Abstract

The feasibility of steel materials classification by support vector machines (SVMs), in combination with laser-induced breakdown spectroscopy (LIBS) technology, was investigated. Multi-classification methods based on SVM, the one-against-all and the one-against-one models, and a combination model, are applied to classify nine types of round steel. Due to the inhomogeneity of steel composition, the data obtained using the one-against-all and one-against-one models were ambiguous and difficult to discriminate; whereas, the combination model, was able to successfully distinguish most of the ambiguous data and control the computation cost within an acceptable range. The studies presented here demonstrate that LIBS–SVM is a useful technique for the identification and discrimination of steel materials, and would be very well-suited for process analysis in the steelmaking industry.

© 2014 Optical Society of America

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2013 (3)

F. J. Fortes, J. Moros, P. Lucena, L. M. Cabalin, and J. J. Laserna, “Laser-induced breakdown spectroscopy,” Anal. Chem. 85, 640–669 (2013).
[CrossRef]

M. A. Khater, “Laser-induced breakdown spectroscopy for light elements detection in steel: state of the art,” Spectrochim. Acta, Part B 81, 1–10 (2013).
[CrossRef]

J. L. Gottfried, “Influence of metal substrates on the detection of explosive residues with laser-induced breakdown spectroscopy,” Appl. Opt. 52, B10–B19 (2013).
[CrossRef]

2012 (6)

A. M. Ollila, J. Lasue, H. E. Newson, R. A. Multari, R. C. Wiens, and S. M. Clegg, “Comparison of two party all least squares-discriminant analysis algorithms for identifying geological samples with the ChemCam laser-induced breakdown spectroscopy instrument,” Appl. Opt. 51, B130–B142 (2012).
[CrossRef]

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84, 2686–2694 (2012).
[CrossRef]

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemom. 26, 143–149 (2012).
[CrossRef]

M. Hoehse, A. Paul, I. Gornushkin, and U. Panne, “Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS,” Anal. Bioanal. Chem. 402, 1443–1450 (2012).
[CrossRef]

D. W. Hahn and N. Omenetto, “Laser-induced breakdown spectroscopy (LIBS), part II: review of instrumental and methodological approaches to material analysis and applications to different fields,” Appl. Spectrosc. 66, 347–419 (2012).
[CrossRef]

2011 (2)

Z. Wang, J. Feng, L. Li, W. Ni, and Z. Li, “A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements,” J. Anal. At. Spectrom. 26, 2175–2182 (2011).
[CrossRef]

J. Feng, Z. Wang, L. West, Z. Li, and W. Ni, “A non-linearized multivariate dominant factor–based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 400, 3261–3271 (2011).

2010 (4)

C. B. Stipe, B. D. Hensley, J. L. Boersema, and S. G. Buckley, “Laser-induced breakdown spectroscopy of steel: a comparison of univariate and multivariate calibration methods,” Appl. Spectrosc. 64, 154–160 (2010).
[CrossRef]

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
[CrossRef]

D. W. Hahn and N. Omenetto, “Laser-induced breakdown spectroscopy (LIBS), part I: review of basic diagnostics and plasma–particle interactions: still-challenging issues within the analytical plasma community,” Appl. Spectrosc. 64, 335A–366A (2010).
[CrossRef]

K. Hasegawa and K. Funatsu, “Non-linear modeling and chemical interpretation with aid of support vector machine and regression,” Curr. Comput. Aided Drug Des. 6, 24–36 (2010).
[CrossRef]

2008 (5)

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

E. G. Snyder, C. A. Munson, J. L. Gottfried, F. C. De Lucia, B. Gullett, and A. Miziolek, “Laser-induced breakdown spectroscopy for the classification of unknown powders,” Appl. Opt. 47, G80–G87 (2008).
[CrossRef]

F. Boué-Bigne, “Laser-induced breakdown spectroscopy applications in the steel industry: rapid analysis of segregation and decarburization,” Spectrochim. Acta, Part B 63, 1122–1129 (2008).
[CrossRef]

