Path-integrated concentration and multi-gas detection in FTIR spectroscopy with deep learning methods

Shane Choo; Dohyun Park; Bernd Burgstaller

doi:10.1117/12.2647338

15 March 2023 Path-integrated concentration and multi-gas detection in FTIR spectroscopy with deep learning methods

Shane Choo, Dohyun Park, Bernd Burgstaller

Proceedings Volume 12438, AI and Optical Data Sciences IV; 124380N (2023) https://doi.org/10.1117/12.2647338
Event: SPIE OPTO, 2023, San Francisco, California, United States

Abstract

The accurate and efficient detection of molecular absorption signatures in FTIR output spectra is a challenging task for traditional filter and statistics-based methods; especially with the quantification of density and robustness to the presence of multiple molecules is concerned. Cross correlation, matched filter and support vector machine techniques generalise poorly to unseen variations of the input. In this work, we employ the powerful embedding capabilities of deep learning models to extract path-integrated concentrations of target gases from the complex spectra generated by HITRAN simulation in the mid-infrared spectrum. A quantitative study is done comparing the applicability of the common neural network types MLP, CNN, and LSTM. The results confirm that convolutional layers are substantially effective at capturing the “spatial” information present in characteristic absorption spectra. Furthermore, we show that such neural networks are robust to noise, temperature and concentration variations, and interference from the presence of other molecules.

Conference Presentation

Citation Download Citation

Shane Choo, Dohyun Park, and Bernd Burgstaller "Path-integrated concentration and multi-gas detection in FTIR spectroscopy with deep learning methods", Proc. SPIE 12438, AI and Optical Data Sciences IV, 124380N (15 March 2023); https://doi.org/10.1117/12.2647338

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