Abstract

Laser-induced breakdown spectroscopy was applied to quantitative analysis of three impurities in Sn alloy. The impurities analysis was based on the internal standard method using the Sn I 333.062-nm line as the reference line to achieve the best reproducible results. Minor-element concentrations (Ag, Cu, Pb) in the alloy were comparatively evaluated by artificial neural networks (ANNs) and calibration curves. ANN was found to effectively predict elemental concentrations with a trend of nonlinear growth due to self-absorption. The limits of detection for Ag, Cu, and Pb in Sn alloy were determined to be 29, 197, and 213ppm, respectively.

© 2010 Optical Society of America

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    [CrossRef]
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2009 (2)

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

F.-Y. Yueh, H. Zheng, J. P. Singh, and S. Burgess, “Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification,” Spectrochim. Acta Part B 64, 1059-1067 (2009).
[CrossRef]

2006 (1)

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

2005 (1)

2004 (1)

2003 (1)

K. Muller and H. Stege, “Evaluation of the analytical potential of laser-induced breakdown spectroscopy (LIBS) for the analysis of historical glasses,” Archaeometry 45, 421-433 (2003).
[CrossRef]

2000 (1)

J. Amador-Hernandez, L. E. Garcõa-Ayuso, J. M. Fernandez-Romero, and M. D. Luque de Castro, “Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry,” J. Anal. At. Spectrom. 15, 587-593 (2000).
[CrossRef]

1999 (1)

1995 (3)

1994 (2)

1992 (1)

J. T. Larsen, W. L. Morgan, and W. H. Goldstein, “Artificial neural network for plasma X-ray spectroscopic analysis,” Rev. Sci. Instrum. 63, 4775-4777 (1992).
[CrossRef]

1990 (1)

K. L. Peterson, “Classification of Cm I energy levels using counterpropagation neural networks,” Phys. Rev. A 41, 2457-2461 (1990).
[CrossRef] [PubMed]

Amador-Hernandez, J.

J. Amador-Hernandez, L. E. Garcõa-Ayuso, J. M. Fernandez-Romero, and M. D. Luque de Castro, “Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry,” J. Anal. At. Spectrom. 15, 587-593 (2000).
[CrossRef]

Blades, M. W.

Bousquet, B.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Branagh, W.

Bree, A. V.

Burgess, S.

F.-Y. Yueh, H. Zheng, J. P. Singh, and S. Burgess, “Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification,” Spectrochim. Acta Part B 64, 1059-1067 (2009).
[CrossRef]

Canionl, L.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Catasus, M.

Cielo, P.

Ciucci, A.

Corsi, M.

de Castro, M. D. Luque

J. Amador-Hernandez, L. E. Garcõa-Ayuso, J. M. Fernandez-Romero, and M. D. Luque de Castro, “Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry,” J. Anal. At. Spectrom. 15, 587-593 (2000).
[CrossRef]

Fahlman, S. E.

S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,” in Advances in Neural information Processing System 2 (Morgan-Kaufmann, 1990).

Fernandez-Romero, J. M.

J. Amador-Hernandez, L. E. Garcõa-Ayuso, J. M. Fernandez-Romero, and M. D. Luque de Castro, “Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry,” J. Anal. At. Spectrom. 15, 587-593 (2000).
[CrossRef]

Freek, L. S.

Garcõa-Ayuso, L. E.

J. Amador-Hernandez, L. E. Garcõa-Ayuso, J. M. Fernandez-Romero, and M. D. Luque de Castro, “Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry,” J. Anal. At. Spectrom. 15, 587-593 (2000).
[CrossRef]

Gautier, M. P.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Goldstein, W. H.

W. L. Morgan, J. T. Larsen, and W. H. Goldstein, “The use of artificial neural networks in plasma spectroscopy,” J. Quant. Radiat. Transfer 51, 247-253 (1994).
[CrossRef]

J. T. Larsen, W. L. Morgan, and W. H. Goldstein, “Artificial neural network for plasma X-ray spectroscopic analysis,” Rev. Sci. Instrum. 63, 4775-4777 (1992).
[CrossRef]

Gorzalka, B. B.

Hecho, I. L.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Inakollu, P.

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

Kozma, L.

B. Német and L. Kozma, “Time-resolved optical emission spectroscopy of Q-switched Nd: YAG laser induced plasmas from copper target in air at atmospheric pressure,” Spectrochim. Acta Part B 50, 1869-1888 (1995).
[CrossRef]

Lal, B.

Larsen, J. T.

W. L. Morgan, J. T. Larsen, and W. H. Goldstein, “The use of artificial neural networks in plasma spectroscopy,” J. Quant. Radiat. Transfer 51, 247-253 (1994).
[CrossRef]

J. T. Larsen, W. L. Morgan, and W. H. Goldstein, “Artificial neural network for plasma X-ray spectroscopic analysis,” Rev. Sci. Instrum. 63, 4775-4777 (1992).
[CrossRef]

Lebiere, C.

