Tenderness classification of fresh broiler breast fillets using visible and near-infrared hyperspectral imaging

Abstract

The aim of this study was to classify and visualize tenderness of intact fresh broiler breast fillets using hyperspectral imaging (HSI) technique. A total of 75 chicken fillets were scanned by HSI system of 400–1000nm in reflectance mode. Warner-Bratzler shear force (WBSF) value was used as reference tenderness indicator and fillets were grouped into least, moderately and very tender categories accordingly. To extract additional image textural features, principal component analysis (PCA) transform of images were conducted and gray level co-occurrence matrix (GLCM) analysis was implemented in region of interests (ROIs) on first three PC score images. Partial least square discriminant analysis (PLS-DA) or radial basis function-support vector machine (RBF-SVM) was developed for predicting tenderness based on full wavelengths (CCR=0.92), selected wavelengths (CCR=0.84), textural or combined data (CCR=0.88). Classification maps were created by pixels prediction in images and breast fillet tenderness was readily discernible. Overall, HSI technique is a promising methodology for predicting tenderness of intact fresh broiler breast meat.