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.
CITATION STYLE
Isaev, I., Burikov, S., Dolenko, T., Laptinskiy, K., & Dolenko, S. (2019). Artificial neural networks for diagnostics of water-ethanol solutions by raman spectra. In Studies in Computational Intelligence (Vol. 799, pp. 167–175). Springer Verlag. https://doi.org/10.1007/978-3-030-01328-8_18
Mendeley helps you to discover research relevant for your work.