Abstract
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.
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Acknowledgments
The authors wish to thank the Agência Nacional do Petróleo, Gás Natural e Biocombustíveis—ANP and FUNDUNESP for financial support, along with CAPES, CNPq and FUNDUNESP for the provision of scholarships, and also CEMPEQC (Centro de Monitoramento e Pesquisa na Qualidade de Combustíveis, Petróleo e Derivados) for offering its infrastructure and its staff for the development of this work.
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Gomes, G.S., Santos, B.C.D.B.d., de Oliveira, J.E. et al. Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis. Chroma 69, 719–727 (2009). https://doi.org/10.1365/s10337-009-0958-6
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DOI: https://doi.org/10.1365/s10337-009-0958-6