Determination Of Detergent And Dispensant Additives In Gasoline By Ring-oven And Near Infrared Hypespectral Imaging.

dc.creatorRodrigues E Brito, Lívia
dc.creatorda Silva, Michelle P F
dc.creatorRohwedder, Jarbas J R
dc.creatorPasquini, Celio
dc.creatorHonorato, Fernanda A
dc.creatorPimentel, Maria Fernanda
dc.date2015-Mar
dc.date2015-11-27T13:46:19Z
dc.date2015-11-27T13:46:19Z
dc.date.accessioned2018-03-29T01:23:44Z
dc.date.available2018-03-29T01:23:44Z
dc.identifierAnalytica Chimica Acta. v. 863, p. 9-19, 2015-Mar.
dc.identifier1873-4324
dc.identifier10.1016/j.aca.2014.12.052
dc.identifierhttp://www.ncbi.nlm.nih.gov/pubmed/25732308
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/202175
dc.identifier25732308
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1302408
dc.descriptionA method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.
dc.description863
dc.description9-19
dc.languageeng
dc.relationAnalytica Chimica Acta
dc.relationAnal. Chim. Acta
dc.rightsfechado
dc.rightsCopyright © 2015 Elsevier B.V. All rights reserved.
dc.sourcePubMed
dc.subjectDetergent Dispersant Additives
dc.subjectGasoline
dc.subjectHyperspectral Imaging
dc.subjectNear Infrared Spectroscopy
dc.subjectRing Oven
dc.titleDetermination Of Detergent And Dispensant Additives In Gasoline By Ring-oven And Near Infrared Hypespectral Imaging.
dc.typeArtículos de revistas


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