Principal component analysis of comprehensive three-dimensional gas chromatography time-of-flight mass spectrometry data

Autor: Paige E. Sudol, Sonia Schöneich, Robert E. Synovec
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of Chromatography Open, Vol 2, Iss , Pp 100043- (2022)
Druh dokumentu: article
ISSN: 2772-3917
DOI: 10.1016/j.jcoa.2022.100043
Popis: Comprehensive three-dimensional (3D) gas chromatography (GC3) coupled to time-of-flight-mass spectrometry (GC3-TOFMS) is an intriguing extension of the well-established comprehensive two-dimensional gas chromatography (GC×GC) technique. Although impressive gains have been made in the instrumentation realm, the utility of non-targeted chemometric analysis of GC3-TOFMS data has yet to be explored. Herein, we present the first application of principal component analysis (PCA) to a GC3-TOFMS dataset of jet fuel samples. Five replicates each of four jet fuels (JP8, J1800A, JP4, and JP7) were collected by GC3-TOFMS with commercial thermal modulation from the first-dimension column (1D) to the second-dimension column (2D), and dynamic pressure gradient modulation (DPGM) from the 2D column to the third-dimension column (3D), thus providing full mass transfer (100% duty cycle both modulation stages). A novel re-registration technique is introduced in which a user-selected series of vacant 3D modulations are removed to correct 2D shifting and effectively “center” the data. This shifting has consistently been observed in the 2D versus 1D view of GC3-TOFMS chromatograms, likely due to the slowed flow on 2D in DPGM and/or temperature programming effects. The 3D PCA loadings of the re-registered data revealed subtle chemical differences between the fuels which would not be as easily elucidated using PCA of GC×GC data. Finally, PCA of the two most chemically similar fuels (J1800A and JP7) revealed additional chemical differences which were drowned out in the initial multi-fuel PCA model, highlighting the potential advantage of “pairwise” PCA for multi-class GC3-TOFMS datasets.
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