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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.


Related in: MedlinePlus

Projection of samples (scores) onto space defined by first two principal components: a samples denoted as plus sign are authentic and samples denoted as empty circle are after the laundering process and b illustration of corresponding pairs of samples authentic plus sign and counterfeit variant empty circle (i.e. after the laundering process). Loadings as a function of retention time for: c PC 1 and d PC 2
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Fig3: Projection of samples (scores) onto space defined by first two principal components: a samples denoted as plus sign are authentic and samples denoted as empty circle are after the laundering process and b illustration of corresponding pairs of samples authentic plus sign and counterfeit variant empty circle (i.e. after the laundering process). Loadings as a function of retention time for: c PC 1 and d PC 2

Mentions: Potential differences in the chemical composition of diesel fuel samples were studied using the PCA method. Preprocessed GC-FID fingerprints (baseline corrected, aligned, and mean-centered) of genuine and counterfeit samples can be modeled with two principal components that describe 73.68 % of the total data variance. Projection of the samples onto the space that was defined by the first two principal components allows some conclusions about their chemical similarities to be drawn. Each point on the PC 1-PC 2 projection (Fig. 3a) represents one GC-FID fingerprint (sample). Genuine and discolored samples were denoted as “+” and “○”, respectively. In Fig. 3b, for a better clarity of presentation pairs of samples authentic and counterfeit are connected with a line. Two groups of diesel fuel samples can be observed along the PC 1 axis and another two groups along the PC 2 axis. By analyzing the score projections in Fig. 3a, one can conclude that the laundering process itself is not substantially influential for the separation of the samples along PC 1 and PC 2. The corresponding loading plots in Fig. 3c, d, which show the ranges of the elution times, indicates the two chromatographic peaks that are responsible for the differences between the two groups of samples. These two peaks correspond to the mixtures that were eluted at ca. 65.21 and 65.24 min. They can be attributed to the methyl esters of fatty acids (FAME). The FAME compounds cannot be considered as possible markers for the laundering process. They are deliberately added to diesel oil during its production and are present in the studied samples regardless the laundering process. In Poland, any manufacturer is allowed to add up to 7 % (V/V) of FAME, but the differences in the total amount of FAME from batch to batch of diesel oil depend on the temporary production and economic requirements. The group of diesel oil samples characterized by positive score values along PC 1 (see Fig. 3a) contains FAME, the content of which varies in the range of 4.1 % (V/V) and 5.3 (V/V). Other variability sources such as different producers, origin of crude oil used in the production process, production process itself have an impact on forming clusters of samples.Fig. 3


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)

Projection of samples (scores) onto space defined by first two principal components: a samples denoted as plus sign are authentic and samples denoted as empty circle are after the laundering process and b illustration of corresponding pairs of samples authentic plus sign and counterfeit variant empty circle (i.e. after the laundering process). Loadings as a function of retention time for: c PC 1 and d PC 2
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig3: Projection of samples (scores) onto space defined by first two principal components: a samples denoted as plus sign are authentic and samples denoted as empty circle are after the laundering process and b illustration of corresponding pairs of samples authentic plus sign and counterfeit variant empty circle (i.e. after the laundering process). Loadings as a function of retention time for: c PC 1 and d PC 2
Mentions: Potential differences in the chemical composition of diesel fuel samples were studied using the PCA method. Preprocessed GC-FID fingerprints (baseline corrected, aligned, and mean-centered) of genuine and counterfeit samples can be modeled with two principal components that describe 73.68 % of the total data variance. Projection of the samples onto the space that was defined by the first two principal components allows some conclusions about their chemical similarities to be drawn. Each point on the PC 1-PC 2 projection (Fig. 3a) represents one GC-FID fingerprint (sample). Genuine and discolored samples were denoted as “+” and “○”, respectively. In Fig. 3b, for a better clarity of presentation pairs of samples authentic and counterfeit are connected with a line. Two groups of diesel fuel samples can be observed along the PC 1 axis and another two groups along the PC 2 axis. By analyzing the score projections in Fig. 3a, one can conclude that the laundering process itself is not substantially influential for the separation of the samples along PC 1 and PC 2. The corresponding loading plots in Fig. 3c, d, which show the ranges of the elution times, indicates the two chromatographic peaks that are responsible for the differences between the two groups of samples. These two peaks correspond to the mixtures that were eluted at ca. 65.21 and 65.24 min. They can be attributed to the methyl esters of fatty acids (FAME). The FAME compounds cannot be considered as possible markers for the laundering process. They are deliberately added to diesel oil during its production and are present in the studied samples regardless the laundering process. In Poland, any manufacturer is allowed to add up to 7 % (V/V) of FAME, but the differences in the total amount of FAME from batch to batch of diesel oil depend on the temporary production and economic requirements. The group of diesel oil samples characterized by positive score values along PC 1 (see Fig. 3a) contains FAME, the content of which varies in the range of 4.1 % (V/V) and 5.3 (V/V). Other variability sources such as different producers, origin of crude oil used in the production process, production process itself have an impact on forming clusters of samples.Fig. 3

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.


Related in: MedlinePlus