Authentication of Covid-19 Vaccines Using Synchronous Fluorescence Spectroscopy.

Area of Science:

• Analytical Chemistry
• Biotechnology
• Data Science

Background:

• Ensuring the authenticity of vaccines is critical for public health.
• COVID-19 vaccines utilize different platforms, including DNA and mRNA technologies.
• Reliable methods are needed to verify vaccine identity and origin.

Purpose of the Study:

• To evaluate synchronous fluorescence spectroscopy combined with multivariate data analysis for COVID-19 vaccine authentication.
• To differentiate between DNA-based and mRNA-based vaccine formulations.
• To assess the efficacy of machine learning algorithms in vaccine verification.

Main Methods:

• Synchronous fluorescence spectroscopy was employed to capture spectral data from various COVID-19 vaccines.
• Multivariate data analysis techniques, including Principal Component Analysis (PCA), PCA-Gaussian Mixture Models (PCA-GMM), and Self-Organising Maps (SOM), were applied.
• Spectral data were analyzed to identify characteristic patterns and classify vaccine types.

Main Results:

• Fluorescence spectra for DNA and DNA-based vaccines were identical, as were spectra for RNA and RNA-based vaccines.
• Principal Component Analysis (PCA) effectively separated DNA-based and mRNA-based vaccines, with the first three PCs explaining 99.5% of variance.
• PCA-GMM further classified mRNA vaccines, identifying three distinct outliers, while SOM demonstrated effectiveness without dimensionality reduction.

Conclusions:

• Synchronous fluorescence spectroscopy coupled with machine learning algorithms (PCA, PCA-GMM, SOM) offers a simple, rapid, and reliable method for COVID-19 vaccine authentication.
• The technique successfully distinguishes between different vaccine platforms.
• This approach holds significant potential for routine vaccination verification processes.