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Laser & Optoelectronics Progress, Volume. 59, Issue 16, 1630004(2022)

Rapid Identification and Classification of Cannabis Oil Based on Data Fusion of Spectroscopy and Chromatography

Jiarui Li1, Jifen Wang1、*, Linyuan Fan1, and Xuejun Shi2
Author Affiliations
  • 1School of Investigation, People’s Public Security University of China, Beijing 100038, China
  • 2Forensic Expertise Center of Beijing Customs Anti-Smuggling Bureau, Beijing 100000, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (10)
    Equations (10)
    References (30)
    Cited By (2)
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    Figures & Tables(10)
    Infrared spectra of cannabis oil

    Fig. 1. Infrared spectra of cannabis oil

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    Infrared spectra of all samples

    Fig. 2. Infrared spectra of all samples

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    FDA confusion matrix. (a) Single model; (b) fusion model

    Fig. 3. FDA confusion matrix. (a) Single model; (b) fusion model

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    Spatial classification detail of cannabis oil

    Fig. 4. Spatial classification detail of cannabis oil

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    Eigenvalue importance

    Fig. 5. Eigenvalue importance

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    K value selection error rate

    Fig. 6. K value selection error rate

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    KNN confusion matrix. (a) Single model; (b) fusion model

    Fig. 7. KNN confusion matrix. (a) Single model; (b) fusion model

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    • Table 1. Information of cannabis oil samples with different species

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      Table 1. Information of cannabis oil samples with different species

      NameIngredient
      IMMUNO BOOSTERMCT Oil，Echinacea Extract，CBD，THC
      NRG BOOSTERMCT Oil，Ashwagandha，Maca Root，Citral-Terpene Blend，CBD，THC
      RELAXMCT Oil，Dang Quai Extract，Red Clover Extract，CBD，THC
      WELLNESSMCT Oil，Passion Flower，Myrcene-Terpene Blend，CBD，THC

    • Table 2. Parameter results of FDA model

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      Table 2. Parameter results of FDA model

      TypeData setSensitivitySpecificityPrecisionAccuracy
      FTIRIMMUNO BOOSTER0.8090.9120.8530.818
      NRG BOOSTER0.8090.9120.853
      RELAX0.8560.6690.729
      WELLNESS0.8330.8620.836
      FTIR+GCIMMUNO BOOSTER0.9831.0000.9920.987
      NRG BOOSTER0.9831.0000.992
      RELAX0.9831.0000.992
      WELLNESS1.0000.9510.974

    • Table 3. Parameter results of KNN model

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      Table 3. Parameter results of KNN model

      TypeParameterTraining setTest set
      IMMUNO BOOSTERNRG BOOSTERRELAXWELLNESSIMMUNO BOOSTERNRG BOOSTERRELAXWELLNESS
      FTIRSensitivity0.8220.8070.8220.8110.8400.8240.8490.849
      Specificity0.7580.9470.7640.8390.8540.9320.7890.789
      Precision0.7860.8640.7900.8240.8470.8710.8160.816
      Accuracy0.7310.9570.7410.8460.8570.9410.7690.842
      FTIR+GCSensitivity1.0001.0001.0001.0001.0001.0001.0001.000
      Specificity1.0001.0001.0001.0001.0001.0001.0001.000
      Precision1.0001.0001.0001.0001.0001.0001.0001.000
      Accuracy1.0001.0001.0001.0001.0001.0001.0001.000
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    Jiarui Li, Jifen Wang, Linyuan Fan, Xuejun Shi. Rapid Identification and Classification of Cannabis Oil Based on Data Fusion of Spectroscopy and Chromatography[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1630004

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    Paper Information

    Category: Spectroscopy

    Received: May. 26, 2021

    Accepted: Jul. 13, 2021

    Published Online: Jul. 22, 2022

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

    DOI:10.3788/LOP202259.1630004

    Topics

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