Comparison of SERS spectra of intact and inactivated viruses via machine learning algorithms for the viral disease’s diagnosis application

Abstract

In this work, Surface-enhanced Raman spectroscopy (SERS) along with machine learning algorithms (MLA) were used to detect and classify the viral particles to assess the possibility of using the spectra of inactivated influenza A viruses for MLA training and spectra database compilation for further study and diagnosis of intact forms of the virus. Viral particles inactivation was performed by formalin, ultraviolet and beta-propiolactone. Support vector method and principal component analysis allowed to classify intact and inactivated viral particles spectra with an accuracy of 80.0–96.7 %. The results obtained suggest that it is not advisable to create a spectral database and train machine learning algorithms for their further application in SERS diagnostics of intact viruses based on the spectra of the inactivated virus particles.