Machine learning for predicting chemical migration from food packaging materials to foods

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.

Automated summary

This study developed a nonlinear machine learning method to predict the migration of chemicals from packaging materials to food, taking into account chemical properties, material type, food type, and temperature. The ensemble model leveraging multiple algorithms provides better performance compared to previous linear regression models. These prediction models can accelerate the assessment of migration of food contact chemicals from package to foods.