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Sensors 2017, 17(11), 2544; https://doi.org/10.3390/s17112544

Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints

1
Department of Analytical Chemistry, Faculty of Sciences, IVAGRO, University of Cadiz, 11510 Puerto Real, Cádiz, Spain
2
Department of Physical Chemistry, Faculty of Sciences, University of Cadiz, 11510 Puerto Real, Cádiz, Spain
*
Author to whom correspondence should be addressed.
Received: 9 October 2017 / Revised: 29 October 2017 / Accepted: 2 November 2017 / Published: 5 November 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
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Abstract

Characterization of petroleum-derived products is an area of continuing importance in environmental science, mainly related to fuel spills. In this study, a non-separative analytical method based on E-Nose (Electronic Nose) is presented as a rapid alternative for the characterization of several different petroleum-derived products including gasoline, diesel, aromatic solvents, and ethanol samples, which were poured onto different surfaces (wood, cork, and cotton). The working conditions about the headspace generation were 145 °C and 10 min. Mass spectroscopic data (45–200 m/z) combined with chemometric tools such as hierarchical cluster analysis (HCA), later principal component analysis (PCA), and finally linear discriminant analysis (LDA) allowed for a full discrimination of the samples. A characteristic fingerprint for each product can be used for discrimination or identification. The E-Nose can be considered as a green technique, and it is rapid and easy to use in routine analysis, thus providing a good alternative to currently used methods. View Full-Text
Keywords: petroleum-derived products; characterization; fingerprints; E-Nose; gasoline; diesel petroleum-derived products; characterization; fingerprints; E-Nose; gasoline; diesel
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Ferreiro-González, M.; Barbero, G.F.; Palma, M.; Ayuso, J.; Álvarez, J.A.; Barroso, C.G. Characterization and Differentiation of Petroleum-Derived Products by E-Nose Fingerprints. Sensors 2017, 17, 2544.

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