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 Fouriertransform 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.