Application of Adaptive Neural Network Algorithms for Determination of Salt Composition of Waters Using Laser Spectroscopy
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.
[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.
[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.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:68137", author = "Tatiana A. Dolenko and Sergey A. Burikov and Alexander O. Efitorov and Sergey A. Dolenko", title = "Application of Adaptive Neural Network Algorithms for Determination of Salt Composition of Waters Using Laser Spectroscopy", 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.", volume = "8", number = "10", pages = "1349-7", }
