Journals Articles Conferences News About CLP
Submit a paper Email Alert
Search
Search
Articles
Journals
News
Advanced Search
Top Searches
2D MaterialsTransformation opticsQuantum PhotonicsSmall lasersSemiconductor UV PhotonicsSupersymmetric microring laser arrays

Laser & Optoelectronics Progress, Volume. 57, Issue 20, 203001(2020)

Application of Kernel Extreme Learning Machine and Laser Induction Fluorescence Technique in Edible Oil Identification

Mengran Zhou, Jinguo Wang*, Hongping Song, Feng Hu, Wenhao Lai, and Kai Bian
Author Affiliations
  • College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui 232001, China
    • show less
    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (0)
    References (15)
    Cited By (2)
    Tools
    Paper Information
    Topics
    Figures & Tables(12)
    Oil sample detection experiment system diagram

    Fig. 1. Oil sample detection experiment system diagram

    Download full size
    Network structure of ELM

    Fig. 2. Network structure of ELM

    Download full size
    Algorithm flowchart

    Fig. 3. Algorithm flowchart

    Download full size
    Original fluorescence spectra of five oil samples

    Fig. 4. Original fluorescence spectra of five oil samples

    Download full size
    Figure of cumulative contribution rate

    Fig. 5. Figure of cumulative contribution rate

    Download full size
    Test classification accuracy of MFO algorithm

    Fig. 6. Test classification accuracy of MFO algorithm

    Download full size
    Comparison of test classification accuracy of three algorithms

    Fig. 7. Comparison of test classification accuracy of three algorithms

    Download full size
    Statistics of classification accuracy and training time of three models. (a) KELM model; (b) ELM model; (c) BP model

    Fig. 8. Statistics of classification accuracy and training time of three models. (a) KELM model; (b) ELM model; (c) BP model

    Download full size
    Comparison of average training time and classification accuracy of three models. (a) Average training time; (b) classification accuracy

    Fig. 9. Comparison of average training time and classification accuracy of three models. (a) Average training time; (b) classification accuracy

    Download full size

    • Table 1. Types and parameters of edible oils

      View table

      Table 1. Types and parameters of edible oils

      TypeBrandLevelProduction processProducing areaProduction time
      Peanut oilLuhuaAPressShandong2019-07-31
      Soybean oilFulinmenBLeachJiangsu2019-07-31
      Corn oilChangshouhuaAPressShandong2019-06-06
      Rapeseed oilTuoniaoDLeachJiangsu2019-01-25
      Sunflower seed oilLuhuaAPressInner Mongolia2018-08-24

    • Table 2. Performance comparison of three algorithms

      View table

      Table 2. Performance comparison of three algorithms

      AlgorithmModel practicaltime /sAverage recognitionrate /%
      MFO196.4395.33
      GWO195.2995.33
      PSO194.7791.16

    • Table 3. Performance comparison of three classification models

      View table

      Table 3. Performance comparison of three classification models

      ModelModel practical time /sAverage recognition rate /%Recognition rate standard deviation
      KELM0.025595.620.0157
      ELM0.002595.166.5940
      BP0.213794.770.0640
    Tools

    Get Citation

    Copy Citation Text

    Mengran Zhou, Jinguo Wang, Hongping Song, Feng Hu, Wenhao Lai, Kai Bian. Application of Kernel Extreme Learning Machine and Laser Induction Fluorescence Technique in Edible Oil Identification[J]. Laser & Optoelectronics Progress, 2020, 57(20): 203001

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Set citation alerts for article
    Save article for my favorites
    Paper Information

    Category: Spectroscopy

    Received: Dec. 4, 2019

    Accepted: Jan. 9, 2020

    Published Online: Oct. 17, 2020

    The Author Email: Jinguo Wang (wangjinguo1023@163.com)

    DOI:10.3788/LOP57.203001

    Topics

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