Developing a multispectral model for detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in fish fillet using physarum network and genetic algorithm (PN-GA) method

A multispectral model for the detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes in grass carp and salmon fillet was developed using physarum network and genetic algorithm (PN-GA) method for the first time. Partial least-squares regression (PLSR), multiple linear regressions (MLR), and principal component regression (PCR) algorithms were used to predict the DHA and EPA using optimal wavelengths selected by PN-GA. The MLR models showed the best DHA prediction results for both grass carp and salmon fillets, and also showed good prediction for EPA in grass carp fillet but poor prediction in salmon fillet. The MLR models were then applied for visualizing the spatial distribution of DHA and EPA changes in two fish fillets. The current results demonstrated that a developed multispectral imaging system could be feasibly constructed for DHA and EPA measurement in fish species with the optimal wavelengths selected by PN-GA method.