Identification of Chinese teas by a colorimetric sensor array based on tea polyphenol induced indicator displacement assay

Unveiling a novel nanozyme cofactor: a highly activated Fe-CDs-derived colorimetric sensor array for comprehensive authentication of diverse tea products

Tea has antioxidant, anti-aging, hypotensive, and other functions. The tea polyphenol profile is of great significance for the identification and quality control of diverse tea products. Herein, an iron-doped carbon dots (Fe-CDs) with peroxidase-like activity was synthesized to construct a colorimetric sensor array for the tea polyphenols analysis in a complex matrix. Unexpectedly, it is found that the addition of tea polyphenols leads to an admirable increase in the activity of Fe-CDs, indicating the role of tea polyphenols as a cofactor of Fe-CDs nanozyme. The exploration based on multiple techniques reveals that the polyphenols can absorb on the Fe-CDs surface to form the new complex and effectively enhance the catalytic performance of Fe-CDs. This enhancement effect is closely related to the elemental doping, surface condition of nanozyme, and the polyphenol concentration. Taking TMB as the substrate, the colorimetric fingerprint generated in the presence of different tea polyphenols are extracted from the sensing array. The results of principal component analysis (PCA) and hierarchical clustering analysis (HCA) show that five tea polyphenols of different types, concentrations, and tea polyphenol mixtures of different proportions are successfully distinguished through pattern recognition methods. Moreover, the identification of tea leaves from different years and types, as well as various tea drinks, has been further realized. This study not only paves a new direction for enhancing the nanozyme activity, but provides an effective and convenient strategy for the quality control of tea products.

Deep Learning-Assisted Multiplexed Electrochemical Fingerprinting for Chinese Tea Identification

Selectively differential identification of natural components with similar chemical structures in complex matrices is still a challenging task by conventional analytical strategies. Herein, we developed a landmark (DaXing airport)-inspired laser engraving sensor array that combined multiplex electrochemical fingerprinting technology with a one-dimensional convolutional neural network (1D-CNN) for rapidly precise detection of three tea polyphenols and the differentiation of 24 distinct types of Chinese teas. This sensing strategy employs a diverse array of three different working electrode configurations as a multivariate sensor (bare electrode, nanoenzyme electrode, and bioenzyme electrode), generating distinct electrochemical fingerprints in complex samples. By utilizing a self-designed 1D-CNN algorithm for feature extraction, the identification of electrochemical fingerprints is significantly improved, thereby enhancing the predictive accuracy for tea polyphenols and Chinese teas. This platform successfully achieves detection of three tea polyphenols, distinguishing six Chinese tea series and 24 tea varieties with accuracy rates of 98.84 and 97.68%, respectively. Notably, the deep learning-assisted multiplexed electrochemical fingerprinting technique achieves better accuracy for tea identification compared with other representative machine learning methods. This advancement offers a rapid and reliable approach to enhancing the development of identification and authentication processes for agricultural products.

A novel colorimetric and fluorometric dual-signal identification of crude baijiu based on La-CDs

The correct classification of strong-flavored crude baijiu affects its quality and overall standard and is crucial for the intelligent development of the baijiu industry. In this work, we developed a novel optical sensing array using lanthanum-doped carbon dots (La-CDs). Using La-CDs with three metal complex dyes-chromium black T, alizarin red, and dimethylphenol orange-we were able to detect organic acids and tannic acid (TA) in crude baijiu in a way that was both colorimetric and fluorescent for the first time. Based on the indicator displacement (IDA) principle, organic acids competitively replace the dyes' binding sites on La3+, causing the dye colors to change to varying degrees. TA quenches the fluorescence of quantum dots through an internal filtering effect. We analyzed the data using pattern recognition algorithms such as HCA, PCA, and LDA, successfully classifying and identifying 16 types of strong-flavored crude baijiu, which included 10 types of carboxylic acids and various grades. In blind tests of 32 crude baijiu samples, the colorimetric method achieved a 94 % accuracy rate, while the fluorescence method achieved 100 %. The sensor demonstrates significant advantages in response speed.

