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Updated: Dec 12, 2025

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Comprehensive Two-Dimensional Gas Chromatography with Mass Spectrometry: Toward a Super-Resolved Separation Technique
Yada Nolvachai1, Laura McGregor2, Natasha Damiana Spadafora3,4
1Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, VIC 3800, Australia.
Abstract:
A data interpretation and processing approach for improved compound identification and data presentation in comprehensive two-dimensional gas chromatography (GC×GC) is described. A footprint peak of a compound in 2D space can be represented by a centroid or peak apex, similar to the data-reduced histogram spectra used in mass spectrometry. The workflow was demonstrated on data from GC×GC-TOFMS. Peaks in a modulated chromatogram were initially detected by conventional chromatographic integration, followed by a curve-fitting approach, which interpolated high-precision, absolute retention times for all modulated peaks. First dimension retention time (1tR) was obtained by using an exponentially modified Gaussian (EMG) fitting model for near-Gaussian distributed subpeaks, polynomial fitting for highly asymmetrical peaks, and parabolic fitting for under-sampled peaks, which allows determination of a precise 1tR, considering the dwell-time arising from modulation and 2tR. Area summation of the modulated peaks belonging to the same compound was then performed to yield the total peak area. Each compound in the GC×GC-MS result was then represented by its position at the intersecting coordinates, (1tR, 2tR), in the 2D separation plane, having a height of the same magnitude as the total component summed area. This results in a novel and uncluttered GC×GC output convention based on the scripted total ion chromatogram (TIC) data with precise 1tR, 2tR, and area. Comparison between the contour plots from the scripted and conventional TIC revealed improved data presentation, accompanied by an apparent enhanced resolution. The described approach was applied to the identification of 177 aroma compounds from peaches as indicators of fruit quality.
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