Application of the Mason-Schamp equation and ion mobility mass spectrometry to identify structurally related compounds in crude oil.

The various components of crude oil were structurally resolved using an atmospheric-pressure solids analysis probe (ASAP) coupled with ion mobility mass spectrometry (IM-MS). An ASAP source was used to broadly fractionate compounds according to their boiling points, thereby simplifying the resulting mass spectra for easier data interpretation. The m/z-mobility plots obtained by IM-MS analysis of crude oil could be used to find the structural relationship between crude oil molecules. That was demonstrated using ion mobility mass spectra from a homologous series of compounds, differing only by the number of alkyl units, found in crude oil. The peaks from this series were linearly aligned in the plot, suggesting a continuous increase of the collisional cross section with an increase of mass values and hence the absence of significant structural differences within the series. In contrast, peaks in a homologous series differing only in the number of pendant hydrogen atoms were not linearly aligned, suggesting a discontinuous increase of the collisional cross section with an increase of mass values and hence significant structural differences due to the addition or removal of hydrogen. Cases in which a slope change was observed at three- or four-peak intervals may be related to the addition of an aromatic ring to existing structures. Overall, ion mobility mass spectrometry demonstrates a useful tool that can be used to elucidate structural relationships between molecules comprising crude oil.

Combination of statistical methods and Fourier transform ion cyclotron resonance mass spectrometry for more comprehensive, molecular-level interpretations of petroleum samples.

Complex petroleum mass spectra obtained by Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS) were successfully interpreted at the molecular level by applying principle component analysis (PCA) and hierarchical clustering analysis (HCA). A total of 40 mass spectra were obtained from 20 crude oil samples using both positive and negative atmospheric pressure photoionization (APPI). Approximately 400,000 peaks were identified at the molecular level. Conventional data analyses would have been impractical with so much data. However, PCA grouped samples into score plots based on their molecular composition. In this way, the overall compositional difference between samples could be easily displayed and identified by comparing score and loading plots. HCA was also performed to group and compare samples based on selected peaks that had been grouped by PCA. Subsequent heat map analyses revealed detailed compositional differences among grouped samples. This study demonstrates a promising new approach for studying multiple, complex petroleum samples at the molecular level.