The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Molecular and biomolecular spectroscopy, 2018-03, Vol.193, p.297-304
• Main Authors: Valizadeh, Maryam, Sohrabi, Mahmoud Reza
• Format: Article
• Language: English
• Subjects: Dorzalamide; Neural network time series; Radial basis network; Simultaneous determination; Support vector regression; Timolol
• ISSN: 1386-1425
• DOI: 10.1016/j.saa.2017.11.056

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions. [Display omitted] •ANNs and SVR coupled with spectrophotometry for using the quantitative prediction of commercial eye drop contents.•ANNs and SVR models were applied for simultaneous determination of TIM and DOR.•These models use as inexpensive and rapid techniques.•ANNs & SVR / spectrophotometry methods were compared to the HPLC method.