Prediction of fat content in salmon fillets based on hyperspectral imaging and residual attention convolution neural network

Abstract

The fat content of salmon is an essential indicator to evaluate its quality, and is related to its commercial value. An attention residual convolutional neural network (CNN), namely RACNN, which added channel attention and residual modules to the traditional CNN structure, was proposed to combine near-infrared hyperspectral imaging (NIR-HSI) (900-1700 nm) to predict fat contents in salmon fillets. Partial least squares regression (PLSR), least squares support vector machine (LSSVM) and CNN models were compared with the proposed RACNN models. Various spectrum preprocessing methods were used at the time. And Three feature extraction algorithms (successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS)) were used to extract the effective wavelengths from the preprocessed NIR spectra. The experimental results showed that, the optimal PLSR and LSSVM models obtained coefficient of determination of prediction (Rp2) values of 0.8781 and 0.8952, respectively. The RACNN model based on full spectrum gained the highest prediction accuracies with Rp2 = 0.9033, root mean square error of prediction (RMSEP) = 1.5143, and residual predictive deviation (RPD) = 3.2707. Therefore, the proposed RACNN model enables the rapid, non-destructive and accurate prediction of the fat content in salmon fillets without additional processing on raw spectra.