Suchacz Bogdan, Wesołowski Marek
Department of Analytical Chemistry, Medical University of Gdansk, al. Gen. J. Hallera 107, Gdansk, Poland.
Talanta. 2006 Mar 15;69(1):37-42. doi: 10.1016/j.talanta.2005.08.026. Epub 2006 Jan 26.
The objective of the paper was to verify if the content of some elements provides enough information for proper classification of the medicinal plant raw materials. Such information could be helpful in standardization process of herbal products. Four elements-zinc, copper, lead and cadmium were determined using inverse voltammetry in commercially available medicinal herbal raw materials. Initially, principal component analysis (PCA) was employed to investigate the relationships among the analyzed trace elements. In the next stage of the study, two different types of feed-forward artificial neural networks (FANNs)--multilayer perceptron (MLP) and radial basis function (RBF)--were applied. The concentrations of the elements were used as input variables to neural networks models, which were to recognize the taxonomy of the plant and the anatomical part it originated from. Although full recognition of the samples with use of FANNs on the basis of some trace elements content was not achieved, it was possible to identify two elements-cadmium and lead as the most important in the classification analysis of medicinal plants.
本文的目的是验证某些元素的含量是否能为药用植物原材料的正确分类提供足够信息。此类信息可能有助于草药产品的标准化进程。采用反向伏安法测定了市售药用植物原材料中的四种元素——锌、铜、铅和镉。首先，运用主成分分析（PCA）来研究分析的微量元素之间的关系。在研究的下一阶段，应用了两种不同类型的前馈人工神经网络（FANNs）——多层感知器（MLP）和径向基函数（RBF）。元素浓度被用作神经网络模型的输入变量，这些模型旨在识别植物的分类学以及其来源的解剖部位。尽管基于某些微量元素含量利用FANNs未能实现对样本的完全识别，但在药用植物分类分析中确定镉和铅这两种元素最为重要是有可能的。