Smartphone-assisted colorimetric biosensor for the rapid visual detection of natural antioxidants in food samples

Quantitative monitoring of the concentrations of epigallocatechin gallate (EGCG) and cysteine (Cys) is of great significance for promoting human health. In this study, iron/aluminum bimetallic MOF material MIL-53 (Fe, Al) was rapidly prepared under room temperature using a co-precipitation method, followed by investigating the peroxidase-like (POD-like) activity of MIL-53(Fe, Al) using 3,3',5,5'-tetramethylbenzidine (TMB) as a chromogenic substrate. The results showed that the Michaelis -Menten constants of TMB and H2O2 as substrates were 0.167 mM and 0.108 mM, respectively. A colorimetric sensing platform for detecting EGCG and Cys was developed and successfully applied for analysis and quantitative detection using a smartphone. The linear detection range for EGCG was 15∼80 μM (R2=0.994) and for Cys was 7∼95 μM (R2=0.998). The limits of detection (LOD) were 0.719 μM and 0.363 μM for EGCG and Cys, respectively. This work provides a new and cost-effective approach for the real-time analysis of catechins and amino acids.

A novel sensor array with ability to respectively identify phenol and ketone for the precise discrimination of origins of raw Pu-erh and its counterfeiting

Most reported sensor arrays for teas were based on the sensing of phenolic hydroxyl group on tea polyphenols. In this work, a novel sensor array was developed based on the simultaneous sensing of phenols and ketones, for the enhanced discrimination of tea polyphenols with/without ketone, and then for the efficient discrimination of raw Pu-erh teas from different origins and the counterfeit, combined with machine learning. This sensor array is consisting of four channels. Channel A is carbon dots, room-temperature carbon nanoparticles (RT-CNPs), whose fluorescence can be quenched by ketone; Channel B is the chromogenic agent, 2,4-Dinitrophenylhydrazine (DNPH), which can combine with ketone to undergo absorption spectral changes. Tea polyphenols without ketone have little effect on Channel A and Channel B. Channel C (RT-CNPs + nanozyme) and Channel D (DNPH + nanozyme) are the addition of nanozyme with polyphenol oxidase activity to Channel A and Channel B. The nanozyme can catalyze the oxidation of phenols to the quinones, which means Channel C and D can react to tea polyphenols with phenols or ketones. Based on the different quenching efficiency of various tea polyphenols on the carbon dots, differences in the color due to the ketone combination with DNPH, and differences in the degrees of tea polyphenol's oxidation by the nanozyme, the proposed four-channel sensor array achieved the enhanced discrimination of tea polyphenols. This sensing technology which can simultaneously recognize phenols and ketones, has an excellent applicating prospects in the recognition and distinguish of tea and its derivatives (such as tea drinks), with oxidation and transformation of tea polyphenols.

Smartphone-assisted colorimetric sensor arrays based on nanozymes for high throughput identification of heavy metal ions in salmon

The rapid, precise, and high-throughput identification of multiple heavy metals ions holds immense importance in ensuring food safety and promoting public health. This study presents a novel smartphone-assisted colorimetric sensor array for the rapid and precise detection of multiple heavy metals ions. The sensor array is based on three signal recognition elements (AuPt@Fe-N-C, AuPt@N-C, and Fe-N-C) and the presence of different heavy metal ions affects the nanozymes-chromogenic substrate (TMB) catalytic color production, enabling the differentiation and quantification of various heavy metal ions. Combined with a smartphone-based RGB mode, the colorimetric sensor array can successfully identify five different heavy metal ions (Hg2+, Pb2+, Co2+, Cr6+, and Fe3+) as low as 0.5 μM and different ratios of binary and ternary mixed heavy metal ions in just 5 min. The sensor array successfully tested seawater and salmon samples with a total heavy metal content of 10 μM in the South China Sea (Haikou and Wenchang). Overall, this study highlights the potential of smartphone-assisted colorimetric sensor arrays for the rapid and precise detection of multiple heavy metal ions, which could significantly contribute to food safety and public health monitoring.

Intelligent identification of picking periods of Lu'an Guapian tea by an indicator displacement colorimetric sensor array combined with machine learning

Lu'an Gua Pian (LAGP) tea is one of the most famous green teas in China. The quality of green tea is related to its picking periods, especially the green tea before Qingming Festival (usually April 6th) is highly praised as precious in the market. In this work, a simple and cheap indicator displacement colorimetric sensor array combined with smartphone was developed to rapidly identify LAGP picked during different picking periods. First, the chemical component contents of LAGP picked before and after Qingming Festival were analyzed. Second, a well-designed colorimetric sensor array was proposed based on the tea component contents differences. Finally, machine learning was used to process the array data taken by a smartphone. By comparison, the accuracy of the best model for the prediction set was 97%. Meanwhile, the multi-channel advantages of the sensing array were demonstrated by an ablation experiment. In addition, the method achieved an AGREE analysis score of 0.88, indicating that it was environmental-friendly.

