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In chromatographic analysis of multicomponent real samples, peak overlapping, high level of noise and background are frequently encountered, making the qualitative and quantitative analysis difficult or even impossible. In this work, an algorithm named as generalized window factor analysis (GWFA) was proposed for quantitative analysis of the target components in the samples with complex matrices by gas chromatography–mass spectrometry (GC–MS). The theory and calculation of GWFA are just similar with the conventional window factor analysis (WFA), but the “window” is defined as the selected channels (mass-to-charge ratios) in the mass spectral dimension of the data matrix, instead of a continuous region in chromatographic dimension along the retention time. Therefore, the generalized window for a target component can be easily determined with the help of the mass spectrum. Then, the calculated mass spectrum can be obtained with the window and quantitative determination can be achieved with the help of the standard. Both simulated and experimental data were investigated with the proposed method. Whether or not a peak shift occurs during the test, accurate results were obtained from the overlapping GC–MS signals with high level of noise and background. [ABSTRACT FROM AUTHOR]

A second-order method, standard signal extraction was proposed for quantitative analysis of target analytes in the samples with complex matrices by gas chromatography-mass spectrometry. In the method, standard addition was adopted. The data of the pure standard, sample, and spiked sample were used in the calculation. By performing principal component analysis on the data aligned with the measured signals of the sample and standard, the standardized signal that is orthogonal to the signals of interferences in the sample can be extracted. Then, the signal of spiked sample was projected to the standardized signal. The concentration ratio of the target analyte in the sample and spiked sample can be obtained. Finally, the concentration of the target analyte in the sample can be calculated with the added concentration in the spiked sample. Both simulated and experimental data were investigated with the proposed approach, compared with rank annihilation factor analysis. The recoveries of standard addition were found in a range of 99-105%, and the relative standard deviations obtained in three repeated measurements were less than 3%. Results show that the method can accurately estimate the quantitative information of analytes of interest in the presence of interferents and can be applied more widely compared with rank annihilation factor analysis. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]