Alignment for Comprehensive Two-Dimensional Gas Chromatography with Dual Secondary Columns and Detectors
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- 作者：Stephen E. Reichenbach ; Davis W. Rempe ; Qingping Tao ; Davide Bressanello ; Erica Liberto ; Carlo Bicchi ; Stefano Balducci ; Chiara Cordero
- 刊名：Analytical Chemistry
- 出版年：2015
- 出版时间：October 6, 2015
- 年：2015
- 卷：87
- 期：19
- 页码：10056-10063
- 全文大小：567K
- ISSN：1520-6882
文摘
In each sample run, comprehensive two-dimensional gas chromatography with dual secondary columns and detectors (GC 脳 2GC) provides complementary information in two chromatograms generated by its two detectors. For example, a flame ionization detector (FID) produces data that is especially effective for quantification and a mass spectrometer (MS) produces data that is especially useful for chemical-structure elucidation and compound identification. The greater information capacity of two detectors is most useful for difficult analyses, such as metabolomics, but using the joint information offered by the two complex two-dimensional chromatograms requires data fusion. In the case that the second columns are equivalent but flow conditions vary (e.g., related to the operative pressure of their different detectors), data fusion can be accomplished by aligning the chromatographic data and/or chromatographic features such as peaks and retention-time windows. Chromatographic alignment requires a mapping from the retention times of one chromatogram to the retention times of the other chromatogram. This paper considers general issues and experimental performance for global two-dimensional mapping functions to align pairs of GC 脳 2GC chromatograms. Experimental results for GC 脳 2GC with FID and MS for metabolomic analyses of human urine samples suggest that low-degree polynomial mapping functions out-perform affine transformation (as measured by root-mean-square residuals for matched peaks) and achieve performance near a lower-bound benchmark of inherent variability. Third-degree polynomials slightly out-performed second-degree polynomials in these results, but second-degree polynomials performed nearly as well and may be preferred for parametric and computational simplicity as well as robustness.