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

支持向量机 规范化(社会学) 主成分分析 计算机科学 人工智能 欧几里德距离 模式识别(心理学) 质谱法 鉴定(生物学) 数据挖掘 算法 色谱法 化学 生物 社会学 植物 人类学
作者
Zhengyong Zhang,Yaju Zhao,Feiyue Guo,Haiyan Wang
出处
期刊:Journal of AOAC International [Oxford University Press]
卷期号:106 (6): 1682-1688 被引量:4
标识
DOI:10.1093/jaoacint/qsad060
摘要

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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
xu发布了新的文献求助10
1秒前
张张发布了新的文献求助10
1秒前
2秒前
科研通AI6应助vagrant采纳,获得10
3秒前
4秒前
。。完成签到 ,获得积分10
4秒前
眼镜起雾完成签到,获得积分10
5秒前
唐僧肉臊子面完成签到,获得积分10
5秒前
yll完成签到,获得积分10
5秒前
wisdom发布了新的文献求助10
5秒前
ZL完成签到,获得积分10
5秒前
浮游应助科研通管家采纳,获得10
6秒前
BowieHuang应助科研通管家采纳,获得10
6秒前
香蕉诗蕊应助科研通管家采纳,获得10
7秒前
科研通AI2S应助科研通管家采纳,获得10
7秒前
科研通AI6应助科研通管家采纳,获得10
7秒前
香蕉诗蕊应助科研通管家采纳,获得10
7秒前
传奇3应助科研通管家采纳,获得10
7秒前
Ranqi应助科研通管家采纳,获得10
7秒前
传奇3应助科研通管家采纳,获得10
7秒前
搜集达人应助科研通管家采纳,获得10
7秒前
科研通AI6应助科研通管家采纳,获得10
7秒前
香蕉诗蕊应助科研通管家采纳,获得10
7秒前
华仔应助科研通管家采纳,获得10
7秒前
浅晨发布了新的文献求助10
8秒前
caifeng发布了新的文献求助10
8秒前
古今奇观完成签到 ,获得积分10
9秒前
8R60d8应助Denmark采纳,获得10
9秒前
michael发布了新的文献求助10
10秒前
10秒前
10秒前
汉堡包应助Merciful采纳,获得10
11秒前
Ava应助孔雀翎采纳,获得10
11秒前
白白白完成签到,获得积分10
12秒前
13秒前
13秒前
xu完成签到,获得积分10
13秒前
在水一方应助renxsh采纳,获得10
14秒前
酷酷的冰淇淋完成签到 ,获得积分10
15秒前
和谐听白发布了新的文献求助10
16秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
List of 1,091 Public Pension Profiles by Region 1621
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] | NHBS Field Guides & Natural History 1500
Lloyd's Register of Shipping's Approach to the Control of Incidents of Brittle Fracture in Ship Structures 1000
Brittle fracture in welded ships 1000
COATING AND DRYINGDEEECTSTroubleshooting Operating Problems 600
涂布技术与设备手册 600
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5569592
求助须知:如何正确求助?哪些是违规求助? 4654253
关于积分的说明 14710045
捐赠科研通 4595902
什么是DOI,文献DOI怎么找? 2522102
邀请新用户注册赠送积分活动 1493376
关于科研通互助平台的介绍 1463987