Identification of Materials Through SVM Classification of Their LIBS Spectra
DOI:
https://doi.org/10.11113/jt.v62.1897Keywords:
Laser Induced Breakdown Spectroscopy (LIBS), machine learning, Support Vector Machines (SVMs), classification, accuracyAbstract
Laser Induced Breakdown Spectroscopy is a strong analytical method for qualitative studies and Support Vector Machines (SVM) is a powerful machine learning technique for pattern recognition and classification. In this paper we present an application of LIBS qualitative capability reinforced by SVM classification. Three different samples were ablated by an Nd:YAG laser and their spectra were recorded by Ocean Optics HR4000 spectrometer. These spectra possess signatures of the ablated materials. Sometimes these are visible to the naked eye while in many cases it is hard to decide about the presence of any pattern identifying a particular material. In addition variations are always found in the spectra obtained from laser induced ablation. In this situation a pattern recognition tool is very useful that sweep through the whole spectrum and record minor details. Here SVM serves the purpose. SVM classifiers were trained with distinct sets of spectra, belonging to specific materials, for classification. The results obtained from this preliminary experiment are encouraging and can lead us on positive grounds for the future work. This combination of tools can prove to be valuable for fast and automated identification and classification.References
Singh, J. P. and S. N. Thakur. 2007. Laser-Induced Breakdown Spectroscopy. Oxford UK: Elsevier.
Noll, R. 2012. Laser-Induced Breakdown Spectroscopy: Fundamentals and Applications. Berlin Heidelberg: Springer-Verlag.
Li, J. 2008. An Empirical Comparison between SVMs and ANNs for Speech Recognition. Department of Computer Science, Rutgers University, USA.
BURGES, C. J. C. 1998. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 2: 121–167.
Abe, S. 2010. Support Vector Machines for Pattern Classification. London: Springer-Verlag
Remus, J. and K. S. Dunsin. 2012. Robust Validation of Pattern Classification Methods for Laser-induced Breakdown Spectroscopy. Applied Optics. 51(7): B49–B56.
Vance, T., N. Reljin, A. Lazarevic, D. Pokrajac, V. Kecman, N. Melikechi, A. Marcano, Y. Markushin and S. McDaniel. 2010. Classification of LIBS Protein Spectra Using Support Vector Machines and Adaptive Local Hyperplanes. IEEE. 1–7.
Dingari, N. C., I. Barman, A. K. Myakalwar, S. P. Tewari and M. K. Gundawar. 2012. Incorporation of Support Vector Machines in the LIBS Toolbox for Sensitive and Robust Classification Amidst Unexpected Sample and System Variability. Analytical Chemistry. 84: 2686−2694.
Cisewski, J., E. Snyder, J. Hannig and L. Oudejans. 2012. Support vector Machine Classification of Suspect Powders Using Laser-Induced Breakdown Spectroscopy (LIBS) spectral data. Journal of Chemometrics. 1–7.
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