Olfactory analysis of oolong tea sensory quality using composite nano-colorimetric sensor array

Chinese oolong tea is famous for its rich and diverse aromas, which is an important indicator for sensor quality evaluation. To accurately and rapidly evaluate sensory quality, a novel colorimetric sensor array (CSA) was developed to detect volatile organic compounds (VOCs) in oolong tea. We further explored the binding mechanism between colorimetric dyes that trigger changes in charge transfer and visible color changes. Based on this, we modified and optimized the CSA to improve the sensitivity by 17.1-234.9% and the stability by 8.7-33.3%. The study also assessed the effectiveness of this method by comparing two linear and two non-linear classification models, with the support vector machine (SVM) model achieving the highest accuracy, identifying different flavor intensity and grades with rates of 100% and 95.83%, respectively. These findings sufficiently demonstrated that the novel CSA, integrated with the SVM model, has promising potential for predicting the sensory quality of oolong tea.

Long-range infrared absorption spectroscopy and fast mass spectrometry for rapid online measurements of volatile organic compounds from black tea fermentation

Fermentation is the key process to determine the quality of black tea. Traditional physical and chemical analyses are time consuming, it cannot meet the needs of online monitoring. The existing rapid testing techniques cannot determine the specific volatile organic compounds (VOCs) produced at different stages of fermentation, resulting in poor model transferability; therefore, the current degree of black tea fermentation mainly relies on the sensory judgment of tea makers. This study used proton transfer reaction mass spectrometry (PTR-MS) and fourier transform infrared spectroscopy (FTIR) combined with different injection methods to collect VOCs of the samples, the rule of change of specific VOCs was clarified, and the extreme learning machine (ELM) model was established after principal component analysis (PCA), the prediction accuracy reached 95% and 100%, respectively. Finally, different application scenarios of the two technologies in the actual production of black tea are discussed based on their respective advantages.

Pattern-based colorimetric sensor array chip for discrimination of Baijiu aromas

Gas mixtures are comprised of numerous complex components, making the accurate identification a continuing challenge due to the significant limitations of existing detection methods. Herein, we developed a low-cost and sensitive pattern-based colorimetric sensor array chip for the identification of typical gas mixtures - Baijiu aroma. Specifically, three nanomaterials (AuNPs, MoS2 and ZIF-8) were prepared to adsorb gas molecules and enhance the reaction of trace gases with sensor arrays. The colorimetric sensor array chip took only 5 min to complete the recognition of Baijiu aromas and effectively avoided recognition errors caused by sommelier olfactory fatigue. Notably, the hierarchical cluster analysis (HCA) revealed no confusion or errors in the results of 80 tests across the five trials involving 16 commercial Baijius. Even fake Baijius with similar ingredients could be easily identified, demonstrating the excellent analytical capabilities of the system in Baijiu identification and its significant potential for quality control of Baijius.

Near-infrared spectroscopy based on colorimetric sensor array coupled with convolutional neural network detecting zearalenone in wheat

Wheat is a vital global cereal crop, but its susceptibility to contamination by mycotoxins can render it unusable. This study explored the integration of two novel non-destructive detection methodologies with convolutional neural network (CNN) for the identification of zearalenone (ZEN) contamination in wheat. Firstly, the colorimetric sensor array composed of six selected porphyrin-based materials was used to capture the olfactory signatures of wheat samples. Subsequently, the colorimetric sensor array, after undergoing a reaction, was characterized by its near-infrared spectral features. Then, the CNN quantitative analysis model was proposed based on the data, alongside the establishment of traditional machine learning models, partial least squares regression (PLSR) and support vector machine regression (SVR), for comparative purposes. The outcomes demonstrated that the CNN model had superior predictive performance, with a root mean square error of prediction (RMSEP) of 40.92 μ g ∙ kg-1 and a coefficient of determination on the prediction (R P 2 ) of 0.91. These results affirmed the potential of integrating colorimetric sensor array with near-infrared spectroscopy in evaluating the safety of wheat and potentially other grains. Moreover, CNN can have the capacity to autonomously learn and distill features from spectral data, enabling further spectral analysis and making it a forward-looking spectroscopic tool.

