Laser desorption/ionization mass spectrometry fingerprinting of complex hydrocarbon mixtures: application to crude oils using data mining techniques

Abstract

Crude oil fingerprints were obtained from four crude oils by laser desorption/ionization mass spectrometry (LDI-MS) using a silver nitrate cationization reagent. Replicate analyses produced spectral data with a large number of features for each sample (>11,000 m/z values) which were statistically analyzed to extract useful information for their differentiation. Individual characteristic features from the data set were identified by a false discovery rate based feature selection procedure based on the analysis of variance models. The selected features were, in turn, evaluated using classification models. A substantially reduced set of 23 features was obtained through this procedure. One oil sample containing a high ratio of saturated/aromatic hydrocarbon content was easily distinguished from the others using this reduced set. The other three samples were more difficult to distinguish by LDI-MS using a silver cationization reagent; however, a minimal number of significant features were still identified for this purpose. Focus is placed on presenting this multivariate statistical method as a rapid and simple analytical procedure for classifying and distinguishing complex mixtures.