Application of Adaptive Neural Network Algorithms for Determination of Salt Composition of Waters Using Laser Spectroscopy

Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko

Abstract:

In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.

Keywords: Inverse problems, multi-component solutions, neural networks, Raman spectroscopy.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1096475

References:

[1] M. H. Hassoun, Fundamentals of Artificial Neural Networks. Massachusetts, Cambridge: MIT Press, 1995.
[2] S. S. Haykin, Neural networks: a comprehensive foundation. Prentice Hall, 1999.
[3] S. A. Dolenko, A. V. Filippov, A. F. Pal, I. G. Persiantsev, and A. O. Serov, "Use of neural network based auto-associative memory as a data compressor for pre-processing optical emission spectra in gas thermometry with the help of neural network,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment (NIMA A), vol. 502, no. 2-3, pp. 523-525, 2003.
[4] S. A. Burikov, A. M. Vervald, I. I. Vlasov, S. A. Dolenko, K. A. Laptinskiy, and T. A. Dolenko, "Use of neural network algorithms for elaboration of fluorescent biosensors on the base of nanoparticles,” Optical Memory and Neural Networks (Information Optics), vol. 22, no. 3, pp. 156–165, 2013.
[5] M. Talone, R. Sabia, A. Camps, M. Vall-llosera, C. Gabarró, and J. Font, "Sea surface salinity retrievals from HUT-2D L-band radiometric measurements,” Remote Sens. Environ., no. 114, pp. 1756-1764, 2010.
[6] A. Camps, J. Font, M. Vall-llossera, I. Corbella, N. Duffo, F. Torres, S. Blanch, A. Aguasca, R. Villarino, C. Gabarró, L. Enrique, J. Miranda, R. Sabia, and M. Talone, "Determination of the sea surface emissivity at Lband and application to SMOS salinity retrieval algorithms: Review of the contributions of the UPC-ICM,” Radio Science, vol. 43, p.RS3008, 2008.
[7] J. Font, J. Boutin, N. Reul, P. Spurgeon, J. Ballabrera-Poy, A. Chuprin, et al., "SMOS first data analysis for sea surface salinity determination,” International J. of Remote Sensing, no. 1, pp. 1-17, 2013.
[8] T. G. Balicheva, and O. A. Lobaneva, Electronic and vibrational spectra of inorganic and coordination compounds. Leningrad: Leningrad State University, 1983, pp. 9-81. (Russian)
[9] S. F. Baldwin, and C. W. Brown, "Detection of ionic water pollutants by laser excited Raman spectroscopy,” Water Research, vol. 6, pp. 1601- 1604, 1972.
[10] W. W. Rudolph, and G. Irmer, "Raman and infrared spectroscopic investigation on aqueous alkali metal phosphate solutions and density functional theory calculations of phosphate-water clusters,” Applied spectroscopy, vol. 61, no 12, pp. 274A-292A, 2007.
[11] F. Rull, and J. A. De Saja, "Effect of electrolyte concentration on the Raman spectra of water in aqueous solutions,” J. Raman Spectroscopy, vol. 17, no. 2, pp.167-172, 1986.
[12] G. E. Walrafen, "Raman studies of the effects of temperature on water and electrolyte solutions,” J. Chem. Phys., vol. 44, no. 4, pp. 1546-1558, 1966.
[13] T. A. Dolenko, I. V. Churina, V. V. Fadeev, and S. M. Glushkov, "Valence band of liquid water Raman scattering: some peculiarities and applications in the diagnostics of water media,” J. Raman Spectroscopy, vol. 31, pp. 863-870, 2000.
[14] T. A. Gogolinskaia, S. V. Patsaeva, and V. V. Fadeev, "The regularities of change of the 3100-3700 cm-1 band of water Raman scattering in salt aqueous solutions,” Doklady Akademii Nauk SSSR, vol. 290, no. 5, pp. 1099-1103, 1986.
[15] G. M. Georgiev, T. K. Kalkanjiev, V. P. Petrov, and Zh. Nickolov, "Determination of salts in water solutions by a skewing parameter of the water Raman band,” Applied Spectroscopy, vol. 38, no. 4, pp. 593-595, 1984.
[16] K. Furic, I. Ciglenecki, and B. Cosovic, "Raman spectroscopic study of sodium chloride water solutions,” J. Molecular Structure, vol. 6, pp. 225-234, 2000.
[17] A. Yu. Bekkiev, T. A. Gogolinskaia, and V. V. Fadeev, "Simultaneous determination of temperature and salinity of sea water by the method of laser Raman spectroscopy,” Doklady Akademii Nauk SSSR, vol. 271, no. 4, pp. 849-853, 1983.
[18] S. A. Burikov, T. A. Dolenko, V. V. Fadeev, and A. V. Sugonyaev, "Identification of inorganic salts and determination their concentrations in water solutions above the water Raman valence band using artificial neural networks,” Pattern Recognition and Image Analysis, vol. 15, no. 2, pp. 520-523, 2005.
[19] S. A. Burikov, T. A. Dolenko, and V. V. Fadeev, "Identification of inorganic salts and determination of their concentrations in multicomponent aqueous solutions by the Raman valence band of water using artificial neural networks,” Neurocomputers: Development, Application, no. 5, pp. 62-72, 2007.
[20] S. A. Burikov, S. A. Dolenko, T. A. Dolenko, and I. G. Persiantsev, "Application of Artificial Neural Networks to Solve Problems of Identification and Determination of Concentration of Salts in Multi- Component Water Solutions by Raman Spectra,” Optical Memory and Neural Networks (Information Optics), vol. 19, no. 2, pp. 140-148, 2010.
[21] S. A. Dolenko, S. A. Burikov, T. A. Dolenko, and I. G. Persiantsev, "Adaptive Methods for Solving Inverse Problems in Laser Raman Spectroscopy of Multi-Component Solutions,” Pattern Recognition and Image Analysis, vol. 22, no. 4, pp. 551-558, 2012.
[22] I. V. Gerdova, S. A. Dolenko, T. A. Dolenko, I. V. Churina, and V. V. Fadeev, "New opportunity solutions to inverse problems in laser spectroscopy involving artificial neural networks,” Izvestiya Akademii Nauk Seriya Fizicheskaya, vol. 66, no. 8, pp. 1116-1124, 2002.
[23] D. Specht, "A General Regression Neural Network,” IEEE Trans. on Neural Networks, vol. 2, no. 6, pp. 568-576, Nov. 1991.
[24] H. R. Madala, and A. G. Ivakhnenko, Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, 1994.