Prediction of chiral separations using combination of experimental designs and artificial neural networks
| Autoři | |
|---|---|
| Rok publikování | 1999 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | Chirality |
| Fakulta / Pracoviště MU | |
| Citace | |
| Obor | Analytická chemie, separace |
| Popis | In this work the advantages of using artificial neural networks (ANNs) combined with experimental design (ED) to optimize the separation of amino acids enantiomers, with a-cyclodextrin as chiral selector, were demonstrated. The results obtained with the ED-ANN approach were compared with those of either partial least squares (PLS) method or response surface methodology where experimental design and the regression equation were used. The ANN approach is quite general, no explicit model is needed and the amount of experimental work can be decreased considerably. |
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