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The Application of Artificial Neural Networks for the Selection of Key Thermoanalytical Parameters in Medicinal Plants Analysis

[ Vol. 6 , Issue. 8 ]


Marek Wesolowski, Bogdan Suchacz and Pawel Konieczynski   Pages 811 - 820 ( 10 )


In the present study three thermoanalytical methods: differential thermal analysis (DTA), thermogravimetric analysis (TGA), and derivative thermogravimetric analysis (DTG) were used to investigate the thermal behavior of medicinal plant raw materials. In order to describe DTA curve, designation of the onset Ti, and peak Tp, temperatures was required. In TGA the mass losses Δm, and in DTG the temperature range of peak ΔT, peak temperature Tp, and peak height h, were recorded. All parameters were read for three stages of the thermal decomposition of plant samples which resulted in obtaining eighteen thermal variables for each sample. Some similarities in the course of thermal decomposition of the same plant organs were recognized, but complexity of the obtained data made it very difficult to determine if they could differentiate between medicinal plant materials and which of them encode the most valuable information about the studied herbals. In order to confirm the existence of any relations between the chemical composition of medicinal plants and their thermal decomposition and to find out which thermoanalytical variables or decomposition stages can be considered as the most significant in terms of their evaluation, it was decided to apply fully connected feedforward artificial neural networks (ANN). Two different training algorithms were used to address the problem: back-propagation of error and conjugate gradient descent. To verify the results two-dimensional (2-D) Kohonen self-organizing feature maps (SOFMs) were employed. Two alternative datasets of thirteen key variables discriminating plant samples have been proposed.


thermogravimetric, neural networks


Department of Analytical Chemistry, Medical University of Gdansk, al. Gen. J. Hallera 107, 80-416Gdansk, Poland.

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