F. B. Gonzaga and C. A. Pasquini, “Complementary metal oxide semiconductor sensor array-based detection system for laser-induced breakdown spectroscopy: evaluation of calibration strategies and application for manganese determination in steel,” Spectrochim. Acta, Part B 63, 56–63 (2008).
[CrossRef]

A. Ramil, A. J. López, and A. Yáňez, “Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser-induced breakdown spectroscopy (LIBS),” Appl. Phys. A 92, 197–202 (2008).
[CrossRef]

2007 (4)

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

F. R. Doucet, T. F. Belliveau, J. L. Fortier, and J. Hubert, “Use of chemometrics and laser-induced breakdown spectroscopy for quantitative analysis of major and minor elements in aluminum alloys,” Appl. Spectrosc. 61, 327–332 (2007).
[CrossRef]

F. Boue-Bigne, “Analysis of oxide inclusions in steel by fast laser-induced breakdown spectroscopy scanning: an approach to quantification,” Appl. Spectrosc. 61, 333–337 (2007).
[CrossRef]

2006 (1)

J. M. Prats-Montalban, A. Ferrer, J. L. Malo, and J. Gorbena, “A comparison of different discriminant analysis techniques in a steel industry welding process,” Chemometr. Intell. Lab. Syst. 80, 109–119 (2006).
[CrossRef]

2005 (2)

2004 (2)

S. Palanco, S. Conesa, and J. J. Laserna, “Analytical control of liquid steel in an induction melting furnace using a remote laser-induced plasma spectrometer,” J. Anal. At. Spectrom. 19, 462–467 (2004).
[CrossRef]

V. Sturm, J. Vrenegor, R. Noll, and M. Hemmerlin, “Bulk analysis of steel samples with surface scale layers by enhanced laser ablation and LIBS analysis of C, P, S, Al, Cr, Cu, Mn, and Mo,” J. Anal. At. Spectrom. 19, 451–456 (2004).
[CrossRef]

2003 (3)

2002 (3)

K. Crammer and Y. Singer, “On the learnability and design of output codes for multiclass problems,” Mach. Learn. 47, 201–233 (2002).
[CrossRef]

L. M. Cabalin, D. Romero, C. C. Garcia, J. M. Baena, and J. J. Laserna, “Time-resolved laser-induced plasma spectrometry for determination of minor elements in steelmaking process samples,” Anal. Bioanal. Chem. 372, 352–359 (2002).
[CrossRef]

C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE. Trans. Neural Netw. 13, 415–425 (2002).

2001 (1)

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

2000 (1)

S. Palanco and J. J. Laserna, “Full automation of a laser-induced breakdown spectrometer for quality assessment in the steel industry with sample handling, surface preparation and quantitative analysis capabilities,” J. Anal. At. Spectrom. 15, 1321–1327 (2000).
[CrossRef]

1998 (1)

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” J. Data Mini. Know. Disc. 2, 121–167 (1998).

1995 (1)

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

1994 (1)

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

1993 (1)

1985 (1)

Aguilera, J. A.

Anabitarte, F.

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

Angelopoulos, G. N.

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

Annen, K. D.

Aragon, C.

Baena, J. M.

L. M. Cabalin, D. Romero, C. C. Garcia, J. M. Baena, and J. J. Laserna, “Time-resolved laser-induced plasma spectrometry for determination of minor elements in steelmaking process samples,” Anal. Bioanal. Chem. 372, 352–359 (2002).
[CrossRef]

Barman, I.

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84, 2686–2694 (2012).
[CrossRef]

Belliveau, J.

Belliveau, T. F.

Bette, H.

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Boersema, J. L.

Bottou, L.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Boue-Bigne, F.

Boué-Bigne, F.

F. Boué-Bigne, “Laser-induced breakdown spectroscopy applications in the steel industry: rapid analysis of segregation and decarburization,” Spectrochim. Acta, Part B 63, 1122–1129 (2008).
[CrossRef]

Braga, J. W. B.

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
[CrossRef]

Brysch, A.

