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

Journal Article · · Analytica Chimica Acta
 [1];  [2];  [1];  [3];  [4];  [1]
  1. Univ. of Washington, Seattle, WA (United States)
  2. Seattle Pacific Univ., WA (United States)
  3. Seattle Univ., WA (United States)
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Gas chromatography (GC) is undoubtedly the analytical technique of choice for analyzing the composition 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, as their complexity 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 chemometrics 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, in an effort 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 techniques will be provided. More specifically, chemometric techniques will be broken down into (1) signal pre-processing, (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 techniques. Overall, 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.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1668702
Alternate ID(s):
OSTI ID: 1809112
Report Number(s):
PNNL-SA--151027
Journal Information:
Analytica Chimica Acta, Journal Name: Analytica Chimica Acta Vol. 1132; ISSN 0003-2670
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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