Artificial neural networks and experimental design in analytical chemistry, especially in separation methods

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Project Identification
GA203/02/1103
Project Period
1/2002 - 12/2004
Investor / Pogramme / Project type
Czech Science Foundation
MU Faculty or unit
Faculty of Science

Artificial neural networks (ANN) in analytical chemistry; research and development of the optimization techniques based on ANN and/or genetic algorithms and their combination with experimental design for a general approach towards analytical process and method development. Experimental optimization combined with the prediction of analyte properties also from the structure (molecular descriptors) and physicochemical constants. Creation of general methodology for optimization and development of analytical methods, especially when the use of classical single variable optimization process of the experimental optimization and previous methods fail in complex chemical systems or when these approaches are too laborious and time consuming. The applications in analytical chemistry, especially in separation methods including chiral (capillary electrophoresis, liquid and ion chromatography), MALDI TOF mass spectrometry and separation techniques combined with mass spectrometry for analysis (method development) of

Publications

Total number of publications: 26


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