Abstract: The most commonly used optimization procedure in multivariant calibration is the least squares method.Usually this kind of methods are nonrobust, the normal distribution of the random variables of the regression model are assumed.Searching robust criteria to replace the least squares criterion would reduce the influence of the outliers in the original data on the analytical results obtained in multi-component analysis.Generalized Simulated Annealing (GSA) as an optimization algorithm has the mechanism walking across local optima.The least absolute deviation is a more robust criterion than widely used least squares, and is more suitable for the data set with possible departure from the -assumption of normal distribution.In this paper, the least absolute deviation was taken as the optimization criterion, and GSAwith variable step size was used as a tool for the multicomponent determination in spectrophotometry.This method was applied to the analysis of two- and three-component drug mixtures with satisfactory results.The precision of the GSAhas been improved by using variable step size in searching process.This method was applied to the analysis of the mixtures of phenol and resorcinol, drug preparations of aminopyrine, antipyrine, barbital and APCtablets.The results were compared with those obtained by target transformation factor analysis and Kalman filtering.Satisfactory agreeable analytical results were obtained.The precision of the GSAhas been substantially improved by using variable step size in the searching process.

Key words: Simulated Annealing, Multivariate calibration, Drug analysis, Robust statistics