DURMUS ÖZDEMIR, ERDAL DINÇ, DUMITRU BALEANU<br>UV-Visible Spectrophotometric Quantitative Analysis of Ternary Mixture Using Multivariate Calibration Methods Optimized <br>by a Genetic Algorithm

DURMUS ÖZDEMIR, ERDAL DINÇ, DUMITRU BALEANU
UV-Visible Spectrophotometric Quantitative Analysis of Ternary Mixture Using Multivariate Calibration Methods Optimized
by a Genetic Algorithm

Abstract:

Simultaneous determination of ternary mixtures of caffeine, paracetamol and metamizol in commercial tablet formulations using UV-visible spectrophotometry combined with classical least squares (CLS) and genetic algorithm (GA) based multivariate calibration methods were demonstrated. The three genetic multivariate calibration methods are named as Genetic Classical Least Squares (GCLS), Genetic Inverse Least Squares (GILS) and Genetic Regression (GR). The GR method is based on a genetic algorithm based wavelength selection followed by a simple linear regression step whereas the GCLS and GILS are multivariate calibration methods modified by a wavelength selection principle using a genetic algorithm. The sample data set contains the UV-visible spectra of 47 synthetic mixtures (4 to 48 mg/mL) and 16 tablets containing these components from two different producers. The spectra cover the range from 200 to 330 nm in 0.1 nm intervals. Several calibration models were built with the four methods for the three components. Overall, the standard error of calibration (SEC) and the standard error of prediction (SEP) for the synthetic data were in the range of 0.04 and 2.34 mg/mL for all the four methods. Predictive ability of the calibration models generated with synthetic samples was tested with actual tablet samples and results obtained from four methods were compared. The SEP values for the tablets were in the range of 0.31and 15.44 mg/tablets. Keywords: UV-visible spectrophotometry, multivariate calibration, Genetic algorithms

Issue: 2010, Volume 61, Issue 2

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