Multivariate Analysis as a Tool to Identify Concentrations from Strongly Overlapping Gas Spectra

Yannick Saalberg; Marcus Wolff

doi:10.3390/s18051562

Multivariate Analysis as a Tool to Identify Concentrations from Strongly Overlapping Gas Spectra

Sensors (Basel). 2018 May 15;18(5):1562. doi: 10.3390/s18051562.

Authors

Yannick Saalberg^{1

2}, Marcus Wolff³

Affiliations

¹ Heinrich Blasius Institute of Physical Technologies, Hamburg University of Applied Sciences, Berliner Tor 21, 20099 Hamburg, Germany. yannick.saalberg@haw-hamburg.de.
² School of Engineering and Computing, University of the West of Scotland, High Street, Paisley PA1 2BE, UK. yannick.saalberg@haw-hamburg.de.
³ Heinrich Blasius Institute of Physical Technologies, Hamburg University of Applied Sciences, Berliner Tor 21, 20099 Hamburg, Germany. marcus.wolff@haw-hamburg.de.

Abstract

We applied a multivariate analysis (MVA) to spectroscopic data of gas mixtures in the mid-IR in order to calculate the concentrations of the single components which exhibit strongly overlapping absorption spectra. This is a common challenge in broadband spectroscopy. Photoacoustic (PA) measurements of different volatile organic compounds (VOCs) in the wavelength region of 3250 nm to 3550 nm served as the exemplary detection technique. Partial least squares regression (PLS) was used to calculate concentrations from the PA spectra. After calibration, the PLS model was able to determine concentrations of single VOCs with a relative accuracy of 2.60%.

Keywords: OPO; PAS; PLS; VOC; concentration determination; multivariate analysis; overlapping spectra; partial least squares regression; photoacoustic spectroscopy.