Study of Resilience of Neural Network Solution of Inverse Problem Based on Integration of Optical Spectroscopic Methods to Noise in Data
Igor Isaev, D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Olga Sarmanova, Faculty of Physics & D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Sergey Burikov, Faculty of Physics & D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Tatiana Dolenko, Faculty of Physics & D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Kirill Laptinskiy, D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Nikita Trifonov, Faculty of Physics & D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Sergey Dolenko, D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Russia
Abstract
In various fields of industry and ecology there is a need for simple, express, non-contact, highly selective methods for determining concentrations of ions dissolved in multi-component solutions. Traditional chemical and analytical methods provide high accuracy of determining the concentrations, but these methods are contact, individual for each ion, and their implementation requires a long time, good sample preparation and consumption of expensive reagents [1,2]. Optical spectroscopy methods do not have these drawbacks: spectra of solutions can be obtained quickly, remotely, without special preparation of samples [3]. However, for many methods of optical spectroscopy there is no analytical and/or direct numerical solution for the inverse problem (IP) of determination of concentrations of each component in multi-component solutions by spectra. Therefore, recently, application of machine learning methods to solve these problems has been actively investigated [4,5]. In addition, IP are often ill-posed or ill-conditioned. This causes high sensitivity of the solution to noise in the data, and low accuracy of the solution.
In the preceding study [6] it has been suggested to use an ensemble of optical spectroscopy methods to increase the accuracy of the solution obtained by machine learning methods. We consider joint use of Raman spectroscopy and optical absorption spectroscopy methods to determine the concentrations of heavy metal ions in water. This complex IP is solved by artificial neural networks as a machine learning method. It is demonstrated that when one of the methods is strong by its results, and the other one is weak, their joint application does not allow one to improve the results of the strong method.
In this paper, we investigate the resilience of the considered IP to noise in data. The task was set to find out whether the joint use of these two types of spectroscopy can improve resilience of the solution to noise in input data of the considered IP in comparison with the case of using each of these types of spectroscopy separately. As possible alternative ways to increase the resilience of the neural network solution of this problem, the previously studied methods [7] of group determination of parameters and the use of ensembles of neural networks were considered.
The main result is similar to that of the previous studies: combination of a “strong” method with a much “weaker” one does not allow one to increase the results of the “strong” method alone. This regards not only the error of the IP solution, but also its resilience to noise in the input data.
This study has been performed at the expense of Russian Science Foundation, project
no.19-11-00333.
1. Crompton T.R. Determination of anions in natural and treated waters. Taylor&Francis, 2002, 828 p.
2. Michalski R., Jablonska M., Szopa S., Łyko A. Application of Ion Chromatography with ICP-MS or MS Detection to the Determination of Selected Halides and Metal. Metalloids Species, Critical Reviews in Analytical Chemistry, 2011, V. 41, pp. 133-150
3. 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, 2007, V.61 (12), pp.274A–292A
4. Isaev I., Burikov S., Dolenko T., Laptinskiy K., Vervald A., Dolenko S. Joint application of group determination of parameters and of training with noise addition to improve the resilience of the neural network solution of the inverse problem in spectroscopy to noise in data. Lecture Notes in Computer Science, 2018, V. 11139, pp. 435–444.
5. Isaev I., Burikov S., Dolenko T., Laptinskiy K., Dolenko S. Artificial neural networks for diagnostics of water-ethanol solutions by Raman spectra. Studies in Computational Intelligence, 2019, V. 799, pp. 167–175.
6. Isaev, I., Trifonov, N., Sarmanova, O., Burikov, S., Dolenko, T., Laptinskiy, K., Dolenko, S. Joint application of raman and optical absorption spectroscopy to determine concentrations of heavy metal ions in water using artificial neural networks. Proceedings of SPIE, Saratov Fall Meeting 2019: Laser Physics, Photonic Technologies, and Molecular Modeling, 2020, V. 11458, pp. 114580R.
7. Isaev I., Burikov S., Dolenko T., Laptinskiy K., Vervald A., Dolenko S. Joint application of group determination of parameters and of training with noise addition to improve the resilience of the neural network solution of the inverse problem in spectroscopy to noise in data. Lecture Notes in Computer Science, 2018, V. 11139, pp. 435–444.
Speaker
Igor Isaev
D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University
Russia
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