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Buckley, S. G.

Burges, C. J. C.

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” J. Data Mini. Know. Disc. 2, 121–167 (1998).

Cabalin, L. M.

F. J. Fortes, J. Moros, P. Lucena, L. M. Cabalin, and J. J. Laserna, “Laser-induced breakdown spectroscopy,” Anal. Chem. 85, 640–669 (2013).
[CrossRef]

L. M. Cabalin, D. Romero, C. C. Garcia, J. M. Baena, and J. J. Laserna, “Time-resolved laser-induced plasma spectrometry for determination of minor elements in steelmaking process samples,” Anal. Bioanal. Chem. 372, 352–359 (2002).
[CrossRef]

Cadwell, L.

Campos, J.

Cisewski, J.

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemom. 26, 143–149 (2012).
[CrossRef]

Clegg, S. M.

Cobo, A.

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

Coleman, K.

Conde, O.

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

Conesa, S.

S. Palanco, S. Conesa, and J. J. Laserna, “Analytical control of liquid steel in an induction melting furnace using a remote laser-induced plasma spectrometer,” J. Anal. At. Spectrom. 19, 462–467 (2004).
[CrossRef]

Cortes, C.

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

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Couris, S.

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

Crammer, K.

K. Crammer and Y. Singer, “On the learnability and design of output codes for multiclass problems,” Mach. Learn. 47, 201–233 (2002).
[CrossRef]

Cremers, D. A.

D. A. Cremers and L. J. Radziemski, Handbook of Laser-Induced Breakdown Spectroscopy (Wiley, 2006).

Cristianini, N.

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machine and Other Kernel-based Learning Methods (Cambridge University, 2000).

De Lucia, F. C.

Denker, J. S.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Dingari, N. C.

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84, 2686–2694 (2012).
[CrossRef]

Doucet, F. R.

Drucker, H.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Feng, J.

Z. Wang, J. Feng, L. Li, W. Ni, and Z. Li, “A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements,” J. Anal. At. Spectrom. 26, 2175–2182 (2011).
[CrossRef]

J. Feng, Z. Wang, L. West, Z. Li, and W. Ni, “A non-linearized multivariate dominant factor–based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 400, 3261–3271 (2011).

Ferrer, A.

J. M. Prats-Montalban, A. Ferrer, J. L. Malo, and J. Gorbena, “A comparison of different discriminant analysis techniques in a steel industry welding process,” Chemometr. Intell. Lab. Syst. 80, 109–119 (2006).
[CrossRef]

Fortes, F. J.

F. J. Fortes, J. Moros, P. Lucena, L. M. Cabalin, and J. J. Laserna, “Laser-induced breakdown spectroscopy,” Anal. Chem. 85, 640–669 (2013).
[CrossRef]

Fortier, J. L.

Franklin, J.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The element of statistical learning: data mining, inference, and prediction,” Math. Intell. 27, 83–85 (2005).
[CrossRef]

Freedman, A.

Friedman, J.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The element of statistical learning: data mining, inference, and prediction,” Math. Intell. 27, 83–85 (2005).
[CrossRef]

Friedman, J. H.

J. H. Friedman, “Another approach to polychotomous classification,” (Department of Statistics, Stanford University, 1996).

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K. Hasegawa and K. Funatsu, “Non-linear modeling and chemical interpretation with aid of support vector machine and regression,” Curr. Comput. Aided Drug Des. 6, 24–36 (2010).
[CrossRef]

Garcia, C. C.

L. M. Cabalin, D. Romero, C. C. Garcia, J. M. Baena, and J. J. Laserna, “Time-resolved laser-induced plasma spectrometry for determination of minor elements in steelmaking process samples,” Anal. Bioanal. Chem. 372, 352–359 (2002).
[CrossRef]

Garcia-Allende, P.

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

Gonzaga, F. B.

