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
上官若男应助完美的冷珍采纳,获得10
1秒前
3秒前
jstagey完成签到 ,获得积分10
3秒前
万能图书馆应助Aniew采纳,获得10
6秒前
6秒前
7秒前
kim完成签到,获得积分20
8秒前
西瓜完成签到 ,获得积分10
11秒前
ok发布了新的文献求助10
11秒前
希望天下0贩的0应助朝歌采纳,获得10
11秒前
FBQ完成签到,获得积分10
14秒前
15秒前
17秒前
沉默秀发布了新的文献求助10
20秒前
闻山发布了新的文献求助10
21秒前
24秒前
米豆爸完成签到,获得积分10
26秒前
bushi完成签到,获得积分10
28秒前
小蘑菇应助李咕噜采纳,获得10
28秒前
mtenxx发布了新的文献求助10
29秒前
30秒前
木木完成签到,获得积分20
31秒前
慕青应助王第一采纳,获得10
33秒前
33秒前
34秒前
哈哈发布了新的文献求助10
35秒前
Eason_C完成签到 ,获得积分10
36秒前
nhanvm完成签到,获得积分10
36秒前
小马甲应助国产好人采纳,获得10
37秒前
文艺寄松完成签到 ,获得积分10
39秒前
唐唐应助结实寒梦采纳,获得10
39秒前
39秒前
快乐的思真完成签到 ,获得积分10
41秒前
42秒前
42秒前
42秒前
42秒前
42秒前
LB应助科研通管家采纳,获得10
42秒前
13313完成签到,获得积分10
42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 1000
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
信任代码:AI 时代的传播重构 450
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 6357408
求助须知:如何正确求助?哪些是违规求助? 8172051
关于积分的说明 17206804
捐赠科研通 5413040
什么是DOI,文献DOI怎么找? 2864862
邀请新用户注册赠送积分活动 1842345
关于科研通互助平台的介绍 1690526