Pattern recognition for screening of crude oils using multivariate circular profiles

Abstract

An appropriate pattern recognition method has been designed to visually discriminate between crude oils belonging to different geographic origins. Each oil is represented by a circular profile that consists of parameter axes that radiate from the centre like spokes on a wheel. One objective of this approach is to provide an optimum set of axes that will best differentiate one circular profile from another. Several parameters were considered for evaluating the oils: photo-acoustic spectroscopy (PAS), carbon-13 nuclear magnetic resonance (13C NMR, for %CA = aromatic carbon content), high-performance liquid chromatography (HPLC, for unsaturated, aromatic, and polar compounds), initial boiling point (IBP), American Petroleum Institute (API) gravity, and ultraviolet–visible spectrophotometry (for λmax = maximum absorption wavelength and εmax = maximum molar absorptivity). By computerized statistical evaluation, the selected parameters are PAS and HPLC for unsaturated, aromatic, and polar compounds.