AI-Based Food Quality Monitoring System
This research project focuses on developing an intelligent monitoring system using artificial intelligence to evaluate food quality in real-time across production and processing environments. The system integrates computer vision, machine learning algorithms, and data analytics to analyze visual and chemical indicators of food quality. It is designed to detect contamination, classify food products, and identify […]
Data-Driven Toxicological Risk Assessment in Food Systems
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 […]