A colorimetric sensor based on 4-MPBA Au@AgNPs for accurately identification of EnshiYulu tea grade.

Enshi Yulu green tea (ESYL) is the most representative traditional steamed green tea in Enshi, Hubei. Different ESYL grades exhibit distinct flavors, tastes, and prices. In this study, a visual sensor based on 4-MPBA Au@AgNPs was developed for the rapid and accurate identification of ESYL grades. The recognition mechanism involved the binding of 4-MPBA Au@AgNPs with polyphenolic compounds in ESYL to form borate esters and the conversion of Ag+ to Ag0, with the generated Ag0 depositing on the surface of 4-MPBA Au@AgNPs. The results showed that the sensor can amplify the color differences of different grades of ESYL. The visual results were also validated by the partial least squares discriminant analysis model, demonstrating an enhancement in recognition accuracy from 68.2 % to 95.5 % compared to the original extraction solution. The colorimetric sensor developed in this study is expected to provide a new approach for traceability research of other foods.

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.

Rapid visual detection of sulfur dioxide residues in food using acid-sensitive CdTe quantum dots-loaded alginate hydrogel beads.

Acid-sensitive CdTe quantum dots-loaded alginate hydrogel (CdTe QDs-AH) beads were designed for the visual detection of SO2 residues. As proof of concept, two types of CdTe QDs were selected as model probes and embedded in AH beads. The entire test was performed within 25 min in a modified double-layer test tube with one bead fixed above the sample solution. Adding citric acid and heating at 70 ℃ for 20 min transformed the sulfites in the solution into SO2 gas, which then quenched the fluorescence of the CdTe QDs-AH beads. Using this assay, qualitative, naked-eye detection of SO2 residues was achieved in the concentration range of 25-300 ppm, as well as precise quantification was possible based on the difference in the average fluorescence brightness of the beads before and after the reaction. Five food types were successfully analysed using this method, which is simpler and more economical than existing methods, and does not require complex pretreatment.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties.

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Three-dimensional fluorescence combined with alternating trilinear decomposition and random forest algorithm for the rapid prediction of species, geographical origin and main components of Glycyrrhizae Radix et Rhizoma (Gancao).

Glycyrrhizae Radix et Rhizoma (Gancao) is a functional food whose quality varies significantly between distinct geographical sources owing to the influence of genetics and the geographical environment. This study employed three-dimensional fluorescence coupled with alternating trilinear decomposition (ATLD) and random forest (RF) algorithms to rapidly predict Gancao species, geographical origins, and primary constituents. Seven fluorescent components were resolved from the three-dimensional fluorescence of the ATLD for subsequent analysis. Results indicated that the RF model distinguished Gancao from various species and origins better than other algorithms, achieving an accuracy of 94.4 % and 88.9 %, respectively. Furthermore, the RF regressor algorithm was used to predict the concentrations of liquiritin and glycyrrhizic acid in Gancao, with 96.4 % and 95.6 % prediction accuracies compared to HPLC, respectively. This approach offers a novel means of objectively evaluating the origin of food and holds substantial promise for food quality assessment.

Nano effect fluorescence visual sensor based on Au-AgNCs: A novel strategy to identify the origin and growth year of Lilium bulbs.

In this study, we developed a cost-effective fluorescence visual sensor strategy based on gold and silver nanocluster (Au-AgNCs) for the rapid identification of the origins and growth years of Lilium bulbs (LB). Au-AgNCs combined with catechins in LB produce aggregation-induced emission (AIE). The catechin content in LB of different origins and growth years varied, resulting in different fluorescence color responses of the sensor system. Furthermore, the RGB values of the fluorescent color were extracted, and the discriminant effect of visual visualisation was verified using the data-driven soft independent modelling of class analogy (DD-SIMCA) and partial least squares discriminant analysis (PLSDA) models. The results showed that the accuracy of DD-SIMCA for identifying LB origins and PLSDA for growth year identification was 100%. These results indicated that the established strategy could accurately identify the quality of LB, which has great potential for application in the rapid and visual identification of other foods.

Authenticity identification of high - Temperature Daqu Baijiu through multi-channel visual array sensor of organic dyes combined with smart phone App.

High - temperature Daqu Baijiu faces a challenge from illegal adulteration of high-grade Baijiu bottles with low-grade Baijiu, affecting its quality and value. This study developed a rapid identification method for high temperature Daqu Baijiu with the same aroma type using a four-channel visual array sensor and detection of color changes caused by competition coordination with Zn2+ and color-changing organic dyes. The array sensor demonstrated high stability and repeatability in targeting flavor components and achieved 97.78 % or more accuracy combined with DD-SIMCA model in detecting adulteration across the Baijiu with same aroma type. The results of GC-MS and Quantum Chemical Calculation showed that esters, acids, and pyrazines played a crucial role. The smart phone App could quickly identify the authenticity of Baijiu with accuracy achieved 93 %. This research provides a foundation for rapid and reliable assessment of Baijiu quality and authenticity, enabling the industry to combat fraudulent practices effectively.

