Path-integrated Concentration and Multi-gas Detection in FTIR Spectroscopy with Deep Learning Methods

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.