Accession Number:

ADA535147

Title:

Classification of Jet Fuels by Fuzzy Rule-Building Expert Systems Applied to Three-Way Data by Fast Gas Chromatography-Fast Scanning Quadrupole Ion Trap Mass Spectrometry

Descriptive Note:

Journal article

Corporate Author:

AIR FORCE RESEARCH LAB WRIGHT-PATTERSON AFB OH PROPULSION DIRECTORATE

Report Date:

2011-01-01

Pagination or Media Count:

11.0

Abstract:

A fast method that can be used to classify unknown jet fuel types or detect possible property changes in jet fuel physical properties is of paramount interest to national defense and the airline industries. While fast gas chromatography GC has been used with conventional mass spectrometry MS to study jet fuels, fast GC was combined with fast scanning MS and used to classify jet fuels into lot numbers or origin for the first time by using fuzzy rule-building expert system FuRES classifiers. In the process of building classifiers, the data were pretreated with and without wavelet transformation and evaluated with respect to performance. Principal component transformation was used to compress the two-way data images prior to classification. Jet fuel samples were successfully classified with 99.80.5 accuracy for both with and without wavelet compression. Ten bootstrapped Latin partitions were used to validate the generalized prediction accuracy. Optimized partial least squares o-PLS regression results were used as positively biased references for comparing the FuRES prediction results. The prediction results for the jet fuel samples obtained with these two methods were compared statistically. The projected difference resolution PDR method was also used to evaluate the fast GC and fast MS data. Two batches of aliquots of ten new samples were prepared and run independently 4 days apart to evaluate the robustness of the method. The only change in classification parameters was the use of polynomial retention time alignment to correct for drift that occurred during the 4-day span of the two collections. FuRES achieved perfect classifications for four models of uncompressed three-way data. This fast GCfast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track change

Subject Categories:

  • Fuels

Distribution Statement:

APPROVED FOR PUBLIC RELEASE