Rapid identification of fish species by laser-induced breakdown spectroscopy and Raman spectroscopy coupled with machine learning methods.

Journal: Food chemistry
PMID: 36058043

Abstract

There has been an increasing demand for the rapid verification of fish authenticity and the detection of adulteration. In this work, we combined LIBS and Raman spectroscopy for the fish species identification for the first time. Two machine learning methods of SVM and CNN are used to establish the classification models based on the LIBS and Raman data obtained from 13 types of fish species. Data fusion strategies including low-level, mid-level and high-level fusions are used for the combination of LIBS and Raman data. It shows that all these data fusion strategies offer a significant improvement in fish classification compared with the individual LIBS or Raman data, and the CNN model works more powerfully than the SVM model. The low-level fusion CNN model provides a best classification accuracy of 98.2%, while the mid-level fusion involved with feature selection improves the computing efficiency and gains the interpretability of CNN.

Authors

  • Lihui Ren
    College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China; Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China.
  • Ye Tian
    State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics and Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, 361102, China.
  • Xiaoying Yang
    From the Department of Radiology (B.A.-V.), Bloomberg School of Public Health (E.G.), and Department of Medicine, Cardiology and Radiology (J.A.C.L.), Johns Hopkins University, Baltimore, MD; George Washington University, DC (X.Y.); Office of Biostatistics, NHLBI, NIH, Bethesda, MD (C.O.W.); Department of Preventive Medicine, Northwestern University Medical School, Chicago, IL (K.L.); Department of Cardiology, Wake Forest University Health Sciences, Winston-Salem, NC (W.G.H.); Department of Biostatistics, University of Washington, Seattle (R.M.); Department of Radiology, UCLA School of Medicine, Los Angeles, CA (A.S.G.); Division of Epidemiology and Community Health, University of Minnesota, Minneapolis (A.R.F.); Departments of Medicine and Epidemiology, Columbia University, New York, NY (S.S.); and Radiology and Imaging Sciences, NIH Clinical Center, Bethesda, MD (D.A.B.).
  • Qi Wang
    Biotherapeutics Discovery Research Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Leshan Wang
    College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao 266100, China.
  • Xin Geng
    BGI-Shenzhen, Shenzhen, 518083, China.
  • Kaiqiang Wang
  • Zengfeng Du
    Key Laboratory of Marine Geology and Environment, Chinese Academy of Sciences, Qingdao 266071, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Hong Lin
    Wenzhou Medical University, Wenzhou, China.

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