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[根据HPLC-QTOF-MS~E和Adaboost进行的Cervi Cornu Pantotrichum的数字识别]

Xiao-Han Guo¹, Xian-Rui Wang¹, Yu Zhang²

¹State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control Beijing 102629, China Institute for Control of Chinese Traditional Medicine and Ethnic Medicine, National Institutes for Food and Drug Control Beijing 102629, China.

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica

|May 12, 2025

概括

这项研究使用UHPLC-QTOF-MS/E和Adaboost开发了Cervi Cornu Pantotrichum的数字识别模型. 该模型准确地识别了这种宝贵的动物制药的来源,确保了药物的安全性和质量控制.

科学领域:

药理学和中国传统医学
分析化学分析化学
生物信息学是一种生物信息学.

背景情况:

宫角 (Cervi Cornu Pantotrichum) 是一种有价值的动物衍生传统中医药.
市场上使用未指定的动物来源的改会危及药物的安全性.
标准化和质量控制对于真正的Cervi Cornu Pantotrichum至关重要.

研究的目的:

开发Cervi Cornu Pantotrichum的数字识别模型.
为了确保质量控制和标准化Cervi Cornu Pantotrichum的使用.
为了准确地区分Cervi Cornu Pantotrichum与各种动物来源.

主要方法:

从西卡鹿,红鹿,鹿和鹿中获得的Cervi Cornu Pantotrichum的多分析,使用超高性能液态染色学并列四极飞行时间质谱法 (UHPLC-QTOF-MS/E).
数字定量质谱数据以获得聚信息.
使用基尼指数进行特征选,并使用自适应增强 (Adaboost) 算法构建数字识别模型.
评估和验证模型的性能,使用回忆率,F1得分和准确性.

主要成果:

基于UHPLC-QTOF-MS/E和Adaboost的数字识别模型实现了高性能.
使用基尼指数来选前100个特征性多,结果是最好的模型性能.
该模型显示了回忆率,F1得分,准确度不低于0.953.
验证分析证实,识别10个样本批次的准确率为100.0%.

结论:

使用UHPLC-QTOF-MS/E和Adaboost算法成功建立了Cervi Cornu Pantotrichum的数字识别方法.
这种方法提供了高效和准确的识别,对于质量控制至关重要.
这些发现为确定Cervi Cornu Pantotrichum的原始动物来源和确保其药用质量提供了有价值的参考.

关键词:

宫角 (Cervi Cornu Pantotrichum) 是一个非常重要的部位.数字识别数字识别机器学习是机器学习.原产地标识来源标识这是一种多类聚.验证真实性的验证.

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