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

被引：4

作者：

Zifarelli, Andrea ^{[1
,2
]}

Patimisco, Pietro ^{[1
,2
]}

Sampaolo, Angelo ^{[1
,2
]}

Giglio, Marilena ^{[1
,2
]}

Menduni, Giansergio ^{[1
,2
,3
]}

Elefante, Arianna ^{[1
,2
]}

Passaro, Vittorio ^{[3
]}

Tittel, Frank K. ^{[4
]}

Spagnolo, Vincenzo ^{[1
,2
]}

机构：

[1] Univ Bari, Dipartimento Interateneo Fis, PolySense Lab, Via Amendola 173, Bari, Italy

[2] Politecn Bari, CNR, IFN, Via Amendola 173, Bari, Italy

[3] Politecn Bari, Photon Res Grp, Dipartimento Ingn Elettr & Informaz, Via Orabona 4, I-70126 Bari, Italy

[4] Rice Univ, Dept Elect & Comp Engn, 6100 Main St, Houston, TX 77005 USA

来源：

QUANTUM SENSING AND NANO ELECTRONICS AND PHOTONICS XVII | 2020年 / 11288卷

关键词：

quartz-enhanced photoacoustic spectroscopy; partial least squares regression; multivariate analysis; multilinear regression; gas mixtures; acetylene; carbon monoxide; nitrous oxide; methane; TUNING FORKS; ALTERNATIVE APPROACH; LASER; SENSOR; PLS; MODE;

D O I：

10.1117/12.2545766

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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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页数：9

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