Combination of spectral and image information from hyperspectral imaging for the prediction and visualization of the total volatile basic nitrogen content in cooked beef

The total volatile basic nitrogen (TVB-N) content is representative index for measuring the freshness of cooked meat. The study investigated integrating spectral and image information from visible and near-infrared hyperspectral imaging for predicting TVB-N values in cooked beef during storage. Nine optimal wavelengths were selected by uninformative variable elimination (UVE) and successive projections algorithm (SPA). Thirty-six singular values as texture features were extracted using discrete wavelet transform (DCT). Partial least squares regression (PLSR) and least squares-support vector machine (LS-SVM) models were established using spectral, image, and their fusion information. The models based on data fusion yielded satisfactory prediction results: PLSR gave R c 2 and R P 2 values of 0.9512 and 0.9401 with RMSEC and RMSEP of 1.9037 and 1.8942; LS-SVM gave R c 2 and R P 2 values of 0.9674 and 0.9579 with RMSEC and RMSEP of 1.6538 and 1.5435. The distribution of TVB-N values from sample images was visualized using the fused LS-SVM model with image processing algorithms. This indicated that integrating spectral and image information from hyperspectral imaging coupled with the LS-SVM or PLSR algorithm could predict the TVB-N value and visualize its distribution in cooked beef.