dc.contributor.author |
Pektaş, Gözde
|
|
dc.contributor.author |
Dinç, Erdal
|
|
dc.contributor.author |
Baleanu, Dumitru
|
|
dc.date.accessioned |
2016-04-28T12:29:25Z |
|
dc.date.available |
2016-04-28T12:29:25Z |
|
dc.date.issued |
2008-10 |
|
dc.identifier.citation |
Baleanu, D. (2008). Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network. Revista De Chimie, 59(10), 1156-1159. |
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dc.identifier.issn |
0034-7752 |
|
dc.identifier.uri |
http://hdl.handle.net/20.500.12416/940 |
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dc.description.abstract |
Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 mu g/mL and 1-14 mu g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds |
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dc.language.iso |
eng |
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dc.publisher |
Chiminform Data S A |
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dc.rights |
info:eu-repo/semantics/closedAccess |
|
dc.subject |
Closulon |
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dc.subject |
Ivermectin |
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dc.subject |
Veterinary Injectable Formulation |
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dc.subject |
Artificial Neural Networks |
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dc.title |
Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network |
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dc.type |
article |
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dc.relation.journal |
Revista De Chimie |
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dc.contributor.authorID |
197407 |
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dc.contributor.authorID |
6981 |
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dc.identifier.volume |
59 |
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dc.identifier.issue |
10S |
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dc.identifier.startpage |
1156 |
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dc.identifier.endpage |
1159 |
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dc.contributor.department |
Çankaya Üniversitesi, Fen Edebiyat Fakültesi, Matematik Bilgisayar Bölümü |
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