WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Article Citation - WoS: 2Citation - Scopus: 3An Application of Principal Component Analysis - Artificial Neural Network for the Simultaneous Quantitative Analysis of a Binary Mixture System(Chiminform Data S A, 2009) Dinc, Erdal; Baleanu, Dumitru; Sen Koktas, Nigar; Köktaş, Nigar; Baleanu, Dumitru; Kökias, Nigar Şen; MatematikArtificial neural networks (ANNs) based on the use of principal components and the original absorbance data were proposed for the simultaneous quantitative analysis of amlodipine (AML) and atorvastatin (ATO) in tablets. A concentration set of mixtures containing ATO and AML in different concentration composition between 0.0-20.0 mu g/mL was prepared in methanol. The measured absorbance data matrix for the concentration data set was obtained and the principal components were extracted. In the next step five principal components were selected as an input data for the artificial neural network. This combined approach was named principal components-artificial neural network (PCA-ANN). The same problem was solved by using the application of the artificial neural network to the original absorbance data matrix. This approach was denoted as ANN. The classical ANN approach was used as a comparison method. Both PCA-ANN and ANN methods were tested by analyzing various synthetic mixtures corresponding to the validation set of AML and ATO compounds. The proposed methods were successfully applied to the quantitative analysis of the commercial tablets and a coincidence was reported between the proposed methods.Article Citation - WoS: 2Citation - Scopus: 3New Spectral Approaches To the Simultaneous Quantitative Resolution of a Combined Veterinary Formulation by Ann and Pca-Ann Methods(Walter de Gruyter & Co, 2011) Baleanu, Dumitru; Sen Koktas, Nigar; Dinc, Erdal; Köktaş, Nigar ŞenThe simultaneous spectral prediction of levamisole (LEV) and triclabendazole (TRI) in combined veterinary formulation was performed by the new chemometric methods, artificial neural network (ANN) and principal component analysis-artificial neural network (PCA-ANN). Despite the overlapping spectra of LEV and TRI in the same wavelength region, the proposed methods do not use any separation procedure for the analysis of the related compounds. Good precision and accuracy were observed for the applications of the proposed artificial neural network models to an independent binary mixture set consisting of the active compounds. These methods were successfully applied for the chemometric quantitation of a veterinary formulation of LEV and TRI.Article Citation - WoS: 37Citation - Scopus: 43Multivariate Analysis of Paracetamol, Propiphenazone, Caffeine and Thiamine in Quaternary Mixtures by Pcr, Pls and Ann Calibrations Applied on Wavelet Transform Data(Elsevier, 2008) Baleanu, Dumitru; Ioele, Giuseppina; De Luca, Michele; Ragno, Gaetano; Dinc, ErdalThe quantitative resolution of a quaternary pharmaceutical mixture consisting of paracetamol, propiphenazone, caffeine and thiamine was performed by the simultaneous use of fractional wavelet transform (FWT) with principal component regression (PCR), partial least squares (PLS) and artificial neural networks (ANN) methods. A calibration set consisting of 22 mixture solutions was prepared by means of an orthogonal experimental design and their absorption spectra were recorded in the spectral range of 210.0-312.3 nm and then transferred into the fractional wavelet domain and processed by FWT. The chemometric calibrations FWT-PCR, FWT-PLS and FWT-ANN were computed by using the relationship between the coefficients provided by FWT method and the concentration data from calibration set. An external validation was carried out by applying the developed methods to the analysis of synthetic mixtures of the related compounds, obtaining successful results. The models were finally used to assay the studied drugs in the commercial pharmaceutical formulations. (c) 2008 Elsevier B.V. All rights reserved.