S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,” in Advances in Neural information Processing System 2 (Morgan-Kaufmann, 1990).

Legnaioli, S.

Miziolek, A.

A. Miziolek, V. Pelleschi, and I. Schechter, Laser Induced Breakdown Spectroscopy (LIBS): Fundamentals and Applications (Cambridge University, 2006).
[CrossRef]

Morgan, W. L.

W. L. Morgan, J. T. Larsen, and W. H. Goldstein, “The use of artificial neural networks in plasma spectroscopy,” J. Quant. Radiat. Transfer 51, 247-253 (1994).
[CrossRef]

J. T. Larsen, W. L. Morgan, and W. H. Goldstein, “Artificial neural network for plasma X-ray spectroscopic analysis,” Rev. Sci. Instrum. 63, 4775-4777 (1992).
[CrossRef]

Muller, K.

K. Muller and H. Stege, “Evaluation of the analytical potential of laser-induced breakdown spectroscopy (LIBS) for the analysis of historical glasses,” Archaeometry 45, 421-433 (2003).
[CrossRef]

Német, B.

B. Német and L. Kozma, “Time-resolved optical emission spectroscopy of Q-switched Nd: YAG laser induced plasmas from copper target in air at atmospheric pressure,” Spectrochim. Acta Part B 50, 1869-1888 (1995).
[CrossRef]

Palleschi, V.

Pelleschi, V.

A. Miziolek, V. Pelleschi, and I. Schechter, Laser Induced Breakdown Spectroscopy (LIBS): Fundamentals and Applications (Cambridge University, 2006).
[CrossRef]

Peterson, K. L.

K. L. Peterson, “Classification of Cm I energy levels using counterpropagation neural networks,” Phys. Rev. A 41, 2457-2461 (1990).
[CrossRef] [PubMed]

Philip, T.

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

Rai, A. K.

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

Sabsabi, M.

Salin, E. D.

Salvetti, A.

Sarger, L.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Schechter, I.

A. Miziolek, V. Pelleschi, and I. Schechter, Laser Induced Breakdown Spectroscopy (LIBS): Fundamentals and Applications (Cambridge University, 2006).
[CrossRef]

Schulze, H. G.

Singh, J. P.

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

F.-Y. Yueh, H. Zheng, J. P. Singh, and S. Burgess, “Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification,” Spectrochim. Acta Part B 64, 1059-1067 (2009).
[CrossRef]

B. Lal, F. Y. Yueh, and J. P. Singh, “Glass batch composition monitoring by laser induced breakdown spectroscopy,” Appl. Opt. 44, 3668-3674 (2005).
[CrossRef] [PubMed]

B. Lal, H. Zheng, F. Y. Yueh, and J. P. Singh, “Parametric study of pellets for elemental analysis with laser induced breakdown spectroscopy,” Appl. Opt. 43, 2792-2797 (2004).
[CrossRef] [PubMed]

J. P. Singh and S. N. Thakur, Laser Induced Breakdown Spectroscopy (Elsevier, 2007).

Sirven, J. B.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Stege, H.

K. Muller and H. Stege, “Evaluation of the analytical potential of laser-induced breakdown spectroscopy (LIBS) for the analysis of historical glasses,” Archaeometry 45, 421-433 (2003).
[CrossRef]

Tellier, S.

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Thakur, S. N.

J. P. Singh and S. N. Thakur, Laser Induced Breakdown Spectroscopy (Elsevier, 2007).

Tognoni, E.

Turner, R. F. B.

Yueh, F. Y.

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

B. Lal, F. Y. Yueh, and J. P. Singh, “Glass batch composition monitoring by laser induced breakdown spectroscopy,” Appl. Opt. 44, 3668-3674 (2005).
[CrossRef] [PubMed]

B. Lal, H. Zheng, F. Y. Yueh, and J. P. Singh, “Parametric study of pellets for elemental analysis with laser induced breakdown spectroscopy,” Appl. Opt. 43, 2792-2797 (2004).
[CrossRef] [PubMed]

Yueh, F.-Y.

F.-Y. Yueh, H. Zheng, J. P. Singh, and S. Burgess, “Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification,” Spectrochim. Acta Part B 64, 1059-1067 (2009).
[CrossRef]

Zheng, H.

