Optical Memory and Neural Networks

, Volume 27, Issue 2, pp 89–99 | Cite as

Use of Adaptive Methods to Solve the Inverse Problem of Determination of Composition of Multi-Component Solutions

  • A. Efitorov
  • S. DolenkoEmail author
  • T. Dolenko
  • K. Laptinskiy
  • S. Burikov


This study considers solving the inverse problem of determination of salt or ionic composition of multi-component solutions of inorganic salts by their Raman spectra using artificial neural networks. From the point of view of data analysis, one of the key problems here is high input dimensionality of the data, as the spectrum is usually recorded in 1–2 thousand channels. The two main approaches used for dimensionality reduction are feature selection and feature extraction. In this paper, three feature extraction methods are compared: channel aggregation, principal component analysis, and discrete wavelet transformation. It is demonstrated that for neural network solution of the inverse problem of determination of salt composition, the best results are provided by channel aggregation.


inverse problems artificial neural networks principal component analysis wavelet analysis aqueous solutions of salts Raman spectroscopy 


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Copyright information

© Allerton Press, Inc. 2018

Authors and Affiliations

  • A. Efitorov
    • 1
  • S. Dolenko
    • 1
    Email author
  • T. Dolenko
    • 1
    • 2
  • K. Laptinskiy
    • 1
    • 2
  • S. Burikov
    • 1
    • 2
  1. 1.Skobeltsyn Institute of Nuclear PhysicsMoscow State UniversityMoscowRussia
  2. 2.Phaculty of PhysicsMoscow State UniversityMoscowRussia

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