Counter-propagating Gaussian beam enhanced Raman spectroscopy for rapid reagentless detection of respiratory pathogens in nasal swab samples

Abstract

Co-circulation of respiratory viruses compounded by similarities in clinical presentation and mode of transmission underscores the need for broad range pathogen detection. Accurate identification and diagnosis at the point-of-need is critical to limiting disease spread. A novel point-of-need Raman spectroscopy-based platform is described for rapid detection of multiple respiratory pathogens in nasal swab samples with high sensitivity and specificity. The system takes advantage of a counter-propagating Gaussian beam focused within the sample chamber that augments the Raman signal of pathogens. Combined with multiclass machine learning spectral analysis via Gradient Boosting Machine, accurate identification of SARS-CoV-2, human coronaviruses OC43, NL63, 229E, Influenza A (H1N1), respiratory syncytial virus, and Streptococcus pyogenes in spiked clinical nasal swab samples was demonstrated at 99% sensitivity and 93% specificity. The limit of detection was assessed using binary class Support Vector Machine with SARS-CoV-2 in nasal swab samples against negative control at 2.2 × 104 virions/swab. The spectrometer can be operated by minimally trained personnel with software-generated diagnostic yes/no results in 2 min or less, making it well suited for point-of-need applications. Furthermore, adaptive algorithms can detect and differentiate new and emerging variants using a Raman spectral database.

Keywords

• Raman spectroscopy
• SARS-CoV-2
• Rapid diagnostics
• Point-of-need
• Respiratory pathogens
• Virus detection