A sensitive fluorescence sensor based on a glutathione modified quantum dot for visual detection of copper ions in real samples.

Copper (Cu2+), as a heavy metal, accumulates in the human body to a certain extent, which can induce various diseases and endanger human health. Rapid and sensitive detection of Cu2+ is highly desired. In present work, a glutathione modified quantum dot (GSH-CdTe QDs) was synthesized and applied in a "turn-off" fluorescence probe to detect Cu2+. The fluorescence of GSH-CdTe QDs could be rapidly quenched in the presence of Cu2+ through aggregation-caused quenching (ACQ), resulting from the interaction between the surface functional groups of GSH-CdTe QDs and Cu2+ and the electrostatic attraction. In the range of 20-1100 nM, the Cu2+ concentration showed a good linear relationship with the fluorescence decline of the sensor, and the LOD is 10.12 nM, which was lower than the U.S. Environmental Protection Agency (EPA) defined limit (20 µM). Moreover, aiming to attain visual analysis, colorimetric method was also used for rapidly detecting Cu2+ by capturing the change in fluorescence color. Interestingly, the proposed approach has successfully been applied for the detection of Cu2+ in real samples (i.e., environment water, food and traditional Chinese medicine) with satisfactory results, which provides a promising strategy for the detection of Cu2+ in practical application with the merits of being rapid, simple and sensitive.

Fast and non-destructive discriminating the geographical origin of Hangbaiju by hyperspectral imaging combined with chemometrics.

Hangbaiju is highly appreciated flower tea for its health benefits, and its quality and price are affected by geographical origin. Fast and accurate identification of the geographical origin of Hangbaiju is very significant for producers, consumers and market regulators. In this work, hyperspectral imaging combined with chemometrics, was used, for the first time, to explore and implement the geographical origin classification of Hangbaiju. The hyperspectral images in the spectral range of 410-2500 nm for 75 samples of five different origins were collected. As a versatile chemometrics tool, bagging classification tree-radial basis function (BAGCT-RBFN), compared with classification tree (CT), radial basis function network (RBFN), was applied to discriminate Hangbaiju samples from different origins. The results showed that BAGCT-RBFN based on optimal wavelengths yielded superior classification performances to CT and RBFN with full wavelengths. The recognition rates (RR) of the training and prediction sets by BAGCT-RBFN were 96.0 % and 92.0 %, respectively. Hyperspectral imaging combined with chemometric can be considered as a powerful, feasible and convenient tool for the classification of Hangbaiju samples from different origins. It promises to be a potential way for origin discriminant analysis and quality monitor in food fields.