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Updated: Dec 7, 2025

Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses
Paige E Sudol1, Karisa M Pierce2, Sarah E Prebihalo1
1University of Washington, USA.
Abstract:
Gas chromatography (GC) is undoubtedly the analytical technique of choice for compositional analysis of petroleum-based fuels. Over the past twenty years, as comprehensive two-dimensional gas chromatography (GC × GC) has evolved, fuel analysis has often been highlighted in scientific reports, since the complexity of fuel analysis allows for illustration of the impressive peak capacity gains afforded by GC × GC. Indeed, several research groups in recent years have applied GC × GC and chemometric data analysis to demonstrate the potential of these analytical tools to address important compliance (tax evasion, tax credits, physical quality standards) and forensic (arson investigations, oil spills) applications involving fuels. None the less, routine use of GC × GC in forensic laboratories has been limited largely by (1) legal and regulatory guidelines, (2) lack of chemometrics training, and (3) concerns about the reproducibility of GC × GC. The goal of this review is to highlight recent advances in one-dimensional GC (1D-GC) and GC × GC analyses of fuels for compliance and forensic applications, to assist scientists in overcoming the aforementioned hindrances. An introduction to 1D-GC principles, GC × GC technology (column stationary phases and modulators) and several chemometric methods is provided. More specifically, chemometric methods will be broken down into (1) signal preprocessing, (2) peak decomposition, identification and quantification, and (3) classification and pattern recognition. Examples of compliance and forensic applications will be discussed with particular emphasis on the demonstrated success of the employed chemometric methods. This review will hopefully make 1D-GC and GC × GC coupled with chemometric data analysis tools more accessible to the larger scientific community, and aid in eventual widespread standardization.
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