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

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. Optimizing the hyperparameters, evaluating the prediction accuracy, and comparing the performance of these AI models reveal that the gradient boosting regressor (GBR) is the superior method for this simulation. The proposed GBR model accurately predicts 1847 experimental datasets, showcasing mean squared error, mean absolute error, root mean squared error, relative absolute error percent, and regressing coefficient, of 0.06, 0.15, 0.24, 6.46%, and 0.9961 respectively. Additionally, implementing a leverage algorithm for outlier detection further affirms the reliability of this modeling study.