Potentiometric Nonlinear Multivariate Calibration with Genetic Algorithm and Simplex Optimization

Abstract

In this contribution, a genetic algorithm (GA) and a modified Simplex technique are investigated as a means of developing nonlinear multivariate calibration models for an array of ion-selective electrodes. The responses of an array of ammonium-, sodium-, potassium-, and calcium-selective electrodes employed in a flow injection analysis system were modeled over the concentration range of 1 × 10-4 to 1 × 10-2 M using the GA and simplex techniques to optimize the cell potentials, slopes, and selectivity coefficient parameters of the Nikolskii−Eisenman equation for each electrode. Correlations between activities predicted from the calibration model and the actual activities of the solutions presented to the array ranged from 0.98 to 0.88 for the four ions.