通过在线全面的二维液态染色学-四极点飞行时间质谱学结合深度学习辅助质量缺陷过分类和多维数据注释策略的智能特征化多元组件在新石平板电脑上
Jiake Wen1, Wei Wei1, Lili Zhang2
1State Key Laboratory of Chinese Medicine Modernization, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, China.
Talanta
|February 28, 2025
概括
通过先进的质谱测量,开发了一种用于分析阳平板电脑 (YXST) 组件的新深度学习策略. 这种方法有效地识别了228种化合物,改善了天然产品分析.
科学领域:
- 分析化学 分析化学
- 药理学是指药理学,即药理学是指药理学.
- 计算化学计算化学
背景情况:
- 阳石药片 (YXST) 是一种复杂的传统医药,需要进行全面的化学分析.
- 现有的方法很难有效地识别和量化YXST中存在的各种各样的化合物.
研究的目的:
- 建立一个智能策略,以全面描述YXST中的多种化学成分.
- 为复杂的自然产品开发和验证一种新的数据采集和分析方法.
主要方法:
- 在线全面的二维液态色谱与四极飞行时间质谱学 (2DLC-Q-TOF-MS/MS) 结合使用.
- 开发了一个深度学习辅助的质量缺陷过智能分类,首选的离子捕获列表和主动排除 (DLA-MDF-PIL-AE) 数据采集模式.
- 分子网络 (MN),相关的中性损失 (NL) 片段和特征诊断离子 (CDI) 用于化合物注释.
- 超高性能液态色谱学与三重四极质谱学 (UHPLC-MS/MS) 结合使用了量化.
主要成果:
- 总共有228种化合物在YXST中初步确定,包括黄类,类,酸,类和沙素.
- 对已识别的化合物进行了来源归因.
- 使用UHPLC-MS/MS.同时量化了39种化合物.
结论:
- 综合战略将DLA-MDF-PIL-AE与MN,NL和CDI结合起来,提供了一种强大的方法来表征复杂的天然产品中的多种成分.
- 这种方法显著提高了天然产品分析的效率和准确性.
- 这些发现有助于更好地了解YXST.的化学成分.
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