Machine learning for predicting chemical migration from food packaging materials to foods
Journal
Food and Chemical Toxicology
Journal Volume
178
Date Issued
2023-08-01
Author(s)
Abstract
Food contact chemicals (FCCs) can migrate from packaging materials to food posing an issue of exposure to FCCs of toxicity concern. Compared to costly experiments, computational methods can be utilized to assess the migration potentials for various migration scenarios for further experimental investigation that can potentially accelerate the migration assessment. This study developed a nonlinear machine learning method utilizing chemical properties, material type, food type and temperature to predict chemical migration from package to food. Nine nonlinear algorithms were evaluated for their prediction performance. The ensemble model leveraging multiple algorithms provides state-of-the-art performance that is much better than previous linear regression models. The developed prediction models were subsequently applied to profile the migration potential of FCCs of high toxicity concern. The models are expected to be useful for accelerating the assessment of migration of FCCs from package to foods.
Subjects
Chemical migration | Ensemble learning | Food contact chemical | Quantitative structure-activity relationship
Type
journal article