Abstract

The simultaneous separation of components in a multi-component drug is not possible by spectrophotometry alone due to their overlap. In this work, UV–Vis spectrophotometry along with continuous wavelet transform (CWT) and multivariate calibration method named partial least squares (PLS) as a fast, easy, inexpensive, and precise method was introduced for the simultaneous determination of paracetamol (PAR), diphenhydramine (DPE), and phenylephrine (PHE) in tablet dosage form without using the time-consuming extraction step. The validation of these techniques was surveyed by analyzing diverse synthetic mixtures with specific concentrations. The absorption of standard solutions of three pure components was processed through different mother wavelet families in the CWT approach. The best wavelet families were selected based on the appropriate zero crossing point and calibration curve. The best results were related to the Daubechies (db1) wavelet with a scaling factor of 21 at 214 nm, Coiflet (Coif2) with scaling factor of 22 at 262 nm, and Gaussian (Gaus2) with a scaling factor of 28 at 249 nm for PAR, DPE, and PHE, respectively. The limit of detection (LOD) and limit of quantification (LOQ) were lower than 0.4 μg mL−1 and 0.6 μg mL−1, respectively. The mean recovery values of synthetic mixtures were found to be 98.65%, 99.33%, and 99.43% for PAR, DPE, and PHE, respectively, where the root mean square error (RMSE) was not more than 1.5. Based on the k-Fold cross-validation of the PLS method, the optimum number of components of PAR, DPE, and PHE were 7, 7, and 6 that their related MSEP values were obtained at 6.04 × 10−4, 0.5258, and 0.0397, respectively. In this method, the mixtures are divided into two parts, training and testing. Mean recovery values of 100.04% for PAR and DPE, as well as 100.02% for PHE were achieved, where RMSE values were <0.07. The proposed methods were applied to the results of the commercial tablet, which were compared to the outcomes of high-performance liquid chromatography (HPLC) using a one-way analysis of variance (ANOVA) test. Regarding to the results, they were in good agreement and revealed no significant differences.

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