F.-Y. Yueh, H. Zheng, J. P. Singh, and S. Burgess, “Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification,” Spectrochim. Acta Part B 64, 1059-1067 (2009).
[CrossRef]

B. Lal, H. Zheng, F. Y. Yueh, and J. P. Singh, “Parametric study of pellets for elemental analysis with laser induced breakdown spectroscopy,” Appl. Opt. 43, 2792-2797 (2004).
[CrossRef] [PubMed]

Anal. Bioanal. Chem. (1)

J. B. Sirven, B. Bousquet, L. Canionl, L. Sarger, S. Tellier, M. P. Gautier, and I. L. Hecho, “Qualitative and quantitative investigation of chromium polluted soils by laser induced breakdown spectroscopy combined with neural networks analysis,” Anal. Bioanal. Chem. 385, 256-262 (2006).
[CrossRef] [PubMed]

Appl. Opt. (2)

Appl. Spectrosc. (4)

Archaeometry (1)

K. Muller and H. Stege, “Evaluation of the analytical potential of laser-induced breakdown spectroscopy (LIBS) for the analysis of historical glasses,” Archaeometry 45, 421-433 (2003).
[CrossRef]

J. Anal. At. Spectrom. (1)

J. Amador-Hernandez, L. E. Garcõa-Ayuso, J. M. Fernandez-Romero, and M. D. Luque de Castro, “Partial least squares regression for problem solving in precious metal analysis by laser induced breakdown spectrometry,” J. Anal. At. Spectrom. 15, 587-593 (2000).
[CrossRef]

J. Quant. Radiat. Transfer (1)

W. L. Morgan, J. T. Larsen, and W. H. Goldstein, “The use of artificial neural networks in plasma spectroscopy,” J. Quant. Radiat. Transfer 51, 247-253 (1994).
[CrossRef]

Phys. Rev. A (1)

K. L. Peterson, “Classification of Cm I energy levels using counterpropagation neural networks,” Phys. Rev. A 41, 2457-2461 (1990).
[CrossRef] [PubMed]

Rev. Sci. Instrum. (1)

J. T. Larsen, W. L. Morgan, and W. H. Goldstein, “Artificial neural network for plasma X-ray spectroscopic analysis,” Rev. Sci. Instrum. 63, 4775-4777 (1992).
[CrossRef]

Spectrochim. Acta Part B (3)

B. Német and L. Kozma, “Time-resolved optical emission spectroscopy of Q-switched Nd: YAG laser induced plasmas from copper target in air at atmospheric pressure,” Spectrochim. Acta Part B 50, 1869-1888 (1995).
[CrossRef]

P. Inakollu, T. Philip, A. K. Rai, F. Y. Yueh, and J. P. Singh, “A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods,” Spectrochim. Acta Part B 64, 99-104 (2009).
[CrossRef]

F.-Y. Yueh, H. Zheng, J. P. Singh, and S. Burgess, “Preliminary evaluation of laser-induced breakdown spectroscopy for tissue classification,” Spectrochim. Acta Part B 64, 1059-1067 (2009).
[CrossRef]

Other (4)

Predict Software Manual (Neural Ware, Inc., 1997).

S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,” in Advances in Neural information Processing System 2 (Morgan-Kaufmann, 1990).

A. Miziolek, V. Pelleschi, and I. Schechter, Laser Induced Breakdown Spectroscopy (LIBS): Fundamentals and Applications (Cambridge University, 2006).
[CrossRef]

J. P. Singh and S. N. Thakur, Laser Induced Breakdown Spectroscopy (Elsevier, 2007).

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Figures (4)

Fig. 1
Fig. 1

Neural network architecture with three layers [12].

Fig. 2
Fig. 2

Variation in intensity (a) and intensity ratio referenced to Sn 333.062 nm (b) of Cu I 327.396 nm lines as a function of sampling sequences. The indices L, M, and S represent the diameter of the circles that the laser pulse sweeps over. The diameters of ablation traces corresponding to L, M, and S are approximately 8, 2.5, and 0 mm , respectively. Each data point is an average of LIBS signals of 100 laser shots.

Fig. 3
Fig. 3

Output computed by neural network training of calibration set as a function of true concentration of Ag. The linear curve is a plot of y = x .

Fig. 4
Fig. 4

Intensity ratio calibration curves for (a) Ag I 338.289 nm , (b) Cu I 327.396 nm , and (c) Pb I 405.782 nm lines. The elemental concentrations (wt.%) of the validation set extracted from the calibration curves are marked.

Tables (5)

Tables Icon

Table 1 Elemental Concentration of the Impurities in the Calibration Set of Sn Alloy (All Values in wt.%)

Tables Icon

Table 2 Analyte Lines Selected for Neural Training a

Tables Icon

Table 3 Statistical Scores of Neural Training

Tables Icon

Table 4 Comparison of Prediction Results for Ag, Cu, and Pb Obtained by Neural Network and Calibration Method with Results of Chemical Analysis

Tables Icon

Table 5 Comparison of Predictions Results from Raw and Optimized Inputs Using Neural Network

Metrics