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Journal
2010 | 8 | 6 | 1298-1304
Article title

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

Content
Title variants
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.
Publisher

Journal
Year
Volume
8
Issue
6
Pages
1298-1304
Physical description
Dates
published
1 - 12 - 2010
online
8 - 10 - 2010
Contributors
  • Department of Analytical Chemistry, Medical University of Gdansk, 80-416, Gdansk, Poland
  • Department of Analytical Chemistry, Medical University of Gdansk, 80-416, Gdansk, Poland, marwes@gumed.edu.pl
References
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.-psjd-doi-10_2478_s11532-010-0105-0
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