Abstract
We report on a statistical tool based on partial least-squares regression (PLSR) able to retrieve single-component concentrations in a multiple-gas mixture characterized by spectrally overlapping absorption features. Absorption spectra of mixtures of CO–N2O and mixtures of C2H2–CH4–N2O, both diluted in N2, were detected in the mid-IR range by exploiting quartz-enhanced photoacoustic spectroscopy (QEPAS) and using two quantum cascade lasers as light sources. Single-gas reference spectra of each target molecule were acquired and used as PLSR-based algorithm training data set. The concentration range explored in the analysis varies from a few parts-per-million (ppm) to thousands of ppm. Within this concentration range, the influence of the gas matrix on nonradiative relaxation processes can be neglected. Exploiting the ability of PLSR to deal with correlated data, these spectra were used to generate new simulated spectra, i.e., linear combinations of the reference ones. A Gaussian noise distribution was added to the created data set, simulating the real QEPAS signal fluctuations around the peak value. Compared with standard multilinear regression, PLSR predicted gas concentrations with a calibration error up to 5 times better, even with absorption features with spectral overlap greater than 97%.
Highlights
We report on a statistical tool based on partial leastsquares regression (PLSR) able to retrieve single-component concentrations in a multiple-gas mixture characterized by spectrally overlapping absorption features
The light sources are shined in sequence[12] or, for the specific instance of a twogas mixture, simultaneously excite the quartz tuning fork (QTF) fundamental and first overtone resonance mode, respectively.[13−15] quartz-enhanced photoacoustic spectroscopy (QEPAS) typically targets isolated absorption features to evaluate the analytes concentrations and avoid interferences from other species contained in the gas matrix
When dealing with complex systems made of correlated data,[16] which is the case for spectroscopic analysis of overlapping absorption features of different components in a gas mixture, these requirements cannot be guaranteed, and the use of an multilinear regression (MLR) approach can result in a lack of precision and accuracy.[17,18]
Summary
We report on a statistical tool based on partial leastsquares regression (PLSR) able to retrieve single-component concentrations in a multiple-gas mixture characterized by spectrally overlapping absorption features. When dealing with complex systems made of correlated data,[16] which is the case for spectroscopic analysis of overlapping absorption features of different components in a gas mixture, these requirements cannot be guaranteed, and the use of an MLR approach can result in a lack of precision and accuracy.[17,18] MLR models can fall into overfitting problems dealing with spectroscopic data, due to the high number of involved variables.[19] Sampaolo[20] and Giglio[21] detected merged absorption features using QEPAS-based sensors and analyzed using MLR. We combined the QEPAS technique with PLSR to identify gas components in a mixture with strongly overlapping absorption features over the full spectral dynamic range of quantum cascade laser (QCL) sources.
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