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
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
CodeCraft应助阔达的念珍采纳,获得10
1秒前
1秒前
二妮儿完成签到,获得积分20
2秒前
CL完成签到 ,获得积分10
2秒前
FashionBoy应助rita_sun1969采纳,获得10
2秒前
黑黑126完成签到,获得积分10
2秒前
3秒前
d甩甩发布了新的文献求助10
4秒前
yolo发布了新的文献求助10
4秒前
5秒前
云遮月发布了新的文献求助10
5秒前
xin发布了新的文献求助10
6秒前
万里发布了新的文献求助10
9秒前
OuO发布了新的文献求助10
9秒前
9秒前
11发布了新的文献求助10
10秒前
ferritin完成签到 ,获得积分10
10秒前
10秒前
关心完成签到,获得积分10
11秒前
上官若男应助楠楠采纳,获得30
11秒前
11秒前
顾矜应助aiuxin采纳,获得10
12秒前
在水一方应助Komorebi采纳,获得10
12秒前
13秒前
guoleileity完成签到,获得积分10
13秒前
QJT发布了新的文献求助10
13秒前
14秒前
西出阳关完成签到,获得积分10
14秒前
彭于晏应助xin采纳,获得10
14秒前
14秒前
hxy完成签到,获得积分10
14秒前
15秒前
15秒前
16秒前
小二郎应助sakatagintoki采纳,获得10
17秒前
17秒前
18秒前
rr发布了新的文献求助10
18秒前
18秒前
阳光毛巾发布了新的文献求助10
18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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
CLSI M100 Performance Standards for Antimicrobial Susceptibility Testing 36th edition 400
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6361350
求助须知:如何正确求助?哪些是违规求助? 8175163
关于积分的说明 17221223
捐赠科研通 5416216
什么是DOI,文献DOI怎么找? 2866187
邀请新用户注册赠送积分活动 1843500
关于科研通互助平台的介绍 1691442