HPLC fingerprints combined with principal component analysis, hierarchical cluster analysis and linear discriminant analysis for the classification and differentiation of Peganum sp. indigenous to China

骆驼蓬 主成分分析 线性判别分析 高效液相色谱法 化学 化学计量学 色谱法 层次聚类 传统医学 植物 生物 人工智能 计算机科学 医学 聚类分析
作者
Xuemei Cheng,Ting Zhao,Tao Yang,Changhong Wang,S. W. Annie Bligh,Zhengtao Wang
出处
期刊:Phytochemical Analysis [Wiley]
卷期号:21 (3): 279-289 被引量:57
标识
DOI:10.1002/pca.1198
摘要

Abstract Introduction – Seeds of wild Peganum harmala Linn., P . multisectum (Maxim) Bobr., P . nigellastrum Bunge and a probable indeterminate species, herein referred to as P . variety, are commonly used in Chinese medicine. These seeds cannot be differentiated based on morphology. Objective – Seeds of P . harmala Linn., P . multisectum (Maxim) Bobr., P . nigellastrum Bunge and P . variety were collected in different provinces in China and their HPLC profiles were recorded for statistical analysis and pattern recognition. Methodology – HPLC chromatograms of seed extracts were recorded under the same conditions. Individual HPLC chromatograms for each species were evaluated against the mean chromatogram for the same species generated using a similarity evaluation computer program. Data from chromatographic fingerprints were also processed using principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). Results – The Peganum sp. seed extracts had similar HPLC fingerprints but with some inter‐specific differences. The chromatographic fingerprints combined with PCA, HCA and LDA could distinguish the seeds of the different species of Peganum investigated. Conclusion – HPLC fingerprints can be used to authenticate and differentiate the seeds of three different species of genus Peganum indigenous to China. The results indicated that the unidentified P . variety might indeed be a new species or variety. Copyright © 2009 John Wiley & Sons, Ltd.
最长约 10秒,即可获得该文献文件

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
生动的金鑫完成签到,获得积分10
1秒前
2秒前
清脆又晴完成签到,获得积分10
2秒前
2秒前
2秒前
3秒前
xuyan完成签到,获得积分10
3秒前
3秒前
3秒前
Eternity完成签到,获得积分10
3秒前
Chan完成签到,获得积分10
4秒前
4秒前
cm发布了新的文献求助10
5秒前
ljs发布了新的文献求助20
5秒前
xiaoguai发布了新的文献求助20
6秒前
Lee发布了新的文献求助10
6秒前
Ki发布了新的文献求助10
6秒前
7秒前
翟炳发布了新的文献求助10
7秒前
8秒前
xf发布了新的文献求助10
8秒前
kuku完成签到 ,获得积分10
8秒前
Two发布了新的文献求助10
8秒前
TSUKI发布了新的文献求助10
9秒前
winter应助Lucia_yx采纳,获得10
10秒前
清爽的胡萝卜完成签到 ,获得积分10
10秒前
10秒前
10秒前
12秒前
Zx发布了新的文献求助20
12秒前
13秒前
jie367发布了新的文献求助10
13秒前
kuku发布了新的文献求助10
15秒前
咔咔发布了新的文献求助10
15秒前
16秒前
16秒前
科研通AI5应助cm采纳,获得10
17秒前
chi发布了新的文献求助10
19秒前
SYLH应助科研通管家采纳,获得10
19秒前
SYLH应助科研通管家采纳,获得10
19秒前
高分求助中
Continuum Thermodynamics and Material Modelling 4000
Production Logging: Theoretical and Interpretive Elements 2700
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
El viaje de una vida: Memorias de María Lecea 800
Novel synthetic routes for multiple bond formation between Si, Ge, and Sn and the d- and p-block elements 700
Neuromuscular and Electrodiagnostic Medicine Board Review 700
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3514884
求助须知:如何正确求助?哪些是违规求助? 3097246
关于积分的说明 9234750
捐赠科研通 2792216
什么是DOI,文献DOI怎么找? 1532342
邀请新用户注册赠送积分活动 711969
科研通“疑难数据库(出版商)”最低求助积分说明 707062