ISSN:
1612-1112
Schlagwort(e):
Ion Chromatography
;
Artificial neural networks
;
Modelling
;
Optimisation
Quelle:
Springer Online Journal Archives 1860-2000
Thema:
Chemie und Pharmazie
Notizen:
Summary An Artificial Neural Network (ANN) was investigated as a method to model retention times of anions in nonsuppressed and suppressed ion chromatography (IC) using a range of eluents and stationary phases, with the results being compared to those obtained using mathematical retention models. The optimal ANN architecture was determined for six specific IC cases of increasing complexity. Analysis of the retention times predicted using the ANN and those predicted by the mathematical models showed that the ANN approach yielded superior performance in all of the above cases. The use of a limited training data set configured in a central composite experimental design was suitable for application of the ANN to non-suppressed IC but was not applicable to suppressed IC, for which a more extensive training data set was necessary.
Materialart:
Digitale Medien
URL:
http://dx.doi.org/10.1007/BF02467746
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