Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

The combination of ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program...

Full description

Saved in:

Bibliographic Details
Published in:	Chromatographia Vol. 69; no. 7-8; pp. 719 - 727
Main Authors:	Gomes, Gláucia Silvério, Santos, Bruno César Diniz Brito dos, de Oliveira, José Eduardo, Flumignan, Danilo Luiz
Format:	Journal Article
Language:	English
Published:	Wiesbaden Wiesbaden : Vieweg Verlag 01-04-2009 Vieweg Verlag Springer
Subjects:	Analytical Chemistry Applied sciences Chemistry Chemistry and Materials Science Chromatographic methods and physical methods associated with chromatography Chromatography Crude oil, natural gas and petroleum products Energy Exact sciences and technology Fuels Gas chromatographic methods Laboratory Medicine Original Pharmacy Prospecting and production of crude oil, natural gas, oil shales and tar sands Proteomics South America Brazil America Brazilian commercial gasoline Pattern recognition multivariate SIMCA Gas chromatography ASTM D6733 Quality control Cluster analysis Chemical analysis Hierarchical classification Chromatogram Flame ionization detector Pattern recognition Gasoline Chemometrics Principal component analysis
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	The combination of ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.
Bibliography:	http://dx.doi.org/10.1365/s10337-009-0958-6
ISSN:	0009-5893 1612-1112
DOI:	10.1365/s10337-009-0958-6