Nanotechnology improves the detection of bacteria: Recent advances and future perspectives

Nanotechnology has advanced significantly, particularly in biomedicine, showing promise for nanomaterial applications. Bacterial infections pose persistent public health challenges due to the lack of rapid pathogen detection methods, resulting in antibiotic overuse and bacterial resistance, threatening the human microbiome. Nanotechnology offers a solution through nanoparticle-based materials facilitating early bacterial detection and combating resistance. This study explores recent research on nanoparticle development for controlling microbial infections using various nanotechnology-driven detection methods. These approaches include Surface Plasmon Resonance (SPR) Sensors, Surface-Enhanced Raman Scattering (SERS) Sensors, Optoelectronic-based sensors, Bacteriophage-Based Sensors, and nanotechnology-based aptasensors. These technologies provide precise bacteria detection, enabling targeted treatment and infection prevention. Integrating nanoparticles into detection approaches holds promise for enhancing patient outcomes and mitigating harmful bacteria spread in healthcare settings.

A novel hybrid sensor array based on the polyphenol oxidase and its nanozymes combined with the machine learning based dual output model to identify tea polyphenols and Chinese teas

A novel sensor array was developed based on the enzyme/nanozyme hybridization for the identification of tea polyphenols (TPs) and Chinese teas. The enzyme/nanozyme with polyphenol oxidase activity can catalyze the reaction between TPs and 4-aminoantipyrine (4-AAP) to produce differences in color, and the sensor array was thus constructed to accurately identify TPs mixed in different species, concentrations, or ratios. In addition, a machine learning based dual output model was further used to effectively predict the classes and concentrations of unknown samples. Therefore, the qualitative and quantitative detection of TPs can be realized continuously and quickly. Furthermore, the sensor array combining the machine learning based dual output model was also utilized for the identification of Chinese teas. The method can distinguish the six teas series in China, and then precisely differentiate the more specific tea varieties. This study provides an efficient and facile strategy for the identification of teas and tea products.

Colorimetric sensor array combined with chemometric methods for the assessment of aroma produced during the drying of tencha

The aroma produced during drying is an important indicator of tencha and needs to be monitored. This study constructed an olfactory visualization system for assessing tencha aroma using colorimetric sensor array (CSA) combined with chemometric methods. The 16 chemically responsive dyes were selected to obtain aroma information of tencha samples and extracted image data of aroma information by machine vision algorithms. Subsequently, k-nearest neighbor, support vector machine, classification and regression tree, and random forest (RF), four qualitative models were applied to build the mathematical models. The RF model with nine principal components was preferred, with recognition rate of 100.00% and 91.07% for the training and prediction sets, respectively. The experimental results showed that CSA combined with the RF model can be effectively applied to assess tencha aroma. This study provided a scientific and novel method to maintain the stability of tencha quality in the production process.

Multilocus Distance-Regulated Sensor Array for Recognition of Polyphenols via Machine Learning and Indicator Displacement Assay

Developing new strategies to construct sensor arrays that can effectively distinguish multiple natural components with similar structures in mixtures is an exceptionally challenging task. Here, we propose a new multilocus distance-modulated indicator displacement assay (IDA) strategy for constructing a sensor array, incorporating machine learning optimization to identify polyphenols. An 8-element array, comprising two fluorophores and their six dynamic covalent complexes (C1-C6) formed by pairing two fluorophores with three distinct distance-regulated quenchers, has been constructed. Polyphenols with diverse spatial arrangements and combinatorial forms compete with the fluorophores by forming pseudocycles with quenchers within the complexes, leading to varying degrees of fluorescence recovery. The array accurately and effectively distinguished four tea polyphenols and 16 tea varieties, thereby demonstrating the broad applicability of the multilocus distance-modulated IDA array in detecting polyhydroxy foods and natural medicines.

