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

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. Copyright © 2010 John Wiley & Sons, Ltd.The characterization of two plants, frequently used as food and medicinal species from South America, Achyrocline satureioides and Achyrocline venosa (Asteraceae:Gnaphalieae), was carried out based on their multielemental composition. Twenty five elements, including toxic elements, were analyzed by means of inductively coupled plasma optical emission spectrometry (ICP-OES) from aerial parts of the plants. Recovery of toxic elements was study. For characterization, the multivariate methods used include Principal Components Analysis, Cluster Analysis and Linear Discriminant Analysis.