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Rapid, Non-destructive Identification of Iron Ores-Based Random Forest (RF) Using Visible and Near-Infrared Spectroscopy

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Abstract

Visible and near-infrared (Vis–NIR) spectroscopy technology can better meet the needs of identification of mineral differences in iron ores by using a large amount of chemical bond vibration information contained in the spectra. In this paper, the original Vis–NIR spectra data of six kinds of iron ore are dimensionally reduced by the principal component analysis (PCA) algorithm, and the influence of sample noise and outliers is eliminated at the same time. After processing, the sum of the contribution rates of the first three principal components of the reflectance spectra reaches 99.27 pct, and the eigenvalue inclusion rate of the principal component analysis data is high, which has a good data processing effect. Then, based on the random forest (RF) algorithm, the spectra data after PCA are classified and predicted. After multiple authentications, the identification accuracy of the algorithm can reach more than 95 pct on average. Finally, the receiver operating characteristic (ROC) curve is used to characterize the prediction success rate of the recognition model and its accuracy for the recognition of single iron ore.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. B.P. Yur’ev and V.A. Dudko: in Materials Science Forum, vol. 1052, Trans Tech Publications, 2022, pp. 262–66.

  2. M. Iljana, T. Paananen, O. Mattila, M. Kondrakov, and T. Fabritius: Metals (Basel), 2022, vol. 12, p. 302.

    Article  CAS  Google Scholar 

  3. Z. He, X. Hu, M. Lan, J. Liu, G. Cheng, X. Xue, and K. Chou: Minerals, 2021, vol. 11, p. 418.

    Article  CAS  Google Scholar 

  4. M. Ostadrahimi, S. Farrokhpay, K. Gharibi, and A. Dehghani: Metall. Mater. Trans. B, 2020, vol. 51B, pp. 505–09.

    Article  Google Scholar 

  5. Y. Cheng, Z. Shen, Y. Yang, Q. Liang, and H. Liu: Metall. Mater. Trans. B, 2022, vol. 53B, pp. 807–15.

    Article  Google Scholar 

  6. K.M. Novack and M.M.C. Elias: ISIJ Int., 2013, vol. 53, pp. 1732–38.

    Article  Google Scholar 

  7. M. Ostadrahimi, S. Farrokhpay, A. Farahani, K. Gharibi, and A. Dehghani: Metall. Mater. Trans. B, 2021, vol. 52B, pp. 3576–82.

    Article  Google Scholar 

  8. T. Angelopoulou, A. Balafoutis, G. Zalidis, and D. Bochtis: Sustainability, 2020, vol. 12, p. 443.

    Article  CAS  Google Scholar 

  9. P. Heidrich, E. Lambert, A. Kessler, M. Gerstenlauer, H. Heißler, T. Weber, J. Zipfel, M. Wüst, and R. Stamminger: J. Near Infrared Spectrosc., 2019, vol. 27, pp. 183–90.

    Article  CAS  Google Scholar 

  10. Z. Yang, L. Cai, L. Han, X. Fan, and X. Liu: J. Near Infrared Spectrosc., 2021, vol. 29, pp. 313–20.

    Article  CAS  Google Scholar 

  11. S. Xu, Y. Zhao, M. Wang, and X. Shi: Soil Sci. Soc. Am. J., 2018, vol. 82, pp. 1497–511.

    Article  CAS  Google Scholar 

  12. D. Xiao, B.T. Le, and T.T.L. Ha: Spectrochim. Acta Part A, 2021, vol. 248, 119168.

    Article  CAS  Google Scholar 

  13. M. Vitelli, H. Mehrtash, A. Assatory, S. Tabtabaei, R.L. Legge, and A.R. Rajabzadeh: Powder Technol., 2021, vol. 381, pp. 620–31.

    Article  CAS  Google Scholar 

  14. L. Wang, Z. Huang, and R. Wang: Infrared Phys. Technol., 2021, vol. 115, 103731.

    Article  CAS  Google Scholar 

  15. M. Lei, X. Yu, M. Li, and W. Zhu: Infrared Phys. Technol., 2018, vol. 92, pp. 177–82.

    Article  CAS  Google Scholar 

  16. X. Zhang, L. Chen, Y. Sun, Y. Bai, B. Huang, and K. Chen: Spectrochim Acta Part A, 2018, vol. 193, pp. 133–40.

    Article  CAS  Google Scholar 

  17. S. Wu, M. He, M. Yang, B. Zhang, F. Wang, and Q. Li: Crystals, 2021, vol. 11, p. 1130.

    Article  CAS  Google Scholar 

  18. J.A. Coblinski, A.V. Inda, J.A.M. Dematte, A.C. Dotto, A. Gholizadeh, and E. Giasson: CATENA, 2021, vol. 203, 105334.

    Article  CAS  Google Scholar 

  19. L.M. Kustov, V.Y. Borovkov, and V.B. Kazansky: J. Catal., 1981, vol. 72, pp. 149–59.

    Article  CAS  Google Scholar 

  20. M. Fonteyne, J. Arruabarrena, J. de Beer, T. Hellings, M. Van Den Kerkhof, T. Burggraeve, A. Vervaet, C. Remon, and J.P. De Beer: J. Pharm. Biomed. Anal., 2014, vol. 100, pp. 21–27.

    Article  CAS  PubMed  Google Scholar 

  21. T. Shi, Y. Chen, Y. Liu, and G. Wu: J. Hazard. Mater., 2014, vol. 265, pp. 166–76.

    Article  CAS  PubMed  Google Scholar 

  22. J. Liu, J. Han, J. Xie, H. Wang, W. Tong, and Y. Ba: Spectrochim. Acta Part A, 2020, vol. 226, 117639.

    Article  CAS  Google Scholar 

  23. R.S. Fard and H.R. Matinfar: J. Geosci., 2016, vol. 9, p. 745.

    Google Scholar 

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Acknowledgments

The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of China (52174291), the Central Universities Foundation of China (06500170), the Beijing New-star Plan of Science and Technology (Z211100002121115), the Guangdong Basic & Applied Basic Research Fund Joint Regional Funds-Youth Foundation Projects (2020A1515111008), and the China Postdoctoral Science Foundation (2021M690369).

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Authors and Affiliations

  1. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, P.R. China

    Zhengjian Liu, Zhen Li, Jianliang Zhang & Longyao Zhang

  2. School of Intelligence Science and Technology, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, P.R. China

    Yaozu Wang

  3. Institute of Artificial Intelligence, University of Science and Technology Beijing, 30th Xueyuan Road, Haidian District, Beijing, 100083, P.R. China

    Yaozu Wang

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  1. Zhengjian Liu
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  2. Zhen Li
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Correspondence to Jianliang Zhang or Yaozu Wang.

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Liu, Z., Li, Z., Zhang, J. et al. Rapid, Non-destructive Identification of Iron Ores-Based Random Forest (RF) Using Visible and Near-Infrared Spectroscopy. Metall Mater Trans B (2024). https://doi.org/10.1007/s11663-024-03119-w

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