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Insight into molecular information of Huolinguole lignite obtained by Fourier transform ion cyclotron resonance mass spectrometry and statistical methods
Author(s) -
Li Bei,
Fan Xing,
Yu YaRu,
Wang Fei,
Li Xian,
Lu Yao,
Wei XianYong,
Ma FengYun,
Zhao YunPeng,
Zhao Wei
Publication year - 2019
Publication title -
rapid communications in mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.528
H-Index - 136
eISSN - 1097-0231
pISSN - 0951-4198
DOI - 10.1002/rcm.8448
Subject(s) - fourier transform ion cyclotron resonance , chemistry , mass spectrometry , principal component analysis , analytical chemistry (journal) , mass spectrum , chemometrics , polyatomic ion , coal , chromatography , ion , artificial intelligence , organic chemistry , computer science
Rationale Fourier transform ion cyclotron resonance mass spectrometry (FT‐ICR MS) was applied to the characterization of organic compounds in coal extracts at the molecular level. Large volumes of data obtained by FT‐ICR MS were processed via statistical methods to extract valuable information on the molecular structures and compositions of organic compounds in coal. Methods A low‐rank coal was subjected to ultrasonic extraction sequentially with six solvents to separate and enrich species with different molecular characteristics. Complex mass spectra of the six extracts were obtained by a FT‐ICR MS system equipped with two ionization sources. Two multivariate statistical methods, hierarchical clustering analysis (HCA) and principle component analysis (PCA), were introduced to mine useful information from the complex MS data and visually exhibit comprehensive molecular details in coal extracts. Results Similarities and differences between the 17 MS data sets from six coal extracts ionized by different ion sources were visually exhibited in plots via data processing using HCA and PCA. For HCA, all of the identified compounds were divided into seven classes (CH, O, N, S, ON, OS, and NS), and detailed differences in the relative abundance were revealed. In addition, PCA discriminated the differences in molecular composition for organic compounds from the six extracts. Conclusions Multivariate statistical analysis is a promising methodology which can interpret the chemical composition of coals and coal derivatives at the molecular level, especially for the analysis of multiple complex samples presenting in a single plot.