Application of visible near-infrared spectroscopy combined with colorimetric sensor array for the aroma quality evaluation in tencha drying process

The drying process is a critical stage in developing the aroma quality of tencha. In our research, visible near infrared (Vis-NIR) and colorimetric sensor array (Vis-NIR-CSA) were used for evaluating the aroma quality of tencha drying process. Vis-NIR recorded the spectral signal of CSA after the reaction in samples. Subsequently, the aroma quality was predicted by a combination of different data fusion strategies and classification and regression tree (CART) in tencha drying process. The high-level fusion strategy showed the best performance, with calibration and prediction set accuracy of 94.68% and 93.48%, respectively. The results indicated that Vis-NIR-CSA combined with high-level data fusion could be applied satisfactorily in the aroma quality evaluation of tencha. Moreover, pentanal was identified to be highly correlated with aroma quality during tencha drying process, which verified the sensor identification results. This study contributed to controlling good manufacturing practices and designing optimal tencha processing systems.

Applications, challenges and prospects of bionic nose in rapid perception of volatile organic compounds of food

Bionic nose, a technology that mimics the human olfactory system, has been widely used to assess food quality due to their high sensitivity, low cost, portability and simplicity. This review briefly describes that bionic noses with multiple transduction mechanisms are developed based on gas molecules' physical properties: electrical conductivity, visible optical absorption, and mass sensing. To enhance their superior sensing performance and meet the growing demand for applications, a range of strategies have been developed, such as peripheral substitutions, molecular backbones, and ligand metals that can finely tune the properties of sensitive materials. In addition, challenges and prospects coexist are covered. Cross-selective receptors of bionic nose will help and guide the selection of the best array for a particular application scenario. It provides an odour-based monitoring tool for rapid, reliable and online assessment of food safety and quality.

Cost-effective colorimetric sensor for authentication of protected designation of origin (PDO) Longjing green tea

Traceability and authentication of protected designation of origin (PDO) tea is an important prerequisite to safeguard its production and distribution system. Here, indicator displacement array (IDA) sensors consisting of natural anthocyanidins and edible metal ions were developed to authenticate PDO and non-PDO Longjing from different origins. Five IDA elements were selected for constructing sensors, achieved by an indicator displacement reaction after adding epigallocatechin gallate solution. The obtained sensors were subsequently used for real tea samples. Unsupervised algorithms were used for data exploration among PDO and non-PDO teas. The supervised support vector machine (SVM) model further achieved accurate authentication of PDO and non-PDO Longjing with a correct classification rate of 100% for the 26 validated samples. The developed IDA sensor thus achieves accurate authentication of PDO tea in a hazard-free and cost-efficient way, providing a useful tool for origin authentication of other agricultural products.

Spectroscopic studies on a natural biomarker for the identification of origin and quality of tea extracts for the development of a portable and field deployable prototype

Even in the era of smart technologies and IoT enabled devices, tea testing technique continues to be a person specific subjective task. In this study, we have employed optical spectroscopy-based detection technique for the quantitative validation of tea quality. In this regard, we have employed the external quantum yield of quercetin at 450 nm (λ_ex = 360 nm), which is an enzymatic product generated by the activity of β-glucosidase on rutin, a naturally occurring metabolite responsible for tea-flavour (quality). We have found that a specific point in a graph representing Optical Density and external Quantum Yield as independent and dependent variables respectively of an aqueous tea extract objectively indicates a specific variety of the tea. A variety of tea samples from various geographical origin have been analysed with the developed technique and found to be useful for the tea quality assessment. The principal component analysis distinctly showed the tea samples originated from Nepal and Darjeeling having similar external quantum yield, while the tea samples from Assam region had a lower external quantum yield. Furthermore, we have employed experimental and computational biology techniques for the detection of adulteration and health benefit of the tea extracts. In order to assure the portability/field use, we have also developed a prototype which confirms the results obtained in the laboratory. We are of the opinion that the simple user interface and almost zero maintenance cost of the device will make it useful and attractive with minimally trained manpower at low resource setting.

A rapid aroma quantification method: Colorimetric sensor-coupled multidimensional spectroscopy applied to black tea aroma

Aroma affects the quality of black tea, and the rapid evaluation of aroma quality is the key to realize the intelligent processing of black tea. A simple colorimetric sensor array coupled with a hyperspectral system was proposed for the rapid quantitative detection of key volatile organic compounds (VOCs) in black tea. Feature variables were screened based on competitive adaptive reweighted sampling (CARS). Furthermore, the performance of the models for VOCs quantitative prediction was compared. For the quantitative prediction of linalool, benzeneacetaldehyde, hexanal, methyl salicylate, and geraniol, the CARS-least-squares support vector machine model's correlation coefficients were 0.89, 0.95, 0.88, 0.80, and 0.78, respectively. The interaction mechanism of array dyes with VOCs was based on density flooding theory. The optimized highest occupied molecular orbital levels, lowest unoccupied molecular orbital energy levels, dipole moments, and intermolecular distances were determined to be strongly correlated with interactions between array dyes and VOCs.

