Identification of Radix Bupleuri From Different Geographic Origins Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry and Support Vector Machine Algorithm.

IF 1.7 4区 农林科学 Q3 CHEMISTRY, ANALYTICAL
Zheng-Yong Zhang, Ya-Ju Zhao, Fang-Jie Guo, Hai-Yan Wang
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引用次数: 0

Abstract

Background: The geographic origin of Radix bupleuri is an important factor affecting its efficacy, which needs to be effectively identified.

Objective: The goal is to enrich and develop the intelligent recognition technology applicable to the identification of the origin of traditional Chinese medicine.

Method: This article establishes an identification method of Radix bupleuri geographic origin based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and support vector machine (SVM) algorithm. The Euclidean distance method is used to measure the similarity between Radix bupleuri samples, and the quality control chart method is applied to quantitatively describe their quality fluctuation.

Results: It is found that the samples from the same origin are relatively similar and mainly fluctuate within the control limit, but the fluctuation range is large, and it is impossible to distinguish the samples from different origins. The SVM algorithm can effectively eliminate the impact of intensity fluctuations and huge data dimensions by combining the normalization of MALDI-TOF MS data and the dimensionality reduction of principal components, and finally achieve efficient identification of the origin of Radix bupleuri, with an average recognition rate of 98.5%.

Conclusions: This newly established approach for identification of the geographic origin of Radix bupleuri has been realized, and it has the advantages of objectivity and intelligence, which can be used as a reference for other medical and food-related research.

Highlights: A new intelligent recognition method of medicinal material origin based on MALDI-TOF MS and SVM has been established.

基于矩阵辅助激光解吸/电离飞行时间质谱和支持向量机算法鉴定不同地理产地柴胡
背景:柴胡的地理来源是影响其药效的重要因素,需要进行有效的鉴别。目的:丰富和发展适用于中药产地识别的智能识别技术。方法:建立了一种基于基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)和支持向量机(SVM)算法的柴胡产地鉴别方法。采用欧几里得距离法测量柴胡样品之间的相似性,采用质量控制图法定量描述其质量波动。结果:发现同一来源的样品相对相似,主要在控制范围内波动,但波动幅度较大,无法区分不同来源的样品。SVM算法将MALDI-TOF MS数据的归一化与主成分的降维相结合,可以有效地消除强度波动和巨大数据维度的影响,最终实现对柴胡产地的有效识别,平均识别率为98.5%。结论:该新建立的柴胡地理产地识别方法已实现,具有客观性和智能性的优点,可为其他医学和食品相关研究提供参考。亮点:建立了一种新的基于MALDI-TOF MS和SVM的药材产地智能识别方法。
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来源期刊
Journal of AOAC International
Journal of AOAC International 医学-分析化学
CiteScore
3.10
自引率
12.50%
发文量
144
审稿时长
2.7 months
期刊介绍: The Journal of AOAC INTERNATIONAL publishes the latest in basic and applied research in analytical sciences related to foods, drugs, agriculture, the environment, and more. The Journal is the method researchers'' forum for exchanging information and keeping informed of new technology and techniques pertinent to regulatory agencies and regulated industries.
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