Limits...
Detection of discoloration in diesel fuel based on gas chromatographic fingerprints.

Krakowska B, Stanimirova I, Orzel J, Daszykowski M, Grabowski I, Zaleszczyk G, Sznajder M - Anal Bioanal Chem (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006, Katowice, Poland.

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.

No MeSH data available.


Histograms of correlation coefficients calculated between each chromatographic fingerprint and a target signal: a before and b after alignment using COW
© Copyright Policy - OpenAccess
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4305096&req=5

Fig2: Histograms of correlation coefficients calculated between each chromatographic fingerprint and a target signal: a before and b after alignment using COW

Mentions: The smallest value of the initial correlation coefficient between a signal and target was about 0.220, whereas the largest value was 0.989. A few fingerprints, which had relatively low correlation coefficients with respect to the target signal (compared to majority of signals), were aligned again using different input parameters. In general, most of the GC-FID fingerprints were characterized by correlation coefficients that were higher than 0.8 after the alignment procedure. The smallest correlation coefficient that was observed was 0.804 and the largest value was 0.999. To illustrate the effect of the alignment procedure, histograms of the initial and the final (after alignment) correlation coefficients that were computed between each fingerprint and the target signal are presented in Fig. 2.Fig. 2


Detection of discoloration in diesel fuel based on gas chromatographic fingerprints.

Krakowska B, Stanimirova I, Orzel J, Daszykowski M, Grabowski I, Zaleszczyk G, Sznajder M - Anal Bioanal Chem (2014)

Histograms of correlation coefficients calculated between each chromatographic fingerprint and a target signal: a before and b after alignment using COW
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4305096&req=5

Fig2: Histograms of correlation coefficients calculated between each chromatographic fingerprint and a target signal: a before and b after alignment using COW
Mentions: The smallest value of the initial correlation coefficient between a signal and target was about 0.220, whereas the largest value was 0.989. A few fingerprints, which had relatively low correlation coefficients with respect to the target signal (compared to majority of signals), were aligned again using different input parameters. In general, most of the GC-FID fingerprints were characterized by correlation coefficients that were higher than 0.8 after the alignment procedure. The smallest correlation coefficient that was observed was 0.804 and the largest value was 0.999. To illustrate the effect of the alignment procedure, histograms of the initial and the final (after alignment) correlation coefficients that were computed between each fingerprint and the target signal are presented in Fig. 2.Fig. 2

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Institute of Chemistry, The University of Silesia, 9 Szkolna Street, 40-006, Katowice, Poland.

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.

No MeSH data available.