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Enzymatic Synthesis of Epoxidized Metabolites of Docosahexaenoic, Eicosapentaenoic, and Arachidonic Acids
Published on: June 28, 2019
Developing a multispectral model for detection of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) changes
Jun-Hu Cheng1, Da-Wen Sun2, Guixiong Liu3
1School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; Academy of Contemporary Food Engineering, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China; Engineering and Technological Research Centre of Guangdong Province on Intelligent Sensing and Process Control of Cold Chain Foods, Guangzhou Higher Education Mega Center, Guangzhou 510006, China.
Abstract:
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.
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