| Autor: |
Yueyi Xia, Xiaoxiao Wang, Chenfei Ma, Xinxin Wang, Chunxia Zhao, Xinjie Zhao, Zhanquan Zhang, Yinglong Yu, Xiaohui Lin, Xin Lu, Guowang Xu |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Journal of Chromatography A. 1673:463194 |
| ISSN: |
0021-9673 |
| DOI: |
10.1016/j.chroma.2022.463194 |
| Popis: |
Online liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) has attracted much attention in the molecular characterization of crude oil. Neither open access nor commercially available petroleomics tools were developed specifically to process LC-HRMS data. Here, a novel data processing pipeline was specifically designed for LC-HRMS-based petroleomics data. A customizable formula database was established deriving from the detected sample, which could avoid the interference caused by a large number of redundant molecules in a conventionally theoretical molecular database. Molecular formula candidates were assigned by the formula database using a low noise threshold, and false-positive assignments were eliminated by the chromatographic retention behaviors. Multi-dimensional information was obtained, including heteroatom class, double bond equivalent (DBE), carbon number, retention time, and MS/MS spectra. The developed method was compared with a popular petroleomics software, similar relative abundance class distribution was obtained, and much more formulas of low abundant components were uniquely extracted by the developed method. Finally, it was applied to reveal variation between feed and product oils in hydrodenitrogenation. Significantly compositional and structural differences were revealed. The developed method provides a useful pipeline for molecular data mining of petroleum samples. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect