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OES Data paper-20200224-Submission-SensorsAndActuatorsB-Preprint.pdf (586.31 kB)

Detecting Trace Methane Levels with Plasma Optical Emission Spectroscopy and Supervised Machine Learning

preprint
submitted on 25.02.2020, 16:05 and posted on 26.02.2020, 11:27 by Jordan Vincent, Hui Wang, Omar Nibouche, PAUL MAGUIRE
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 (PLS-DA) was implemented for CH4 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.

History

Email Address of Submitting Author

pd.maguire@ulster.ac.uk

Institution

University of Ulster

Country

Northern Ireland, UK

ORCID For Submitting Author

0000-0002-2725-4647

Declaration of Conflict of Interest

None

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