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Prediction of chiral separations using a combination of experimental design and artificial neural networks

✍ Scribed by Vlastimil Dohnal; Marta Farková; Josef Havel

Publisher
John Wiley and Sons
Year
1999
Tongue
English
Weight
208 KB
Volume
11
Category
Article
ISSN
0899-0042

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✦ Synopsis

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 ␣-cyclodextrin as chiral selector, were demonstrated. The results obtained with the ED-ANN approach were compared with those of either the partial least-squares (PLS) method or the 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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