F. B. Gonzaga and C. A. Pasquini, “Complementary metal oxide semiconductor sensor array-based detection system for laser-induced breakdown spectroscopy: evaluation of calibration strategies and application for manganese determination in steel,” Spectrochim. Acta, Part B 63, 56–63 (2008).
[CrossRef]

Gorbena, J.

J. M. Prats-Montalban, A. Ferrer, J. L. Malo, and J. Gorbena, “A comparison of different discriminant analysis techniques in a steel industry welding process,” Chemometr. Intell. Lab. Syst. 80, 109–119 (2006).
[CrossRef]

Gornushkin, I.

M. Hoehse, A. Paul, I. Gornushkin, and U. Panne, “Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS,” Anal. Bioanal. Chem. 402, 1443–1450 (2012).
[CrossRef]

Gottfried, J. L.

Griffin, H.

Gullett, B.

Gundawar, M. K.

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84, 2686–2694 (2012).
[CrossRef]

Guyon, I.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Hahn, D. W.

Hannig, J.

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemom. 26, 143–149 (2012).
[CrossRef]

Harmon, R. S.

Hasegawa, K.

K. Hasegawa and K. Funatsu, “Non-linear modeling and chemical interpretation with aid of support vector machine and regression,” Curr. Comput. Aided Drug Des. 6, 24–36 (2010).
[CrossRef]

Hastie, T.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The element of statistical learning: data mining, inference, and prediction,” Math. Intell. 27, 83–85 (2005).
[CrossRef]

Hemmerlin, M.

V. Sturm, J. Vrenegor, R. Noll, and M. Hemmerlin, “Bulk analysis of steel samples with surface scale layers by enhanced laser ablation and LIBS analysis of C, P, S, Al, Cr, Cu, Mn, and Mo,” J. Anal. At. Spectrom. 19, 451–456 (2004).
[CrossRef]

Hensley, B. D.

Hoehse, M.

M. Hoehse, A. Paul, I. Gornushkin, and U. Panne, “Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS,” Anal. Bioanal. Chem. 402, 1443–1450 (2012).
[CrossRef]

Hsu, C. W.

C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE. Trans. Neural Netw. 13, 415–425 (2002).

Hubert, J.

Huwel, L.

Iannarilli, F. J.

Jackel, L. D.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Jones, S.

Jurado-López, A.

Khater, M. A.

M. A. Khater, “Laser-induced breakdown spectroscopy for light elements detection in steel: state of the art,” Spectrochim. Acta, Part B 81, 1–10 (2013).
[CrossRef]

Kolm, I.

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

Kraushaar, M.

M. Kraushaar, R. Noll, and H.-U. Schmitz, “Slag analysis with laser-induced breakdown spectrometry,” Appl. Spectrosc. 57, 1282–1287 (2003).
[CrossRef]

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Krug, F. J.

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
[CrossRef]

Lacour, J. L.

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

Laserna, J. J.

F. J. Fortes, J. Moros, P. Lucena, L. M. Cabalin, and J. J. Laserna, “Laser-induced breakdown spectroscopy,” Anal. Chem. 85, 640–669 (2013).
[CrossRef]

S. Palanco, S. Conesa, and J. J. Laserna, “Analytical control of liquid steel in an induction melting furnace using a remote laser-induced plasma spectrometer,” J. Anal. At. Spectrom. 19, 462–467 (2004).
[CrossRef]

L. M. Cabalin, D. Romero, C. C. Garcia, J. M. Baena, and J. J. Laserna, “Time-resolved laser-induced plasma spectrometry for determination of minor elements in steelmaking process samples,” Anal. Bioanal. Chem. 372, 352–359 (2002).
[CrossRef]

S. Palanco and J. J. Laserna, “Full automation of a laser-induced breakdown spectrometer for quality assessment in the steel industry with sample handling, surface preparation and quantitative analysis capabilities,” J. Anal. At. Spectrom. 15, 1321–1327 (2000).
[CrossRef]

Lasue, J.

LeCun, Y.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Li, L.

Z. Wang, J. Feng, L. Li, W. Ni, and Z. Li, “A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements,” J. Anal. At. Spectrom. 26, 2175–2182 (2011).
[CrossRef]

Li, Z.

