Machine Learning for Toxicological Risk Identification in Food Products
Problem: Identifying toxicological risks in food products is a complex process that requires accurate analysis of chemical contaminants and exposure levels, which is often time-consuming using traditional methods.
Method: Machine learning models were developed to analyze chemical composition data and predict toxicity levels. The system integrated laboratory data with environmental exposure datasets to enhance the accuracy of risk assessment.
Result: The model improved the speed and reliability of toxicological risk identification, enabling faster decision-making in food safety management and supporting regulatory compliance.
