| dc.creator |
AKTAŞ, Ahmet Hakan |
|
| dc.creator |
KITIS, Filiz |
|
| dc.date |
2014-03-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T09:49:45Z |
|
| dc.date.available |
2020-10-06T09:49:45Z |
|
| dc.identifier |
4a27ba61-5797-4ad4-b473-ef8ceed2f5aa |
|
| dc.identifier |
10.5562/cca2214 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/4a27ba61-5797-4ad4-b473-ef8ceed2f5aa/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/59298 |
|
| dc.description |
Three multivariate calibration-prediction techniques, principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANN) were applied to the spectrometric multicomponent analysis of the drug containing paracetamol (PCT) and caffeine (CAF) without any separation step. The selection of variables was studied. A series of synthetic solution containing different concentrations of PCT and CAF were used to check the prediction ability of the PCR, PLS and ANN. The results obtained in this investigation strongly encourage us to apply these techniques for a routine analysis and quality control of the drug. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
Spectrophotometric Simultaneous Determination of Caffeine and Paracetamol in Commercial Pharmaceutical by Principal Component Regression, Partial Least Squares and Artificial Neural Networks Chemometric Methods |
|
| dc.type |
info:eu-repo/semantics/article |
|