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

摘要

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