Antibiotic dregs contain antibiotic residues and pose safety risks if they are used illegally in animal feed protein materials. The objective of this study was to develop a shortwave infrared (SWIR) hyperspectral imaging system (HSI) to quantify and qualify antibiotic dregs for the adulteration of feed protein materials (FPMs). Three FPMs were adulterated with oxytetracycline dregs (OD) over a range of 2%–98% (w/w). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and one-class partial least squares classifier (OCPLS) models were developed based on different spectral preprocessing techniques to predict adulterants. Furthermore, mean spectra with partial least squares (PLS) regression and images were used to predict adulterant concentrations. The results showed that FPMs and adulterated FPMs could not be completely distinguished using PCA. PLS-DA and OCPLS exhibited the highest classification accuracies (100%), and PLS-DA exhibited better recognition results for the pixel spectra. Using the mean spectra, PLS models were successfully established to predict adulteration levels in FPMs. The optimal parameters of residual predictive deviation (RPD) were 14.5, 6.1, and 7.6. Overall, the developed HSI system and optimized model demonstrated a high potential for discriminating antibiotic dregs in FPMs and allowed for quantitative evaluation.
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