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Chinese Journal of Lasers, Volume. 48, Issue 21, 2111001(2021)

Quantitative Analysis of Laser-Induced Breakdown Spectroscopy Iron Ore Slurry Based on Cyclic Variable Filtering and Nonlinear Partial Least Squares

Dong Shang1,2,3,4, Lanxian Sun1,2,3、*, Lifeng Qi1,2,3, Yuanming Xie1,2,3,5, and Tong Chen1,2,3,4
Author Affiliations
  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 2Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China
  • 3Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110169, China
  • 4University of Chinese Academy of Sciences, Beijing 100049, China
  • 5Shenyang University of Chemical Technology, Shenyang, Liaoning 110142, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
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    References (22)
    Cited By (10)
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    Figures & Tables(13)
    LIBS iron ore slurry system. (a) Schematic of the system; (b) photo of the experimental device

    Fig. 1. LIBS iron ore slurry system. (a) Schematic of the system; (b) photo of the experimental device

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    Iron ore concentrate slurry samples

    Fig. 2. Iron ore concentrate slurry samples

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    Plasma spectrum example of iron ore concentrate slurry sample

    Fig. 3. Plasma spectrum example of iron ore concentrate slurry sample

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    Flow chart of the nonlinear PLS algorithm based on cyclic variable filtering

    Fig. 4. Flow chart of the nonlinear PLS algorithm based on cyclic variable filtering

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    RMSE of validation set as a function of number of components in the traditional PLS model

    Fig. 5. RMSE of validation set as a function of number of components in the traditional PLS model

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    Analysis results of Fe mass fraction in the traditional PLS model

    Fig. 6. Analysis results of Fe mass fraction in the traditional PLS model

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    RMSE of validation set as function of number of components in the nonlinearity PLS model

    Fig. 7. RMSE of validation set as function of number of components in the nonlinearity PLS model

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    Analysis results of Fe mass fraction in the nonlinear PLS model

    Fig. 8. Analysis results of Fe mass fraction in the nonlinear PLS model

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    RMSE of validation set as function of variable selection times in the nonlinear PLS model based on cyclic variable filtering

    Fig. 9. RMSE of validation set as function of variable selection times in the nonlinear PLS model based on cyclic variable filtering

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    RMSE of validation set after filtering variables as a function of number of components

    Fig. 10. RMSE of validation set after filtering variables as a function of number of components

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    Analysis results of Fe mass fraction in the nonlinear PLS model based on cyclic variable filtering

    Fig. 11. Analysis results of Fe mass fraction in the nonlinear PLS model based on cyclic variable filtering

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    • Table 1. Selected analytical lines

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      Table 1. Selected analytical lines

      Serial No.Emission lines /nmEi /eVEj /eV
      1Fe Ⅰ 252.284904.913304
      2Fe Ⅰ 271.902704.558830
      3Fe Ⅰ 322.77962.4255976.265889
      4Fe Ⅱ 259.939604.768628
      5Fe Ⅱ 261.18740.0477114.793558
      6Fe Ⅱ 261.38250.1069584.849263
      7Fe Ⅱ 273.95480.9863985.511082
      8Fe Ⅱ 274.64841.0763125.589570
      9Fe Ⅱ 274.93211.0405385.549138
      10Fe Ⅱ 275.57370.9863985.484502
      11Si Ⅰ 250.68970.0095624.954129
      12Si Ⅰ 251.92020.0095624.929980
      13Si Ⅰ 252.41080.0095624.920417
      14Si Ⅰ 252.85090.0276704.929980
      15Si Ⅰ 288.15770.7810115.082689

    • Table 2. Comparison of the results of the three algorithms

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      Table 2. Comparison of the results of the three algorithms

      AlgorithmRMSE /%R2
      PLS1.150.51
      Nonlinear PLS0.850.73
      Nonlinear PLS based on cyclic variable filtering0.700.86
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    Dong Shang, Lanxian Sun, Lifeng Qi, Yuanming Xie, Tong Chen. Quantitative Analysis of Laser-Induced Breakdown Spectroscopy Iron Ore Slurry Based on Cyclic Variable Filtering and Nonlinear Partial Least Squares[J]. Chinese Journal of Lasers, 2021, 48(21): 2111001

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    Paper Information

    Category: spectroscopy

    Received: Mar. 15, 2021

    Accepted: Apr. 19, 2021

    Published Online: Nov. 2, 2021

    The Author Email: Sun Lanxian (sunlanxiang@sia.cn)

    DOI:10.3788/CJL202148.2111001

    Topics

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