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Proceedings Paper

Partial least squares regression as novel tool for gas mixtures analysis in quartz-enhanced photoacoustic spectroscopy
Author(s): Andrea Zifarelli; Pietro Patimisco; Angelo Sampaolo; Marilena Giglio; Giansergio Menduni; Arianna Elefante; Vittorio Passaro; Frank K. Tittel; Vincenzo Spagnolo
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Paper Abstract

Gas mixtures analysis is a challenging task because of the demand for sensitive and highly selective detection techniques. Partial least squares regression (PLSR) is a statistical method developed as generalization of standard multilinear regression (MLR), widely employed in multivariate analysis for relating two data matrices even with noisy and strongly correlated experimental data. In this work, PLSR is proposed as a novel approach for the analysis of gas mixtures spectra acquired with quartz-enhanced photoacoustic spectroscopy (QEPAS). Results obtained analyzing CO/N2O and CH4/C2H2/N2O gas mixtures are presented. A comparison with standard MLR approach highlights a prediction errors reduction up to 5 times.

Paper Details

Date Published: 31 January 2020
PDF: 9 pages
Proc. SPIE 11288, Quantum Sensing and Nano Electronics and Photonics XVII, 112882B (31 January 2020); doi: 10.1117/12.2545766
Show Author Affiliations
Andrea Zifarelli, Univ. and Politecnico of Bari, CNR-IFN (Italy)
Pietro Patimisco, Univ. and Politecnico of Bari, CNR-IFN (Italy)
Angelo Sampaolo, Univ. and Politecnico of Bari, CNR-IFN (Italy)
Marilena Giglio, Univ. and Politecnico of Bari, CNR-IFN (Italy)
Giansergio Menduni, Univ. and Politecnico of Bari, CNR-IFN (Italy)
Politecnico di Bari (Italy)
Arianna Elefante, Univ. and Politecnico of Bari, CNR-IFN (Italy)
Vittorio Passaro, Politecnico di Bari (Italy)
Frank K. Tittel, Rice Univ. (United States)
Vincenzo Spagnolo, Univ. and Politecnico of Bari, CNR-IFN (Italy)


Published in SPIE Proceedings Vol. 11288:
Quantum Sensing and Nano Electronics and Photonics XVII
Manijeh Razeghi; Jay S. Lewis; Giti A. Khodaparast; Pedram Khalili, Editor(s)

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