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

• Publisher: De Gruyter Open
• 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

References

• [1] H.B. Matthews, G.W. Lucier, K.D. Fisher, Environmental Health Perspectives 107, 773 (1999) http://dx.doi.org/10.2307/3454572[Crossref]
• [2] P.R.G.M. De Smet, K. Keller, R. Hansel, R.F. Chandler, Adverse Effects of Herbal Drugs (Springer-Verlag, Heidelberg, 1997) vol. 1–3
• [3] W.M. Bandaranayake, In: I. Ahmad, F. Aqil, M. Owais (Eds.), Modern Phytomedicine. Turning Medicinal Plants into Drugs (Wiley-VCH, Weinheim, 2006) 25
• [4] M.Y. Lovkova, G.N. Buzuk, S.M. Sokolova, N.I. Kliment’eva, Applied Biochemistry and Microbiology 37, 229 (2001) http://dx.doi.org/10.1023/A:1010254131166[Crossref]
• [5] C. Bishop, Neural networks for pattern recognition (Oxford University Press, Oxford 1995)
• [6] S. Haykin, Neural networks: A Comprehensive foundation, 2nd edition (Prentice Hall, New Jersey, 1999)
• [7] Statistica Neural Networks (manual) (Statsoft Inc, Tulsa OK, 2007)
• [8] M. Wesolowski, P. Konieczynski, Chemia Analityczna (Warsaw) 41, 377 (1996)
• [9] M. Wesolowski, P. Konieczynski, B. Ulewicz, Journal of Thermal Analysis and Calorimetry 60, 299 (2000) http://dx.doi.org/10.1023/A:1010142517579[Crossref]
• [10] M. Wesolowski, P. Konieczynski, V. Medrzycka, Chemia Analityczna (Warsaw) 46, 697 (2001)
• [11] M. Wesolowski, P. Konieczynski, Thermochimica Acta 397, 171 (2003) http://dx.doi.org/10.1016/S0040-6031(02)00319-2[Crossref]
• [12] M. Wesolowski, P. Konieczynski, International Journal of Pharmaceutics 262, 29 (2003) http://dx.doi.org/10.1016/S0378-5173(03)00317-X[Crossref]
• [13] M. Wesolowski, B. Suchacz, Fresenius Journal of Analytical Chemistry 371, 323 (2001) http://dx.doi.org/10.1007/s002160100921[Crossref]
• [14] J. Zupan, J. Gastaiger, Neural networks in chemistry and drug design (Wiley, New York, 1999)

Identifiers

DOI

10.2478/s11532-010-0105-0