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

[Display omitted] •Using thioglycolic acid (TGA)-capped cadmium-telluride (CdTe) quantum dots (QDs) as an optical nanoprobe.•Employing various kinetic properties of the interaction between a binary mixture of cysteine enantiomers and QDs.•Analysis of data using multivariate analysis methods (RBF-ANN and PLS) without previous extraction steps.•Comparison of the prediction performance of the RBF-ANN model (nonlinear regression) and the PLS model (linear regression)•Investigation of the interference of a number of available amino acids that may coexist with the cysteine in sample matrixes. 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.