The identification of elements inside materials is very essential and important part of spectroscopy. Support Vector Machine(SVM) has proven to be powerful in spectroscopy. Support vector based identification of elements from its spectrum is proposed in this study. Here in this study HG-AR(Mercury-Argon) samples are collected from Ocean Optics Spectrometer HR4000 in order to identify Mercury from any unknown substance. Spectral features are extracted from raw data in terms of peak intensity and their wave-length to differentiate two categories.Support vector machine as binary classifier is used to identify element from their spectral features. The identification results are achieved from Nu-SVM and Polynomial SVM classifier in under different parameters. The best identification results were achieved by Nu-SVM classifier. The identification accuracy was 100\textdiscount~ at nu=0.1,0.2 and polynimial degree=2 and 3. The overall results fortify that spectroscopy with SVM can be coherent and rapid to identify elements from any unknown substances.

The identification of elements inside materials is very essential and important part of spectroscopy. Support Vector Machine(SVM) has proven to be powerful in spectroscopy. Support vector based identification of elements from its spectrum is proposed in this study. Here in this study HG-AR(Mercury-Argon) samples are collected from Ocean Optics Spectrometer HR4000 in order to identify Mercury from any unknown substance. Spectral features are extracted from raw data in terms of peak intensity and their wave-length to differentiate two categories.Support vector machine as binary classifier is used to identify element from their spectral features. The identification results are achieved from Nu-SVM and Polynomial SVM classifier in under different parameters. The best identification results were achieved by Nu-SVM classifier. The identification accuracy was 100\textdiscount~ at nu=0.1,0.2 and polynimial degree=2 and 3. The overall results fortify that spectroscopy with SVM can be coherent and rapid to identify elements from any unknown substances.

[1] V. N. Vapnik, “An overview of statistical learning theory.,” IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 988–99, 1999.
[2] V. Vapnik, "The nature of statistical learning theory, Springer science & business media,2013.
[3] R. G. Brereton and G. R. Lloyd, “Support Vector Machines for classification and regression,” Analyst, vol. 135, no. 2, pp. 230–267, 2010.
[4] B. Scholkopf, A. J. Smola, R. C. Williamson, and P. L. Bartlett, “New support vector algorithms.,” vol. 1245, pp. 1207–1245, 2000.
[5] M. Brunner, G. Nagy, and O. Wilhelm, “A Tutorial on ν-Support Vector Machines,” J. Pers., vol. 80, no. 4, pp. 796–846, 2012.
[6] Q. Chen, J. Zhao, C. H. Fang, and D. Wang, “Feasibility study on identification of green, black and Oolong teas using near-infrared reflectance spectroscopy based on support vector machine (SVM),” Spectrochim. Acta - Part A Mol. Biomol. Spectrosc., vol. 66, no. 3, pp. 568–574, 2007.
[7]S. Dai, Z. Gao, Z. Shi, and L. Huang, “Material Intelligent Identification Based on Hyperspectral Imaging and SVM,” Proc. - 2015 1st Int. Conf. Comput. Intell. Theory, Syst. Appl. CCITSA 2015, pp. 69–72, 2016.
[8] P. B. Garcia-Allende, F. Anabitarte, O. M. Conde, J. Mirapeix, F. J. Madruga, and J. M. Lopez-Higuera, “Support vector machines in hyperspectral imaging spectroscopy with application to material identification,” Proc. SPIE, Algorithms Technol. Multispectral, Hyperspectral, Ultraspectral Imag., vol. 6966, no. April 2008, p. 69661V–69661V–11, 2008.
[9] B. Zhou, A. K. Cheema, and H. W. Ressom, “SVM-based spectral matching for metabolite identification,” 2010 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBC’10, pp. 756–759, 2010.
[10] J. Alfred, C. Ulson, S. H. Benez, I. Nunes, and A. N. De Souza, “Nitrogen Content Identification in Crop Plants Using Spectral Reflectance and Artificial Neural Networks,” pp. 1–5.
[11] D. Yang, H. Li, C. Cao, F. Chen, Y. Zhou, and Z. Xiu, “Analysis of the oil content of rapeseed using artificial neural networks based on near infrared spectral data,” J. Spectrosc., vol. 2014, 2014.
[12] S. Ji-Yong et al., “Rapid detecting total acid content and classifying different types of vinegar based on near infrared spectroscopy and least-squares support vector machine,” Food Chem., vol. 138, no. 1, pp. 192–199, 2013.
[13] F. E. Spetale, P. Bulacio, S. Guillaume, J. Murillo, and E. Tapia, “A spectral envelope approach towards effective SVM-RFE on infrared data,” Pattern Recognit. Lett., vol. 71, pp. 59–65, 2016.
[14] W. Bi, A. Tan, Y. Zhao, and M. Gao, “Identification of oil spills by near-infrared spectroscopy (NIR) and support vector machine (SVM),” Proc. SPIE - Int. Soc. Opt. Eng., vol. 7511, no. November 2009, p. 75111R, 2009.
[15] Y. Shao and Y. He, “Visible/near infrared spectroscopy and chemometrics for the prediction of trace element (Fe and Zn) levels in rice leaf,” Sensors (Switzerland), vol. 13, no. 2, pp. 1872–1883, 2013.
[16] F. Chauchard, R. Cogdill, S. Roussel, J. M. Roger, and V. Bellon-Maurel, “Application of LS-SVM to non-linear phenomena in NIR spectroscopy: Development of a robust and portable sensor for acidity prediction in grapes,” Chemom. Intell. Lab. Syst., vol. 71, no. 2, pp. 141–150, 2004.
[17] W. Suphamitmongkol, G. Nie, R. Liu, S. Kasemsumran, and Y. Shi, “An alternative approach for the classification of orange varieties based on near infrared spectroscopy,” Comput. Electron. Agric., vol. 91, pp. 87–93, 2013.
[18] Y. Langeron, M. Doussot, D. J. Hewson, and J. Duchêne, “Classifying NIR spectra of textile products with kernel methods,” Eng. Appl. Artif. Intell., vol. 20, no. 3, pp. 415–427, 2007.