QCL spectroscopy combined with the least squares method for substance analysis

Samsonov, D. A.; Tabalina, Anastasiya S.; Fufurin, I. L.

doi:10.1088/1742-6596/918/1/012034

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Ground-based prototypes are described in papers [ 34 , 35 , 36 ]. Substance concentrations are determined by the depth of spectral lines according to the Beer–Lambert law [ 35 , 37 , 38 ].…”

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

“…Для решения обратной задачи спектроскопии, как правило, используются статистические подходы, основанные на корреляции Пирсона [14 -16] или методе наименьших квадратов [17], что позволяет найти наилучшие совпадения между экспериментальным спектром и эталонным спектром из базы данных. Обратная задача является некорректно поставленной, и описанные методы решения не дают универсального подхода для решения в случае большого числа компонентов.…”

Section: Introductionunclassified

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

et al. 2022

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In this paper, the application of machine learning and deep learning in the spectral analysis of multicomponent gas mixtures is considered. The experimental setup consists of a quantum cascade laser with a tuning range of 5.3–12.8 µm, a peak power of up to 150 mW, and an astigmatic Herriott gas cell with an optical path length of up to 76 m. Acetone, ethanol, methanol, and their mixtures are used as test substances. For the detection and clustering of substances, including molecular biomarkers, methods of machine learning, such as stochastic embedding of neighbors with a t-distribution, principal component analysis and classification methods, such as random forest, gradient boosting, and logistic regression, are proposed. A shallow convolutional neural network based on TensorFlow (Google) and Keras is used for the spectral analysis of gas mixtures. Model spectra of substances are used as a training sample, and model and experimental spectra are used as a test sample. It is shown that neural networks trained on model spectra (NIST database) can recognize substances in experimental gas mixtures. We propose using machine learning methods for clustering and classification of pure substances and gas mixtures and neural networks for the identification of gas mixture components. Using the experimental setup described, the experimentally obtained concentration limits are 80 ppb for acetone and 100–120 ppb for ethanol and methanol. The possibility of using the proposed methods for analyzing spectra of human exhaled air is shown, which is significant for biomedical applications.

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Identification of substances from diffuse reflectance spectra of a broadband quantum cascade laser using Kramers–Kronig relations

et al. 2020

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QCL spectroscopy combined with the least squares method for substance analysis

Cited by 4 publications

References 5 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

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

Identification of substances from diffuse reflectance spectra of a broadband quantum cascade laser using Kramers–Kronig relations

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