Artificial neural networks for quantification in unresolved capillary electrophoresis peaks
| Autor: | Marta Farková, Rosa Latorre, Josef Havel, Gaston Bocaz-Beneventi |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Normalization (statistics)
Chromatography Artificial neural network Chemistry Peak shift Computer Science::Neural and Evolutionary Computation 010401 analytical chemistry 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Biochemistry Physics::Geophysics 0104 chemical sciences Analytical Chemistry Electropherogram Capillary electrophoresis Environmental Chemistry 0210 nano-technology Biological system Spectroscopy |
| Zdroj: | Analytica Chimica Acta. 452:47-63 |
| ISSN: | 0003-2670 |
| DOI: | 10.1016/s0003-2670(01)01445-3 |
| Popis: | The application of the combination of experimental design (ED) and artificial neural networks (ANNs) for the quantification of overlapped peaks in capillary zone electrophoresis is described. When the total separation cannot be achieved by separation techniques, the use of ED-ANN can be a suitable approach. The unstability of EOF causes peak shift that has to be corrected in order to apply ED-ANN methods. In this work, normalization procedure of electropherograms with consequent application of ANNs for quantification purpose was developed. Both, spectra and electropherograms can be used as multivariate data. In general, both kinds of data were found to be suitable for unresolved peaks quantification by ED-ANN approach. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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