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 novel 3D-fluorescence sensing strategy based on HN-chitosan polymer probe for rapid identification and quantification of potential adulteration in saffron.

Saffron is a candidate for various kinds of fraud to make huge profits. The present study proposed an efficient three-dimensional (3D) fluorescence sensing strategy based on hydrophilic hydrazine-naphthalimide functionalized chitosan (HN-chitosan) polymer probe for rapid identification and quantification of potential adulteration in saffron. The amino functional group in the HN-chitosan probe reacted specifically with the Oxygen-containing group of active ingredients in saffron, amplifying the signal difference between saffron and the adulterants, which was comprehensively characterized by 3D fluorescence. Four advanced chemometrics methods were applied for the classification of saffron and adulterated saffron, and good performance were obtained in both training and prediction sets. Furthermore, the PLS regression model was applied to the prediction of adulteration level in saffron and showed satisfactory accuracy. This strategy provides a new solution for rapid identification and quantification of potential adulteration in saffron, which contributes to the healthy development of its industry.

Highly selective detection of epinephrine by a "turn-off" fluorescent sensor based on N-doped carbon quantum dots.

Epinephrine (EP) is a catecholamine hormone with a variety of physiological activities. Monitoring the concentration of EP in drugs, food, biological samples and cosmetics is of great significance for their quality control. Herein, a novel fluorescence sensing method was designed for the high-specificity detection of EP based on N-doped carbon quantum dots (N-CDs). The EP could interact with the fluorescent senor of N-CDs which emits blue fluorescence to produce concentration- dependent fluorescence quenching through the photo-induced electron transfer (PET). The established sensing method has good linearity in the range of 0.5-10 µM with the LOD of 0.15 µM. More importantly, it is highly selective because similar components with phenolic hydroxyl groups or primary amino groups, even norepinephrine (NEP), could not interfere with the detection. This method can provide a low-cost, rapid and simple new way for the detection of EP, and has a good application prospect in point-of-care assay and in situ test.

Classification of Chinese traditional cereal vinegars and antioxidant property predication by fluorescence spectroscopy.

In this work, a rapid and accurate strategy for classification of Chinese traditional cereal vinegars (CTCV) and antioxidant property predication was proposed by using the combination fluorescence spectroscopy and machine learning. Three characteristic fluorescent components were extracted by parallel factor analysis (PARAFAC), which have correlations greater than 0.8 with antioxidant activity of CTCV obtained by Pearson correlation analysis. Machine learning methods, including linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and N-way partial least squares discriminant analysis (N-PLS-DA), were used for the classification of different types of CTCV, and the correct classification rates was higher than 97%. The antioxidant property of CTCV were further quantified by using optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). The proposed strategy provides a basis for further research on antioxidant active ingredients and antioxidant mechanisms of CTCV, and enable the continued exploration and application of CTCV from different types.

A sensitized ratiometric fluorescence probe based on N/S doped carbon dots and mercaptoacetic acid capped CdTe quantum dots for the highly selective detection of multiple tetracycline antibiotics in food.

A ratiometric fluorescent probe based on N/S doped carbon dots (N/S-CQDs) and mercaptoacetic acid capped CdTe quantum dots (TGA-CdTe QDs) with sensitized and self-calibration functions was constructed to sensitively detect multiple tetracycline antibiotics (TCs). N/S-CQDs could attach stably to TGA-CdTe QDs and form a new composite ratiometric fluorescent probe that had a more than tenfold increase in sensitivity to TCs compared with each single QD. The probe could detect four common TCs as the color of the probe changed from bright red to dark red, and the limit of detection (LOD) was 1.47 × 10-2-1.78 × 10-2 mg/L. Practical applications of the probe in food and urine were also verified with recovery rates of 95.21%-104.97%. Due to the abundant spectral fingerprints provided by both QDs, this novel probe could accurately recognize not only different single TCs but also mixed TC samples even in actual samples combined with chemometrics.

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.

UHPLC-QTOF-MS-based untargeted metabolomics and mineral element analysis insight into the geographical differences of Chrysanthemum morifolium Ramat cv. "Hangbaiju" from different origins.

