Integration of spectral and textural data for enhancing hyperspectral prediction of K value in pork meat

• Data fusion was used for enhancing hyperspectral prediction of K value in pork.
• 6 optimal wavebands were identified by SPA.
• 24 textural variables were extracted by GLCM.
• Spectral and textural variables were fused by feature level fusion.
• The PLSR model was enhanced by data fusion with an improvement of at least 17.5%.

K value is an important freshness indicator of meat. This study investigated the integration of spectral and textural data for enhancing the hyperspectral prediction ability of K value in pork meat. In this study, six feature wavebands (407, 481, 555, 578, 633, and 973 nm) were identified by successive projections algorithm (SPA). Meanwhile, the texture data of the grayscale images at the feature wavebands were extracted by gray level co-occurrence matrix (GLCM). The spectral and textural data were integrated by feature level fusion and the partial least square regression (PLSR) model built based on data fusion yielded excellent results, an improvement of at least 17.5% was obtained in model performance compared to those when either spectral data or textural data were used alone, indicating that data fusion is an effective way to enhance hyperspectral imaging ability for the determination of K values for freshness evaluation in pork meat.