Mapping of Fat and Moisture Distribution in Atlantic Salmon Using Near-Infrared Hyperspectral Imaging

Abstract

A nondestructive and rapid method using near-infrared (NIR) hyperspectral imaging was investigated to determine the spatial distribution of fat and moisture in Atlantic salmon fillets. Altogether, 100 samples were studied, cutting out from different parts of five whole fillets. For each sample, the hyperspectral image was collected with a pushbroom NIR (899–1,694 nm) hyperspectral imaging system followed by chemical analysis to measure its reference fat and moisture contents. Mean spectrum were extracted from the region of interest inside each hyperspectral image. The quantitative relationships between spectral data and the reference chemical values were successfully developed based on partial least squares (PLS) regression with correlation coefficient of prediction of 0.93 and 0.94, and root mean square error of prediction of 1.24 and 1.06 for fat and moisture, respectively. Then the PLS models were applied pixel-wise to the hyperspectral images of the prediction samples to produce chemical images for visualizing fat and moisture distribution. The results were promising and demonstrated the potential of this technique to predict constituent distribution in salmon fillets.