Partial least squares regression as novel tool for gas mixtures analysis in quartz-enhanced photoacoustic spectroscopy

Andrea Zifarelli; Pietro Patimisco; Angelo Sampaolo; Marilena Giglio; Giansergio Menduni; Arianna Elefante; Vittorio Passaro; Frank K. Tittel; Vincenzo Spagnolo

doi:10.1117/12.2545766

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31 January 2020 Partial least squares regression as novel tool for gas mixtures analysis in quartz-enhanced photoacoustic spectroscopy

Andrea Zifarelli, Pietro Patimisco, Angelo Sampaolo, Marilena Giglio, Giansergio Menduni, Arianna Elefante, Vittorio Passaro, Frank K. Tittel, Vincenzo Spagnolo

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Proceedings Volume 11288, Quantum Sensing and Nano Electronics and Photonics XVII; 112882B (2020) https://doi.org/10.1117/12.2545766
Event: SPIE OPTO, 2020, San Francisco, California, United States

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.

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Andrea Zifarelli, Pietro Patimisco, Angelo Sampaolo, Marilena Giglio, Giansergio Menduni, Arianna Elefante, Vittorio Passaro, Frank K. Tittel, and Vincenzo Spagnolo "Partial least squares regression as novel tool for gas mixtures analysis in quartz-enhanced photoacoustic spectroscopy", Proc. SPIE 11288, Quantum Sensing and Nano Electronics and Photonics XVII, 112882B (31 January 2020); https://doi.org/10.1117/12.2545766

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