ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION

ABSTRACT In this study, least squares support vector machine (LS-SVM) and artificial neural networks (ANNs) as intelligent methods combined with spectrophotometry method, were used for determination of Sofosbuvir (SOF) and Ledipasvir (LED) in synthetic mixtures and Harvoni tablet simultaneously. In...

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Autores principales:	Safakhoo,Negin, Sohrabi,Mahmoud Reza, Khalili,Mahsa, Mofavvaz,Shirin
Lenguaje:	English
Publicado:	Sociedad Chilena de Química 2019
Materias:	least square support vector machine Artificial neural networks Sofosbuvir Ledipasvir Harvoni
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072019000104310
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spelling	oai:scielo:S0717-970720190001043102019-04-11ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATIONSafakhoo,NeginSohrabi,Mahmoud RezaKhalili,MahsaMofavvaz,Shirin least square support vector machine Artificial neural networks Sofosbuvir Ledipasvir Harvoni ABSTRACT In this study, least squares support vector machine (LS-SVM) and artificial neural networks (ANNs) as intelligent methods combined with spectrophotometry method, were used for determination of Sofosbuvir (SOF) and Ledipasvir (LED) in synthetic mixtures and Harvoni tablet simultaneously. In the LS-SVM method, Radial Basis Function (RBF) was used as kernel function. Then, the regularization parameter (γ) and Bandwidth (2) were optimized and root mean square error prediction (RMSE) was 0.4164, 0.6033 for SOF and LED respectively. Afterwards, Feed-forward back-propagation network with different training algorithms was used in artificial neural network method. These training algorithms compared with each other for selecting the best model. On the other hand, radial basis function neural network (RBFNN) was applied as an efficient network. Finally, these methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. According to one way analysis of variance (ANOVA) test at the 95 % confidence level, there were no significant differences between LS-SVM, ANN and reference methods.info:eu-repo/semantics/openAccessSociedad Chilena de QuímicaJournal of the Chilean Chemical Society v.64 n.1 20192019-03-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072019000104310en10.4067/s0717-97072019000104310
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	least square support vector machine Artificial neural networks Sofosbuvir Ledipasvir Harvoni
spellingShingle	least square support vector machine Artificial neural networks Sofosbuvir Ledipasvir Harvoni Safakhoo,Negin Sohrabi,Mahmoud Reza Khalili,Mahsa Mofavvaz,Shirin ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
description	ABSTRACT In this study, least squares support vector machine (LS-SVM) and artificial neural networks (ANNs) as intelligent methods combined with spectrophotometry method, were used for determination of Sofosbuvir (SOF) and Ledipasvir (LED) in synthetic mixtures and Harvoni tablet simultaneously. In the LS-SVM method, Radial Basis Function (RBF) was used as kernel function. Then, the regularization parameter (γ) and Bandwidth (2) were optimized and root mean square error prediction (RMSE) was 0.4164, 0.6033 for SOF and LED respectively. Afterwards, Feed-forward back-propagation network with different training algorithms was used in artificial neural network method. These training algorithms compared with each other for selecting the best model. On the other hand, radial basis function neural network (RBFNN) was applied as an efficient network. Finally, these methods were compared to the high- performance liquid chromatography (HPLC) as a reference method. According to one way analysis of variance (ANOVA) test at the 95 % confidence level, there were no significant differences between LS-SVM, ANN and reference methods.
author	Safakhoo,Negin Sohrabi,Mahmoud Reza Khalili,Mahsa Mofavvaz,Shirin
author_facet	Safakhoo,Negin Sohrabi,Mahmoud Reza Khalili,Mahsa Mofavvaz,Shirin
author_sort	Safakhoo,Negin
title	ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
title_short	ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
title_full	ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
title_fullStr	ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
title_full_unstemmed	ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
title_sort	enhancement spectral resolution for the prediction amount of sofosbuvir and ledipasvir using least squares support vector machine and artificial neural networks in pharmaceutical formulation
publisher	Sociedad Chilena de Química
publishDate	2019
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-97072019000104310
work_keys_str_mv	AT safakhoonegin enhancementspectralresolutionforthepredictionamountofsofosbuvirandledipasvirusingleastsquaressupportvectormachineandartificialneuralnetworksinpharmaceuticalformulation AT sohrabimahmoudreza enhancementspectralresolutionforthepredictionamountofsofosbuvirandledipasvirusingleastsquaressupportvectormachineandartificialneuralnetworksinpharmaceuticalformulation AT khalilimahsa enhancementspectralresolutionforthepredictionamountofsofosbuvirandledipasvirusingleastsquaressupportvectormachineandartificialneuralnetworksinpharmaceuticalformulation AT mofavvazshirin enhancementspectralresolutionforthepredictionamountofsofosbuvirandledipasvirusingleastsquaressupportvectormachineandartificialneuralnetworksinpharmaceuticalformulation
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ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION

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