Z. Wang, J. Feng, L. Li, W. Ni, and Z. Li, “A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements,” J. Anal. At. Spectrom. 26, 2175–2182 (2011).
[CrossRef]

J. Feng, Z. Wang, L. West, Z. Li, and W. Ni, “A non-linearized multivariate dominant factor–based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 400, 3261–3271 (2011).

Lin, C. J.

C. W. Hsu and C. J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE. Trans. Neural Netw. 13, 415–425 (2002).

López, A. J.

A. Ramil, A. J. López, and A. Yáňez, “Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser-induced breakdown spectroscopy (LIBS),” Appl. Phys. A 92, 197–202 (2008).
[CrossRef]

Lopez-Highera, J.

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

Lopez-Higuera, J. M.

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

Lucena, P.

F. J. Fortes, J. Moros, P. Lucena, L. M. Cabalin, and J. J. Laserna, “Laser-induced breakdown spectroscopy,” Anal. Chem. 85, 640–669 (2013).
[CrossRef]

Luque de Castro, M. D.

Madruga, F.

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

Malo, J. L.

J. M. Prats-Montalban, A. Ferrer, J. L. Malo, and J. Gorbena, “A comparison of different discriminant analysis techniques in a steel industry welding process,” Chemometr. Intell. Lab. Syst. 80, 109–119 (2006).
[CrossRef]

Manhès, G.

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

Mauchien, P.

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

Maurice, S.

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

McNesby, K. L.

Member, S.

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

Mirapeix, J.

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

P. Garcia-Allende, F. Anabitarte, O. Conde, J. Mirapeix, F. Madruga, and J. Lopez-Highera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE 6966, 69661V (2008).
[CrossRef]

Miziolek, A.

Miziolek, A. W.

Moench, I.

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Moros, J.

F. J. Fortes, J. Moros, P. Lucena, L. M. Cabalin, and J. J. Laserna, “Laser-induced breakdown spectroscopy,” Anal. Chem. 85, 640–669 (2013).
[CrossRef]

Muller, U. A.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Multari, R. A.

Munson, C. A.

Myakalwar, A. K.

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84, 2686–2694 (2012).
[CrossRef]

Newson, H. E.

Ni, W.

J. Feng, Z. Wang, L. West, Z. Li, and W. Ni, “A non-linearized multivariate dominant factor–based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 400, 3261–3271 (2011).

Z. Wang, J. Feng, L. Li, W. Ni, and Z. Li, “A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements,” J. Anal. At. Spectrom. 26, 2175–2182 (2011).
[CrossRef]

Noll, R.

V. Sturm, J. Vrenegor, R. Noll, and M. Hemmerlin, “Bulk analysis of steel samples with surface scale layers by enhanced laser ablation and LIBS analysis of C, P, S, Al, Cr, Cu, Mn, and Mo,” J. Anal. At. Spectrom. 19, 451–456 (2004).
[CrossRef]

M. Kraushaar, R. Noll, and H.-U. Schmitz, “Slag analysis with laser-induced breakdown spectrometry,” Appl. Spectrosc. 57, 1282–1287 (2003).
[CrossRef]

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Nunes, L. C.

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
[CrossRef]

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Omenetto, N.

Oudejans, L.

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemom. 26, 143–149 (2012).
[CrossRef]

Palagas, C.

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

Palanco, S.

S. Palanco, S. Conesa, and J. J. Laserna, “Analytical control of liquid steel in an induction melting furnace using a remote laser-induced plasma spectrometer,” J. Anal. At. Spectrom. 19, 462–467 (2004).
[CrossRef]

S. Palanco and J. J. Laserna, “Full automation of a laser-induced breakdown spectrometer for quality assessment in the steel industry with sample handling, surface preparation and quantitative analysis capabilities,” J. Anal. At. Spectrom. 15, 1321–1327 (2000).
[CrossRef]

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A. W. Miziolek, V. Palleschi, and I. Schechter, Laser-Induced Breakdown Spectroscopy (Cambridge University, 2006).

