Hybrid of non-selective quantum dots for simultaneous determination of TNT and 4-nitrophenol using multivariate chemometrics methods

Abstract

Herein, we applied the overall fluorescence of a nanohybrid fluorescence probe comprised of carbon quantum dots (CQDs) and CdTe QDs to pattern-based discrimination of different analytes using principal component analysis and to simultaneous determination in a binary mixture of analytes using multivariate chemometrics methods such as partial least-squares (PLS) and artificial neural network (ANN). The fluorescence intensity of both QDs was quenched in the presence of six different nitro-compounds with more or less different quenching constants. It was shown that unlike individual QDs, the overall fluorescence response of the hybrid system allowed pattern-based discrimination of different samples of nitro-compounds. Then, we demonstrate that the nanohybrid system can be used for simultaneous determination of 2,4,6-trinitrotoluene (TNT) and 4-nitrophenol. A calibration set including 36 samples in the concentration ranges of 2–30 μM were used for building the PLS model and training the ANN. Accordingly, average errors lower than 10% were found in prediction of both analytes in the test set. However, nonlinear modeling (ANN) showed greater potential for quantitative analysis of the data investigated than the linear model (PLS). In order to investigate the feasibility of the simultaneous determination in a binary mixture at different selectivity and spectral overlapping cases, analysis of a series of simulated examples generated with two hypothetical fluorophores in the presence of two quenchers were considered and the results of PLS and ANN were compared.