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Laser & Optoelectronics Progress, Volume. 55, Issue 6, 063003(2018)

Mid-Infrared Spectroscopy Analysis Combined with Support Vector Machine for Rapid Discrimination of Botanical Origin of Honey

Tianyang Xu1,2,3、1; 2; 3; , Juan Yang1、1; , Xiaorong Sun4,5、4; 5; , Cuiling Liu4,5、4; 5; , Yi Li1,2,3、1; 2; 3; , Jinhui Zhou1,2,3、1; 2; 3; , and Lanzhen Chen1,2、1; 2; 3*;
Author Affiliations
  • 1 Institute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China
  • 2 Key Laboratory of Bee Products for Quality and Safety Control, Ministry of Agriculture, Beijing 100093, China
  • 3 Laboratory of Risk Assessment for Quality and Safety of Bee Products, Ministry of Agriculture, Beijing 100093, China
  • 4 School of Computer and Information Engineer, Beijing Technology and Business University, Beijing 100048, China
  • 5 Beijing Key Laboratory of Large Data Technology for Food Safety, Beijing 100048, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (21)
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    Figures & Tables(8)
    Mid-infrared spectra of different honey samples

    Fig. 1. Mid-infrared spectra of different honey samples

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    Actual and predicted classifications of test set using SVM algorithm when recognition rate is 100% and 20-dimensional feature data are input

    Fig. 2. Actual and predicted classifications of test set using SVM algorithm when recognition rate is 100% and 20-dimensional feature data are input

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    Actual and predicted classifications of test set using SVM algorithm when recognition rate is 99.23% and 20-dimensional feature data are input

    Fig. 3. Actual and predicted classifications of test set using SVM algorithm when recognition rate is 99.23% and 20-dimensional feature data are input

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    Actual and predicted classifications of test set using LSSVM algorithm when recognition rate is 100% and 20-dimensional feature data are input

    Fig. 4. Actual and predicted classifications of test set using LSSVM algorithm when recognition rate is 100% and 20-dimensional feature data are input

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    Actual and prediction classifications of test set using LSSVM algorithm when recognition rate is 97.69% and 20-dimensional feature data are input

    Fig. 5. Actual and prediction classifications of test set using LSSVM algorithm when recognition rate is 97.69% and 20-dimensional feature data are input

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    Transformation graph of support vector from 1-dimensional space to 2-dimensional space

    Fig. 6. Transformation graph of support vector from 1-dimensional space to 2-dimensional space

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    • Table 1. Cumulative variance contribution rate of different principal components

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      Table 1. Cumulative variance contribution rate of different principal components

      Dimension1235101520
      Contribution rate /%46.53982.27291.90896.05499.18899.12999.856

    • Table 2. Average discrimination rate of different dimension feature data from linear SVM and LSSVM classifier models%

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      Table 2. Average discrimination rate of different dimension feature data from linear SVM and LSSVM classifier models%

      Method5-dimension10-dimension15-dimension20-dimension
      PCA-SVM84.5487.3896.3897.77
      PCA-LSSVM87.3192.5496.1597.69
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    Tianyang Xu, Juan Yang, Xiaorong Sun, Cuiling Liu, Yi Li, Jinhui Zhou, Lanzhen Chen. Mid-Infrared Spectroscopy Analysis Combined with Support Vector Machine for Rapid Discrimination of Botanical Origin of Honey[J]. Laser & Optoelectronics Progress, 2018, 55(6): 063003

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    Paper Information

    Category: Spectroscopy

    Received: Nov. 23, 2017

    Accepted: --

    Published Online: Sep. 11, 2018

    The Author Email: Lanzhen Chen ( chenlanzhen2005@126.com)

    DOI:10.3788/LOP55.063003

    Topics

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