Skip to content
2000
Volume 6, Issue 8
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

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.

Loading

Article metrics loading...

/content/journals/cchts/10.2174/138620703771826928
2003-12-01
2025-07-14
Loading full text...

Full text loading...

/content/journals/cchts/10.2174/138620703771826928
Loading

  • Article Type:
    Review Article
Keyword(s): neural networks; thermogravimetric
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test