Visible–Near-Infrared Hyperspectral Imaging for Rapid Quantitative Detection and Visualization of Complex Multicomponent Adulteration in Beef

Multicomponent beef adulteration is challenging to detect rapidly due to high concealment, complex composition, and overlapping spectral features. This study employed visible–near-infrared hyperspectral imaging (400–1000 nm) combined with a Deep Feature-Enhanced Partial Least Squares Regression (DF-PLSR) model for quantitative detection and visualization of adulteration in beef. A total of 2322 samples were prepared across seven adulteration systems—single (chicken, duck, pork), binary, and ternary—with adulteration ratios from 5% to 50%. The DF-PLSR model outperformed traditional PLSR in all systems, achieving the best performance for chicken adulteration (R2p = 0.9942, RMSEP = 1.31%), with RPD > 5 for all systems. CARS and SPA reduced spectral dimensionality by 85.9% while maintaining R2p > 0.93. Pixel-level visualization achieved prediction errors < 3%, enabling intuitive identification of adulterant spatial distribution. This method provides a rapid, non-destructive, and accurate approach for detecting complex adulteration in beef, with strong potential for food safety monitoring.