Open AccessDetecting trace methane levels with plasma optical emission spectroscopy and supervised machine learning
Author(s) -
Jordan Vincent,
Hui Wang,
Omar Nibouche,
Paul Maguire
Publication year - 2020
Publication title -
plasma sources science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.9
H-Index - 108
eISSN - 1361-6595
pISSN - 0963-0252
DOI - 10.1088/1361-6595/aba488
Subject(s) - trace gas , methane , spectroscopy , trace (psycholinguistics) , spectral line , analytical chemistry (journal) , chemistry , linear discriminant analysis , spectrometer , emission spectrum , artificial intelligence , optics , computer science , physics , environmental chemistry , linguistics , philosophy , organic chemistry , astronomy , quantum mechanics
Trace methane detection in the parts per million range is reported using a novel detection scheme based on optical emission spectra from low temperature atmospheric pressure microplasmas. These bright low-cost plasma sources were operated under non-equilibrium conditions, producing spectra with a complex and variable sensitivity to trace levels of added gases. A data-driven machine learning approach based on partial least squares discriminant analysis was implemented for CH 4 concentrations up to 100 ppm in He, to provide binary classification of samples above or below a threshold of 2 ppm. With a low-resolution spectrometer and a custom spectral alignment procedure, a prediction accuracy of 98% was achieved, demonstrating the power of machine learning with otherwise prohibitively complex spectral analysis. This work establishes proof of principle for low cost and high-resolution trace gas detection with the potential for field deployment and autonomous remote monitoring.