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Source attribution and structure classification-assisted strategy for comprehensively profiling Chinese herbal formula: Ganmaoling granule as a case

Chinese herbal formula (CHF) has extremely complex chemical composition. Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and granule (GMLG) was selected as a representative case to illust... Full description

Journal Title: Journal of Chromatography A 16 September 2016, Vol.1464, pp.102-114
Main Author: Chen, Jinfeng
Other Authors: Shi, Ziyi , Song, Yuelin , Guo, Xiaoyu , Zhao, Mingbo , Tu, Pengfei , Jiang, Yong
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0021-9673 ; DOI: 10.1016/j.chroma.2016.08.028
Link: https://www.sciencedirect.com/science/article/pii/S0021967316310901
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recordid: elsevier_sdoi_10_1016_j_chroma_2016_08_028
title: Source attribution and structure classification-assisted strategy for comprehensively profiling Chinese herbal formula: Ganmaoling granule as a case
format: Article
creator:
  • Chen, Jinfeng
  • Shi, Ziyi
  • Song, Yuelin
  • Guo, Xiaoyu
  • Zhao, Mingbo
  • Tu, Pengfei
  • Jiang, Yong
subjects:
  • Chinese Herbal Formula
  • Source Attribution
  • Structure Classification
  • Ganmaoling Granule
  • Scheduled Multiple Reaction Monitoring
  • Online Parameter Optimization
  • Chemistry
ispartof: Journal of Chromatography A, 16 September 2016, Vol.1464, pp.102-114
description: Chinese herbal formula (CHF) has extremely complex chemical composition. Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and granule (GMLG) was selected as a representative case to illustrate such a strategy and to confirm its applicability. Firstly, comprehensive data acquisition was achieved using neutral losses along with full scan on a liquid chromatography coupled with hybrid ion trap-time of flight mass spectrometer (LC-IT-TOF-MS). Then, the detected precursor and product ions were paired to construct a list of ion transitions for profiling GMLG and its constituent herbs using the scheduled multiple reaction monitoring (sMRM) mode on a LC coupled with hybrid triple quadrupole-linear ion trap mass spectrometer (LC-Q-Trap-MS). The mass parameters of sMRM were optimized using an online optimization strategy to achieve the highest sensitivity, and the automated source attribution was performed with the assistant of the “Quantitate” function of Analyst software. The target peaks were then structurally classified into seven classes through integrating the mass defect filtering (MDF) and diagnostic fragment ion filtering (DFIF), and identified by combination of the mass fragmentation rules and a ‘structure extension’ approach. Using this strategy, 261 components, including 148 trace ones (with the intensity lower than 100,000 cps), were tentatively characterized. The findings demonstrated that such a comprehensive source attribution and structure classification-assisted strategy is qualified to be an efficient approach for rapidly and globally characterizing the chemical profile of CHF.
language: eng
source:
identifier: ISSN: 0021-9673 ; DOI: 10.1016/j.chroma.2016.08.028
fulltext: fulltext
issn:
  • 0021-9673
  • 00219673
url: Link


