Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat

This study was carried out to investigate the feasibility of combining spectral with texture features in order to improve pH prediction for salted pork. Average spectra were extracted from the region of interest (ROI) of hyperspectral images over the wavelength region of 400-1000 nm and 9 characteristic spectral variables were then selected by principal components analysis (PCA). Meanwhile, gray-level gradient cooccurrence matrix (GLGCM) analysis was implemented on the first PC image (accounted for 96% of the total variance) to extract 13 textural feature variables. Partial least-squares regression (PLSR) was developed for predicting pH based on spectral, textural or combined data. Coefficient of determination (R2P) of 0.794 for the prediction samples based on data fusion was achieved, which was superior to the results based on spectra (R2P = 0.783) or texture (R2P = 0.593) alone. Hence, methods of combining spectral with texture analyses are effective for improving meat quality prediction.