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Laser & Optoelectronics Progress, Volume. 60, Issue 10, 1030003(2023)

Terahertz Time-Domain Spectral Pattern Recognition for Weight Loss Drugs Based on Feature Fusion

Zhaowei Jie1, Zhiyu Wang1, Jifen Wang1、*, Yijian Sun1, Zhen Zhang1, Wenping Li2, and Yiqing Kong3
Author Affiliations
  • 1School of Investigation, People's Public Security University of China, Beijing 100038, China
  • 2Qingdao Qingyuanfengda Terahertz Technology Co., Ltd., Qingdao 266100, Shandong, China
  • 3Anti-Doping Center of the State Administration of Sports, Beijing 100029, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
    Equations (4)
    References (26)
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    Figures & Tables(13)
    Original spectra of seven weight loss drugs

    Fig. 1. Original spectra of seven weight loss drugs

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    Characteristic peak interval of seven weight loss drugs

    Fig. 2. Characteristic peak interval of seven weight loss drugs

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    Flow chart of random forest model

    Fig. 3. Flow chart of random forest model

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    Recognition accuracy of PSO-LSSVM under different feature fusion methods for seven weight loss drugs. (a) Original spectra; (b) feature fusion spectra after Hilbert transform; (c) feature fusion spectra after SNV+DT transform; (d) feature fusion spectra after FFT low-pass filter transform; (e) feature fusion spectra after Butterworth low-pass filter transform

    Fig. 4. Recognition accuracy of PSO-LSSVM under different feature fusion methods for seven weight loss drugs. (a) Original spectra; (b) feature fusion spectra after Hilbert transform; (c) feature fusion spectra after SNV+DT transform; (d) feature fusion spectra after FFT low-pass filter transform; (e) feature fusion spectra after Butterworth low-pass filter transform

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    Confusion matrix of random forest model under different feature fusion methods for seven weight loss drugs. (a) Original spectra; (b) Hilbert transform; (c) SNV+DT transform; (d) FFT low-pass filter transform; (e) Butterworth low-pass filter transform

    Fig. 5. Confusion matrix of random forest model under different feature fusion methods for seven weight loss drugs. (a) Original spectra; (b) Hilbert transform; (c) SNV+DT transform; (d) FFT low-pass filter transform; (e) Butterworth low-pass filter transform

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    Accuracy of random forest model under different feature fusion methods for seven weight loss drugs. (a) Original spectra; (b) Hilbert transform; (c) SNV+DT transform; (d) FFT low-pass filter transform; (e) Butterworth low-pass filter transform

    Fig. 6. Accuracy of random forest model under different feature fusion methods for seven weight loss drugs. (a) Original spectra; (b) Hilbert transform; (c) SNV+DT transform; (d) FFT low-pass filter transform; (e) Butterworth low-pass filter transform

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    Classification and recognition accuracy of PSO-LSSVM and RF. (a) PSO-LSSVM; (b) RF

    Fig. 7. Classification and recognition accuracy of PSO-LSSVM and RF. (a) PSO-LSSVM; (b) RF

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    THz time-domain spectrum of sample S-1

    Fig. 8. THz time-domain spectrum of sample S-1

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    THz time-domain spectrum of sample S-2

    Fig. 9. THz time-domain spectrum of sample S-2

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    THz time-domain spectrum of sample S-3

    Fig. 10. THz time-domain spectrum of sample S-3

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    • Table 1. Statistics of weight loss drug samples

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      Table 1. Statistics of weight loss drug samples

      Serial No.BrandNumber of samples
      1Co founder of Star Coffee20
      2Real owner’s representative20
      3Star brand counter20
      4Weiyun coffee shop20
      5Star Coffee head office20
      6My name is Xiao Ji20
      7Zhao SiSi XiaoLe shop20

    • Table 2. Basic information of equipment and parameters

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      Table 2. Basic information of equipment and parameters

      NameContent
      ModelQT-TS2000
      Spectral width0.1-4 THz
      Spectral resolution8 GHz
      Dynamic range>70 dB
      Working modeReflex/Transmission
      Working power supply24 V/DC
      Equipment quality13 kg

    • Table 3. Classification result of three samples to be judged

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      Table 3. Classification result of three samples to be judged

      Sample to be determinedContaining sildenafilSildenafil free
      S-1√
      S-2√
      S-3√
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    Zhaowei Jie, Zhiyu Wang, Jifen Wang, Yijian Sun, Zhen Zhang, Wenping Li, Yiqing Kong. Terahertz Time-Domain Spectral Pattern Recognition for Weight Loss Drugs Based on Feature Fusion[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1030003

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    Paper Information

    Category: Spectroscopy

    Received: Feb. 21, 2022

    Accepted: Apr. 19, 2022

    Published Online: May. 17, 2023

    The Author Email: Wang Jifen (wangjifen58@126.com)

    DOI:10.3788/LOP220773

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology