Genetic algorithm-based method for selecting conditions in multivariate determination of povidone-iodine using hand scanner

Abstract

Application of hand scanner in multivariate quantification of povidone-iodine (PVI), as a popular antiseptic agent, in some of pharmaceutical products is presented. Brightness, contrast, and mixed gamma were the adjustable scanner parameters. For selection of optimum values of the scanner parameters, partial least squares (PLS) and multiple linear regression (MLR), coupled with genetic algorithm, were performed. For the selected variables, both MLR and PLS performances were similar and appropriate. From the results obtained, it was concluded that the simpler method of MLR could be successfully applied instead of PLS, which requires more statistical experience. The considered concentration range for PVI in the calibration and prediction samples was 0.0–10.0% (w/v). For the analysis of pharmaceutical samples, generalized standard addition method (GSAM) was applied (on the variables selected by GA) and desirable results were obtained. Relative standard error (RSE) of less than 8% was obtained for the majority of samples analyzed.