistically relate measured F _C to properties of chemicals, food, packaging, and experimental conditions. We found a negative relationship between the molecular weight (MW) and F _C , and a positive relationship with the fat content of the food depending on the octanol-water partitioning coefficient of the migrant. We also showed that large chemicals (MW > 400 g/mol) have a higher migration potential in packaging with low crystallinity compared with high crystallinity. The predictive performance of the LMM for chemicals not included in the database in contact with untested food items but known packaging material was higher (Coefficient of Efficiency (CoE) = 0.21) compared with a recently developed PMM (CoE = -5.24). We conclude that our empirical model is useful to predict chemical migration from packaging to food and prioritise chemicals in the absence of measurements.