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.

The urea breath test is a non-invasive diagnostic method for Helicobacter pylori infections, which relies on the change in the proportion of ¹³CO₂ in exhaled air. Nondispersive infrared sensors are commonly used for the urea breath test in laboratory equipment, but Raman spectroscopy demonstrated potential for more accurate measurements. The accuracy of the Helicobacter pylori detection via the urea breath test using ¹³CO₂ as a biomarker is affected by measurement errors, including equipment error and δ¹³C measurement uncertainty. We present a Raman scattering-based gas analyzer capable of δ¹³C measurements in exhaled air. The technical details of the various measurement conditions have been discussed. Standard gas samples were measured. ¹²CO₂ and ¹³CO₂ calibration coefficients were determined. The Raman spectrum of the exhaled air was measured and the δ¹³C change (in the process of the urea breath test) was calculated. The total error measured was 6% and does not exceed the limit of 10% that was analytically calculated.

The reservoir computing approach based on a semiconductor laser with optoelectronic time-delay feedback is distinguished by its simplicity, speed, and reliability. However, the response of the reservoir to the input signal in this configuration is a superposition of the laser relaxation oscillations, which has limited complexity, which may therefore reduce computational performance when solving problems requiring high nonlinearity. We experimentally demonstrate a combination of postprocessing techniques that overcome this limitation through upsampling and state space expansion using a nonlinear transformation of nodal functions, and show how this results in an order-of-magnitude reduction in prediction error for the Santa Fe task.

A scheme of a high-power Cr:LiSAF laser operating in active Q-switching mode with a Pockels cell is developed. A method for shortening the laser pulse duration and increasing the peak power by means of intracavity transverse mode selection using an aperture diaphragm and external feedback with Bragg grating is proposed. As a result, the duration of the laser pulse was 19 ns and the pulse energy of 7.7 mJ, which corresponds to 0.41 MW of peak power at the central wavelength of 830 nm. The laser wavelength tuning was also adjusted in the range of 825–865 nm with a width of 2 nm. The results of the research obtained in this work will serve as a basis for the development of new systems for spectrometry for environmental monitoring of the atmosphere using the differential optical absorption spectroscopy method.

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.