Effect of Genetic Algorithm–Based Wavelength Selection as a Preprocessing Tool on Multivariate Simultaneous Determination of Paracetamol, Orphenadrine Citrate, and Caffeine in the Presence of p-Aminophenol Impurity

Abstract Background: The utilization of selection methods such as genetic algorithm (GA) aims to construct better partial least squares (PLS) and principal component regression (PCR) models than those established from the full-spectrum range. Objective: Determination of paracetamol (PAR), orphenadrine citrate (Or.cit), and caffeine (CAF) in the presence of PAR nephrotoxic impurity [p-aminophenol (PAP)]. GA was applied to select the optimum wavelengths used. Methods: A calibration set was prepared in which the three drugs, together with PAP, were modeled by multilevel multifactor design. This calibration set was used to build the PLS and PCR models, either with or without preprocessing the data using GA. Results: Results were compared with and without preprocessing, and this revealed that GA can find an optimized combination of spectral wavelengths, yielding a lower root mean square error of prediction as well as a lower number of latent variables used. The results of the two models show that simultaneous determination of the aforementioned drugs can be performed in the concentration ranges of 20–60, 3–11, and 1–9 μg/mL for PAR, Or.cit, and CAF, respectively. Conclusions: The proposed models were applied for the determination of the three drugs in their pharmaceutical formulations, and the results were verified by the standard addition technique. Highlights: GA can be useful as a wavelength selection tool before applying multivariate PLS and PCR methods. GA gives an improvement in the predictive ability of the models with lower RMSEP and less number of latent variables (LVs). The proposed PLS, PCR, GA-PLS, and GA-PCR spectrophotometric methods were able to determine paracetamol, orphenadrine citrate, and caffeine in the presence of p-aminophenol and severe spectral overlapping.