“Green spectrophotometric determination of rupatadine, its toxic impurity, and methyl paraben: comparative AI-assisted univariate versus multivariate chemometrics”

Abstract Sensitive and selective green UV-spectrophotometric univariate and multivariate chemometric methods were developed to resolve the overlapped spectra of rupatadine fumarate (RUP), its impurity desloratadine (DES), and methyl paraben (MP). The univariate approach employed was the dual-wavelength (DW) method. Guided by artificial intelligence (AI) in the selection of the optimal wavelength pair, the determination of RUP was performed at 230 and 284.3 nm, and for MP at 236.6 and 253.8 nm. Among the multivariate methods, ANN demonstrated superior predictive accuracy (lowest RMSEP values), followed by PLS-1 and MCR-ALS, highlighting the power of machine learning for complex mixture resolution, particularly in the presence of the preservative MP. A three-factor, five-level experimental design was adopted to construct a calibration set comprising 25 mixtures with varying component ratios, along with 6 independent validation mixtures. The developed methods were successfully applied to pharmaceutical formulation containing the studied drug and were validated in accordance with International Conference of Harmonization (ICH) guidelines. The results obtained were reliable and reproducible, confirming the suitability of these methods for routine analysis and quality control in laboratories. Moreover, the GLANCE tool (Graphical Layout for Analytical Chemistry Evaluation) offered a clear visual summary of the twelve structured method attributes.