A fluorescence ionic probe utilizing Cu2+ assisted competition for detecting glyphosate abused in green tea.

Food fraud caused by the violation of glyphosate use in tea is frequently exposed, posing a potential health risk to consumers and undermining trust in food safety. In the work, an ionic fluorescent probe "[P66614] [4HQCA]-Cu2+ (PHQCA-Cu2+)" was constructed using Cu2+ and ionic liquids coordination through a competitive coordination strategy to detect glyphosate. This probe exhibited a prominent "turn-on" fluorescence response in glyphosate detection. PHQCA-Cu2+was destroyed by glyphosate with its strong coordination capability, and a new complex re-formed simultaneously between glyphosate and the Cu2+ in it, where Cu2+ served as an "invisible indicator" influencing fluorescence changes. Remarkably, PHQCA-Cu2+formed rapidly within 5 s, demonstrated exceptional sensitivity and selectivity, and satisfactory detection performance on paper strips impregnated withPHQCA-Cu2+.Importantly,PHQCA-Cu2+showed excellent recoveries in various green tea, which offered a viable method for identifying contaminated products from the supply chain quickly to enhance overall food safety surveillance.

Discrimination and prediction of Qingzhuan tea storage year using quantitative chemical profile combined with multivariate analysis: Advantages of MRMHR based targeted quantification metabolomics.

The duration of storage significantly influences the quality and market value of Qingzhuan tea (QZT). Herein, a high-resolution multiple reaction monitoring (MRMHR) quantitative method for markers of QZT storage year was developed. Quantitative data alongside multivariate analysis were employed to discriminate and predict the storage year of QZT. Furthermore, the content of the main biochemical ingredients, catechins and alkaloids, and free amino acids (FAA) were assessed for this purpose. The results show that targeted marker-based models exhibited superior discrimination and prediction performance among four datasets. The R2Xcum, R2Ycum and Q2cum of orthogonal projection to latent structure-discriminant analysis discrimination model were close to 1. The correlation coefficient (R2) and the root mean square error of prediction of the QZT storage year prediction model were 0.9906 and 0.63, respectively. This study provides valuable insights into tea storage quality and highlights the potential application of targeted markers in food quality evaluation.

A novel comprehensive strategy for high-thoroughly studying released compounds during the combustion process of herbs. A case study for Artemisia argyi Levl. et Vant.

The comprehensive study of compound variations in released smoke during the combustion process is a great challenge in many scientific fields related to analytical chemistry like traditional Chinese medicine, environment analysis, food analysis, etc. In this work, we propose a new comprehensive strategy for efficiently and high-thoroughly characterizing compounds in the online released complex smokes: (i) A smoke capture device was designed for efficiently collecting chemical constituents to perform gas chromatography-mass spectrometry (GC-MS) based untargeted analysis. (ii) An advanced data analysis tool, AntDAS-GCMS, was used for automatically extracting compounds in the original acquired GC-MS data files. Additionally, a GC-MS data analysis guided instrumental parameter optimizing strategy was proposed for the optimization of parameters in the smoke capture device. The developed strategy was demonstrated by the study of compound variations in the smoke of traditional Chinese medicine, Artemisia argyi Levl. et Vant. The results indicated that more than 590 components showed significant differences among released smokes of various moxa velvet ratios. Finally, about 88 compounds were identified, of which phenolic compounds were the most abundant, followed by aromatics, alkenes, alcohols and furans. In conclusion, we may provide a novel approach to the studies of compounds in online released smoke.

Revealing the chemical differences and their application in the storage year prediction of Qingzhuan tea by SWATH-MS based metabolomics analysis.

It's generally believed that the longer the storage, the better the quality of dark tea, but the chemical differences of Qingzhuan tea (QZT) with different storage years is still unclear. Herein, in this work, an untargeted metabolomic approach based on SWATH-MS was established to investigate the differential compounds of QZT with 0-9 years' storage time. These QZT samples were roughly divided into two categories by principal component analysis (PCA). After orthogonal projections to latent structures discriminant analysis (OPLS-DA), 18 differential compounds were putatively identified as chemical markers for the storage year variation of QZT. Heatmap visualization showed that the contents of catechins, fatty acids, and some phenolic acids significantly reduced, flavonoid glycosides, triterpenoids, and 8-C N-ethyl-2-pyrrolidinone-substituted flavan-3-ols (EPSFs) increased with the increase of storage time. Furthermore, these chemical markers were verified by the peak areas corresponding to MS2 ions from SWATH-MS. Based on the extraction chromatographic peak areas of MS and MS2 ions, a duration time prediction model was built for QZT with correlation coefficient R2 of 0.9080 and 0.9701, and RMSEP value of 0.85 and 1.24, respectively. This study reveals the chemical differences of QZT with different storage years and provides a theoretical basis for the quality evaluation of stored dark tea.

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.

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 %.

Colorimetric assay based on peroxidase-like activity of dodecyl trimethylammonium bromide-tetramethyl zinc (4-pyridinyl) porphyrin for detection of organophosphorus pesticides.

