Prediction of chiral separations using a combination of experimental design and artificial neural networks
Jazyk angličtina Země Spojené státy americké Médium print
Typ dokumentu časopisecké články
PubMed
10467312
DOI
10.1002/(sici)1520-636x(1999)11:8<616::aid-chir2>3.0.co;2-r
PII: 10.1002/(SICI)1520-636X(1999)11:8<616::AID-CHIR2>3.0.CO;2-R
Knihovny.cz E-zdroje
- MeSH
- aminokyseliny analýza MeSH
- elektroforéza kapilární MeSH
- neuronové sítě * MeSH
- stereoizomerie MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- aminokyseliny MeSH
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 alpha-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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