Abstract
The paper presents a study of aspects of using single and multiple output artificial neural networks to determine concentrations of inorganic salts in multicomponent water solutions by processing their Raman spectra. The dependence of the results on complexity of the inverse problem has been demonstrated. The results are compared for two data arrays including spectra of solutions of: (1) 5 salts composed of 10 different ions, and (2) 10 salts composed of 10 different ions.
This study has been performed at the expense of the grant of Russian Science Foundation (project no. 14-11-00579).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baldwin, S.F., Brown, C.W.: Detection of ionic water pollutants by laser excited Raman spectroscopy. Water Res. 6, 1601–1604 (1972)
Rudolph, W.W., Irmer, G.: Raman and infrared spectroscopic investigation on aqueous alkali metal phosphate solutions and density functional theory calculations of phosphate-water clusters. Appl. Spectrosc. 61(12), 274A–292A (2007)
Furic, K., Ciglenecki, I., Cosovic, B.: Raman spectroscopic study of sodium chloride water solutions. J. Mol. Struct. 6, 225–234 (2000)
Dolenko, T.A., Churina, I.V., Fadeev, V.V., Glushkov, S.M.: Valence band of liquid water Raman scattering: some peculiarities and applications in the diagnostics of water media. J. Raman Spectrosc. 31(8–9), 863–870 (2000)
Terpstra, P., Combes, D., Zwick, A.: Effect of salts on dynamics of water: a Raman spectroscopy study. J. Chem. Phys. 92(1), 65–70 (1989)
Gogolinskaia (Dolenko), T.A., Patsaeva, S.V., Fadeev, V.V.: The regularities of change of the 3100–3700 cm\(^{-1}\) band of water Raman scattering in salt aqueous solutions. Dokl. Akad. Nauk SSSR 290(5), 1099–1103 (1986)
Gerdova, I.V., Dolenko, S.A., Dolenko, T.A., Churina, I.V., Fadeev, V.V.: New opportunity solutions to inverse problems in laser spectroscopy involving artificial neural networks. Izv. Akad. Nauk Ser. Fiz. 66(8), 1116–1124 (2002)
Dolenko, S., Burikov, S., Dolenko, T., Efitorov, A., Gushchin, K., Persiantsev, I.: Neural network approaches to solution of the inverse problem of identification and determination of partial concentrations of salts in multi-component water solutions. In: Wermter, S., et al. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 805–812. Springer, Switzerland (2014)
Efitorov, A.O., Burikov, S.A., Dolenko, T.A., Persiantsev, I.G., Dolenko, S.A.: Comparison of the quality of solving the inverse problems of spectroscopy of multi-component solutions with neural network methods and with the method of projection to latent structures. Opt. Mem. Neural Netw. (Inf. Opt.) 24(2), 93–101 (2015)
Efitorov, A., Burikov, S., Dolenko, T., Laptinskiy, K., Dolenko, S.: Significant feature selection in neural network solution of an inverse problem in spectroscopy. Procedia Comput. Sci. 66, 93–102 (2015)
Dolenko, S., Efitorov, A., Burikov, S., Dolenko, T., Laptinskiy, K., Persiantsev, I.: Neural network approaches to solution of the inverse problem of identification and determination of the ionic composition of multi-component water solutions. In: Iliadis, L., et al. (eds.) EANN 2015. CCIS, vol. 517, pp. 109–118. Springer, Heidelberg (2015). doi:10.1007/978-3-319-23983-5_11
Zhang, Y., Pulliainen, J., Koponen, S., Hallikainen, M.: Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sens. Environ. 81, 327–336 (2002)
Plaza, J., Martinez, P., Perez, R., Plaza, A., Cantero, C.: Nonlinear neural-network-based mixture model for estimating the concentration of nitrogen salts in turbid inland waters using hyperspectral imagery. In: Proceedings of the SPIE 5584, Chemical and Biological Standoff Detection II, p. 165 (2004). doi:10.1117/12.579805
Chen, L., Zhang, X.: Application of artificial neural networks to classify water quality of the yellow river. Fuzzy information and engineering. Adv. Soft Comput. 54, 15–23 (2009)
Liu, M., Liu, X., Jiang, J., Xia, X.: Artificial neural network and random forest approaches for modeling of sea surface salinity. Int. J. Remote Sens. Appl. 3(4), 229–234 (2013)
Hongwei, J.I., Yan, X.U., Shuang, L.I., Huizhen, X., Hengxia, C.: Simultaneous determination of calcium and magnesium in water using artificial neural network. Spectro-photometric method. J. Ocean Univ. Chin. 9(3), 229–234 (2010)
Hongwei, J.I., Yan, X.U., Shuang, L.I., Huizhen, X., Hengxia, C.: Simultaneous determination of iron and manganese in water using artificial neural network. Catalytic spectrophotometric method. J. Ocean Univ. Chin. 11(3), 323–330 (2012)
Dolenko, S., Isaev, I., Obornev, E., Persiantsev, I., Shimelevich, M.: Study of influence of parameter grouping on the error of neural network solution of the inverse problem of electrical prospecting. In: Iliadis, L., Papadopoulos, H., Jayne, C. (eds.) EANN 2013, Part I. CCIS, vol. 383, pp. 81–90. Springer, Heidelberg (2013)
Dolenko, S.A., Isaev, I.V., Persiantsev, I.G., Obornev, I.E., Obornev, E.A., Shimelevich, M.I.: Elaboration of a complex algorithm of neural network solution of the inverse problem of electrical prospecting based on data classification. In: Proceedings of the 10th International Conference Problems of Geocosmos, St. Petersburgh, Russia, pp. 11–16, 6–10 October 2014. http://geo.phys.spbu.ru/materials_of_a_conference_2014/C2014/01_Dolenko.pdf
Efitorov, A., Dolenko, T., Burikov, S., Laptinskiy, K., Dolenko, S.: Neural network solution of an inverse problem in Raman spectroscopy of multi-component solutions of inorganic salts. In: Samsonovich, A.V., Klimov, V.V., Rybina, G.V. (eds.) Biologically Inspired Cognitive Architectures (BICA) for Young Scientists. AISC, vol. 449, pp. 273–279. Springer, Switzerland (2016)
Werbos, P.J.: Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Harvard University, Cambridge, MA (1974)
Madala, H.R., Ivakhnenko, A.G.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boca Raton (1994)
Wehrens, R.: Chemometrics with R, p. 286. Springer, Heidelberg (2011)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Efitorov, A., Dolenko, T., Burikov, S., Laptinskiy, K., Dolenko, S. (2016). Solution of an Inverse Problem in Raman Spectroscopy of Multi-component Solutions of Inorganic Salts by Artificial Neural Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9887. Springer, Cham. https://doi.org/10.1007/978-3-319-44781-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-44781-0_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44780-3
Online ISBN: 978-3-319-44781-0
eBook Packages: Computer ScienceComputer Science (R0)