Laser & Optoelectronics Progress, Volume. 58, Issue 14, 1404003(2021)

Research on Spectral Recognition of Drug Mixture Based on SVM-MLP Fusion Model

Wenjie Yan1, Wenhui Lu2, and Jifen Wang1、*
Author Affiliations
  • 1School of Investigation, People's Public Security University of China, Beijing 102600, China
  • 2Henan Police College, Zhengzhou, Henan 450000, China
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    Figures & Tables(10)
    Distribution of contribution rate of each component
    Schematic diagram of support vector machine
    Accuracy comparison chart
    MLP simple structure schematic diagram
    Classification effects diagram of two models under different training ratios
    • Table 1. Composition of heroin and methamphetamine mixed sample

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      Table 1. Composition of heroin and methamphetamine mixed sample

      SamplesHeroin quality /mgAdditivesAdditive quality /mgHeroin mass score
      10.5Caffeine4.50.1
      20.5Glucose4.50.1
      30.5Phenacetin4.50.1
      40.5Starch4.50.1
      50.5Sucrose4.50.1
      61.0Caffeine4.00.2
      71.0Glucose4.00.2
      81.0Phenacetin4.00.2
      91.0Starch4.00.2
      101.0Sucrose4.00.2
      111.5Caffeine3.50.3
      121.5Glucose3.50.3
      131.5Phenacetin3.50.3
      141.5Starch3.50.3
      151.5Sucrose3.50.3
      162.0Caffeine3.00.4
      172.0Glucose3.00.4
      SamplesHeroin quality /mgAdditivesAdditive quality /mgHeroin mass score
      182.0Phenacetin3.00.4
      192.0Starch3.00.4
      202.0Sucrose3.00.4
      212.5Caffeine2.50.5
      222.5Glucose2.50.5
      232.5Phenacetin2.50.5
      242.5Starch2.50.5
      252.5Sucrose2.50.5
      263.0Caffeine2.00.6
      273.0Glucose2.00.6
      283.0Phenacetin2.00.6
      293.0Starch2.00.6
      303.0Sucrose2.00.6
      313.5Caffeine1.50.7
      323.5Glucose1.50.7
      333.5Phenacetin1.50.7
      343.5Starch1.50.7
      353.5Sucrose1.50.7
      364.0Caffeine1.00.8
      374.0Glucose1.00.8
      384.0Phenacetin1.00.8
      394.0Starch1.00.8
      404.0Sucrose1.00.8
      414.5Caffeine0.50.9
      424.5Glucose0.50.9
      434.5Phenacetin0.50.9
      444.5Starch0.50.9
      454.5Sucrose0.50.9
      460.5Caffeine4.50.1
      470.5Glucose4.50.1
      480.5Paracetamol4.50.1
      490.5Phenacetin4.50.1
      500.5Starch4.50.1
      511.0Caffeine4.00.2
      521.0Glucose4.00.2
      531.0Paracetamol4.00.2
      541.0Phenacetin4.00.2
      551.0Starch4.00.2
      561.5Caffeine3.50.3
      571.5Glucose3.50.3
      581.5Paracetamol3.50.3
      SamplesHeroin quality /mgAdditivesAdditive quality /mgHeroin mass score
      591.5Phenacetin3.50.3
      601.5Starch3.50.3
      612.0Caffeine3.00.4
      622.0Glucose3.00.4
      632.0Paracetamol3.00.4
      642.0Phenacetin3.00.4
      652.0Starch3.00.4
      662.5Caffeine2.50.5
      672.5Glucose2.50.5
      682.5Paracetamol2.50.5
      692.5Phenacetin2.50.5
      702.5Starch2.50.5
      713.0Caffeine2.00.6
      723.0Glucose2.00.6
      733.0Paracetamol2.00.6
      743.0Phenacetin2.00.6
      753.0Starch2.00.6
      763.5Caffeine1.50.7
      773.5Glucose1.50.7
      783.5Paracetamol1.50.7
      793.5Phenacetin1.50.7
      803.5Starch1.50.7
      814.0Caffeine1.00.8
      824.0Glucose1.00.8
      834.0Paracetamol1.00.8
      844.0Phenacetin1.00.8
      854.0Starch1.00.8
      864.5Caffeine0.50.9
      874.5Glucose0.50.9
      884.5Paracetamol0.50.9
      894.5Phenacetin0.50.9
      904.5Starch0.50.9
    • Table 2. Principal component analysis scores of partial data

