Abstract: Hyperspectral imaging technology integrates the strengths of spectral analysis and spatial imaging, allowing for non-destructive, high-throughput, and simultaneous detection of food components, contaminants, and foreign substances. This paper systematically elucidated the technical principles and system architecture of hyperspectral imaging technology, with a focus on its advanced applications in the quantitative analysis of food components (moisture, protein, fat, carbohydrates), the identification of biological and chemical contaminants (microorganisms, pesticide and veterinary drug residues, heavy metals), and the detection of foreign objects. Furthermore, this study investigated the critical role of algorithm optimization in enhancing detection accuracy. It also identified several key technical challenges, such as high equipment costs, limited real-time performance, and insufficient model generalization. It further proposed corresponding solutions, such as the development of lightweight hardware, the construction of cross-scenario universal models, and the implementation of edge intelligence decision-making systems, thereby providing a solid theoretical foundation and feasible technical pathways for intelligent monitoring of food quality and safety.