Prediction of the packaging chemical migration into food and water by cutting-edge machine learning techniques.

ABSTRACT: Chemicals transfer from the packaging materials and their dissolution in food and water can create health risks. Due to the costly and time-intensive nature of experimental measurements, employing artificial intelligence (AI) methodologies is beneficial. This research uses five renowned AI-based techniques (namely, long short-term memory, gradient boosting regressor, multi-layer perceptron, Random Forest, and convolutional neural networks) to anticipate chemical migration from packaging materials to the food/water structure, considering variables such as temperature, chemical characteristics, and packaging/food types. The relevance analysis has been employed for monitoring the way that these explanatory variables impact the chemical migration from packaging materials into foods and water.

SUBMITTER: Vaferi B 

PROVIDER: S-EPMC11885667 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature