We demonstrate the possibility of applying surface-enhanced Raman spectroscopy (SERS) combined with machine learning technology to detect and differentiate influenza type A and B viruses in a buffer environment. The SERS spectra of the influenza viruses do not possess specific peaks that allow for their straight classification and detection. Machine learning technologies (particularly, the support vector machine method) enabled the differentiation of samples containing influenza A and B viruses using SERS with an accuracy of 93% at a concentration of 200 μg/mL. The minimum detectable concentration of the virus in the sample using the proposed approach was ~0.05 μg/mL of protein (according to the Lowry protein assay), and the detection accuracy of a sample with this pathogen concentration was 84%.

A compact Raman laser gas spectrometer is developed. It comprises a high-power green laser at 532.123 nm as an excitation source and a specially designed gas cell with an internal volume of less than 0.6 cm³ that can withstand gas pressures up to 100 atm. The resolution of the spectrometer is ~1 cm⁻¹. The Raman spectra of chemically pure isotopically enriched carbon dioxide (¹²CO₂, ¹³CO₂) and methane (¹²CH₄, ¹³CH₄) gases are studied. The expected limit of detection (LOD) is less than 100 ppm for the isotopologues of CO₂ and less than 25 ppm for those of CH₄ (at a gas pressure of 50 atm.), making the developed spectrometer promising for studying the sources of emissions of greenhouse gases by resolving their isotopologue composition. We also show the suitability of the spectrometer for Raman spectroscopy of human exhalation.

SPIE Digital Library Proceedings

We present the Raman-scattering based gas analyzer having the relative error of 2%, which allows to measure concentration of gases including carbon stable isotopes of low concentrations.