Chrysanthemum morifolium Ramat cv. "Hangbaiju" (HBJ), known as one of the "eight flavors of Zhejiang", is commonly used as a classical tea material for both food and medicine over three thousand years in China. The quality of HBJ is closely related to its geographical origins. However, the mechanism underlying the geographical differences of HBJ remains to be elucidated. In this study, an untargeted metabolomic strategy based on UHPLC-QTOF-MS was established to discover the differential metabolites in HBJ samples from four different origins and explore the possible relationship with mineral elements in planting soils by chemometric analysis. Eight compounds were screened and identified as the key differential metabolites in HBJ samples from different origins. Among them, four important pharmacodynamic compounds including L-arginine, rutin, chlorogenic acid and apigenin-7-O-glucoside are the most abundant in HBJ samples from Tongxiang region, which suggests that HBJ planted in Tongxiang has higher medicinal values. Pearson correlation analysis revealed that the contents of soil mineral elements are positively correlated with those of chlorogenic acid, rutin, apigenin-7-O-glucoside in HBJ samples. Furthermore, an interrelationship model based on random forest algorithm was established to successfully predict the contents of differential metabolites in HBJ samples by soil mineral elements. All these results indicated that the contents of differential metabolites in HBJ samples seemed to be affected by soil mineral elements and therefore resulted in the geographical differences of HBJ.

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.

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.

Digital image colorimetry in combination with chemometrics for the detection of carbaryl based on the peroxidase-like activity of nanoporphyrins and the etching process of gold nanoparticles.

Carbaryl is a typical carbamate pesticide that plays an essential role in agricultural production, but its residues cause serious harm to the environment and human health. Here, we developed a polychromatic colorimetric sensor based on ZnTPyP-DTAB peroxidase activity and gold nano-bipyramids (Au NBPs) etching to detect carbaryl. ZnTPyP-DTAB catalyzes the decomposition of H2O2 to hydroxyl radicals, and Au NBPs are etched. The coordination of zinc and nitrogen in nanometer porphyrins was affected by the steric effects of carbaryl, which resulted in decreased activity of ZnTPyP-DTAB peroxidase. The detection limit of carbaryl was 0.26 mg/kg. The recoveries of carbaryl in reaal sample ranged from 91 % to 107% (RSD ≤ 0.7%). The sensor platform displayed a series of high-resolution multicolor variations of rainbow colors within the above concentration range. The rich color variation facilitates the acquisition of digital images. RGB value transformation combined with partial least squares regression model can accurately and quantitatively detect carbaryl in vegetables, fruits and Chinese medicinal materials.

[Research progress on common pesticide residues in Chinese medicinal materials].

Chinese medicinal materials are the precious resources of China and favored by patients at home and abroad because of their natural sources and curative effects. Pesticides are often used to prevent and control diseases and insect pests and regulate the growth of Chinese medicinal plants, so as to improve the yield and quality of Chinese medicinal materials. Most of the pesticides can play a role in pest control through systemic action, stomach toxicity, contact, fumigation and other ways, especially the systemic pesticides can kill hidden pests by entering the Chinese medicinal plants. Despite the good pest control effect, it is difficult to remove the systemic pesticides by simple cleaning, which poses a great risk to the safety of Chinese medicinal materials. At the same time, excessive or non-standard use of pesticides leads to serious pesticide residues in Chinese medicinal materials, which affects not only the quality and efficacy of the materials and harm human health but also the international development of Chinese medicinal materials industry. Pesticide residues have become a bottleneck affecting the industry development and hindering the export of Chinese medicinal materials. Therefore, it is of great significance to study how to quickly, sensitively, and accurately detect and remove pesticide residues in Chinese medicinal materials. We reviewed the common pesticide residues in Chinese medicinal materials in recent years in terms of characteristics, harm, and detection and removal techniques, and discussed the future development of the detection and removal deve-lopment. With this review, we aimed to provide a reference for the quality control of Chinese medicinal materials and promote the healthy development of Chinese medicine industry.

Untargeted metabolomic analysis of Chinese red wines for geographical origin traceability by UPLC-QTOF-MS coupled with chemometrics.

Identifying geographical origins of red wines made in specific regions is of significance since the false claim of geographical origins has been frequently exposed in China's wine industry. In this work, an untargeted metabolomic approach based on UPLC-QTOF-MS was established to discriminate geographical origins of Chinese red wines. The principal component analysis (PCA) showed significant differences between wine samples from three famous geographical origins in China. The metabolites contributing to the differentiation were screened by orthogonal partial least squares-discriminant analysis (OPLS-DA) with pairwise modeling. 40 and 46 differential metabolites in positive and negative ionization modes were putatively identified as chemical markers. Furthermore, heatmap visualization and OPLS-DA models were constructed based on these identified markers and external verification wine samples from different regions were successfully discriminated, with recognition rate up to 96.7%. This study indicated that UPLC-QTOF-MS-based untargeted metabolomics has great potential for the geographical origin traceability of Chinese red wines.

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.