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Updated: Jan 23, 2026

On-line Analysis of Nitrogen Containing Compounds in Complex Hydrocarbon Matrixes
Published on: August 5, 2016
Impact of Column Load and Signal-to-Noise Threshold on the Accuracy and Repeatability of Jet A Hydrocarbon Profiling
Brent A Modereger1, Louis Edwards Caceres-Martinez2, Michael E Peretich3
1Department of Chemistry, Purdue University, West Lafayette, Indiana, USA.
Abstract:
Comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC/FID) is a powerful technique for quantifying hydrocarbons in jet fuel. However, factors such as column load and the signal-to-noise (S/N) ratio threshold used for peak inclusion can significantly influence the accuracy and repeatability of hydrocarbon composition measurements. Despite their importance, the effects of these parameters have not been systematically studied. Accurate and precise quantitation of individual hydrocarbon groups (e.g., C7 isoalkanes) is particularly critical, as specific jet fuel properties, such as flash point, freezing point, and kinematic viscosity, are highly dependent on the concentrations of specific hydrocarbon classes. In this study, we investigated the influence of column load (0.05 vs. 2.5 nL) and S/N threshold (0-150) on the measured hydrocarbon composition of Jet A fuel using GC×GC/FID. Results show that both variables significantly affected the number of compounds detected, their measured weight percentages, and the repeatability of the analysis. A higher column load (2.5 nL) generally resulted in more accurate and repeatable measurements compared to a lower load (0.05 nL). In addition, an inverse relationship was found for both the accuracy and repeatability of the hydrocarbon composition measurements versus the S/N threshold value used. These findings demonstrate that GC×GC/FID measurement quality in terms of accuracy and repeatability can be optimized by maximizing the column load value (without incurring on chromatographic performance deterioration) and by minimizing the S/N threshold value.
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