Laser & Optoelectronics Progress, Volume. 62, Issue 5, 0530002(2025)

Raman Spectroscopic Identification of Hazardous Chemicals Based on a Deep Neural Network

Yuhao Xie*, Qianmin Dong, Shangzhong Jin, and Pei Liang
Author Affiliations
  • College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, Zhejiang , China
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    Figures & Tables(8)
    Schematic diagram of Raman spectrum sample acquisition and model training process
    Raman spectra of some hazardous chemicals. (a) CH₃OH; (b) C₇H₈; (c) CH₂O; (d) C₂H₆O₂; (e) C₂H₅NH₂; (f) CH₃CN; (g) C₆H₁₂; (h) H₂SO₄; (i) HNO₃; (j) KNO₃
    Raman spectra of CH₃OH and NH4NO3. (a) CH₃OH; (b) NH4NO3
    Structure of VGG network
    Sturctures of proposed model. (a) SE-VGG model; (b) SE module
    Variation curves of accuracy for four models
    ROC curves of four models. (a) LCNN; (b) MCNN; (c) VGG; (d) SE-VGG
    Performance indicators of LCNN, MCNN, VGG, and SE-VGG models
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    Yuhao Xie, Qianmin Dong, Shangzhong Jin, Pei Liang. Raman Spectroscopic Identification of Hazardous Chemicals Based on a Deep Neural Network[J]. Laser & Optoelectronics Progress, 2025, 62(5): 0530002

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    Paper Information

    Category: Spectroscopy

    Received: Jul. 4, 2024

    Accepted: Aug. 13, 2024

    Published Online: Feb. 26, 2025

    The Author Email:

    DOI:10.3788/LOP241633

    CSTR:32186.14.LOP241633

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