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

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.PMID:37451598 | DOI:10.1016/j.fct.2023.113942

Source: Food and Chemical Toxicology - July 14, 2023 Category: Food Science Authors: Shan-Shan Wang Pinpin Lin Chia-Chi Wang Ying-Chi Lin Chun-Wei Tung Source Type: research