NDLI: Optimization of Artificial Neural Networks for Modeling of Atorvastatin and Its Impurities Retention in Micellar Liquid Chromatography

Content Provider	Springer Nature Link
Author	Malević, Anđelija Jančić Stojavić, Biljana Kostić, Nađa Ivavić, Darko Medenica, Mirjana
Copyright Year	2011
Abstract	Artificial Neural Networks (ANNs) present a powerful tool for the modeling of chromatographic retention. In this paper, the main objective was to use ANNs as a tool in modeling of atorvastatin and its impurities’ retention in a micellar liquid chromatography (MLC) protocol. Factors referred to MLC were evaluated through 30 experiments defined by the Central Composite Design. In this manner, 5–x–3 topology as a starting point for ANNs’ optimization was defined too. In the next step, in order to set the network with the best performance, network optimization was done. In the first part, the number of nodes in the hidden layer and the number of experimental data points in training set were simultaneously varied, and their importance was estimated with suitable statistical parameters. Furthermore, a series of training algorithms was applied to the current network. The Back Propagation, Conjugate Gradient-descent, Quick Propagation, Quasi-Newton, and Delta-bar-Delta algorithms were used to obtain the optimal network. Finally, the predictive ability of the optimized neural network was confirmed through several statistical tests. The obtained network showed high ability to predict chromatographic retention of atorvastatin and its impurities in MLC.
Starting Page	993
Ending Page	998
Page Count	6
File Format	PDF
ISSN	00095893
Journal	Chromatographia
Volume Number	73
Issue Number	9-10
e-ISSN	16121112
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-03-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Micellar liquid chromatography Artificial neural networks Network’s optimization Atorvastatin Impurities Plant Sciences Pharmacy Organic Chemistry Proteomics Biochemistry Analytical Chemistry
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Analytical Chemistry Biochemistry Clinical Biochemistry

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