Flumignan, Danilo Luiz [UNESP]Boralle, Nivaldo [UNESP]Oliveira, Jose Eduardo de [UNESP]2014-05-202014-05-202010-06-30Talanta. Amsterdam: Elsevier B.V., v. 82, n. 1, p. 392-397, 2010.0039-9140http://hdl.handle.net/11449/26012In this work, the combination of carbon nuclear magnetic resonance ((13)C NMR) fingerprinting with pattern-recognition analyses provides an original and alternative approach to screening commercial gasoline quality. Soft Independent Modelling of Class Analogy (SIMCA) was performed on spectroscopic fingerprints to classify representative commercial gasoline samples, which were selected by Hierarchical Cluster Analyses (HCA) over several months in retails services of gas stations, into previously quality-defined classes. Following optimized (13)C NMR-SIMCA algorithm, sensitivity values were obtained in the training set (99.0%), with leave-one-out cross-validation, and external prediction set (92.0%). Governmental laboratories could employ this method as a rapid screening analysis to discourage adulteration practices. (C) 2010 Elsevier B.V. All rights reserved.392-397engBrazilian commercial gasolineQuality controlCarbon nuclear magnetic resonancespectroscopic fingerprintingPattern-recognition multivariate SIMCAANP Regulation 309Artigo10.1016/j.talanta.2010.04.058WOS:000279488900056Acesso restrito9352141379363877