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Chinese Journal of Lasers, Volume. 48, Issue 16, 1611002(2021)

High-accuracy Quantitatively Analysis of Iron Content in Mineral Based on Laser-Induced Breakdown Spectroscopy

Suling Qiu1, An Li1, Xianshuang Wang1, Denan Kong1, Xiao Ma1, Yage He1, Yunsong Yin1, Yufei Liu2, Lijie Shi1, and Ruibin Liu1、*
Author Affiliations
  • 1School of Physics, Beijing Institute of Technology, Beijing 100081, China
  • 2Bright-ray Laser Technology (Changzhou) Co., Ltd., Changzhou, Jiangsu 213000, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (22)
    Cited By (13)
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    Experimental apparatus and optical path

    Fig. 1. Experimental apparatus and optical path

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    Performance results of anomalous spectrum. (a) Comparison of abnormal and normal spectra; (b) spectral line fluctuation before and after removal of abnormal spectrum

    Fig. 2. Performance results of anomalous spectrum. (a) Comparison of abnormal and normal spectra; (b) spectral line fluctuation before and after removal of abnormal spectrum

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    Partial original spectrograms of three types of ores. (a) GBW07820; (b) ZBK332; (c) YSBC28766-2008

    Fig. 3. Partial original spectrograms of three types of ores. (a) GBW07820; (b) ZBK332; (c) YSBC28766-2008

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    Explanation rates and scores of different principal components. (a) Interpretation rate of each principal component and cumulative interpretation rate of each principal component; (b) 3D scores of first three principal components

    Fig. 4. Explanation rates and scores of different principal components. (a) Interpretation rate of each principal component and cumulative interpretation rate of each principal component; (b) 3D scores of first three principal components

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    Calibration curves of full spectrum PLS. (a) 35 kinds of ore species; (b) 14 kinds of iron ore; (c) 12 kinds of manganese ores; (d) 9 kinds of chromium ores

    Fig. 5. Calibration curves of full spectrum PLS. (a) 35 kinds of ore species; (b) 14 kinds of iron ore; (c) 12 kinds of manganese ores; (d) 9 kinds of chromium ores

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    Relationship between RMSEP and correlation coefficient C

    Fig. 6. Relationship between RMSEP and correlation coefficient C

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    Relationship between RMSEP and modeling set determination coefficient with number of principal components

    Fig. 7. Relationship between RMSEP and modeling set determination coefficient with number of principal components

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    Calibration curves for R-PLS. (a) 14 kinds of iron ore; (b) 12 kinds of manganese ore; (c) 9 kinds of chromium ore; (d) all ores

    Fig. 8. Calibration curves for R-PLS. (a) 14 kinds of iron ore; (b) 12 kinds of manganese ore; (c) 9 kinds of chromium ore; (d) all ores

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    • Table 1. Sample type and Fe content of iron ore, manganese ore, and chromium ore

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      Table 1. Sample type and Fe content of iron ore, manganese ore, and chromium ore

      NumberSample nameFe /%CategoryNumberSample nameFe /%Category
      1ZBK32362.63000Iron ore19GBW072662.07000Manganese ore
      2YSBC28767-200863.0700020ZBK3338.05000
      3ZBK32266.5200021GBW072651.40000
      4YSBC28766-200862.6500022YSBC26704-20136.03000
      5ZBK39368.2900023GSB03-2590-20102.75000
      6ZBK39265.7100024QD 09-9612.00000
      7ZBK32146.9300025YSBC26701-201310.50000
      8YSBC28768-200866.1800026Mn-k2-169.66000
      9ZBK39164.4200027GBW078198.28090Chromium ore
      10GBW0782964.4900028ZBK44112.90000
      11YSBC28769-200867.8400029ZBK4409.76000
      12GBW(E)07008764.8200030GBW0782010.72880
      13GBW(E)07008563.9300031k3-411.19000
      14YSB46701a64.3700032k3-19.71000
      15YSBC16701-20073.65000Manganese ore33GSBD33001-949.53000
      16GBW072622.2400034GBW078187.39266
      17ZBK3348.1000035GSBD33001.1-949.71000
      18ZBK3326.71000

    • Table 2. Classification results of three different kinds of ore by support vector machine model

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      Table 2. Classification results of three different kinds of ore by support vector machine model

      CategorySpectrum numberTraining /%Test /%
      Iron oreManganese oreChrome oreIron oreManganese oreChrome ore
      Iron ore1400100100
      Manganese ore1200100100
      Chrome ore900100100

    • Table 3. Classification and comparison of quantitative analysis results before and after R-PLS

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      Table 3. Classification and comparison of quantitative analysis results before and after R-PLS

      ParameterPLSClassification+PLSClassification+R-PLS
      All oreIron oreManganese oreChrome oreIron oreManganese oreChrome ore
      R20.9880.9900.9980.9800.9750.9550.952
      RMSEP /%3.2273.3383.9950.3770.9750.4180.123
      ARE /%31.753.4894.282.931.466.721.09
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    Suling Qiu, An Li, Xianshuang Wang, Denan Kong, Xiao Ma, Yage He, Yunsong Yin, Yufei Liu, Lijie Shi, Ruibin Liu. High-accuracy Quantitatively Analysis of Iron Content in Mineral Based on Laser-Induced Breakdown Spectroscopy[J]. Chinese Journal of Lasers, 2021, 48(16): 1611002

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    Paper Information

    Category: spectroscopy

    Received: Jan. 5, 2021

    Accepted: Feb. 18, 2021

    Published Online: Aug. 9, 2021

    The Author Email: Ruibin Liu (liusir@bit.edu.cn)

    DOI:10.3788/CJL202148.1611002

    Topics

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