Journal: |
Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy
Elsevier
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Abstract: |
Counterfeit drugs have adverse effects on public health; chromatographic methods can be used but they are
costly. In this study, we developed cost-effective and environmentally friendly methodology for the analysis of
terazosin HCl (TZ) in the presence prazosin hydrochloride (PZ) using UV spectroscopy in conjunction with machine
learning (ML) models. Variable selection algorithms were applied to select most informative spectral variables.
Thirty-five ML modelswere assessed and their performances were compared. The models covered a wide
range of prediction mechanisms, such as tree-based, linear, self-organizing maps, neural network, Gaussian process,
boosting, bagging, Bayesian models, kernel methods, and quantile regression. The values of the root mean
square error (RMSE), coefficient of determination (R2), and absolute mean error (MAE) were obtained for the
evaluation of the developed models. According to the results of these performance indices, linear model showed
the highest prediction capacity among all other models.RMSE, R2 andMAE values of (0.159, 0.997 and 0.131) and
(0.196, 0.99 and 0.161)were obtained for train and test datasets, respectively. The predictivemodels in this study
can be useful for the researchers who are interested to work on the determination of active ingredients in pharmaceutical
dosage forms in the presence of interference using UV spectroscopy; therefore, it was used to determine
TZ without interference of PZ.
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