Diagnostics of water-ethanol solutions by Raman spectra with artificial neural networks: Methods to improve resilience of the solution to distortions of spectra

Abstract

In this study, we consider adding noise during training of a neural network as a method of improving the stability of its solution to noise in the data. We tested this method in solving the inverse problem of Raman spectroscopy of aqueous ethanol solutions, for a special type of distortion caused by changes in the power of laser pump leading to compression or stretching of the spectrum. In addition, we tested the method on the spectra of real alcoholic beverages.