SIGNAL (programming language)

Methane

Statistics

Materials science

Chromatography

Social psychology

Computer science

Photoacoustic spectroscopy

Matrix (chemical analysis)

Laser

Physics

Organic chemistry

Psychology

Mathematics

Optics

Analyte

Relaxation (psychology)

Partial least squares regression

Analytical Chemistry (journal)

Chemistry

Programming language

Machine learning

Photoacoustics

A quartz enhanced photoacoustic spectroscopy (QEPAS) sensor capable to detect high concentrations of methane (C1) and ethane (C2) is here reported. The hydrocarbons fingerprint region around 3 µm was exploited using an interband cascade laser (ICL). A standard quartz tuning fork (QTF) coupled with two resonator tubes was used to detect the photoacoustic signal generated by the target molecules. Employing dedicated electronic boards to both control the laser source and collect the QTF signal, a shoe-box sized QEPAS sensor was realized. All the generated mixtures were downstream humidified to remove the influence of water vapor on the target gases. Several natural gas-like samples were generated and subsequently diluted 1:10 in N2. In the concentration ranges under investigation (1%-10% for C1 and 0.1%-1% for C2), both linear and nonlinear responses of the sensor were measured and signal variations due to matrix effects were observed. Partial least squares regression (PLSR) was employed as a multivariate statistical tool to accurately determine the concentrations of C1 and C2 in the mixtures, compensating the matrix relaxation effects. The achieved results extend the range of C1 and C2 concentrations detectable by QEPAS technique up to the percent scale.

High-concentration methane and ethane QEPAS detection employing partial least squares regression to filter out energy relaxation dependence on gas matrix composition