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      Table 2. Principal component analysis scores of partial data

      Sample namePCA1PCA2PCA3PCA4PCA5PCA6PCA7PCA8
      Heroin 10-1.734991.05294-0.244040.546951.238301.86434-0.850630.30997
      Heroin 20-1.387421.08753-0.493410.631361.240091.91988-0.823600.01083
      Heroin 30-1.361641.31395-0.466240.024190.534901.559770.996590.82939
      Heroin 40-1.265951.30657-0.48177-0.022750.570381.412930.986880.84195
      Heroin 50-1.426311.11977-0.43161-0.064170.419471.822240.551250.68340
      Methamphetamine 10-1.828490.65761-0.191630.392331.023522.11987-1.598720.70246
      Methamphetamine 20-1.62927-0.30444-0.28452-0.265240.565801.76207-1.451150.99943
      Methamphetamine 30-1.61720-0.11446-0.20642-0.041460.626001.77511-0.812980.14576
      Methamphetamine 40-1.37645-0.47555-0.20890-0.025130.679961.46555-0.434670.06310
      Methamphetamine 50-1.27684-0.60591-0.35037-0.091530.580281.56357-0.939700.27797
      Sample namePCA9PCA10PCA11PCA12PCA13PCA14PCA15
      Heroin 100.668740.048750.146640.230640.50656-0.152180.11773
      Heroin 200.96444-0.503030.214010.478141.49474-0.32094-0.05898
      Heroin 301.406140.641590.527900.61595-0.820690.88689-0.50806
      Heroin 401.352710.368990.558410.43866-0.341790.78488-0.37276
      Heroin 501.194630.667210.215361.00858-0.250170.69153-0.39813
      Methamphetamine 100.188010.11184-0.28867-0.68909-0.52643-0.590250.38801
      Methamphetamine 20-0.120650.07006-0.46789-0.04990-0.34572-0.955160.10060
      Methamphetamine 300.462650.17059-0.36223-0.22779-0.59669-0.41832-0.08377
      Methamphetamine 400.582640.26791-0.275220.823190.05651-0.76757-0.06932
      Methamphetamine 500.323920.04199-0.441180.035760.18528-0.673030.35902
    • Table 3. Accuracy of sample classification under four kernel functions unit: %

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      Table 3. Accuracy of sample classification under four kernel functions unit: %

      FunctionComprehensive classification accuracyHeroin classification accuracyClassification accuracy of methamphetamine
      RBF-SVM48.997.80
      Polynomial-SVM47.895.60
      Sigmoid-SVM35.671.20
      Linear-SVM48.997.80
    • Table 4. Classification effect with polynomial SVM model with different Gamma values

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      Table 4. Classification effect with polynomial SVM model with different Gamma values

      GamaNumber of heroin samplesNumber of correctly classified samplesClassification accuracy /%
      0.01453986.7
      0.05453986.7
      0.10454395.6
      1.00454395.6
      10.00454395.6
      100.00454395.6
    • Table 5. Average classification accuracy under different training ratios unit: %

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      Table 5. Average classification accuracy under different training ratios unit: %

      Training sample ratioConstitutionAdditives
      5084.996.5
      6080.491.5
      7077.891.9
      8074.285.9
      9071.188.4
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    Wenjie Yan, Wenhui Lu, Jifen Wang. Research on Spectral Recognition of Drug Mixture Based on SVM-MLP Fusion Model[J]. Laser & Optoelectronics Progress, 2021, 58(14): 1404003

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    Paper Information

    Category: Detectors

    Received: Oct. 20, 2020

    Accepted: Nov. 18, 2020

    Published Online: Jul. 14, 2021

    The Author Email: Jifen Wang (1450201565@qq.com)

    DOI:10.3788/LOP202158.1404003

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