Detection of fish bones in fillets by Raman hyperspectral imaging technology

Abstract

Fish products are important foodstuffs for most consumers worldwide. However, fish bones are considered as a serious hazard in fish products, and new detection techniques are increasingly needed to effectively detect fish bones. For this reason, a new method of fish-bone detection based on Raman hyperspectral imaging technology was developed to improve the detection ratio and realize automatic detection. This study describes the proposed method and the corresponding validation experiments with grass carp fillets. The differences in Raman spectra between fish bone and fish meat were investigated, and the optimal band information was selected using a fuzzy-rough set model based on the thermal-charge algorithm (FRSTCA). Then the support vector data description (SVDD) classification model was established for the selected band information (961 and 965 cm−1) to realize the automatic identification of fish bones. Finally, the composition of each pixel in the Raman hyperspectral image of the fillet sample was classified and judged by the established detection model, the fish bone position and a fish bone distribution image were finally obtained. Experiments on 191 fish bones from 22 grass carp fillets showed that our method can effectively detect fish bones with a depth of up to 2.5 mm and yielded a detection performance of 90.5%. The proposed method may open new possibilities in the field of automated fish-bone detection in grass carp and other similar fish and for the further automatic detection of other foreign bodies such as fish bone in the future.