Tags

Type your tag names separated by a space and hit enter

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.
Talanta. 2011 Jan 30; 83(4):1260-8.T

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 99.8 ± 0.5% 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 GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes.

Links

Publisher Full Text

Authors+Show Affiliations

Sun X
Center for Intelligent Chemical Instrumentation, Clippinger Laboratories, Department Of Chemistry and Biochemistry, Ohio University, Athens, OH 45701, USA.
Zimmermann CM
No affiliation info available
Jackson GP
No affiliation info available
Bunker CE
No affiliation info available
Harrington PB
No affiliation info available

MeSH

ClassificationCluster AnalysisExpert SystemsFuzzy LogicGas Chromatography-Mass SpectrometryHydrocarbonsLeast-Squares AnalysisPrincipal Component AnalysisReproducibility of ResultsTime FactorsTransportation

Pub Type(s)

Journal Article

Language

eng

PubMed ID

21215862

Citation

Sun, Xiaobo, et al. "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." Talanta, vol. 83, no. 4, 2011, pp. 1260-8.
Sun X, Zimmermann CM, Jackson GP, et al. 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. Talanta. 2011;83(4):1260-8.
Sun, X., Zimmermann, C. M., Jackson, G. P., Bunker, C. E., & Harrington, P. B. (2011). 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. Talanta, 83(4), 1260-8. https://doi.org/10.1016/j.talanta.2010.05.063
Sun X, et al. 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. Talanta. 2011 Jan 30;83(4):1260-8. PubMed PMID: 21215862.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - 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. AU - Sun,Xiaobo, AU - Zimmermann,Carolyn M, AU - Jackson,Glen P, AU - Bunker,Christopher E, AU - Harrington,Peter B, Y1 - 2010/06/08/ PY - 2010/01/15/received PY - 2010/05/25/revised PY - 2010/05/28/accepted PY - 2011/1/11/entrez PY - 2011/1/11/pubmed PY - 2011/4/9/medline SP - 1260 EP - 8 JF - Talanta JO - Talanta VL - 83 IS - 4 N2 - 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 99.8 ± 0.5% 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 GC/fast MS method furnishes characteristics of high speed, accuracy, and robustness. This mode of measurement may be useful as a monitoring tool to track changes in the chemical composition of fuels that may also lead to property changes. SN - 1873-3573 UR - https://www.unboundmedicine.com/medline/citation/21215862/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_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0039-9140(10)00419-4 DB - PRIME DP - Unbound Medicine ER -