Laser & Optoelectronics Progress, Volume. 55, Issue 4, 043001(2018)

Application of Counterfeit Liquor Recognition Based on Deep Learning and Laser Induced Fluorescence

Wenhao Lai*, Mengran Zhou, Ya Wang, Feng Hu, Datong Li, and Shun Zhao
Author Affiliations
  • 1School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui 232000, China
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    Figures & Tables(10)
    LIF system structure diagram
    Fluorescence spectra of four samples
    (a) Fluorescence spectra of samples with same brand and different degrees; (b) local diagram of the first wave peak; (c) local diagram of the second wave peak; (d) local fluorescence spectra in band of 465-500 nm
    (a) Fluorescence spectra of samples with different brands and same degree; (b) local diagram of the first wave peak; (c) local diagram of the second wave peak; (d) local fluorescence spectra in band of 465-500 nm
    (a) Fluorescence spectra of samples with different brands and same degree of 42; (b) local diagram of the first wave peak; (c) local diagram of the second wave peak; (d) local fluorescence spectra in band of 465-500 nm
    Spectra of the input model. (a) Sample A; (b) sample B; (c) sample C; (d) sample D
    Deep learning model flow chart
    Deep learning model training process curves
    • Table 1. Classification test result of liquor spectrum

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      Table 1. Classification test result of liquor spectrum

      Iteration numberRecognition rate /%
      300098.44
    • Table 2. Results of classification test of liquor spectrum

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      Table 2. Results of classification test of liquor spectrum

      AlgorithmRecognition rate /%
      Deep learning98.44
      BP neural network93.75
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    Wenhao Lai, Mengran Zhou, Ya Wang, Feng Hu, Datong Li, Shun Zhao. Application of Counterfeit Liquor Recognition Based on Deep Learning and Laser Induced Fluorescence[J]. Laser & Optoelectronics Progress, 2018, 55(4): 043001

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    Paper Information

    Category: Spectroscopy

    Received: --

    Accepted: --

    Published Online: Sep. 11, 2018

    The Author Email:

    DOI:10.3788/LOP55.043001

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