A simple and sensitive colorimetric assay for detecting organophosphorus pesticides (OPs) was developed based on 3,3',5,5'-tetramethylbenzidine (TMB)/hydrogen peroxide (H2O2)/dodecyl trimethylammonium bromide (DTAB)-tetramethyl zinc (4-pyridinyl) porphyrin (ZnTPyP). In this system, based on the peroxidase-like activity of DTAB-ZnTPyP, H2O2 decomposes to produce hydroxyl radicals, which oxidize TMB, resulting in blue oxidation products. The OPs (trichlorfon, dichlorvos, and thimet) were first combined with DTAB-ZnTPyP through electrostatic interactions. The OPs caused a decrease in the peroxidase-like activity of DTAB-ZnTPyP due to spatial site blocking. At the same time, π-interactions occurred between them, and these interactions also inhibited the oxidation of TMB (652 nm), thus making the detection of OPs possible. The limits of detection for trichlorfon, dichlorvos, and thimet were 0.25, 1.02, and 0.66 µg/L, respectively, and the corresponding linear ranges were 1-35, 5-45, and 1-40 µg/L, respectively. Moreover, the assay was successfully used to determine OPs in cabbage, apple, soil, and traditional Chinese medicine samples (the recovery ratios were 91.8-109.8%), showing a great promising potential for detecting OPs also in other complex samples.

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.

Differentiating Westlake Longjing tea from the first- and second-grade producing regions using ultra high performance liquid chromatography with quadrupole time-of-flight mass spectrometry-based untargeted metabolomics in combination with chemometrics.

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.

ZnCdSe-CdTe quantum dots: A "turn-off" fluorescent probe for the detection of multiple adulterants in an herbal honey.

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds.

A novel enhanced fluorescence method based on multifunctional carbon dots for specific detection of Hg2+ in complex samples.

Carbon quantum dots (CQDs), especially originated from biomass, have emerged as a rising star for the construction of metal ion sensor because they can serve as sensitive, selective and biocompatible probes. The present work describes a novel kind of ascorbic acid (AA)-enhanced CQDs which are synthesized with a kind of famous green teas, Maojian, serving as carbon source. Compared with the CQDs only based on Maojian teas, citric acid (CA)-enhanced and ascorbic acid (AA)-enhanced CQDs had the enhanced fluorescence intensity, and different response characteristics. In addition, the (AA)-enhanced CQDs showed more sensitive and specific fluorescence response to Hg2+ than simple ones, with a detection limit of 6.32 × 10-9 nmol·L-1. A linear response range from 2.00 × 10-7 mol·L-1 to 6.00 × 10-5 mol·L-1 was also achieved. The (AA)-enhanced CQDs also demonstrate good stability. They could effectively sense the Hg2+ in complex samples including waste water, tea and rice. Therefore, these versatile (AA)-enhanced CQDs fluorescence method hold a promising potential in other promising applications such as pharmaceutical quality, environmental quality, and food safety monitoring.

"Turn-off" fluorescent sensor based on double quantum dots coupled with chemometrics for highly sensitive and specific recognition of 53 famous green teas.

Fluorescent "turn-off" sensors based on double quantum dots (QDs) has attracted increasing attention in the detection of many materials due to their properties such as more useful information, higher fluorescence efficiency and stability compared with the fluorescent "turn-off" sensors based on single QDs. In this work, highly sensitive and specific method for recognition of 53 different famous green teas was developed based on the fluorescent "turn-off" model with water-soluble ZnCdSe-CdTe double QDs. The fluorescence of the two QDs can be quenched by different teas with varying degrees, which results in the differences in positions and intensities of two peaks. By the combination of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 100% for prediction set, respectively. The fluorescent "turn-off" sensors based on the single QDs (either ZnCdSe QDs or CdTe QDs) coupled with PLSDA were also employed to recognize the 53 famous green teas with unsatisfactory results. Therefore, the fluorescent "turn-off" sensors based on the double QDs is more appropriate for the large-class-number classification (LCNC) of green teas. Herein, we have demonstrated, for the first time, that so many kinds of famous green teas can be discriminated by the "turn-off" model of double QDs combined with chemometrics, which has largely extended the capability of traditional fluorescence and chemometrics, as well as exhibits great potential to perform LCNC in other practical applications.

"Turn-off" fluorescent sensor for highly sensitive and specific simultaneous recognition of 29 famous green teas based on quantum dots combined with chemometrics.

Fluorescent "turn-off" sensors based on water-soluble quantum dots (QDs) have drawn increasing attention owing to their unique properties such as high fluorescence quantum yields, chemical stability and low toxicity. In this work, a novel method based on the fluorescence "turn-off" model with water-soluble CdTe QDs as the fluorescent probes for differentiation of 29 different famous green teas is established. The fluorescence of the QDs can be quenched in different degrees in light of positions and intensities of the fluorescent peaks for the green teas. Subsequently, with aid of classic partial least square discriminant analysis (PLSDA), all the green teas can be discriminated with high sensitivity, specificity and a satisfactory recognition rate of 100% for training set and 98.3% for prediction set, respectively. Especially, the "turn-off" fluorescence PLSDA model based on second-order derivatives (2nd der) with reduced least complexity (LVs = 3) was the most effective one for modeling. Most importantly, we further demonstrated the established "turn-off" fluorescent sensor mode has several significant advantages and appealing properties over the conventional fluorescent method for large-class-number classification (LCNC) of green teas. This work is, to the best of our knowledge, the first report on the rapid and effective identification of so many kinds of famous green teas based on the "turn-off" model of QDs combined with chemometrics, which also implies other potential applications on complex LCNC classification system with weak fluorescence or even without fluorescence to achieve higher detective response and specificity.