Background: Simultaneous determination of medication components in pharmaceutical
samples using ordinary methods have some difficulties and therefore these determinations usually
were made by expensive methods and instruments. Chemometric methods are an effective way to
analyze several components simultaneously.
Objective: In this paper, a novel approach based on Bayesian regularized artificial neural network
is developed for the determination of Loratadine, Naproxen, and Diclofenac in water using UV-Vis
Methods: A dataset is collected by performing several chemical experiments and recording the UV-Vis
spectra and actual constituent values. The effect of a different number of neurons in the hidden
layer was analyzed based on final mean square error, and the optimum number was selected. Principle
Component Analysis (PCA) was also applied to the data. Other back-propagation methods,
such as Levenberg-Marquardt, scaled conjugate gradient, and resilient backpropagation, were tested.
Results: In order to see the proposed network performance, it was performed on two crossvalidation
methods, namely partitioning data into train and test parts, and leave-one-out technique.
Mean square errors between expected results and predicted ones implied that the proposed method
has a strong ability in predicting the expected values.
Conclusion: The results showed that the Bayesian regularization algorithm has the best performance
among other methods for simultaneous determination of Loratadine, Naproxen, and Diclofenac
in water samples.