dc.creatorRodrigues e Brito L.
dc.creatorda Silva M.P.F.
dc.creatorRohwedder J.J.R.
dc.creatorPasquini C.
dc.creatorHonorato F.A.
dc.creatorPimentel M.F.
dc.date2015
dc.date2015-06-25T12:50:25Z
dc.date2015-11-26T14:57:59Z
dc.date2015-06-25T12:50:25Z
dc.date2015-11-26T14:57:59Z
dc.date.accessioned2018-03-28T22:09:45Z
dc.date.available2018-03-28T22:09:45Z
dc.identifierAnalytica Chimica Acta. Elsevier, v. , n. , p. - , 2015.
dc.identifier32670
dc.identifier10.1016/j.aca.2014.12.052
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84920528673&partnerID=40&md5=bec4b5c5ceb61a216993edbfa007ab43
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/85139
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/85139
dc.identifier2-s2.0-84920528673
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1255766
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.languageen
dc.publisherElsevier
dc.relationAnalytica Chimica Acta
dc.rightsfechado
dc.sourceScopus
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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