Numerical methods of spectral analysis of multicomponent gas mixtures and human exhaled breath

Golyak, Igor S.; Kareva, Elisabeth R.; Fufurin, I. L.; Anfimov, Dmitriy R.; Scherbakova, Anastasia V.; Nebritova, A.O.; Demkin, Pavel P.; Morozov, A. N.

doi:10.18287/2412-6179-co-1058

Cited by 10 publications

(2 citation statements)

References 29 publications

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“…The described FTIR spectrometer can be used for trace gas analysis [ 44 ] and remote sensing [ 34 ]. Mathematical methods of spectral analysis are described in [ 45 , 46 ]. The main technical characteristics of the compact, low-resolution spacecraft-based spectrometer are given in Table 2 .…”

Section: Methodsmentioning

confidence: 99%

Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy

Mayorova,

Morozov,

Golyak

et al. 2023

Sensors

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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 CO2 and CH4 concentrations using Fourier transform infrared spectroscopy (FTIR) is proposed. An FTIR spectrometer mockup, operating in the wavelength range from 1.0 to 1.7 μm with a spectral resolution of 10 cm−1, is described. The results of CO2 and CH4 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 μm spectral range for CO2 and CH4 bands, a 0.75–0.80 μm range for reference O2 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.

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Section: Methodsmentioning

confidence: 99%

Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy

Mayorova,

Morozov,

Golyak

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…В работе [26] по данным инфракрасной фотоакустической спектроскопии. В работах [29,30] методы машинного обучения применялись для анализа многокомпонентных газовых смесей. В работах [31,32] показаны возможности применения машинного и глубокого обучения для анализа выдыхаемого человеком воздуха.…”

Section: Introductionunclassified

Применение Машинного Обучения Для Диагностики Некоторых Социально Значимых Заболеваний По Выдыхаемому Человеком Воздуху Методом Инфракрасной Лазерной Спектроскопии

Голяк,

Бережанский,

Седова

et al. 2023

Оптика И Спектроскопия

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The infrared spectra of the air exhaled by several groups of volunteers were studied: those suffering from type 1 diabetes, bronchial asthma, and pneumonia. To record infrared spectra, a tunable quantum-cascade laser (QCL) was used. QCL emits in the wavelength range from 5.3 to 12.8 μm in a pulsed mode with a pulse width of 50 ns, a power of up to 150 mW, and a tuning step of 1 cm-1. The laser is optically coupled to an astigmatic gas cell of the Herriot type with an optical path length of 76 m. A difference was found in the intensity of selective lines of biomarker molecules in the spectra of exhaled air of healthy volunteers compared to similar indicators of volunteers suffering from a certain disease. For an example of methods such as the support vector machine (SVM), the k-nearest neighbors (k-NN) and the random forest algorithm (RandomForest), the possibility of classifying volunteers by the infrared spectra of their exhaled air is shown. The use of dimensionality reduction methods (PCA and t-SNE) made it possible to increase the accuracy of disease classification up to 98% in terms of the accuracy metric.

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A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases

Golyak,

Anfimov,

Demkin

et al. 2024

Journal of Biophotonics

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Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm (780–1890 cm−1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid‐infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations.

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Numerical methods of spectral analysis of multicomponent gas mixtures and human exhaled breath

Cited by 10 publications

References 29 publications

Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy

Determination of Greenhouse Gas Concentrations from the 16U CubeSat Spacecraft Using Fourier Transform Infrared Spectroscopy

A hybrid learning approach to better classify exhaled breath's infrared spectra: A noninvasive optical diagnosis for socially significant diseases

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