Predicting and Inspecting Food Contamination Using AI based Hyperspectral Imaging - Sudha, S.; Nafeeza, S. (University of Science and Computer Technology, 2025)
Growing consumer demands and intricate supply networks are making it more difficult for the global food industry to maintain high standards of quality and ensure food safety. Conventional inspection techniques sometimes take a lot of time, cause damage, and are inaccurate enough to miss contaminants or quality problems early. These drawbacks emphasize how sophisticated, effective, and non-invasive technology is required for food quality monitoring. This effort aims to investigate the use of Hyperspectral Imaging (HSI) in conjunction with Artificial Intelligence (AI) for food contamination inspection and prediction. Food's chemical and physical characteristics that are undetectable to the human eye can be revealed by hyperspectral imaging, which takes pictures at a variety of wavelengths. The findings show that AI-based HSI offers notable advantages over traditional techniques in terms of quick, accurate, and non-destructive examination. It makes early contamination detection possible and aids in preserving food quality throughout the supply chain. By reducing waste, guaranteeing product authenticity, and boosting customer trust in food items, our effort helps worldwide food safety and advance the development of smarter food inspection systems.