OSA's Digital Library

Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 22, Iss. 4 — Feb. 24, 2014
  • pp: 3895–3901

Accuracy improvement on polymer identification using laser-induced breakdown spectroscopy with adjusting spectral weightings

Y. Yu, L. B. Guo, Z. Q. Hao, X. Y. Li, M. Shen, Q. D. Zeng, K. H. Li, X. Y. Zeng, Y. F. Lu, and Z. Ren  »View Author Affiliations

Optics Express, Vol. 22, Issue 4, pp. 3895-3901 (2014)

View Full Text Article

Enhanced HTML    Acrobat PDF (1345 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



A new approach to polymer identification by laser-induced breakdown spectroscopy (LIBS) with adjusting spectral weightings (ASW) was developed in this work aiming at improving the identification accuracy. This approach has been achieved through increasing the intensities of specific characteristic spectral lines which are important to polymer identification but difficult to be excited. Using the ASW method, the identification accuracies of all 11 polymers were increased to nearly 100%, while the accuracies of PE, PU, PP and PC were only 98%, 74%, 90% and 98%, respectively, without using the ASW method.

© 2014 Optical Society of America

OCIS Codes
(140.3440) Lasers and laser optics : Laser-induced breakdown
(160.5470) Materials : Polymers
(350.5400) Other areas of optics : Plasmas
(300.6365) Spectroscopy : Spectroscopy, laser induced breakdown

ToC Category:

Original Manuscript: December 18, 2013
Revised Manuscript: January 26, 2014
Manuscript Accepted: January 26, 2014
Published: February 12, 2014

Virtual Issues
Physics and Applications of Laser Dynamics (2014) Optics Express

Y. Yu, L. B. Guo, Z. Q. Hao, X. Y. Li, M. Shen, Q. D. Zeng, K. H. Li, X. Y. Zeng, Y. F. Lu, and Z. Ren, "Accuracy improvement on polymer identification using laser-induced breakdown spectroscopy with adjusting spectral weightings," Opt. Express 22, 3895-3901 (2014)

