Selective sustainable spectrophotometric techniques for simultaneous estimation of certain binary and ternary mixtures of anti-infective drugs: application in pharmaceutical formulations and spiked human plasma

Abstract A set of green, selective, sustainable, and rapid UV spectrophotometric techniques combined with chemometric models was established for the simultaneous estimation of metronidazole (MET), diloxanide furoate (DIL), and mebeverine hydrochloride (MEB) in overlapping binary and ternary mixtures without prior chemical separation. A total of 25 synthetic mixtures were prepared for model development and partitioned into 17 mixtures for calibration and 8 for external validation. Principal component regression (PCR), classical least squares (CLS), and partial least squares (PLS) models were constructed. The PLS framework utilized the SIMPLS algorithm, retaining 3–5 latent variables determined by the minimum root mean square error of cross-validation (RMSECV). PCR retained 4–6 principal components, optimized by cumulative variance and predicted residual error sum of squares (PRESS) minimization. Model validation was performed using RMSE, RMSECV, root mean square error of prediction (RMSEP), and the coefficient of determination ( $${R}^{2}$$ R 2 ), and bias metrics. The optimized PLS model achieved RMSEP values ranging from 0.12 to 0.61 µg mL⁻ 1 . Analytical performance was evaluated using external prediction errors, recovery, precision, and statistical comparison with the reported method. The established chemometric models were successfully applied to synthetic mixtures, pharmaceutical formulations, and drug-spiked human plasma as a proof-of-concept assessment of performance in a complex biological matrix. The overall eco-friendliness of the procedure was additionally verified using comprehensive green metrics, supporting its potential as a sustainable alternative for pharmaceutical quality control and further bioanalytical development.