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Updated: Apr 13, 2026

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Two-dimensional retention indices improve component identification in comprehensive two-dimensional gas
Ming Jiang, Chadin Kulsing1, Yada Nolvachai1
1‡Australian Centre for Research on Separation Science, School of Chemistry, Monash University, Wellington Road, Clayton, Victoria 3800, Australia.
Abstract:
Comprehensive two-dimensional gas chromatography hyphenated with accurate mass time-of-flight mass spectrometry (GC × GC-accTOFMS) was applied for improved analytical accuracy of saffron analysis, by using retention indices in the two-dimensional separation. This constitutes 3 dimensions of identification. In addition to accTOFMS specificity, and first dimension retention indices ((1)I), a simple method involving direct multiple injections with stepwise isothermal temperature programming is described for construction of isovolatility curves for reference alkane series in GC × GC. This gives access to calculated second dimension retention indices ((2)I). Reliability of the calculated (2)I was evaluated by using a Grob test mixture, and saturated alkanes, revealing good correlation between previously reported I values from the literature, with R(2) correlation being 0.9997. This essentially recognizes the retention property of peaks in the GC × GC 2D space as being reducible to a retention index in each dimension, which should be a valuable tool supporting identification. The benefit of (2)I data, in supplementing (1)I and MS library matching, was clearly demonstrated by the progressive reduction of the number of possible compound matches for peaks observed in saffron. 114 analytes were assessed according to (1)I and (2)I values within ±20 index unit of reference values, and by MS spectrum matching above a match statistic of 750 (including mass accuracy of the molecular ion <20 ppm) and their possible identities derived. The described method provides a new avenue to utilize the full capability of the two-dimensional separation (GC × GC), in combination with MS library matching in complex sample analysis, to provide improved component identification.
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