Indicator displacement assay for freshness monitoring of green tea during storage

Aging of green tea leads to reductions in its flavor and health value, yet in situ testing methods for green tea freshness are lacking. A novel sensitive indicator displacement assay (IDA) sensor was constructed and applied for monitoring of green tea freshness during storage. Low-cost pH dyes and metal ions were used as indicators and receptors, respectively, for the targeted detection of catechins in tea samples. The feasibility of the IDA reaction was verified using images and UV-vis spectroscopy, respectively. IDA combined with supervised algorithms achieved accurate identification of green tea freshness with an accuracy of 86.67%, and acceptable accuracies in the prediction of catechin monomers and total catechins with ratio of prediction to deviation values over 1.5. Thus, the developed IDA sensor is capable of qualitative and quantitative monitoring of the green tea freshness during storage, providing a new option for quality evaluation and control of green teas.

Optical sensor arrays for the detection and discrimination of natural products

Covering: up to the end of 2022Natural products (NPs) have found uses in medicine, food, cosmetics, materials science, environmental protection, and other fields related to our life. Their beneficial properties along with potential toxicities make the detection and discrimination of NPs crucial for their applications. Owing to the merits of low cost and simple operation, optical sensor arrays, including colorimetric and fluorometric sensor arrays, have been widely applied in the detection of small molecule NPs and discrimination of structurally similar small molecule NPs or complex mixtures of NPs. This review provides a brief introduction to the optical sensor array and focuses on its progress toward the detection and discrimination of NPs. We summarized the design principle of sensor arrays toward various NPs (i.e., saccharides and polyhydroxy compounds, organic acids, flavonoids, organic sulfur compounds, amines, amino acids, and saponins) based on their functional groups and characteristic chemical properties, along with representative examples. Moreover, the challenges and potential directions for further research of optical sensor arrays for NPs are proposed.

Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method

Apo pickle is a traditional Chinese fermented vegetable. However, the traditional fermentation process of Apo pickle is slow, easy to ruin, and cannot be judged with regard to time. To improve fermentation, LP-165 (L. Plantarum), which has a high salt tolerance, acidification, and growth capacity, was chosen as the starter culture. Meanwhile, a colorimetric sensor array (CSA) sensitive to pickle volatile compounds was developed to differentiate Apo pickles at varying degrees of fermentation. The color components were extracted from each dye in the color change profiles and were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). The fermentation process of the Apo pickle was classified into four phases by LDA. The accuracy of backward substitution verification was 99% and the accuracy of cross validation was 92.7%. Furthermore, the partial least squares regression (PLSR) showed that data from the CSA were correlated with pH total acid, lactic acid, and volatile acids of the Apo pickle. These results illustrate that the CSA reacts quickly to inoculated Apo pickle and could be used to detect fermentation.

A hybrid boronate affinity probe for the selective detection of cis-diols containing compounds in tea beverages

UiO-66-NH₂ nanocomposite was post-modified with 4-mercaptophenylboronic acid (MPBA) by the method of in-situ hybridization reaction. The hybrid boronate affinity material UiO-NH₂ @P (TEPIC-co-MPBA) was characterized by Scanning electron microscope, X-ray diffraction, Fiurier transform infrared spectroscopy. It was applied as fluorescent probe for the detection of cis-diols containing compounds based on the boronate affinity mechanism, and exhibited high specific selectively. The proposed method exhibited good liearnity for the detection of catechol in the range of 0.50-8.00 μg·mL^-1 . The detection limit was 0.13 μg·mL^-1 . The tactic was successfully applied to analyze the total polyphenols in tea beverages for catechol, and relative recovery was in 98.86-106.00%. Therefore, this work provided a promising strategy for recognization of cis-diols containing compounds.

