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Fuzzy rule-building expert system classification of fuel using solid-phase microextraction two-way gas chromatography differential mobility spectrometric data. | LitMetric

Fuzzy rule-building expert system classification of fuel using solid-phase microextraction two-way gas chromatography differential mobility spectrometric data.

Anal Chem

Clippinger Laboratories, Center for Intelligent Chemical Instrumentation, Department of Chemistry and Biochemistry, Ohio University, Athens, Ohio 45701-2979, USA.

Published: February 2007

AI Article Synopsis

  • Gas chromatography/differential mobility spectrometry (GC/DMS) is being used to analyze and classify various types of fuels like rocket and jet fuels.
  • The technology combines gas chromatography with differential mobility spectrometry to gather more detailed information through two-dimensional data from fuel samples collected using solid-phase microextraction.
  • A fuzzy rule-based expert system (FuRES) successfully classified these fuel samples with an impressive accuracy of 95% and maintained perfect classification even a month later.

Article Abstract

Gas chromatography/differential mobility spectrometry (GC/DMS) has been investigated for characterization of fuels. Neat fuel samples were sampled using solid-phase microextraction (SPME) and analyzed using a micromachined differential mobility spectrometer with a photoionization source interfaced to a gas chromatograph. The coupling of DMS to GC offers an additional order of information in that two-way data are obtained with respect to compensation voltages and retention time. A fuzzy rule-building expert system (FuRES) was used as a multivariate classifier for the two-way gas chromatograms of fuels, including rocket (RP-1, RG-1), diesel, and jet (JP-4, JP-5, JP-7, JP-TS, JetA-3639, Jet A-3688, Jet A-3690, Jet A-3694, and Jet A-generic) fuels. The GC-DMS with SPME was able to produce characteristic profiles of the fuels and a classification rate of 95 +/- 0.3% obtained with a FuRES model. The classification system also had perfect classification for each fuel sample when applied one month later.

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Source
http://dx.doi.org/10.1021/ac060527fDOI Listing

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