Sort:  Author  |  Year  |  Journal  |  Reset  


  1. V. Goodship, Introduction to Plastics Recycling (iSmithers Rapra, 2007).
  2. E. A. Bruno, “Automated Sorting of Polymers for Recycling,” 2011, http://infohouse.p2ric.org/ref/09/08620.pdf .
  3. M. Chanda and S. K. Roy, Plastics Technology Handbook (CRC, 2010).
  4. G. R. Gunning, “Applications of ED-XRF technology to on-line analysis,” Adv. X-Ray Anal. 36, 105–109 (1993).
  5. W. H. A. M. van den Broek, E. P. P. A. Derks, E. W. van de Ven, D. Wienke, P. Geladi, L. M. C. Buydens, “Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS),” Chemometr. Intell. Lab. 35, 187–197 (1996).
  6. W. H. A. M. Van Den Broek, D. Wienke, W. J. Melssen, L. M. C. Buydens, “Plastic material identification with spectroscopic near infrared imaging and artificial neural networks,” Anal. Chim. Acta 361(1-2), 161–176 (1998). [CrossRef]
  7. T. Huth-Fehre, R. Feldhoff, T. Kantimm, L. Quick, F. Winter, K. Cammann, W. van den Broek, D. Wienke, W. Melssen, L. Buydens, “NIR - Remote sensing and artificial neural networks for rapid identification of post consumer plastics,” J. Mol. Struct. 348, 143–146 (1995). [CrossRef]
  8. P. Dinger, “Automatic sorting for mixed polymers,” BioCycle: Journal of Composting & Organics Recycling 33, 80–82 (1992).
  9. N. Eisenreich, T. Rohe, “Infrared spectroscopy in analysis of polymers recycling,” Kunststoffe 2, 222–224 (1996).
  10. D. A. Cremers and L. J. Radziemski, Handbook of Laser-Induced Breakdown Spectroscopy (John Wiley, 2006).
  11. A. W. Miziolek, V. Palleschi, and I. Schechter, Laser-Induced Breakdown Spectroscopy: Fundamentals and Applications (Cambridge University, 2006).
  12. X. N. He, W. Hu, C. M. Li, L. B. Guo, Y. F. Lu, “Generation of high-temperature and low-density plasmas for improved spectral resolutions in laser-induced breakdown spectroscopy,” Opt. Express 19(11), 10997–11006 (2011). [CrossRef] [PubMed]
  13. L. B. Guo, C. M. Li, W. Hu, Y. S. Zhou, B. Y. Zhang, Z. X. Cai, X. Y. Zeng, Y. F. Lu, “Plasma confinement by hemispherical cavity in laser-induced breakdown spectroscopy,” Appl. Phys. Lett. 98(13), 131501 (2011). [CrossRef]
  14. L. B. Guo, W. Hu, B. Y. Zhang, X. N. He, C. M. Li, Y. S. Zhou, Z. X. Cai, X. Y. Zeng, Y. F. Lu, “Enhancement of optical emission from laser-induced plasmas by combined spatial and magnetic confinement,” Opt. Express 19(15), 14067–14075 (2011). [CrossRef] [PubMed]
  15. D. A. Rusak, K. D. Weaver, B. L. Taroli, “Laser-Induced Breakdown Spectroscopy for Analysis of Chemically Etched Polytetrafluoroethylene,” Appl. Spectrosc. 62(7), 773–777 (2008). [CrossRef] [PubMed]
  16. I. Moench, R. Sattmann, R. Noll, “High-speed identification of polymers by laser-induced breakdown spectroscopy,” Proc. SPIE 3100, 64–74 (1997). [CrossRef]
  17. R. Sattmann, I. Monch, H. Krause, R. Noll, S. Couris, A. Hatziapostolou, A. Mavromanolakis, C. Fotakis, E. Larrauri, R. Miguel, “Laser-Induced Breakdown Spectroscopy for Polymer Identification,” Appl. Spectrosc. 52(3), 456–461 (1998). [CrossRef]
  18. M. Stepputat, R. Noll, “On-Line Detection of Heavy Metals and Brominated Flame Retardants in Technical Polymers with Laser-Induced Breakdown Spectrometry,” Appl. Opt. 42(30), 6210–6220 (2003). [CrossRef] [PubMed]
  19. J. M. Anzano, I. B. Gornushkin, B. W. Smith, J. D. Winefordner, “Laser-induced plasma spectroscopy for polymer identification,” Polym. Eng. Sci. 40(11), 2423–2429 (2000). [CrossRef]
  20. M. Boueri, V. Motto-Ros, W. Q. Lei, Q. L. Ma, L. J. Zheng, H. P. Zeng, J. Yu, “Identification of Polymer Materials Using Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Networks,” Appl. Spectrosc. 65(3), 307–314 (2011). [CrossRef] [PubMed]
  21. S. Grégoire, M. Boudinet, F. Pelascini, F. Surma, V. Detalle, Y. Holl, “Laser-induced breakdown spectroscopy for polymer identification,” Anal. Bioanal. Chem. 400(10), 3331–3340 (2011). [CrossRef] [PubMed]
  22. B. E. Boser, I. Guyon, V. Vapnik, “A training algorithm for optimal margin classiers,” in Proceedings of the Fifth Annual Workshop on Computational Learning Theory (ACM, 1992), pp. 144–152. [CrossRef]
  23. C. Cortes, V. Vapnik, “Support-vector network,” Mach. Learn. 20(3), 273–297 (1995). [CrossRef]
  24. C. H. Wu, G. H. Tzeng, Y. J. Goo, W. C. Fang, “A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy,” Expert Syst. Appl. 32(2), 397–408 (2007). [CrossRef]
  25. C. C. Chang and C. J. Lin, “LIBSVM: a library for support vector machines,” ACM TIST 2, 1–27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm [CrossRef]
  26. M. Boueri, 2010 Ph.D. Dissertation (Lyon:Universite de Lyon) (in France).
  27. D. Filmer, L. H. Pritchett, “Estimating Wealth Effects Without Expenditure Data--Or Tears: An Application to Educational Enrollments In States Of India,” Demography 38(1), 115–132 (2001). [PubMed]
  28. V. Motto-Ros, A. S. Koujelev, G. R. Osinski, A. E. Dudelzak, “Quantitative multi-elemental laser-induced breakdown spectroscopy using artificial neural networks,” Journal of the European Optical Society-Rapid Publications 3, 08011 (2008).
  29. Y. Yu, Z.-Q. Hao, C.-M. Li, L.-B. Guo, K.-H. Li, Q.-D. Zeng, X.-Y. Li, Z. Ren, X.-Y. Zeng, “Identification of plastics by laser-induced breakdown spectroscopy combined with support vector machine algorithm,” Acta Phys. Sin. 62, 215201 (2013).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Fig. 1 Fig. 2 Fig. 3
Fig. 4

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited