An estimated 10.5% of the world’s population aged 20–79 years are currently living with diabetes in 2021. An urgent task is to develop a non-invasive express-diagnostics of diabetes with high accuracy. Type 1 diabetes mellitus (T1DM) diagnostic method based on infrared laser spectroscopy of human exhaled breath is described. A quantum cascade laser emitting in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm and Herriot multipass gas cell with an optical path length of 76 m were used. We propose a method for collecting and drying an exhaled human air sample and have measured 1200 infrared exhaled breath spectra from 60 healthy volunteers (the control group) and 60 volunteers with confirmed T1DM (the target group). A 1-D convolutional neural network for the classification of healthy and T1DM volunteers with an accuracy of 99.7%, recall 99.6% and AUC score 99.9% was used. The demonstrated results require clarification on a larger dataset and series of clinical studies and, further, the method can be implemented in routine medical practice.

<!-- *** Custom HTML *** --> We consider the possibility of using a combination of machine and deep learning in the spectral analysis for multicomponent gas mixtures. The experimental setup consists of a quantum cascade laser with a tuning range of 5.3 to 12.8 μm, a peak power up to 150 mW, and a Herriot astigmatic gas cell with an optical path of up to 76 m. We used acetone, ethanol, methanol, acetaldehyde, and ethylene as test substances for the described techniques. For detection and clustering biomarkers, we used machine learning methods such as t-distributed stochastic neighbor embedding, principal component analysis, and classification methods such as decision tree, k-nearest neighbors, logistic regression, and support vector machines. We used a shallow convolutional neural network (CNN) based on TensorFlow (Google) and Keras for spectral analysis of gas mixtures. We modeled IR spectra of pure substances using an NIST database as training and validation sets. Then we used experimental spectra as a test set. We showed that logistic regression gives us the best result for pure substances’ classification. Next, we modeled gas mixtures from synthetic IR spectra for CNN as training and validation sets. We showed that neural networks trained on synthetic spectra can recognize synthetic gas mixtures and experimental individual gaseous substances. We suggest using machine learning methods for pure substances clustering and classification and CNN for gas mixture components identification. We experimentally obtained minimum detectable concentration at ppm level for pure substances. Finally, we estimated that detection limits for the described experimental setup and numerical techniques occurs at levels around 10 to 50 ppb.

Greenhouse gases absorb the Earth’s thermal radiation and partially return it to the Earth’s surface. When accumulated in the atmosphere, greenhouse gases lead to an increase in the average global air temperature and, as a result, climate change. In this paper, an approach to measuring CO${}_2$ and CH${}_4$ concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 $\mathsf{\mu }$m with a spectral resolution of 10 cm${}^{-1}$, is described. The results of CO${}_2$ and CH${}_4$ observations throughout a day in urban conditions are presented. A low-resolution FTIR spectrometer for the 16U CubeSat spacecraft is described. The FTIR spectrometer has a 2.0–2.4 $\mathsf{\mu }$m spectral range for CO${}_2$ and CH${}_4$ bands, a 0.75–0.80 $\mathsf{\mu }$m range for reference O${}_2$ bands, an input field of view of 10${}^{-2}$ rad and a spectral resolution of 2 cm${}^{-1}$. The capabilities of the 16U CubeSat spacecraft for remote sensing of greenhouse gas emissions using a developed FTIR spectrometer are discussed. The design of a 16U CubeSat spacecraft equipped with a compact, low-resolution FTIR spectrometer is presented.

In the present paper a problem of substance identification in FTIR (Fourier transform infrared) spectroscopy is considered. The spectral library hitlist search is chosen as the main tactic. In the paper the Pearson correlation coefficient as a similarity criterion between two spectra is suggested. A situation when one of the measured spectra has an additive narrowband white noise component with a Gaussian distribution is considered. In that case the probability density of the correlation coefficient is found. A concept of the probability of correct detection is proposed and a theoretical expression is found. In addition, we consider a boundary correlation coefficient search algorithm, which allows one to find a boundary value providing the required correct detection. Computational experiments have shown the applicability of the method.

<!-- *** Custom HTML *** --> A real-time automated system for remote substance identification on various surfaces without preliminary sample preparation is presented. In practice, it can be used, for example, as an alerting system to signal the presence of some contaminants. The main components of the system are diffuse reflectance spectra acquisition module, data processing module, and identification module. Development of each module was based on the choice of appropriate devices and algorithms, either existing or newly designed. The experimental setup consists of a quantum cascade laser emitting in the spectral range of 5.3 to 12.8 μm with a HgCdTe photodetector. To achieve better selectivity of substance recognition, identification algorithms were based on the absorption and transmission spectra calculated from the recorded diffuse reflectance spectra. Spectra conversion algorithms employed Kramers–Kronig relations, phase spectra extrapolation, and phase correction. The system was supplied with the recognition database composed of certain commercially available substances. The experiments showed that the usage of transmittance spectra significantly improved the sensitivity of the identification method; the remote identification limit of 30 μg acetylsalicylic acid has been experimentally confirmed. For similar substances, such limit was estimated as 10 μg / cm² at a distance of 1 m.