This research project focuses on developing data-driven approaches for toxicological risk assessment in food systems, combining environmental data, chemical analysis, and predictive modeling techniques.

The project utilizes machine learning algorithms to evaluate exposure levels and assess the potential health risks associated with chemical contaminants in food products. Large datasets from laboratory analysis and environmental monitoring are integrated to improve accuracy and reliability.

The findings support the development of risk assessment frameworks that enhance regulatory decision-making and food safety management practices.

This project contributes to advancing scientific understanding in toxicology and promotes the use of innovative technologies in public health protection.