Hyperspectral imaging coupled with CNN: A powerful approach for quantitative identification of feather meal and fish by-product meal adulterated in marine fishmeal

Abstract

Marine fishmeal (MFM) adulterated with low-cost processed animal proteins (PAPs) such as hydrolyzed feather meal (HFM) and fish by-product meal (FBM) has frequently occurred in the Chinese trade market. This commercial fraud generates a serious threat to farmed animal health and even human food safety. This study aims to develop a rapid detection method using near-infrared hyperspectral imaging (NIR-HSI) combined with deep learning modeling for qualitative and quantitative identification of MFM adulterated with HFM, FBM, and the binary adulterant (HFM-FBM). Three convolutional neural network (CNN) architectures with optimized parameters were constructed to predict sample classes, adulterant concentration, and amino acid content of adulterated samples, respectively. Partial least squares (PLS) and support vector machine (SVM) models were compared with the proposed CNN models. The overall results showed that the CNN outperformed the PLS and SVM on both classification and regression. The six-classification accuracy obtained by the CNN was up to 99.37%, while the R2 of CNN regression prediction varied from 0.984 to 0.997. This study demonstrates that NIR-HSI coupled with CNN calibration provides a promising technique for the detection of MFM adulterated with PAPs.