Foodborne illnesses affect 48 million people in the U.S. annually, causing 128,000 hospitalizations and 3,000 deaths. Traditional detection methods are accurate, but they are costly, slow, and often destructive. Image analysis based on artificial intelligence (AI) offers a faster, nondestructive alternative but struggles to detect internal or microbial contamination. This project will use an electronic nose (e-nose) to detect food contamination by analyzing volatile organic compounds (VOCs). A major challenge is that VOCs from multiple foods can blend, making detection difficult. The team will develop AI models trained on thousands of VOC samples to enable the e-nose to separate odors and identify contamination, even in complex mixtures and varying environments. The improved e-nose will enhance food safety and will have broader applications in healthcare (e.g., disease detection from a patient's breath), security, and robotics. The project will be integrated into machine learning courses at KSU, offering students hands-on experience with AI-driven sensors and promoting interdisciplinary training in olfactory sensing. Electronic noses (e-noses) detect odors by sensing volatile organic compounds (VOCs) and provide fast, non-destructive screening for food safety. However, VOCs often blend in multi-food environments, making it difficult to identify contamination in specific items. These challenges are further complicated by environmental variations, such as changes in tempera