Smart chemometrics-assisted spectrophotometric methods for efficient resolution and simultaneous determination of paracetamol, caffeine, drotaverine HCl along with three of their corresponding related impurities

Abstract

Three novel, simple and accurate multivariate spectrophotometric assisted mathematical techniques were developed for determination of paracetamol, caffeine, drotaverine HCl and their related impurities. The used multivariate algorithms are principal component regression (PCR), partial least squares (PLS), and synergy intervals partial least squares (siPLS). Linearity of the suggested methods was found to be (1.00–14.60, 1.40–7.00, 1.40–3.80, 1.00–3.00, 1.50–3.50 and 2.50–4.50 µg/mL) for paracetamol, caffeine, drotaverine HCl, and their related impurities; p-aminophenol, theophylline and homoveratric acid, correspondingly. The presented methods were effectively implemented in the determination of the cited compounds in their laboratory prepared mixtures. Commercially available tablet preparation was also analyzed using the applied methods where no impurities were detected and without interference from tablet additives. Moreover, statistical analysis did not reveal any noticeable differences between the obtained results and those acquired from the reported method in terms of accuracy and precision. The developed multivariate algorithms were validated by means of internal and external validation sets. The obtained results showed the siPLS algorithm’s superiority to PCR and PLS according to the values of correlation coefficient values (r) and the lowest root mean square error of prediction (RMSEP). The combination of four subintervals [10, 12, 14, and 17] produced the highest efficiency model. Furthermore, these methods may be an applicable substitute to HPLC ones in quality control laboratories during rush of analyses where several samples have to be analyzed in a short time.