An eco-friendly modified methodology for the resolution of binary pharmaceutical mixtures based on self-deconvolution of the UV spectrophotometric spectra in the Fourier domain: Application of Fourier self-deconvolution in UV spectroscopy

Abstract

UV spectrophotometry is a rapid and robust technique in resolving several challenging pharmaceutical combinations. Several mathematical treatments are available for the resolution of complex multicomponent UV spectra as; wavelet transformation, derivatization, and deconvolution-curve fitting models. Fourier self deconvolution (FSD) is a mathematical computational methodology for resolving interfering signals in many disciplines and applications. In the current work, we describe a modified FSD based methodology in resolving different binary pharmaceutical mixtures, which overcome the complexity of applying the traditional deconvolution-curve fitting technique on UV spectroscopic spectral data. The current approach differs from the conventional FSD by using the individual spectra of each component as a probing tool to avoid artifacts or errors on the deconvoluted spectra for accuracy of determinations. The utilized approach managed to resolve the binary mixtures of telmisartan/hydrochlorothiazide and ramipril/hydrochlorothiazide in their pharmaceutical dosage forms. The advantage of the current methodology over the traditional deconvolution-curve fitting is the simplicity of application, less time consuming, no need for sophisticated software, and higher sensitivity as revealed by the limit of detection (LOD). The linear ranges for telmisartan, ramipril, and hydrochlorothiazide were 1–25 µg/ml, 5–35 µg/ml, and 1–10 µg/ml, respectively, and the LOD values were in the ranges of 0.067–0.747 µg/ml. The developed FSD approach was validated as per the ICH recommendations regarding the accuracy, precision, linearity, selectivity, and limits of detection and quantitation. The recoveries obtained from the proposed approach were statistically compared with the corresponding reported methods and found no statistical difference between the obtained results.