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

  • Research Article
  • Published:
Central European Journal of Chemistry

Abstract

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.

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Authors and Affiliations

  1. Department of Analytical Chemistry, Medical University of Gdansk, 80-416, Gdansk, Poland

    Bogdan Suchacz & Marek Wesolowski

Authors
  1. Bogdan Suchacz
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  2. Marek Wesolowski
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Correspondence to Marek Wesolowski.

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Suchacz, B., Wesolowski, M. Herbal drug raw materials differentiation by neural networks using non-metals content. cent.eur.j.chem. 8, 1298–1304 (2010). https://doi.org/10.2478/s11532-010-0105-0

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  • DOI: https://doi.org/10.2478/s11532-010-0105-0

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