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

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[2]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[3]  Chris W. Brown,et al.  Detection of ionic water pollutants by laser excited raman spectroscopy , 1972 .

[4]  W. Marsden.  I and J , 2012 .

[5]  I. G. Persiantsev,et al.  Adaptive methods for solving inverse problems in laser raman spectroscopy of multi-component solutions , 2012, Pattern Recognition and Image Analysis.

[6]  W. Rudolph,et al.  Raman and Infrared Spectroscopic Investigations on Aqueous Alkali Metal Phosphate Solutions and Density Functional Theory Calculations of Phosphate—Water Clusters , 2007, Applied spectroscopy.

[7]  I. G. Persiantsev,et al.  Application of artificial neural networks to solve problems of identification and determination of concentration of salts in multi-component water solutions by Raman spectra , 2010, Optical Memory and Neural Networks.

[8]  Tatiana A. Dolenko,et al.  Valence band of liquid water Raman scattering: some peculiarities and applications in the diagnostics of water media , 2000 .

[9]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[10]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[11]  Z. Nickolov,et al.  Determination of Salts in Water Solutions by a Skewing Parameter of the Water Raman Band , 1984 .

[12]  M. Vall-llossera,et al.  Determination of the sea surface emissivity at L‐band and application to SMOS salinity retrieval algorithms: Review of the contributions of the UPC‐ICM , 2008 .

[13]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[14]  I. G. Persiantsev,et al.  New opportunity solutions to inverse problems in laser spectroscopy involving artificial neural networks , 2002 .

[15]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[16]  K. Furic,et al.  Raman spectroscopic study of sodium chloride water solutions , 2000 .

[17]  K. A. Laptinskiy,et al.  Use of neural network algorithms for elaboration of fluorescent biosensors on the base of nanoparticles , 2013, Optical Memory and Neural Networks.

[18]  I. G. Persiantsev,et al.  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 , 2002 .

[19]  Adriano Camps,et al.  Sea surface salinity retrievals from HUT-2D L-band radiometric measurements , 2010 .

[20]  J. Saja,et al.  Effect of electrolyte concentration on the Raman spectra of water in aqueous solutions , 1986 .

[21]  Svetlana V. Patsaeva,et al.  The regularities of change of the 3100-3700 cm-1 band of water raman-scattering in salt aqueous-solutions , 1986 .