DIAGNOSTICS OF AQUEOUS ETHANOL SOLUTIONS USING RAMAN SPECTROSCOPY AND ARTIFICIAL NEURAL NETWORKS

Read more In this paper, the method of artificial neural networks was used to solve the inverse problem of spectroscopy – to determine the concentration of ethanol, methanol, fusel oil, ethyl acetate in aqueous ethanol solutions using spectra of Raman scattering. The following accuracy of concentration determination was obtained: 0.2 % for ethanol, 2.7 % for methanol, 0.4 % for fusel oil, 1.9 % for ethyl acetate. The results of the solution of the inverse problem demonstrated prospects of the proposed methods for the diagnostics of water-ethanol solutions and alcoholic beverages. The obtained results demonstrate the prospects of using Raman spectroscopy in combination with modern methods of data processing (artificial neural networks) for solution of the problems of diagnostics of aqueous ethanol solutions and alcoholic beverages. The proposed approaches can be further used for development of the 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:
Raman spectroscopy, artificial neural networks, aqueous ethanol solutions. 
УДК 535.3; 661.72; 004.8

DIAGNOSTICS OF AQUEOUS ETHANOL SOLUTIONS USING RAMAN SPECTROSCOPY AND ARTIFICIAL NEURAL NETWORKS