A data processing pipeline for petroleomics based on liquid chromatography-high resolution mass spectrometry

Area of Science:

• Petroleum Chemistry
• Analytical Chemistry
• Mass Spectrometry

Background:

• Online liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) is crucial for crude oil molecular characterization.
• Existing petroleomics tools are not optimized for processing LC-HRMS data.

Purpose of the Study:

• To develop a novel data processing pipeline specifically for LC-HRMS-based petroleomics.
• To improve the accuracy and comprehensiveness of molecular characterization in petroleum samples.

Main Methods:

• A customizable formula database was created from sample data to minimize interference from redundant molecules.
• Molecular formula candidates were assigned using a low noise threshold and validated by chromatographic retention behaviors.
• Multi-dimensional data including heteroatom class, double bond equivalent (DBE), carbon number, retention time, and MS/MS spectra were obtained.

Main Results:

• The developed pipeline was compared to a popular petroleomics software, yielding similar relative abundance class distributions.
• The novel method uniquely extracted a greater number of formulas for low-abundance components.
• Application to hydrodenitrogenation revealed significant compositional and structural differences between feed and product oils.

Conclusions:

• The developed pipeline provides an effective tool for molecular data mining of petroleum samples.
• This method enhances the ability to characterize complex petroleum mixtures using LC-HRMS data.
• The pipeline offers a valuable approach for understanding variations in oil composition, particularly in processes like hydrodenitrogenation.