Partial Least-Squares Regression as a Tool to Retrieve Gas Concentrations in Mixtures Detected Using Quartz-Enhanced Photoacoustic Spectroscopy

[Image: see text] 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–N(2)O and mixtures of C(2)H(2)–CH(4)–N(2)O, both diluted in N(2), 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%.