Dual channel sensor array based on ZnCdSe QDs - KMnO4: An effective tool for analysis of catechins and green teas.

A novel dual-channel sensor array based on the "off" and "off-on" phenomenon of ZnCdSe quantum dots (QDs) - KMnO4 system was established for the effective distinguish of 30 green teas with various species, grades and origins. Starting from optimization of QDs for the construction of the sensing system, their sensitivity and selectivity performances towards 10 representative green teas were tested first and the sensor systems based on ZnCdSe QDs were finally established. An obvious "off" response brought by the interactions between amino acids, quercetin, et al. and ZnCdSe QDs was obtained while an "off-on" phenomenon brought by the interactions between catechins and the ZnCdSe-KMnO4 system had also been verified. Furthermore, based on the differential fluorescence "off-on" response of seven catechins, the qualitative and quantitative analyses of these catechins were successfully carried out with the linear range from 0.5 to 10 µg/mL. Most importantly, through adopting the dual-channel sensor, 30 green teas with different species, origins and grades can be accurately identified with the LDA model according to the spectral signals and the recognition accuracy could achieve 100 %.

Visual paper-based sensor for the highly sensitive detection of caffeine in food and biological matrix based on CdTe-nano ZnTPyP combined with chemometrics.

Caffeine naturally occurs in tea and cocoa, which is also used as an additive in beverages and has pharmacological effects such as refreshing, antidepressant, and digestion promotion, but excessive caffeine can cause harm to the human body. In this work, based on the specific response between nano zinc 5, 10, 15, 20-tetra(4-pyridyl)-21H-23H-porphine (nano ZnTPyP)-CdTe quantum dots (QDs) and caffeine, combined with chemometrics, a visual paper-based sensor was constructed for rapid and on-site detection of caffeine. The fluorescence of QDs can be quenched by nano ZnTPyP. When caffeine is added to the system, it can pull nano ZnTPyP off the surface of the QDs to achieve fluorescence recovery through electrostatic attraction and nitrogen/zinc coordination. The detection range is 5 × 10-11~3 × 10-9 mol L-1, and the detection limit is 1.53 × 10-11 mol L-1 (R2 = 0.9990) (S/N = 3). The paper-based sensor constructed exhibits good results in real samples, such as tea water, cell culture fluid, newborn bovine serum, and human plasma. Therefore, the sensor is expected to be applied to the rapid instrument-free detection of caffeine in food and biological samples.Graphical abstract.

Determination of l-theanine in tea water using fluorescence-visualized paper-based sensors based on CdTe quantum dots/corn carbon dots and nano-porphyrin with chemometrics.

BACKGROUND: The quality of tea is influenced by numerous factors, especially l-theanine, which is one of the important markers used to evaluate the sweetness and freshness of tea. Sensitive, rapid, and accurate detection of l-theanine is therefore useful to identify the grade and quality of tea. RESULTS: A high-sensitivity, paper-based fluorescent sensor combined with chemometrics was established to detect l-theanine in tea water based on CdTe quantum dots / corn carbon dots and nano tetra pyridel-porphine zinc (ZnTPyP). To verify the reliability of this method, fluorescence spectra and fluorescence-visualized paper-based sensors were compared. The fluorescence spectrum method demonstrated a linear range of 1 to 10 000 nmol L-1 and a limit of detection (LOD) of 0.19 nmol L-1 . In the fluorescence-visualized paper-based sensors there was a linear range of 10-1000 nmol L-1 , and the LOD was 10 nmol L-1 . Partial least squares discriminant analysis (PLSDA) and partial least squares regression analysis (PLSR) were used successfully to determine l-theanine accurately in tea water with this approach. The accuracy of the PLSDA model was 100% both in the training set and the predicting set, and the correlation coefficient between the actual concentration and the predicted concentration was greater than 0.9997 in the PLSR model. CONCLUSION: This fluorescence-visualized paper-based sensor, combined with chemometrics, could be applied efficiently to the practical analysis of tea water samples, which provides a new idea to ensure the flavor and quality of tea. © 2020 Society of Chemical Industry.