| Title: | TSMC-Net: Deep-Learning Multigas Classification Using THz Absorption Spectra |
| Author(s): | Chowdhury MAZ; Rice TE; Oehlschlaeger MA; |
| Address: | "Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180-3522, United States" |
| DOI: | 10.1021/acssensors.2c02615 |
| ISSN/ISBN: | 2379-3694 (Electronic) 2379-3694 (Linking) |
| Abstract: | "The identification of gas mixture speciation from a complex multicomponent absorption spectrum is a problem in gas sensing that can be addressed using machine-learning approaches. Here, we report on a deep convolutional neural network for multigas classification using terahertz (THz) absorption spectra, THz spectra mixture classifier network or TSMC-Net. TSMC-Net has been developed to identify eight volatile organic compounds in mixtures based on their fingerprint rotational absorption spectra in the 220-330 GHz frequency range. A data set consisting of simulated absorption spectra for randomly generated mixtures, with absorption greater than thresholds representing detectable limits and annotated with multiple labels, was prepared for model development. The supervised multilabel classification problem, i.e., the identification of individual gases in a mixture, is converted to a supervised multiclass classification problem via label powerset conversion. The trained model is validated and tested against simulated spectra for gas mixtures, with and without white Gaussian noise. The trained model exhibits high precision, recall, and accuracy for each pure compound. Class activation maps illustrate the complex decision-making process of the model and highlight relevant frequency regions that are needed to identify unique mixtures. Finally, the model was demonstrated against measured THz absorption spectra for pure species and mixtures, acquired using a microelectronics-based THz absorption spectrometer. The data set generation strategy and deep convolutional neural network approach are generalized and can be extrapolated to other spectroscopy types, frequency ranges, and sensors" |
| Keywords: | "*Deep Learning *Terahertz Spectroscopy Machine Learning Neural Networks, Computer THz spectroscopy classification convolutional neural network deep learning gas mixtures species identification;" |
| Notes: | "MedlineChowdhury, M Arshad Zahangir Rice, Timothy E Oehlschlaeger, Matthew A eng Research Support, U.S. Gov't, Non-P.H.S. 2023/02/24 ACS Sens. 2023 Mar 24; 8(3):1230-1240. doi: 10.1021/acssensors.2c02615. Epub 2023 Feb 23" |
