Abstract

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

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2013

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).

2011

2008

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).

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]

2007

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]

2003

2001

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]

2000

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]

1998

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]

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]

1997

I. Moench, R. Sattmann, R. Noll, “High-speed identification of polymers by laser-induced breakdown spectroscopy,” Proc. SPIE 3100, 64–74 (1997).
[CrossRef]

1996

N. Eisenreich, T. Rohe, “Infrared spectroscopy in analysis of polymers recycling,” Kunststoffe 2, 222–224 (1996).

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).

1995

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]

C. Cortes, V. Vapnik, “Support-vector network,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

1993

G. R. Gunning, “Applications of ED-XRF technology to on-line analysis,” Adv. X-Ray Anal. 36, 105–109 (1993).

1992

P. Dinger, “Automatic sorting for mixed polymers,” BioCycle: Journal of Composting & Organics Recycling 33, 80–82 (1992).

Anzano, J. M.

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]

Boser, B. E.

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]

Boudinet, M.

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]

Boueri, M.

Buydens, L.

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]

Buydens, L. M. C.

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]

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).

Cai, Z. X.

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]

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]

Cammann, K.

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]

Cortes, C.

C. Cortes, V. Vapnik, “Support-vector network,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

Couris, S.

Derks, E. P. P. A.

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).

Detalle, V.

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]

Dinger, P.

P. Dinger, “Automatic sorting for mixed polymers,” BioCycle: Journal of Composting & Organics Recycling 33, 80–82 (1992).

Dudelzak, A. E.

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).

Eisenreich, N.

N. Eisenreich, T. Rohe, “Infrared spectroscopy in analysis of polymers recycling,” Kunststoffe 2, 222–224 (1996).

Fang, W. C.

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]

Feldhoff, R.

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]

Filmer, D.

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]

Fotakis, C.

Geladi, P.

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).

Goo, Y. J.

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]

Gornushkin, I. B.

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]

Grégoire, S.

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]

Gunning, G. R.

G. R. Gunning, “Applications of ED-XRF technology to on-line analysis,” Adv. X-Ray Anal. 36, 105–109 (1993).

Guo, L. B.

Guo, L.-B.

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).

Guyon, I.

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]

Hao, Z.-Q.

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).

Hatziapostolou, A.

He, X. N.

Holl, Y.

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]

Hu, W.

Huth-Fehre, T.

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]

Kantimm, T.

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]

Koujelev, A. S.

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).

Krause, H.

Larrauri, E.

Lei, W. Q.

Li, C. M.

Li, C.-M.

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).

Li, K.-H.

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).

Li, X.-Y.

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).

Lu, Y. F.

Ma, Q. L.

Mavromanolakis, A.

Melssen, W.

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]

Melssen, W. J.

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]

Miguel, R.

Moench, I.

I. Moench, R. Sattmann, R. Noll, “High-speed identification of polymers by laser-induced breakdown spectroscopy,” Proc. SPIE 3100, 64–74 (1997).
[CrossRef]

Monch, I.

Motto-Ros, V.

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]

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).

Noll, R.

Osinski, G. R.

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).

Pelascini, F.

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]

Pritchett, L. H.

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]

Quick, L.

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]

Ren, Z.

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).

Rohe, T.

N. Eisenreich, T. Rohe, “Infrared spectroscopy in analysis of polymers recycling,” Kunststoffe 2, 222–224 (1996).

Rusak, D. A.

Sattmann, R.

Smith, B. W.

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]

Stepputat, M.

Surma, F.

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]

Taroli, B. L.

Tzeng, G. H.

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]

van de Ven, E. W.

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).

van den Broek, W.

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]

Van Den Broek, W. H. A. M.

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]

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).

Vapnik, V.

C. Cortes, V. Vapnik, “Support-vector network,” Mach. Learn. 20(3), 273–297 (1995).
[CrossRef]

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]

Weaver, K. D.

Wienke, D.

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]

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).

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]

Winefordner, J. D.

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]

Winter, F.

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]

Wu, C. H.

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Figures (4)

Fig. 1
Fig. 1

Schematic diagram of the experimental setup

Fig. 2
Fig. 2

Identification result of the 11 kinds of polymers using the SVM model without ASW.

Fig. 3
Fig. 3

Average normalized intensity of characteristic spectral lines.

Fig. 4
Fig. 4

Identification result of the 11 kinds of polymers using SVM model with ASW.

Tables (4)

Tables Icon

Table 1 Molecular formulas of 11 kinds of polymers

Tables Icon

Table 2 Characteristic spectral lines for the SVM inputs.

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Table 3 Classification weightings of normalized characteristic spectral lines determined by PCA.

Tables Icon

Table 4 Identification results without and with ASW for different groups of training and test sets.

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