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基于多元素组成的化学计量学方法对两种南美食用药用植物的特征描述。

Characterisation of two South American food and medicinal plants by chemometric methods based on their multielemental composition.

机构信息

Facultad de Ciencias Exactas y Naturales, Universidad Nacional de La Pampa, La Pampa, Argentina.

出版信息

Phytochem Anal. 2010 Nov-Dec;21(6):550-5. doi: 10.1002/pca.1231.

Abstract

INTRODUCTION

The chemometric characterisation of two plants frequently used as food and medicinal species, Achyrocline satureioides and Achyrocline venosa (Asteraceae: Gnaphalieae), was carried out based on their mineral composition. Both species, known by the common name of 'marcelas', are very similar in their morphological features but they have different medicinal and food properties.

OBJECTIVE

To develop multivariate models for the classification of A. satureiodes and A. venosa based on their mineral content.

METHODOLOGY

The analytic determinations were made by means of inductively coupled plasma optical emission spectrometry from aerial parts of the plants. An internal standard was used to evaluate the accuracy in the sample treatment and the recovery of toxic elements was studied. The multivariate methods used include principal components analysis, cluster analysis and linear discriminant analysis.

RESULTS

Classification for both A. satureioides and A. venosa was successful in all cases using only four variables: aluminium, iron, magnesium and sulphur content. The concentrations of the following elements were determined: Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, La, Mg, Mn, Mo, Na, Ni, P, Pb, S, Sr, Ti, V, Y and Zn.

CONCLUSIONS

This method is useful to identify both species in raw material in order to detect eventual errors of selection.

摘要

简介

基于矿物质组成,对常被用作食物和药用植物的两种植物(天蓝绣球科:蓝目菊族),天蓝属的天蓝和天蓝副天蓝进行了化学计量学特征描述。这两个物种,俗称“marcelas”,在形态特征上非常相似,但具有不同的药用和食用特性。

目的

基于矿物质含量,开发用于天蓝属天蓝和天蓝副天蓝分类的多元模型。

方法

通过电感耦合等离子体光学发射光谱法对植物的地上部分进行分析测定。采用内标法评估样品处理的准确性,并研究了有毒元素的回收率。所使用的多元方法包括主成分分析、聚类分析和线性判别分析。

结果

仅使用 4 个变量(铝、铁、镁和硫的含量)就成功地对天蓝属天蓝和天蓝副天蓝进行了分类。测定了以下元素的浓度:Al、As、Ba、Ca、Cd、Co、Cr、Cu、Fe、K、La、Mg、Mn、Mo、Na、Ni、P、Pb、S、Sr、Ti、V、Y 和 Zn。

结论

该方法可用于识别原料中的两种植物,以检测可能的选择错误。

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