Determination of 137Ba Isotope Abundances in Water Samples by Inductively Coupled Plasma-optical Emission Spectrometry Combined with Least-squares Support Vector Machine Regression

Document Type : Research Paper


1 NFCRS, Nuclear Science and Technology Research Institute, Tehran, Iran

2 Environmental Laboratory, NSTRI, Tehran, Iran


A simple and rapid method for the determination of 137Ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (ICP-OES) coupled with least-squares support vector machine regression (LS-SVM) is reported. By evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination of 137Ba abundances. After recording the emission spectra in the range of 493.362-493.467 nm, quantification of 137Ba abundances was performed with the aid of LS-SVM algorithm. The obtained results revealed that using LS-SVM as a nonlinear modeling approach improves the predictive quality of the developed models compared with partial least squares (PLS) method. The calculated results proved that the combination of ICP-OES and LS-SVM is a suitable and low cost technique for the determination of 137Ba abundances. Performance of the proposed method was examined through measuring 137Ba abundances in synthetic mixtures and water samples.


[1]         CRC Handbook of Chemistry and Physics, 87th edition, 2006.
[2]         P. Desmoulins, Stable Isotopes, Application-Production, Gif-Sur-Yvette, France, 1994.
[3]         Z. Muccio, G.P. Jackson, Analyst 134 (2009) 213.
[4]         L. Simon, C. Lecuyer, C. Marechal, N. Coltice, Chem. Geol. 225 (2006) 61.
[5]         K. Allmen, M.E. Böttcher, E. Samankassou, T.F. Nägler, Chem. Geol. 277 (2010) 70.
[6]         A. Sonoda, Y. Makita, K. Ooi, T. Hirotsu, J. Nucl. Sci. Technol. 39 (2002) 295.
[7]         D. Beauchemin, Anal. Chem. 78 (2006) 4111.
[8]         S. Misra, P.N. Froelich, J. Anal. At. Spectrom. 24 (2009) 1524.
[9]         J.S. Becker, H. Sela, J. Dobrowolska, M. Zoriy, J.S. Becker, Int. J. Mass Spectrom. 270 (2008) 1.
[10]     M. Khayatzadeh Mahani, A.R. Khanchi, M. Heidari, A. Ahmadi, J. Anal. At. Spectrom. 25 (2010) 1659.
[11]     S. Thangavel, S.V. Rao, K. Dash, J. Arunachalam, Spectrochim. Acta Part B 61 (2006) 314.
[12]     J.A. Wheat, Appl. Spectrosc. 25 (1971) 328.
[13]     P.L. Larkins, Spectrochim. Acta Part B 39 (1984) 1365.
[14]     J.A. Goleb, Anal. Chem. 35 (1963) 1978.
[15]     M. Krachler, D.H. Wegen, J. Anal. At. Spectrom. 27 (2012) 335.
[16]     E. Zolfonoun, S.J. Ahmadi, Spectrochim. Acta Part B 81 (2013) 64.
[17]     X. Mao, A.A. Bol'shakov, D.L. Perry, O. Sorkhabi, R.E. Russo, Spectrochim. Acta Part B 66 (2011) 604.
[18]     A.A. Ganeyev, S.E. Sholupov, Spectrochim. Acta Part B 47 (1992) 1325.
[19]     J. Moros, S. Garrigues, M. Guardia, Trends Anal. Chem. 29 (2010) 578.
[20]     A. Rouhollahi, H. Tavakoli, S. Nayebi, J Ghasemi, M. Noroozi, M. Hashemi, Iran. J. Chem. Chem. Eng. 26 (2007) 41.
[21]     J.B. Ghasemi, E. Zolfonoun, Spectrochim. Acta Part A 115 (2013) 357.
[22]    G.C.Y. Chan, X. Mao, I. Choi, A. Sarkar, O.P. Lama, D.K. Shuh, R.E. Russo, Spectrochim. Acta part B 89 (2013) 40.
[23]    E. Zolfonoun, J. Anal. At. Spectrom. 30 (2015) 2003.
[24]     C. Cortes, V. Vapnik, Mach. Learn. 20 (1995) 273.
[25]     V. Vapnik, Statistical Learning Theory, John Wiley, New York, 1998.
[26]     J.A.K. Suykens, J. Vandewalle, Neural Process. Lett. 9 (1999) 293.
[27]     J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least-Squares Support Vector Machines, World Scientifics, Singapore, 2002.
[28]     A. Niazi, J. Ghasemi, M. Zendehdel, Talanta 74 (2007) 247.
[29]     A.A. Ensafi, F. Hasanpour, T. Khayamian, A. Mokhtari, M. Taei, Spectrochim. Acta A 75 (2010) 867.
[30]     J.B. Ghasemi, E. Zolfonoun, Environ. Monit. Assess. 184 (2012) 3971.