Deep machine learning identified fish flesh using multispectral imaging.

Food fraud is widespread in the aquatic food market, hence fast and non-destructive methods of identification of fish flesh are needed. In this study, multispectral imaging (MSI) was used to screen flesh slices from 20 edible fish species commonly found in the sea around Yantai, China, by combining identification based on the mitochondrial gene. We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species.