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

Jun-Hu Cheng; Da-Wen Sun; Guixiong Liu; Yu-Nan Chen

doi:10.1016/j.foodchem.2018.07.013

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

Food Chem. 2019 Jan 1:270:181-188. doi: 10.1016/j.foodchem.2018.07.013. Epub 2018 Jul 3.

Authors

Jun-Hu Cheng¹, Da-Wen Sun², Guixiong Liu³, Yu-Nan Chen¹

Affiliations

¹ School 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.
² School 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; Food Refrigeration and Computerized Food Technology (FRCFT), Agriculture and Food Science Centre, University College Dublin, National University of Ireland, Belfield, Dublin 4, Ireland. Electronic address: http://www.ucd.ie/refrig.
³ School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.

PMID: 30174033
DOI: 10.1016/j.foodchem.2018.07.013

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.

Keywords: DHA; EPA; Hyperspectral imaging; MLR; Physarum network genetic algorithm.

MeSH terms

Algorithms
Animals
Docosahexaenoic Acids / analysis*
Eicosapentaenoic Acid / analysis*
Least-Squares Analysis
Physarum
Seafood / analysis*

Substances

Docosahexaenoic Acids
Eicosapentaenoic Acid