A global profiling strategy for identification of the total constituents in Chinese herbal medicine based on online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with intelligentized chemical classification guidance
Copyright © 2023 Elsevier B.V. All rights reserved..
A comprehensive strategy for effective identification of total constituents in Chinese patent medicine has been advanced applying full scan-preferred parent ions capture-static and active exclusion (FS-PIC-SAE) acquisition coupled with intelligent deep-learning supported mass defect filter (MDF) process, with Naoxintong capsule (NXT) as a case. Online comprehensive two-dimensional liquid chromatography (2DLC) coupled with Q-TOF-MS/MS system was established for obtaining the excellent separation and detection performance of total components, which could exhibit excellent peak capacity with 1052 and orthogonality with 0.69. In addition, a total of 901 unknown compounds could be classified into nine chemical classes rapidly and effectively, based on the intelligent deep-learning algorithm supported MDF model with 96.4% accuracy. Consequently, 276 compounds were successfully identified from NXT, especially including 44 flavonoids, 27 phenolic acids, 25 fatty acids, 17 saponins, 21 phthalocyanines, 20 triterpenes, 10 monoterpenes, 13 diterpenoid ketones, 14 amino acids, and others. It is concluded that the proposed program is an effective and practical strategy enabling the in-depth chemical profiling of complex herbal and biological samples.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:1710 |
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| Enthalten in: |
Journal of chromatography. A - 1710(2023) vom: 08. Nov., Seite 464387 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Du, Kunze [VerfasserIn] |
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| Links: |
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| Themen: |
2DLC-Q-TOF-MS/MS |
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| Anmerkungen: |
Date Revised 20.10.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1016/j.chroma.2023.464387 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM362538166 |
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| 245 | 1 | 2 | |a A global profiling strategy for identification of the total constituents in Chinese herbal medicine based on online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with intelligentized chemical classification guidance |
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| 500 | |a published: Print-Electronic | ||
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| 520 | |a Copyright © 2023 Elsevier B.V. All rights reserved. | ||
| 520 | |a A comprehensive strategy for effective identification of total constituents in Chinese patent medicine has been advanced applying full scan-preferred parent ions capture-static and active exclusion (FS-PIC-SAE) acquisition coupled with intelligent deep-learning supported mass defect filter (MDF) process, with Naoxintong capsule (NXT) as a case. Online comprehensive two-dimensional liquid chromatography (2DLC) coupled with Q-TOF-MS/MS system was established for obtaining the excellent separation and detection performance of total components, which could exhibit excellent peak capacity with 1052 and orthogonality with 0.69. In addition, a total of 901 unknown compounds could be classified into nine chemical classes rapidly and effectively, based on the intelligent deep-learning algorithm supported MDF model with 96.4% accuracy. Consequently, 276 compounds were successfully identified from NXT, especially including 44 flavonoids, 27 phenolic acids, 25 fatty acids, 17 saponins, 21 phthalocyanines, 20 triterpenes, 10 monoterpenes, 13 diterpenoid ketones, 14 amino acids, and others. It is concluded that the proposed program is an effective and practical strategy enabling the in-depth chemical profiling of complex herbal and biological samples | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 2DLC-Q-TOF-MS/MS | |
| 650 | 4 | |a Deep-learning algorithm | |
| 650 | 4 | |a Mass loss filter | |
| 650 | 4 | |a Naoxintong capsule | |
| 700 | 1 | |a Liu, Tianyu |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Wentao |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Jiading |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Shujing |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Jiake |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Rui |e verfasserin |4 aut | |
| 700 | 1 | |a Cui, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Shuangqi |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Jin |e verfasserin |4 aut | |
| 700 | 1 | |a Chang, Yanxu |e verfasserin |4 aut | |
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