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Orzel J.,University of Silesia | Daszykowski M.,University of Silesia | Grabowski I.,Customs Chamber of Customs Laboratory in Biala Podlaska | Zaleszczyk G.,Customs Chamber of Customs Laboratory in Biala Podlaska | Sznajder M.,Customs Chamber of Customs Laboratory in Biala Podlaska
Fuel | Year: 2014

The fluorescent fingerprints of diesel oil samples were investigated in order to develop a fast and cost-effective method to facilitate the discrimination of rebated tax diesel fuel from oil that is illegally processed by the sorption process. In the experiment oil samples were spiked with a fiscal marker (Solvent Yellow 124) and a dye (Solvent Red 19) and then these were removed using a simulated sorption process. The excitation-emission fluorescence fingerprints were recorded for each sample. Discriminant models were constructed on the basis of fluorescence spectra to distinguish oil samples with respect to four possible discrimination schemes (corresponding to the concentrations of chemical additives). Using discriminant partial least squares models, in all of the discrimination cases that were considered, 100% of the samples from the model set were discriminated correctly. Prediction results for the test set samples were encouraging and varied between 77% and 100% of correctly discriminated samples. © 2013 Elsevier Ltd. All rights reserved. Source

Krakowska B.,University of Silesia | Stanimirova I.,University of Silesia | Orzel J.,University of Silesia | Daszykowski M.,University of Silesia | And 3 more authors.
Analytical and Bioanalytical Chemistry | Year: 2015

Abstract In the countries of the European Community, diesel fuel samples are spiked with Solvent Yellow 124 and either Solvent Red 19 or Solvent Red 164. Their presence at a given concentration indicates the specific tax rate and determines the usage of fuel. The removal of these so-called excise duty components, which is known as fuel "laundering", is an illegal action that causes a substantial loss in a government's budget. The aim of our study was to prove that genuine diesel fuel samples and their counterfeit variants (obtained from a simulated sorption process) can be differentiated by using their gas chromatographic fingerprints that are registered with a flame ionization detector. To achieve this aim, a discriminant partial least squares analysis, PLS-DA, for the genuine and counterfeit oil fingerprints after a baseline correction and the alignment of peaks was constructed and validated. Uninformative variables elimination (UVE), variable importance in projection (VIP), and selectivity ratio (SR), which were coupled with a bootstrap procedure, were adapted in PLS-DA in order to limit the possibility of model overfitting. Several major chemical components within the regions that are relevant to the discriminant problem were suggested as being the most influential. We also found that the bootstrap variants of UVE-PLS-DA and SR-PLS-DA have excellent predictive abilities for a limited number of gas chromatographic features, 14 and 16, respectively. This conclusion was also supported by the unitary values that were obtained for the area under the receiver operating curve (AUC) independently for the model and test sets. © 2014 The Author(s). Source

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