Journal of Applied Spectroscopy

, Volume 86, Issue 4, pp 655–660 | Cite as

A Novel Two-Step Spectral Recovery Framework for Coal Quality Assessment by Near-Infrared Spectroscopy

  • M. Lei
  • X. Yu
  • M. LiEmail author
  • Zh. Rao
  • X. Dai

Near-infrared spectroscopy (NIRS) is an effective and efficient technique for evaluating coal quality. The original spectra might be contaminated by scattering interference and random noise. We propose a novel artifact removal framework to recover the buried information and to overcome limitations of currently available preprocessing techniques, such as the multiplicative scatter correction (MSC), as well as a smoothing process. The two-step framework is mainly constructed by MSC and Savitzky–Golay convolution (S-SGC) . Moreover, a particle swarm optimization (PSO) algorithm is used to search the optimal parameters within the framework. In addition, the spectra are collected from coal samples with different particle sizes (i.e., 0.2 and 3 mm), which may carry different characteristics and interfering information. We have analyzed seven kinds of coal properties, such as moisture (%), ash (%), volatile matter (%), and heating value (MJ/kg) via partial least square regression (PLSR) models in order to verify the effectiveness of the proposed method. The results show that the proposed two-step method provides superior performances for zooming in the spectral characteristic peaks and filtering the random noise simultaneously, which mainly benefits from the appropriate combination of MSC and S-SGC.


near-infrared spectroscopy coal quality analysis multiplicative scatter correction smoothing processing fusion mode 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. M. Andrés and M. T. Bona, Talanta, 70, No. 4, 711–719 (2006)CrossRefGoogle Scholar
  2. 2.
    Q. Zhu, IEA Clean Coal Centre, 4 (2014).Google Scholar
  3. 3.
    Y. Zhao, L. Zhang, S. X. Zhao, Y. F. Li, Y. Gong, L. Dong, W. G. Ma, W. B. Yin, S. C. Yao, J. D. Lu, L. T. Xiao, and S. T. Jia, Front. Phys.-Beijing, 11, No. 6, 114211 (2016).ADSCrossRefGoogle Scholar
  4. 4.
    Y. S. Wang, M. Yang, G. Wei, R. Hu, Z. Luo, and G. Li, Sensor. Actuat. B: Chem., 193, No. 3, 723–729 (2014).CrossRefGoogle Scholar
  5. 5.
    M. Lei, M. Li, and Y. Shi, CIESC J., 63, No. 12, 3991–3995 (2012).Google Scholar
  6. 6.
    M. T. Bona and J. M. Andrés, Talanta, 72, 1423–1431 (2007).CrossRefGoogle Scholar
  7. 7.
    W. K. Dong, J. M. Lee, and J. S. Kim, Korean. J. Chem. Eng., 26, No. 2, 489–495 (2009).CrossRefGoogle Scholar
  8. 8.
    J. M. Andrés and M. T. Bona, Anal. Chim. Acta, 535, Nos. 1–2, 123–132 (2005).CrossRefGoogle Scholar
  9. 9.
    H. Jia, Q. Fu, C. J. Han, D. B. Zou, and W. L. Chen, Spectrosc. Spectr. Anal., 32, No. 11, 3010 (2012).Google Scholar
  10. 10.
    P. Sirisomboon and J. Nawayon, J. Near. Infrared Spectrosc., 24, No. 2, 191–198 (2016).ADSCrossRefGoogle Scholar
  11. 11.
    R. Tang, K. Chen, C. Jiang, and Ch. Li, J. Appl. Spectrosk., 84, No. 4, 627–632 (2017).ADSCrossRefGoogle Scholar
  12. 12.
    H. Sato, M. Kiguchia, F. Kawaguchib, and A. Makiac, Neuroimage, 21, No. 4, 1554–1562 (2004).CrossRefGoogle Scholar
  13. 13.
    Å. Rinnan, F. Berg, and S. Engelsen, Trac-Trend. Anal. Chem., 28, No. 10, 1201–1222 (2009).CrossRefGoogle Scholar
  14. 14.
    X. Yang and F. Wang, Adv. Mater. Res., 898, 831–834 (2014).CrossRefGoogle Scholar
  15. 15.
    D. B. Kovačević, J. G. Kljusurić, P. Putnik, T. Vukušić, Z. Herceg, and V. Dragović-Uzelac, Food Chem., 212, 323–331 (2016).CrossRefGoogle Scholar
  16. 16.
    Z. D. Lin, Y. B. Wang, R. J. Wang, L. S. Wang, C. P. Lu, Z. Y. Zhang, L. T. Song, and Y. Liu, J. Appl. Spectrosc., 84, No. 3, 529–534 (2017).ADSCrossRefGoogle Scholar
  17. 17.
    P. Geladi, D. Macdougall, and H. Martens, Appl. Spectrosc., 39, No. 3, 491–500 (1985).ADSCrossRefGoogle Scholar
  18. 18.
    P. A. Gorry, Anal. Chem., 62, No. 6, 570–573 (1990).CrossRefGoogle Scholar
  19. 19.
    Y. Shao, R. S. Lunetta, B. Wheeler, J. S. Liames, and J. B. Campbell, Remote Sens. Environ., 174, 258–265 (2016).ADSCrossRefGoogle Scholar
  20. 20.
    P. Kubelka and F. Munk, Zeit. für Tekn. Physik, 12, 593 (1931).Google Scholar
  21. 21.
    H. Martens, J. P. Nielsen, and S. B. Engelsen, Anal. Chem., 75, No. 3, 394–404 (2003).CrossRefGoogle Scholar
  22. 22.
    H. Q. Yang, B.Y. Kuang, and A. M. Mouazen, Key Eng. Mater., 10, No. 3, 467–469 (2011).Google Scholar
  23. 23.
    M. Lei, M. Li, N. Wu, and Y. N. Li, Spectrosc. Spectr. Anal., 33, No. 1, 65–68 (2013).ADSGoogle Scholar
  24. 24.
    H. Chen, T. Pan, J. Chen, and Q. Lu, Chemometr. Intell. Lab., 107, No. 1, 139–146 (2011).CrossRefGoogle Scholar
  25. 25.
    H. Chen, Q. Song, G. Tang, Q. Feng, and L. Lin, ISRN Spectrosc., 2013, 1–9 (2013).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouChina
  2. 2.University of British ColumbiaVancouverCanada

Personalised recommendations