Quantification and visualization of meat quality traits in pork using hyperspectral imaging

Accurate and rapid determination of meat quality traits plays key roles in food industry and pig breeding. Currently, most of the spectroscopic instruments developed for meat quality determination can only obtain the spectral average value of the sample, so it is difficult to evaluate the spatial variation of meat quality traits. In this study, we evaluated the predictive potential of 14 meat quality traits based on large-scale VIS/NIR hyperspectral images collected by SpecimIQ. When predictions were based solely on hyperspectral data, the prediction accuracy (R2cv) for the majority of meat qualities ranged from 0.60 to 0.70. After adding texture information, the prediction accuracy of all traits is improved by different magnitudes (R2cv increases from 1.5% to 16.4%). Finally, the best model was utilized to visualize the spatial distribution of Fat (%) and Moisture (%) to assess their homogeneity. These results suggest that hyperspectral imaging has great potential for predicting and visualizing various meat qualities, as well as industrial applications for automated measurements.