多维染色指纹融合与机器学习:基于值的特征评估用于TCM质量标志物发现
Jiamu Ma1, Fang Lv1, Letian Ying1
1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Fangshan District, 100029, Beijing, China.
Talanta
|February 5, 2026
概括
这项研究整合了多维染色学和机器学习,以识别传统中医 (TCM) 中的关键化合物. 这种方法有效地解构复杂的信号,确定洛贝林和人参化物Rf作为Fufang E'jiao Jiang.关键的质量标记.
科学领域:
- 分析化学 分析化学
- 化学信息学 化学信息学
- 药理学是指药理学,它是指药理学.
背景情况:
- 染色学对于中国传统医学 (TCM) 质量控制至关重要.
- 复杂的染色学数据与重叠的信号阻碍了准确的特征识别.
- 现有的方法难以应对TCM复杂的化学特征.
研究的目的:
- 开发一种综合方法,将多维色谱指纹和机器学习结合起来.
- 为了追踪在Fufang E'jiao Jiang (FEJ) 中特征性化合物的分子起源.
- 建立一个可扩展的计算框架,用于复杂样品的质量控制.
主要方法:
- 多维色谱指纹 (TLC,LC-HRMS) 用于生成数据集 (> 1700 个特征).
- 机器学习算法 (例如,随机森林) 用于歧视性特征选择.
- 修改的和障碍因子用于特征评估和权重.
主要成果:
- 在FEJ中确定了5个模式,在中间物质中确定了7个模式,主要是人参化物.
- 罗贝林和人参化物Rf被确定为关键的与质量相关的标记物.
- 实验验证证了重叠信号的有效解卷.
结论:
- 综合方法有效地识别了复杂染色指纹中的关键质量标记.
- 该方法提供了适用于各种复杂样本的通用,可扩展的计算框架.
- 这一战略加强了TCM和其他复杂化学系统的质量控制.
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