Comparing surface-enhanced Raman spectroscopy and Raman microscopy with machine learning for the authentication of Covid-19 vaccines

Covid-19 is a novel coronavirus that emerged in 2019 and spread across the globe, establishing a worldwide pandemic. Vaccination was presented as the most effective solution against the virulence of Covid-19. Accelerated vaccination programmes pushed several nucleic acid-based vaccines into production. Global desperation and limited vaccine supply allowed substandard and falsified (SF) Covid-19 vaccines to enter the supply chain. Conventional analytical methods can be cumbersome, costly and sophisticated to operate. Thus, this study presented a comparison of handheld surface-enhanced Raman spectroscopy (SERS) and Raman microscopy with machine learning algorithms (MLAs) for the rapid authentication of Covid-19 vaccines. Measurements were taken using the Metrohm MIRA XTR DS handheld Raman spectrometer and the Horiba XploRA Plus Raman microscope. Raman spectroscopy showed strong potential as a vaccine authentication method, allowing identification of nucleic acid-specific bands in spectra. SERS showed enhancement of up to $498 \%$ when applied to vaccines of sufficient concentration. Clustering based on principal component analysis (PCA) showed some accuracy but indicated poor repeatability for SERS, although, multiple classification models obtained $100 \%$ accuracy and area under the curve (AUC) for vaccine brand prediction based on spectral characteristics. Raman microscopy produced variable results with improved spectral quality over Raman spectroscopy for a number of samples. However, significant fluorescence was observed in numerous vaccine spectra, limiting the identification potential of the method. Clustering based on PCA showed accuracy in distinguishing between vaccine samples, but showed limited performance in vaccine brand identification. Therefore, this paper presents proof of concept for the use of both handheld Raman spectroscopy and confocal Raman microscopy alongside MLAs for the rapid, on-site authentication of Covid-19 vaccines, with further method optimisation required to combat fluorescence interference in vaccine spectra and expansion of sample size to address the potential of overfitting in the MLAs.