Combined cluster and discriminant analysis: An efficient chemometric approach in diesel fuel characterization

Combined cluster and discriminant analysis (CCDA) as a chemometric tool in compound specific isotope analysis of diesel fuels was studied. The stable carbon isotope ratios (δ¹³C) of n-alkanes in diesel fuel can be used to characterize or differentiate diesels originating from different sources. We investigated 25 diesel fuel samples representing 20 different brands. The samples were collected from 25 different service stations in 11 European countries over a 2 year period. The n-alkane fraction of diesel fuels was separated using solid-state urea clathrate formation combined with silica gel fractionation. The stable carbon isotope ratios of C10-C24 n-alkanes were measured with gas chromatography-isotope ratio mass spectrometry (GC-IRMS) using perdeuterated n-alkanes as internal standards. Beside the 25 samples one additional diesel fuel was prepared and measured three times to get totally homogenous samples in order to test the performance of our analytical and statistical routine. Stable isotope ratio data were evaluated with hierarchical cluster analysis (HCA), principal component analysis (PCA) and CCDA. CCDA combines two multivariate data analysis methods hierarchical cluster analysis with linear discriminant analysis (LDA). The main idea behind CCDA is to compare the goodness of preconceived (based on the sample origins) and random groupings. In CCDA all the samples were compared pairwise. The results for the parallel sample preparations showed that the analytical procedure does not have any significant effect on the δ¹³C values of n-alkanes. The three parallels proved to be totally homogenous with CCDA. HCA and PCA can be useful tools when the examining of the relationship among several samples is in question. However, these two techniques cannot be always decisive on the origin of similar samples. The initial hypothesis that all diesel fuel samples are considered chemically unique was verified by CCDA. The main advantage of CCDA is that it gives an objective index number about the level of similarity among the investigated samples. Thus the application of CCDA supplemented by the traditionally used multivariate methods greatly improves the efficiency of statistical analysis in the CSIA of diesel fuel samples.