ABSTRACT
The coupling of large volume injection (LVI) with comprehensive two-dimensional gas chromatography (GC × GC) can be a powerful technique in the analysis of trace-level complex samples. The coupling of LVI and GC × GC using a cost efficiently operable pneumatic modulator based on capillary flow technology has been examined. The aim was to optimize the LVI parameters in the case of samples with compounds covering a wide boiling range. For the optimization of LVI 25 microliters of a solution containing 27 target compounds modelling the composition and the boiling range of diesel oils was used. The injection parameters were evaluated for peak shapes, reproducibility and peak volumes relative to peak volumes obtained using cold splitless injection. For all GC × GC experiments a non-polar first column (Rxi-5ms) and a polar second column (HP-INNOWax) were applied. Through extensive method optimization solvent vent proved to be an unsuitable technique for the injection of compounds covering a wide boiling range: at lower vent times peaks split, while higher vent times caused severe losses of highly volatile compounds. Therefore, a split-splitless LVI method was optimized. Injection speed, split vent time, splitless vent time and vent flow during split vent have been optimized. The developed method is suitable for the coupling of LVI with flow modulated GC × GC. Using the optimized split-splitless LVI parameters no peak distortion of the target compounds was observed. The relative peak volumes were between 60-120% for all compounds (80-120% for 13 compounds).
ABSTRACT
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.