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

• Authors: Bogdan Suchacz , Marek Wesolowski
• Languages of publication: EN

Abstracts

EN

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.

Keywords

EN

Herbal raw materials; Artificial neural networks; Multilayer perceptron; Radial basis function network; Non-metals

• Journal: Open Chemistry
• Year: 2010
• Volume: 8
• Issue: 6
• Pages: 1298-1304

Dates

published

1 - 12 - 2010

online

8 - 10 - 2010

Contributors

author

Bogdan Suchacz

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

author

Marek Wesolowski

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

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Identifiers

DOI

10.2478/s11532-010-0105-0