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Abstracts
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
Issue
Pages
1298-1304
Physical description
Dates
published
1 - 12 - 2010
online
8 - 10 - 2010
Contributors
author
- Department of Analytical Chemistry, Medical University of Gdansk, 80-416, Gdansk, Poland
author
- Department of Analytical Chemistry, Medical University of Gdansk, 80-416, Gdansk, Poland
References
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.-psjd-doi-10_2478_s11532-010-0105-0