Abstract
The studied inverse problem is determination of ionic composition of inorganic salts (concentrations of up to 10 ions) in multi-component water solutions by their Raman spectra. The regression problem was solved in two ways: 1) by a multilayer perceptron trained on the large dataset, composed of spectra of all possible mixing options of ions in water; 2) dividing the data set into compact clusters and creating regression models for each cluster separately. Within the first approach, we used supervised training of neural network, achieving good results. Unfortunately, this method isn’t stable enough; the results depend on data subdivision into training, test, and out-of-sample sets. In the second approach, we used algorithms of unsupervised learning for data clustering: Kohonen networks, k-means, k-medoids and hierarchical clustering, and built partial least squares regression models on the small datasets of each cluster. Both approaches and their results are discussed in this paper.
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References
Baldwin, S.F., Brown, C.W.: Detection of Ionic Water Pollutants by Laser Excited Raman Spectroscopy. Water Research 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. Applied Spectroscopy 61(12), 274A–292A (2007)
Furic, K., Ciglenecki, I., Cosovic, B.: Raman Spectroscopic Study of Sodium Chloride Water Solutions. J. Molecular Structure 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 Spectroscopy 31, 863–870 (2000)
Burikov, S.A., Dolenko, T.A., Fadeev, V.V., Sugonyaev, A.V.: New Opportunities in the Determination of Inorganic Compounds in Water by the Method of Laser Raman Spectroscopy. Laser Physics 15(8), 1–5 (2005)
Burikov, S.A., Dolenko, T.A., Fadeev, V.V., Sugonyaev, A.V.: Identification of Inorganic Salts and Determination of Their Concentrations in Water Solutions from the Raman Valence Band Using Artificial Neural Networks. Pattern Recognition and Image Analysis 17(4), 554–559 (2007)
Burikov, S.A., Dolenko, S.A., Dolenko, T.A., Persiantsev, I.G.: Neural network solution of the inverse problem of identification and determination of partial concentrations of inorganic salts in multi-component water solution. In: Proceedings of the XIIth All-Russian scientific and technical conference on Neuroinformatics-2010, part 2, pp. 100-110. MEPhI, Moscow (2010). (In Russian)
Burikov, S.A., Dolenko, S.A., Dolenko, T.A., Persiantsev, I.G.: Use of adaptive neural network algorithms to solve problems of identification and determination of concentrations of salts in multi-component water solution by Raman spectra. Neurocomputers: development, application, No. 3, 55-69 (2010). (In Russian)
Burikov, S.A., Dolenko, S.A., Dolenko, T.A., Persiantsev, I.G.: 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) 19(2), 140–148 (2010)
Dolenko, S.A., Burikov, S.A., Dolenko, T.A., Persiantsev, I.G.: Adaptive Methods for Solving Inverse Problems in Laser Raman Spectroscopy of Multi-Component Solutions. Pattern Recognition and Image Analysis 22(4), 551–558 (2012)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Deboeck, G., Kohonen, T.: Visual explorations in finance with self-organizing maps. Springer-Verlag London Limited (1998)
Seiffert, U., Jain, L.C.: Self-Organizing neural networks: recent advances and applications. Physica-Verlag, Heidelberg (2002)
Self-Organizing Maps - Applications and Novel Algorithm Design (2011). http://www.intechopen.com/books/self-organizing-maps-applications-and-novel-algorithm-design
Wehrens, R.: Chemometrics with R, p. 286. Springer-Verlag, Heidelberg (2011)
Zhao, Y.: R and Data Mining: Examples and Case Studies, p. 256. Academic Press, Elsevier (2012)
Wold, S., Geladi, P., Esbensen, K., Öhman, J.: Multi-way principal components-and PLS-analysis. J. of Chemometrics 1(1), 41–56 (1987)
Gerdova, I.V., Dolenko, S.A., Dolenko, T.A., Persiantsev, I.G., Fadeev, V.V., Churina, I.V.: New opportunity solutions to inverse problems in laser spectroscopy involving artificial neural networks. Izv.AN SSSR. Seriya Fizicheskaya 66(8), 1116–1124 (2002)
Dolenko, S., Burikov, S., Dolenko, T., Efitorov, A., Persiantsev, I.: Methods of input data compression in neural network solution of inverse problems of spectroscopy of multi-component solutions. In: Proceedings of the 11th International Conference on Pattern Recognition and Image Analysis: New Information Technologies (PRIA-11-2013), Samara, September 23–28, 2013, vol. II, pp. 541–544. IPSI RAS, Samara (2013)
Dolenko, S., Dolenko, T., Burikov, S., Fadeev, V., Sabirov, A., Persiantsev, I.: Comparison of input data compression methods in neural network solution of inverse problem in laser raman spectroscopy of natural waters. In: Villa, A.E., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part II. LNCS, vol. 7553, pp. 443–450. Springer, Heidelberg (2012)
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-сomponent water solutions. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 805–812. Springer, Heidelberg (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 Mult-Component Solutions with Neural Network Methods and with the Method of Projection to Latent Structures. Optical Memory and Neural Networks (Information Optics) 24(2), 93–101 (2015)
Desgraupes, B.: Clustering Indices. (University of Paris Ouest - Lab Modal’X), p. 34 (2013). http://www.r-project.org
Dunn, J.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Cybernetics and Systems 3(3), 32–57 (1973)
Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(4), 841–847 (1991)
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Dolenko, S., Efitorov, A., Burikov, S., Dolenko, T., Laptinskiy, K., Persiantsev, I. (2015). 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., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2015. Communications in Computer and Information Science, vol 517. Springer, Cham. https://doi.org/10.1007/978-3-319-23983-5_11
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DOI: https://doi.org/10.1007/978-3-319-23983-5_11
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