Development of gas chromatographic pattern recognition and classification tools for compliance and forensic analyses of fuels: A review.

Autor: Sudol PE; University of Washington, USA., Pierce KM; Seattle Pacific University, USA., Prebihalo SE; University of Washington, USA., Skogerboe KJ; Seattle University, USA., Wright BW; Pacific Northwest National Laboratory, USA., Synovec RE; University of Washington, USA. Electronic address: synovec@chem.washington.edu.
Jazyk: angličtina
Zdroj: Analytica chimica acta [Anal Chim Acta] 2020 Oct 02; Vol. 1132, pp. 157-186. Date of Electronic Publication: 2020 Jul 30.
DOI: 10.1016/j.aca.2020.07.027
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This work was supported by the Internal Revenue Service (IRS) under an Interagency Agreement with the U.S. Department of Energy (DOE) under Contract DE-AC05-76RLO 1830. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute. The views, opinions, and findings contained within this report are those of the authors and should not be construed as an official position, policy, or decision of the DOE or IRS unless designated by other documentation.
(Copyright © 2020 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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