ABSTRACT
Five neat diesel samples were analyzed by gas chromatography-mass spectrometry and total ion chromatograms as well as extracted ion profiles of the alkane and aromatic compound classes were generated. A retention time alignment algorithm was employed to align chromatograms prior to peak area normalization. Pearson product moment correlation coefficients and principal components analysis were then employed to investigate association and discrimination among the diesel samples. The same procedures were also used to investigate the association of a diesel residue to its neat counterpart. Current limitations in the retention time alignment algorithm and the subsequent effect on the association and discrimination of the diesel samples are discussed. An understanding of these issues is crucial to ensure the accuracy of data interpretation based on such chemometric procedures.
ABSTRACT
Diesel fuel samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and chemometric procedures to associate and discriminate samples for potential use in forensic and environmental applications. Twenty-five diesel samples, representing 13 different brands, were collected from service stations in the Lansing, Michigan area. From the GC-MS data, mass-to-charge ratios were identified to represent aliphatic (m/z 57) and aromatic (m/z 91 and 141) compounds. The total ion chromatogram (TIC) and extracted ion chromatograms (EICs) of the chosen ions were evaluated using Pearson product moment correlation (PPMC) and principal component analysis (PCA). Diesel samples from the same brand showed higher PPMC coefficients, while those from different brands showed lower values. EICs generally provided a wider range of correlation coefficients than the TIC, with correspondingly increased discrimination among samples for EIC m/z 91. PCA grouped the diesel samples into four distinct clusters for the TIC. The first cluster consisted of four samples from the same brand, two clusters contained one diesel sample each of different brands, and the fourth cluster contained the remaining diesel samples. The same trend was observed using each EIC, with an increase in the number of clusters formed for EIC m/z 57 and 91. Both statistical procedures suggest aromatic components (specifically, those with m/z 91) provide the greatest discrimination among diesel samples. This conclusion was supported by identifying the chemical components that contribute the most to the variance. The relative amount of aliphatic versus aromatic components was found to cause the greatest discrimination among samples in the data set.
