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Herbal drug raw materials differentiation by neural networks using non-metals content

100%

Suchacz B. , Wesolowski M.

Open Chemistry

2010

tom 8

nr 6

1298-1304

Three-layer artificial neural networks (ANN) capable of recognizing the type of raw material (herbs, leaves, flowers, fruits, roots or barks) using the non-metals (N, P, S, Cl, I, B) contents as inputs were designed. Two different types of feed-forward ANNs - multilayer perceptron (MLP) and radial basis function (RBF), best suited for solving classification problems, were used. Phosphorus, nitrogen, sulfur and boron were significant in recognition; chlorine and iodine did not contribute much to differentiation. A high recognition rate was observed for barks, fruits and herbs, while discrimination of herbs from leaves was less effective. MLP was more effective than RBF.

The analysis of heavy metals content in herbal infusions

100%

Suchacz B. , Wesolowski M.

Open Medicine

2012

tom 7

nr 4

457-464

The content of 8 heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn) was evaluated in infusions prepared from 13 different herbal compositions commercially available in drug or herbal stores. The mixtures were produced by a Polish manufacturer “Herbapol”. The concentration of heavy metals was determined using flame atomic absorption spectrometry (FAAS). In the herbal infusions Mn was found in the highest concentration varying from 3.03 to 129.01 mg/kg. The element of the lowest content was Cd in the range of 0.024–0.153 mg/kg. According to interquartile ranges the concentrations of studied heavy metals in infusions decreased in the following descending order: Mn>Fe>Zn>Cu>Ni>Cr>Pb>Cd. Cluster analysis allowed for the division of herbal infusions into groups described by comparable levels of heavy metals. In water extracts made from Urosan, Nervosan, Infektoten and Cholagoga, distinctive levels of Mn, Fe and Cr were determined. According to WHO regulations, the concentrations of the elements did not exceed the allowable limits.