Membrane introduction mass spectrometry has been coupled with multivariate calibration for the analysis of volatile organic compound (VOC) mixtures. Three mixtures of increasing complexity were modeled using multivariate calibration methods. Multivariate calibration models built using partial least-squares (PLS) regression were compared with univariate calibration for the analysis of a benzene, toluene, and p-xylene (BTX) mixture and an ethylbenzene and p-xylene (EX) mixture. The univariate and multivariate calibration methods performed similarly for the BTX mixture with prediction errors of <15%. For the isomer EX mixture, the PLS model outperformed the univariate model having prediction errors between 8 and 13% compared to errors of 40% for the univariate model. The third mixture, containing all four analytes (benzene, toluene, ethylbenzene, p-xylene), was modeled using PLS and resulted in calibration relative errors of 6−15% and prediction relative errors of 3−48%. This paper demonstrates the feasibility of using membrane introduction mass spectrometry with multivariate calibration for the analysis of complex VOC mixtures.
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