The transfer of chemicals from packaging materials into food and water poses significant health risks. Traditional experimental methods are costly and time-consuming, prompting researchers to explore artificial intelligence (AI) techniques. A recent study evaluated five AI models—long short-term memory (LSTM), gradient boosting regressor (GBR), multi-layer perceptron (MLP), Random Forest (RF), and convolutional neural networks (CNN)—to predict chemical migration influenced by factors like temperature, chemical properties, and packaging types. The GBR emerged as the most accurate model, demonstrating superior performance with minimal error rates. This research underscores the potential of AI in enhancing food safety by accurately predicting chemical migration.

Chemical migration from packaging materials into food and beverages is a critical concern for public health. Researchers have developed advanced machine learning algorithms to forecast this phenomenon more efficiently. By analyzing 1847 experimental datasets, they assessed various AI models, focusing on key variables such as temperature, chemical characteristics, and packaging types. The study found that the Gradient Boosting Regressor (GBR) outperformed other models, achieving high prediction accuracy with minimal errors. Specifically, the GBR model showed remarkable precision in estimating the partition coefficient (logKpf), a crucial parameter for evaluating chemical migration.

The investigation revealed that logKpf, which measures the chemical concentration ratio between packaging and food, is significantly influenced by factors such as the octanol-water partition coefficient (logKow) and ethanol equivalency (EtOH-eq). The study also highlighted the complex relationship between temperature and chemical migration. Initially, higher temperatures reduce migration, but beyond a certain point, they enhance it. The GBR model's ability to capture these intricate dependencies makes it an invaluable tool for ensuring food safety.

To validate the GBR model's reliability, researchers employed a leverage algorithm to detect outliers and ensure data integrity. Only 56 out of 1847 samples were identified as problematic, indicating the robustness of the model. The study further explored the impact of different packaging materials, revealing that low-density polyethylene (LDPE), high-density polyethylene (HDPE), and silicone rubber had the most extensive datasets. These findings underscore the importance of selecting appropriate packaging materials to minimize chemical migration.

In conclusion, the application of machine learning, particularly the Gradient Boosting Regressor, offers a promising approach to predicting chemical migration from packaging materials into food and water. This technology can significantly enhance food safety protocols by providing precise predictions and reducing reliance on expensive and time-consuming experimental methods. Future research may focus on expanding these models to include emerging packaging materials, thereby advancing our understanding of chemical interactions within the food supply chain.