Hyperspectral AI for Meat Safety and Inspection Systems

Abstract

Hyperspectral AI provides a non-destructive approach for meat safety inspection by combining spectral sensing, image analysis, and data-driven classification. Meat contamination detection requires sensitivity to weak biochemical signals, robustness to sample heterogeneity, and interpretability for practical inspection environments. Hyperspectral unmixing is valuable because contamination often appears as a mixed signal rather than a clearly separated visual object. Benchmark datasets help define consistent evaluation criteria for weak-signal detection and support comparison across traditional chemometric models, deep learning methods, and spectral unmixing algorithms. This topic focuses on inspection systems that combine spectral decomposition with AI-based decision support. The literature emphasizes the importance of standardized acquisition, preprocessing, model validation, and quality-control interpretability for food safety applications.