Thermodynamic modeling of comprehensive two dimensional gas chromatography isovolatility curves for second dimension retention indices based analyte identification

Comprehensive two‐dimensional gas chromatography— A discussion on recent innovations

“…Jaramillo and Dorman [138] studied the prediction errors in 2 t R values and they derived an empirical correction, based on a polynomial fit to the data obtained for alkanes. They later described an easier way to obtain retention data for comparison and identification purposes [139].…”

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review

“…Multiple efforts were dedicated to developing approaches capable of accurately describing all aspects of the chromatographic separations. This yielded a wide variety of methods, from models describing the kinetics and thermodynamics of the separation to computer-based models [30][31][32][33][34]. Despite these achievements, there is still room for improvement as some aspects may require further refinement to achieve greater accuracy.…”

“…Several approaches were used to generate these curves using serial or continuous injections of reference compounds [28,30,[42][43][44]54]. Most of these methods require collecting a significant amount of retention data and could potentially lead to a decrease in the 1 D peak resolution due to the use of long modulation periods [31]. Although early developed models heavily relied on the use of RIs, multiple studies showed that other numerical approaches involving retention factor (k) [32] and flow calculations [55,56] could be considered viable options for retention time predictions.…”

30th Anniversary of comprehensive two‐dimensional gas chromatography: Latest advances

“…In this context, retention time prediction in both the first and second dimensions, using thermodynamic modeling, was introduced, in which the authors developed their model for both vacuum outlet (GC×GC-MS) and atmospheric outlet (GC×GC-FID). 32,33 Nolvachai et al aimed at further enhancing analytes identification with the automated generation of peak centroid for each analyte in the 2D separation plane. The authors highlighted highly precise and reproducible retention times in both dimensions, with 0.003-0.066%RSD for the 1 D retention time and 0.305-0.551%RSD for the 2 D retention time, from the analysis of peach aroma compounds using flow modulated GC×GC-MS.…”

“…Retention modeling and prediction could greatly help in GC×GC method development. In this context, retention time prediction in both the first and second dimensions, using thermodynamic modeling, was introduced, in which the authors developed their model for both vacuum outlet (GC×GC‐MS) and atmospheric outlet (GC×GC‐FID) 32,33 …”