Deep Learning Enabling Analysis of Exhaled Breath Using Fourier Transform Spectroscopy in the Mid-Infrared

Hesham, Adham; Zeyad, Lama; Elzahraa, Fatma; El-Gamal, A. F.; Mohammed, Passant; Sakr, M. S.; Sabry, Yasser M.

doi:10.1109/icicis52592.2021.9694262

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…The ability of FTIR spectroscopy to identify unique spectral patterns based on material molecular structures extends beyond chemical gases to various materials. Ongoing research explores performance enhancement using machine learning and deep learning [ 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Jang,

Kwon,

Nam

et al. 2024

Sensors

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Chemical warfare agents pose a serious threat due to their extreme toxicity, necessitating swift the identification of chemical gases and individual responses to the identified threats. Fourier transform infrared (FTIR) spectroscopy offers a method for remote material analysis, particularly in detecting colorless and odorless chemical agents. In this paper, we propose a deep neural network utilizing a semi-supervised autoencoder (SSAE) for the classification of chemical gases based on FTIR spectra. In contrast to traditional methods, the SSAE concurrently trains an autoencoder and a classifier attached to a latent vector of the autoencoder, enhancing feature extraction for classification. The SSAE was evaluated on laboratory-collected FTIR spectra, demonstrating a superior classification performance compared to existing methods. The efficacy of the SSAE lies in its ability to generate denser cluster distributions in latent vectors, thereby enhancing gas classification. This study established a consistent experimental environment for hyperparameter optimization, offering valuable insights into the influence of latent vectors on classification performance.

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Section: Introductionmentioning

confidence: 99%

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Jang,

Kwon,

Nam

et al. 2024

Sensors

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“…The study of simultaneous detection of an assortment of molecules with different functional groups is receiving increasing attention from the deep learning community. [22][23][24][25][26][27] There have been many valuable studies on component identification or concentration retrieval of gas mixtures by constructing artificial neural networks in various application scenarios. However, few studies have realized the importance of integrating the two abilities into one model.…”

mentioning

confidence: 99%

Analysis of Gas Mixtures with Broadband Dual Frequency Comb Spectroscopy and Unsupervised Learning Neural Network

Tian,

Kolomenskii,

Schuessler

et al. 2023

Advanced Intelligent Systems

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Broadband mid‐infrared spectroscopy not only offers supreme sensitivity for the massively parallel detection of trace gases but also presents many challenges. Herein, a new platform combining the advantages of a mid‐infrared dual‐comb spectrometer based on two difference‐frequency generation combs pumped by femtosecond Er‐doped fiber comb oscillators and an unsupervised deep learning neural network consisting of information extraction and information mapping blocks is presented. The scarce data problem, the uncertainties of apparatus, and manual operations intrinsic to multicomponent gas mixture analysis are overcome by coupling an unsupervised leaning approach with a model‐agnostic, physics‐informed data augmentation strategy using simulated data from spectral databases. The system provides reliable simultaneous identification of gas species, concentration retrieval, as well as ambient pressure prediction and eliminates the negative impacts on the measurement, such as model error, baseline fluctuation, and unknown absorbers. Parallel optical detection of 31 different mixtures of 5 gas species over a 2900–3100 cm−1 spectral range with a sub part‐per‐billion sensitivity is demonstrated showing the potential in various applications such as atmospheric monitoring, diagnostics with breath biomarkers, and capturing rapid chemical reaction kinetics.

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Deep Learning Enabling Analysis of Exhaled Breath Using Fourier Transform Spectroscopy in the Mid-Infrared

Cited by 2 publications

References 12 publications

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Semi-Supervised Autoencoder for Chemical Gas Classification with FTIR Spectrum

Analysis of Gas Mixtures with Broadband Dual Frequency Comb Spectroscopy and Unsupervised Learning Neural Network

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