slice-based multivariate calibration strategy for quantification of polycyclic aromatichydrocarbons in oil fractions by means of gc×gc-tofms

Combining gc×gc with mass spectrometry (ms) enhances the power of the instrumentationbeyond conventional chromatography methods. the gc×gc–tofms method offers severalpractical advantages, including higher peak capacity, greater separation power, high sensitivity,and improved selectivity [1]. nevertheless, the complexity of multidimensional chromatographypresents a significant challenge in handling and interpreting the vast amount of data generated [2].in this study, the focus was on quantitation of a mixture of polycyclic aromatic hydrocarbons(pahs) including dibenzoanthracene, fluorene, dibenzothiophene, naphthalene, 1-methylnaphthalene, 3,6-dimethylnaphthalene, pyrene, 1-methylphenanthrene, phenanthrene, andanthracene. to address these issues, a novel slice-based multivariate calibration strategy wasintroduced, utilizing the total ion chromatogram (tic). the data related to distinct segments ofthe target analytes injected from the first column into the second chromatography column wereorganized, creating a new matrix. the concentration of each segment was then determined basedon the ratio of the total concentration of that analyte in the first column. subsequently, multivariatecalibration techniques of partial least squares regression (plsr), support vector machine (svm)and radial basis function-artificial neural network (rbf-ann) method, were employed toconstruct appropriate models, and analytical figures of merit (afoms) were obtained for eachmethod. the plsr model outperformed the other ones according to the two criteria: r2 (0.991-0.999) and rmse (0.02-0.07). compared to the conventional approach of using the entirechromatogram in the form of pixels and applying data volume reduction methods, the advantageof this approach lies in its reliance on the concept of two-dimensional chromatography itself,eliminating the need for pre-processing methods to reduce data volume. this leads to a moreefficient analysis of complex samples with less loss of important information [3]. for example,the values of sensitivity, analytical sensitivity and loqmin obtained for analytes in the plsrmethod are in the range of 2.64×105-2.41×106, 7.18-102.88 and 0.43-2.42, respectively. finally,to prove the potential of the proposed strategy in real samples, quantification of pahs in the heavyoil sample was successfully performed.