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titleSource attribution and structure classification-assisted strategy for comprehensively profiling Chinese herbal formula: Ganmaoling granule as a case
creatorChen, Jinfeng ; Shi, Ziyi ; Song, Yuelin ; Guo, Xiaoyu ; Zhao, Mingbo ; Tu, Pengfei ; Jiang, Yong
ispartofJournal of Chromatography A, 16 September 2016, Vol.1464, pp.102-114
identifierISSN: 0021-9673 ; DOI: 10.1016/j.chroma.2016.08.028
subjectChinese Herbal Formula ; Source Attribution ; Structure Classification ; Ganmaoling Granule ; Scheduled Multiple Reaction Monitoring ; Online Parameter Optimization ; Chemistry
descriptionChinese herbal formula (CHF) has extremely complex chemical composition. Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and granule (GMLG) was selected as a representative case to illustrate such a strategy and to confirm its applicability. Firstly, comprehensive data acquisition was achieved using neutral losses along with full scan on a liquid chromatography coupled with hybrid ion trap-time of flight mass spectrometer (LC-IT-TOF-MS). Then, the detected precursor and product ions were paired to construct a list of ion transitions for profiling GMLG and its constituent herbs using the scheduled multiple reaction monitoring (sMRM) mode on a LC coupled with hybrid triple quadrupole-linear ion trap mass spectrometer (LC-Q-Trap-MS). The mass parameters of sMRM were optimized using an online optimization strategy to achieve the highest sensitivity, and the automated source attribution was performed with the assistant of the “Quantitate” function of Analyst software. The target peaks were then structurally classified into seven classes through integrating the mass defect filtering (MDF) and diagnostic fragment ion filtering (DFIF), and identified by combination of the mass fragmentation rules and a ‘structure extension’ approach. Using this strategy, 261 components, including 148 trace ones (with the intensity lower than 100,000 cps), were tentatively characterized. The findings demonstrated that such a comprehensive source attribution and structure classification-assisted strategy is qualified to be an efficient approach for rapidly and globally characterizing the chemical profile of CHF.
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titleSource attribution and structure classification-assisted strategy for comprehensively profiling Chinese herbal formula: Ganmaoling granule as a case
description

Chinese herbal formula (CHF) has extremely complex chemical composition. Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and

granule (GMLG) was selected as a representative case to illustrate such a strategy and to confirm its applicability. Firstly, comprehensive data acquisition was achieved using neutral losses along with full scan on a liquid chromatography coupled with hybrid ion trap-time of flight mass spectrometer (LC-IT-TOF-MS). Then, the detected precursor and product ions were paired to construct a list of ion transitions for profiling GMLG and its constituent herbs using the scheduled multiple reaction monitoring (sMRM) mode on a LC coupled with hybrid triple quadrupole-linear ion trap mass spectrometer (LC-Q-Trap-MS). The mass parameters of sMRM were optimized using an online optimization strategy to achieve the highest sensitivity, and the automated source attribution was performed with the assistant of the “Quantitate” function of Analyst software. The target peaks were then structurally classified into seven classes through integrating the mass defect filtering (MDF) and diagnostic fragment ion filtering (DFIF), and identified by combination of the mass fragmentation rules and a ‘structure extension’ approach. Using this strategy, 261 components, including 148 trace ones (with the intensity lower than 100,000 cps), were tentatively characterized. The findings demonstrated that such a comprehensive source attribution and structure classification-assisted strategy is qualified to be an efficient approach for rapidly and globally characterizing the chemical profile of CHF.

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abstract

Chinese herbal formula (CHF) has extremely complex chemical composition. Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and

granule (GMLG) was selected as a representative case to illustrate such a strategy and to confirm its applicability. Firstly, comprehensive data acquisition was achieved using neutral losses along with full scan on a liquid chromatography coupled with hybrid ion trap-time of flight mass spectrometer (LC-IT-TOF-MS). Then, the detected precursor and product ions were paired to construct a list of ion transitions for profiling GMLG and its constituent herbs using the scheduled multiple reaction monitoring (sMRM) mode on a LC coupled with hybrid triple quadrupole-linear ion trap mass spectrometer (LC-Q-Trap-MS). The mass parameters of sMRM were optimized using an online optimization strategy to achieve the highest sensitivity, and the automated source attribution was performed with the assistant of the “Quantitate” function of Analyst software. The target peaks were then structurally classified into seven classes through integrating the mass defect filtering (MDF) and diagnostic fragment ion filtering (DFIF), and identified by combination of the mass fragmentation rules and a ‘structure extension’ approach. Using this strategy, 261 components, including 148 trace ones (with the intensity lower than 100,000 cps), were tentatively characterized. The findings demonstrated that such a comprehensive source attribution and structure classification-assisted strategy is qualified to be an efficient approach for rapidly and globally characterizing the chemical profile of CHF.

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