Silver doping induced fluorescence enhancement of gold nanoclusters combined with machine learning for detection of quercetin.

A novel fluorescence sensing method of quercetin (Que) was constructed based on gold and silver bimetallic nanoclusters (L-Cys@Au-Ag NCs) that were prepared by sonochemical synthesis. In comparison with gold nanoclusters, the bimetallic nanoclusters produced enhanced fluorescence along with a blue-shift, which is achieved by Ag doping to engineer the aggregation states of the Au(I)-thiolate motifs in the NC shell. Since the fluorescence was quenched after the addition of Que, a rapid-response and highly selective method to detect Que has thereby been established using L-Cys@Au-Ag NCs. This analytical method exhibited good sensitivity in the range of 9.07-115 μM with a limit of detection (LOD) of 1.064 μM, and it was used for the quantitative analysis of quercetin in grape juice samples. Moreover, the accuracy and feasibility of the developed fluorescence sensing method were validated by combining machine learning and HPLC. This machine learning-assisted analytical method offered a promising tool for food safety monitoring and human health surveillance.