ArticleEnergy & fuels : an American Chemical Society journal2025
Methane, Ethane, and Propane Detection Using a Quartz-Enhanced Photoacoustic Sensor for Natural Gas Composition Analysis.
Article in Energy & fuels : an American Chemical Society journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Simultaneous photoacoustic detection of coal spontaneous combustion indicator gases using single laser and absorption feature selection-optimized partial least squares regression.Photoacoustics · 2026Article
- Intelligent Algorithm-Assisted Indirect Absorption Spectroscopy for Trace Gas Sensing.Sensors (Basel, Switzerland) · 2026Review
- Vibrational Fingerprinting of Gas Mixtures Using COCO-QEPAS.Sensors (Basel, Switzerland) · 2026Article
- Simultaneous Detection ofACS sensors · 2026Article
- Conductance-photoacoustic spectroscopy for fast and concurrent sensing of hydrogen and hydrocarbons.Photoacoustics · 2025Article
- Spectroscopic Study of Volatile Organic Compounds for the Assessment of Coffee Authenticity.Molecules (Basel, Switzerland) · 2025Article
- Greenhouse Gases Detection Exploiting a Multi-Wavelength Interband Cascade Laser Source in a Quartz-Enhanced Photoacoustic Sensor.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
10 authors.
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Abstract
A compact and portable gas sensor based on quartz-enhanced photoacoustic spectroscopy (QEPAS) for the detection of methane (C1), ethane (C2), and propane (C3) in natural gas (NG)-like mixtures is reported. An interband cascade laser (ICL) emitting at 3367 nm is employed to target absorption features of the three alkanes, and partial least-squares regression analysis is employed to filter out spectral interferences and matrix effects characterizing the examined gas mixtures. Spectra of methane, ethane, and propane mixtures diluted in nitrogen are employed to train and test the regression algorithm, achieving a prediction accuracy of ∼98%, ∼96%, and ∼93% on C1, C2, and C3, respectively. With respect to previously reported QEPAS sensors for natural gas analysis, the high prediction accuracy as well as the capability to discriminate and detect C3 within natural gas-like complex mixtures provided by the employment of partial least-squares regression mark significant improvements. Furthermore, these results enable an improved performance of the sensor for in situ, real-time, and online natural gas composition analysis.
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Registered trials
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