Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: Pattern-recognition analyses for screening quality control purposes


Autoria(s): Flumignan, Danilo Luiz; Boralle, Nivaldo; Oliveira, Jose Eduardo de
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

20/05/2014

20/05/2014

30/06/2010

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