| dc.creator |
ERTOKUS, Guezide Pekcan |
|
| dc.creator |
AKTAŞ, Ahmet Hakan |
|
| dc.date |
2007-12-31T22:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:48:10Z |
|
| dc.date.available |
2020-10-06T10:48:10Z |
|
| dc.identifier |
8d0d3bcd-8ef9-4174-95a6-29435e850591 |
|
| dc.identifier |
10.2298/jsc0801087a |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/8d0d3bcd-8ef9-4174-95a6-29435e850591/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/65982 |
|
| dc.description |
In this Study, three anti-inflammatory agents, namely ibuprofen, indomethacin and naproxen, were titrated potentiometrically using tetrabutyl-ammonium hydroxide in acetonitrile solvent under a nitrogen atmosphere at 25 degrees C. MATLAB 7.0 software was applied for data treatment as a multivariate calibration tool in the potentiometric titration procedure. An artificial neural network (ANN) was used as a multivariate calibration tool in the potentiometric titration to model the complex non-linear relationship between ibuprofen, indomethacin and naproxen concentrations and the millivolt (mV) of the solutions measured after the addition of different volumes of the titrant. The optimized network predicted the concentrations of agents in synthetic mixtures. The results showed that the employed ANN can precede the titration data with an average relative error of prediction of less than 2.30%. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Potentiometric determination of ibuprofen, indomethacin and naproxen using an artificial neural network calibration |
|
| dc.type |
info:eu-repo/semantics/article |
|