Radial basis function-artificial neural network (RBF-ANN) for simultaneous fluorescent determination of cysteine enantiomers in mixtures

Abstract

The determination of chiral compounds is critically important in chemical and pharmaceutical sciences. Cysteine amino acid is one of the important chiral compounds where each enantiomer (L and D) has different effects on fundamental physiological processes. The unique optical properties of nanoparticles make them a suitable probe for the determination of different analytes. In this work, the water-soluble thioglycolic acid (TGA)-capped cadmium-telluride (CdTe) quantum dots (QDs) were applied as optical nanoprobe for the simultaneous determination of cysteine enantiomers. The difference in the kinetics of the interactions between L- and D-cysteine with CdTe QDs is used for multivariate quantitative analysis. Multivariate methods are superior to univariate methods in determining the concentration of each enantiomer in the mixture without the information about the total chiral analyte concentration. As a nonlinear calibration method the radial basis function -artificial neural network (RBF-ANN) model was more successful in predicting L-and D-cysteine concentrations than the linear partial least squares regression (PLS) model.