Selecting a reduced suite of diagnostic ratios calculated between petroleum biomarkers and polycyclic aromatic hydrocarbons to characterize a set of crude oils

A set of 34 crude oils was analysed by GC-MS (SIM mode) and a suite of 28 diagnostic ratios (DR) calculated. They involved 18 ratios between biomarker molecules (hopanes, steranes, diasteranes and triaromatic steroids) and 10 quotients between polycyclic aromatic hydrocarbons. Three unsupervised pattern recognition techniques (i.e., principal components analysis, heatmap hierarchical cluster analysis and Kohonen neural networks) were employed to evaluate the final dataset and, thus, ascertain whether the crude oils grouped as a function of their geographical origin. In addition, an objective variable selection procedure based on Procrustes Rotation was undertaken to select a reduced set of DR that comprised for most of the information in the original data without loosing relevant information. A reduced set of four DR (namely; TA21, D2/P2, D3/P3 and B(a)F/4-Mpy) demonstrated to be sufficient to characterize the crude oils and the groups they formed.