Panne, U.

M. Hoehse, A. Paul, I. Gornushkin, and U. Panne, “Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS,” Anal. Bioanal. Chem. 402, 1443–1450 (2012).
[CrossRef]

Papamantellos, D. C.

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

Pasquini, C. A.

F. B. Gonzaga and C. A. Pasquini, “Complementary metal oxide semiconductor sensor array-based detection system for laser-induced breakdown spectroscopy: evaluation of calibration strategies and application for manganese determination in steel,” Spectrochim. Acta, Part B 63, 56–63 (2008).
[CrossRef]

Paul, A.

M. Hoehse, A. Paul, I. Gornushkin, and U. Panne, “Multivariate classification of pigments and inks using combined Raman spectroscopy and LIBS,” Anal. Bioanal. Chem. 402, 1443–1450 (2012).
[CrossRef]

Peter, L.

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Portilla, O. M. C.

F. Anabitarte, J. Mirapeix, O. M. C. Portilla, J. M. Lopez-Higuera, S. Member, and A. Cobo, “Sensor for the detection of protective coating traces on boron steel with aluminium–silicon covering by means of laser-induced breakdown spectroscopy and support vector machines,” IEEE. Sens. J. 12, 64–70 (2012).
[CrossRef]

Prats-Montalban, J. M.

J. M. Prats-Montalban, A. Ferrer, J. L. Malo, and J. Gorbena, “A comparison of different discriminant analysis techniques in a steel industry welding process,” Chemometr. Intell. Lab. Syst. 80, 109–119 (2006).
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D. A. Cremers and L. J. Radziemski, Handbook of Laser-Induced Breakdown Spectroscopy (Wiley, 2006).

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A. Ramil, A. J. López, and A. Yáňez, “Application of artificial neural networks for the rapid classification of archaeological ceramics by means of laser-induced breakdown spectroscopy (LIBS),” Appl. Phys. A 92, 197–202 (2008).
[CrossRef]

Romero, D.

L. M. Cabalin, D. Romero, C. C. Garcia, J. M. Baena, and J. J. Laserna, “Time-resolved laser-induced plasma spectrometry for determination of minor elements in steelmaking process samples,” Anal. Bioanal. Chem. 372, 352–359 (2002).
[CrossRef]

Rufini, I. A.

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
[CrossRef]

Sackinger, E.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Sallé, B.

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

Santos, D.

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
[CrossRef]

Schechter, I.

A. W. Miziolek, V. Palleschi, and I. Schechter, Laser-Induced Breakdown Spectroscopy (Cambridge University, 2006).

Schmitz, H.-U.

Shawe-Taylor, J.

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machine and Other Kernel-based Learning Methods (Cambridge University, 2000).

Simard, P.

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

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K. Crammer and Y. Singer, “On the learnability and design of output codes for multiclass problems,” Mach. Learn. 47, 201–233 (2002).
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Sirven, J. B.

J. B. Sirven, B. Sallé, P. Mauchien, J. L. Lacour, S. Maurice, and G. Manhès, “Feasibility study of rock identification at the surface of Mars by remote laser-induced breakdown spectroscopy and three chemometric methods,” J. Anal. At. Spectrom. 22, 1471–1480 (2007).
[CrossRef]

Snyder, E.

J. Cisewski, E. Snyder, J. Hannig, and L. Oudejans, “Support vector machine classification of suspect powders using laser-induced breakdown spectroscopy (LIBS) spectral data,” J. Chemom. 26, 143–149 (2012).
[CrossRef]

Snyder, E. G.

Stavropoulos, P.

C. Palagas, P. Stavropoulos, S. Couris, G. N. Angelopoulos, I. Kolm, and D. C. Papamantellos, “Investigation of the parameters influencing the accuracy of rapid steelmaking slag analysis with laser-induced breakdown spectroscopy,” Steel Res. Int. 78, 693–703 (2007).

Stipe, C. B.

Sturm, V.

