A chemometric approach based on the combined use of the principal component analysis (PCA) and artificialneural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine(MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without anychemical separation. The predictive ability of the ANN method was compared with the classical linear regressionmethod Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteenquaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noisecoming from instrumentation. Several spectral ranges and different numbers of principal components (PCs)were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN,using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transferfunction in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of themodels when subjected to external validation. PCA-ANN showed better prediction ability in the determination ofPPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both componentsare characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability inconsidering all non-linear information from noise or interfering excipients.

Artificial neural network combined with principal component analysis (ANN-PCA) for resolution of complex pharmaceutical formulations

IOELE, Giuseppina;DE LUCA M;RAGNO G.
2011

Abstract

A chemometric approach based on the combined use of the principal component analysis (PCA) and artificialneural network (ANN) was developed for the multicomponent determination of caffeine (CAF), mepyramine(MEP), phenylpropanolamine (PPA) and pheniramine (PNA) in their pharmaceutical preparations without anychemical separation. The predictive ability of the ANN method was compared with the classical linear regressionmethod Partial Least Squares 2 (PLS2). The UV spectral data between 220 and 300 nm of a training set of sixteenquaternary mixtures were processed by PCA to reduce the dimensions of input data and eliminate the noisecoming from instrumentation. Several spectral ranges and different numbers of principal components (PCs)were tested to find the PCA-ANN and PLS2 models reaching the best determination results. A two layer ANN,using the first four PCs, was used with log-sigmoid transfer function in first hidden layer and linear transferfunction in output layer. Standard error of prediction (SEP) was adopted to assess the predictive accuracy of themodels when subjected to external validation. PCA-ANN showed better prediction ability in the determination ofPPA and PNA in synthetic samples with added excipients and pharmaceutical formulations. Since both componentsare characterized by low absorptivity, the better performance of PCA-ANN was ascribed to the ability inconsidering all non-linear information from noise or interfering excipients.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/134017
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