Non-destructive determination of fat and moisture contents in Salmon (Salmo salar) fillets using near-infrared hyperspectral imaging coupled with spectral and textural features

Abstract

The feasibility of using NIR hyperspectral imaging technique for predicting fat and moisture contents in salmon fillets was assessed by integrating both characteristic wavelengths and image texture features. Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) were combined to extract characteristic wavelengths. Ten textural features of the principal component images were obtained using histogram statistics (HS) and gray level co-occurrence matrices (GLCMs) methods. Three types of models (PLS, MLR and LS-SVM) were established based on different types of inputs including only characteristic wavelengths, only texture parameters and combination both characteristic wavelengths and textures, respectively. Compared among all models, LS-SVM model coupled with wavelength and texture information gave the highest prediction accuracies with RP = 0.9685, RMSEP = 1.1750, RPD = 4.0162 for fat and RP = 0.9688, RMSEP = 0.8021, RPD = 4.0357 for moisture, respectively. This study showed that the prediction accuracy can be improved by combining spectral features with textural features and the fusion of characteristic wavelength and textural features had better potential than single spectral information in assessing the fat and moisture contents of salmon. Satisfactory prediction results confirmed the suitability of NIR hyperspectral imaging for quantitative prediction of fat and moisture in salmon.