V. Sturm, J. Vrenegor, R. Noll, and M. Hemmerlin, “Bulk analysis of steel samples with surface scale layers by enhanced laser ablation and LIBS analysis of C, P, S, Al, Cr, Cu, Mn, and Mo,” J. Anal. At. Spectrom. 19, 451–456 (2004).
[CrossRef]

R. Noll, H. Bette, A. Brysch, M. Kraushaar, I. Moench, L. Peter, and V. Sturm, “Laser-induced breakdown spectrometry applications for production control and quality assurance in the steel industry,” Spectrochim. Acta, Part B 56, 637–649 (2001).
[CrossRef]

Tewari, S. P.

N. C. Dingari, I. Barman, A. K. Myakalwar, S. P. Tewari, and M. K. Gundawar, “Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability,” Anal. Chem. 84, 2686–2694 (2012).
[CrossRef]

Tibshirani, R.

T. Hastie, R. Tibshirani, J. Friedman, and J. Franklin, “The element of statistical learning: data mining, inference, and prediction,” Math. Intell. 27, 83–85 (2005).
[CrossRef]

Trevizan, L. C.

J. W. B. Braga, L. C. Trevizan, L. C. Nunes, I. A. Rufini, D. Santos, and F. J. Krug, “Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser-induced breakdown spectrometry,” Spectrochim. Acta, Part B 65, 66–74 (2010).
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C. Cortes and V. Vapnik, “Support-vector network,” Mach. Learn. 20, 273–297 (1995).

L. Bottou, C. Cortes, J. S. Denker, H. Drucker, I. Guyon, L. D. Jackel, Y. LeCun, U. A. Muller, E. Sackinger, P. Simard, and V. Vapnik, “Comparison of classifier methods: a case study in handwriting digit recognition,” Pattern Recogn. 2, 77–87 (1994).

Vapnik, V. N.

V. N. Vapnik, Statistical Learning Theory (Wiley, 1998).

Vrenegor, J.

V. Sturm, J. Vrenegor, R. Noll, and M. Hemmerlin, “Bulk analysis of steel samples with surface scale layers by enhanced laser ablation and LIBS analysis of C, P, S, Al, Cr, Cu, Mn, and Mo,” J. Anal. At. Spectrom. 19, 451–456 (2004).
[CrossRef]

Wang, Z.

Z. Wang, J. Feng, L. Li, W. Ni, and Z. Li, “A non-linearized PLS model based on multivariate dominant factor for laser-induced breakdown spectroscopy measurements,” J. Anal. At. Spectrom. 26, 2175–2182 (2011).
[CrossRef]

J. Feng, Z. Wang, L. West, Z. Li, and W. Ni, “A non-linearized multivariate dominant factor–based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 400, 3261–3271 (2011).

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J. Feng, Z. Wang, L. West, Z. Li, and W. Ni, “A non-linearized multivariate dominant factor–based partial least squares (PLS) model for coal analysis by using laser-induced breakdown spectroscopy,” Appl. Spectrosc. 400, 3261–3271 (2011).

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Figures (2)

Fig. 1.
Fig. 1.

Representative LIBS spectra of the round steel samples.

Fig. 2.
Fig. 2.

Flow diagram of the combination model for multi-classification.

Tables (5)

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Table 1. Certified Elemental Composition of Steel Samples (in wt. %)

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Table 2. Classification Results of One-Against-All Model for Round Steel

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Table 3. Classification Results of One-Against-One Model for Round Steel

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Table 4. Computation Time of Three Models

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Table 5. Classification Results of Combination Model for Round Steel

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

yi[(wtxi)+b]1ξi(i=1,2,3,,n),ξi0,
min12w2+Ci=0nξisubject toyi[(wtxi)+b]1ξi(i=1,2,3,,n),ξi0,
f(x)=sgn(wtxtest+b)=sgn[(i=1nyiαixi)txtest+b],
f(x)=sgn(i=1nyiαiK(xtest,xi)+b).
K(xtest,xi)=(xtest,xi+1)d.

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