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.

Rapid identification of the geographical origin of Eucommia ulmoides by using excitation-emission matrix fluorescence combined with chemometric methods.

Eucommia ulmoides is an important and valuable traditional Chinese medicine with various medical functions, and has been widely used as health food in China, Japan, South Korea and other Asian countries for many years. The efficacy and quality of E. ulmoides are closely associated with the geographical origin. In this work, the potential of excitation-emission matrix (EEMs) fluorescence coupled with chemometric methods was investigated for simple, rapid and accurate for identification E. ulmoides from different geographical origins. Parallel factor analysis (PARAFAC) was applied for characterizing the fluorescence fingerprints of E. ulmoides samples. Moreover, k-nearest neighbor (kNN), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA) models were used for the classification of E. ulmoides samples according to their geographical origins. The results showed that kNN model was more suitable for identification of E. ulmoides samples from different provinces. The kNN model could identify E. ulmoides samples from eight different geographical origins with 100% accuracy on the training and test sets. Therefore, the proposed method was available for conveniently and accurately determining the geographical origin of E. ulmoides, which can expect to be an attractive alternative method for identifying the geographic origin of other traditional Chinese medicines.

Accurate identification of the geographical origins of lily using near-infrared spectroscopy combined with carbon dot-tetramethoxyporphyrin nanocomposite and chemometrics.

Near-infrared spectroscopy technique is a prevailing tool for quality control of foods and traditional Chinese medicines. However, it usually faced the problems of severe peak overlap, low classification accuracy and poor specificity. In this work, the potential of carbon dot-tetramethoxyporphyrin nanocomposite-based nano-effect near-infrared spectroscopy sensor combined with chemometric method was investigated for the accurate identification lily from different geographical origins. Partial least squares-discriminant analysis (PLS-DA) was used for differentiating geographical origins of lily based on the collected traditional and nano-effect near-infrared spectroscopy. Compared with traditional near-infrared spectroscopy, the nano-effect near-infrared spectroscopy obtains superior classification performance with 100% accuracy on the training and test set. The results showed that the proposed method based on near-infrared spectroscopy combined with nanocomposites and chemometrics could be considered as a promising tool for rapid discrimination of the authenticity of food and traditional Chinese medicine in the future.