Carbon nuclear magnetic resonance spectroscopic fingerprinting of commercial gasoline: Pattern-recognition analyses for screening quality control purposes
Nenhuma Miniatura disponível
Data
2010-06-30
Autores
Flumignan, Danilo Luiz [UNESP]
Boralle, Nivaldo [UNESP]
Oliveira, Jose Eduardo de [UNESP]
Título da Revista
ISSN da Revista
Título de Volume
Editor
Elsevier B.V.
Resumo
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.
Descrição
Palavras-chave
Brazilian commercial gasoline, Quality control, Carbon nuclear magnetic resonance, spectroscopic fingerprinting, Pattern-recognition multivariate SIMCA, ANP Regulation 309
Como citar
Talanta. Amsterdam: Elsevier B.V., v. 82, n. 1, p. 392-397, 2010.