Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: Pattern-recognition analyses for screening quality control purposes
Contribuinte(s) |
Universidade Estadual Paulista (UNESP) |
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Data(s) |
20/05/2014
20/05/2014
30/06/2010
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Resumo |
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) In 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. |
Formato |
392-397 |
Identificador |
http://dx.doi.org/10.1016/j.talanta.2010.04.058 Talanta. Amsterdam: Elsevier B.V., v. 82, n. 1, p. 392-397, 2010. 0039-9140 http://hdl.handle.net/11449/26012 10.1016/j.talanta.2010.04.058 WOS:000279488900056 |
Idioma(s) |
eng |
Publicador |
Elsevier B.V. |
Relação |
Talanta |
Direitos |
closedAccess |
Palavras-Chave | #Brazilian commercial gasoline #Quality control #Carbon nuclear magnetic resonance #spectroscopic fingerprinting #Pattern-recognition multivariate SIMCA #ANP Regulation 309 |
Tipo |
info:eu-repo/semantics/article |