Combination of spectra and texture data of hyperspectral imaging for differentiating between free-range and broiler chicken meats

- Fresh free-range and broiler chicken meats were discriminated based on HSI.
- Nine optimal wavelengths were selected by successive projection algorithm.
- Texture data of two main principal component images was extracted by GLGCM.
- Classification models were built with different characteristic information.
- Model based on data fusion was superior to that based on spectra or texture.

This study investigated the potential of visible and near infrared (Vis/NIR) hyperspectral imaging (HSI) to differentiate between free-range and broiler chicken meats. 120 hyperspectral images of chicken fillets were acquired and then calibrated for reflectance. Spectral data were extracted from the region of interest (ROI), followed by multiple scatter correction (MSC) to reduce the noise. Successive projection algorithm (SPA) was used to select optimal wavelengths from the full spectra. On the other hand, principal component analysis (PCA) was applied to select optimum characteristic images, and the first two principal component (PC) images were selected because PC1 and PC2 explained over 95% of variances of all spectra. Then, gray-level gradient co-occurrence matrix (GLGCM) was implemented on PC1 and PC2 images to extract 30 textural variables in total. Based on data fusion, classification models were established, in which the radial basis function-support vector machine (RBF-SVM) model gave the best results with high correct classification rate (CCR) of 93.33% for the prediction samples, demonstrating that combining spectra with texture data were effective for differentiating between free-range and broiler chicken meats.