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Artificial Neural Networks for Diagnostics of Water-Ethanol Solutions by Raman Spectra

  • Igor Isaev
  • Sergey Burikov
  • Tatiana Dolenko
  • Kirill Laptinskiy
  • Sergey Dolenko
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 799)

Abstract

The present paper is devoted to an elaboration of a method of diagnosis of alcoholic beverages using artificial neural networks: the inverse problem of spectroscopy – determination of concentrations of ethanol, methanol, fusel oil, ethyl acetate in water-ethanol solutions – was solved using Raman spectra. We obtained the following accuracies of concentration determination: 0.25% vol. for ethanol, 0.19% vol. for fusel oil, 0.35% vol. for methanol, and 0.29% vol. for ethyl acetate. The obtained results demonstrate the prospects of using Raman spectroscopy in combination with modern data processing methods (artificial neural networks) for the elaboration of an express non-contact method of detection of harmful and dangerous impurities in alcoholic beverages, as well as for the detection of counterfeit and low-quality beverages.

Keywords

Neural networks Inverse problems Raman spectroscopy Water-ethanol solutions 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.D.V. Skobeltsyn Institute of Nuclear PhysicsM.V. Lomonosov Moscow State UniversityMoscowRussia
  2. 2.Physical DepartmentM.V. Lomonosov Moscow State UniversityMoscowRussia

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