Classification of crude oil samples through statistical analysis of APPI FTICR mass spectra

Abstract

Characterization and classification of crude oil is done following different approaches. Geochemistry investigations on one hand are focused on finding and quantifying markers characteristic of the oil, to provide detailed information about the geological history, the conditions of sedimentation of the oil and of the source rock. On the other hand Fourier transform ion cyclotron mass spectrometry (FTICR MS) has been proved to be a powerful tool in the analysis of crude oil at the molecular level. In this study, a group of fourteen samples of crude oil coming from different fields around the world has been analyzed through Atmospheric Pressure Photoionization (APPI) coupled to FTICR MS. Since the comparison of the many complex APPI FTICR mass spectra is complicated, we have applied statistical methods such as Principal Component Analysis (PCA) and hierarchical clustering (HCA) considering the peaks of the mass spectra. The statistical analysis has found to be able to group the oils according to the well where they have been extracted and from their geographical origin.