Authentication of Geographical Origin in Hainan Partridge Tea (Mallotus obongifolius) by Stable Isotope and Targeted Metabolomics Combined with Chemometrics

Partridge tea (Mallotus oblongifolius (Miq.) Müll.Arg.) is a local characteristic tea in Hainan, the southernmost province of China, and the quality of partridge tea may be affected by the producing areas. In this study, stable isotope and targeted metabolomics combined chemometrics were used as potential tools for analyzing and identifying partridge tea from different origins. Elemental analysis-stable isotope ratio mass spectrometer and liquid chromatography-tandem mass spectrometrywas used to analyze the characteristics of C/N/O/H stable isotopes and 54 chemical components, including polyphenols and alkaloids in partridge tea samples from four regions in Hainan (Wanning, Wenchang, Sanya and Baoting). The results showed that there were significant differences in the stable isotope ratios and polyphenol and alkaloid contents of partridge tea from different origins, and both could accurately classify partridge tea from different origins. The correct separation and clustering of the samples were observed by principal component analysis and the cross-validated Q² values by orthogonal partial least squares discriminant analysis (OPLS-DA) were 0.949 (based on stable isotope) and 0.974 (based on polyphenol and alkaloid), respectively. Potential significance indicators for origin identification were screened out by OPLS-DA and random forest algorithm, including three stable isotopes (δ¹³C, δ D, and δ¹⁸O) and four polyphenols (luteolin, protocatechuic acid, astragalin, and naringenin). This study can provide a preliminary guide for the origin identification of Hainan partridge tea.

Indicator displacement assays: from concept to recent developments

Overcoming the synthetic burden related to covalently connected receptors with appropriate indicators for sensing various analytes via an indicator spacer receptor (ISR) approach, the indicator displacement assay (IDA) seems to be a very sophisticated and versatile supramolecular sensing paradigm, and it has taken the phenomenon of molecular recognition to the next level in the realm of host-guest chemistry. Due to the unavailability of a comprehensive report on what has been done in the last decade in relation to IDAs, we decided to set down this account illustrating diverse indicator displacement assays (IDAs) in detail from the concept stage to recent developments relating to the detection of cationic, anionic, and neutral analytes. The authors conclude this account with future perspectives and highlight the limitations and challenges relating to IDAs which need to be overcome in order to realize the full potential of this popular sensing phenomenon. While we were finalizing our account for publication, a tutorial review by the research groups of Anslyn, Sessler, and Sun was published, which focuses mainly on diverse aspects of the chemistry related to IDAs. As can be seen, our review, besides discussing various basic IDA concepts, has a vast collection of information published in the past decade and hence, hopefully, will be very informative for the supramolecular community. We believe that this work will offer new insights for the construction of novel sensors operating through the IDA approach.

Rapid discrimination of beer based on quantitative aroma determination using colorimetric sensor array

In this study, 6 beers from Tsingtao Brewery were analyzed by using colorimetric GC-MS and sensor array (CSA). First, forty volatile compounds of six beers, including 16 esters, 10 alcohols, 4 acids and 4 aldehydes, were identified by GC-MS. Beers from the same category were grouped using principal component analysis (PCA) score plot and hierarchical clustering analysis (HCA) dendrogram. Discrimination of the beers was subsequently implemented using a 4 × 4 CSA combined with multivariate analysis. A linear discriminant analysis (LDA) model achieved a 100% recognition rates of the 6 beers. In addition, a partial least square (PLS) model could be used to quantitatively determine ethyl octanoate, phenethyl acetate, isoamyl alcohol and octanoic acid, with correlation coefficients over 0.85 for both the calibration curves of the training and prediction sets. Hence, CSA could be used for rapid and non-destructive determination of beer quality.

Lanthanide Metal-Organic Framework-Based Fluorescent Sensor Arrays to Discriminate and Quantify Ingredients of Natural Medicine

The discrimination and quantification of the ingredients from natural medicines are a challenging issue due to their complicated and various structures. Metal-organic frameworks (MOFs) have shown great promise in sensing applications. Here, we report a fluorescent sensor array for rapid identification of some natural compounds using a sensor array composed of four kinds of lanthanide (Eu³⁺ and Tb³⁺) fluorescent MOFs (Ln-MOFs), which have diversified fluorescent responses to 26 active/toxic compounds including 12 saponins, 7 flavonoids, 3 stilbenes, and 4 anthraquinones. The fluorescence of the Ln-MOFs after reaction with the compounds was summarized as datasets and processed by principle component analysis (PCA) and hierarchical cluster analysis (HCA) methods. The corresponding responses of the 4 types of compounds are well separated on 2D/3D PCA score plots and HCA dendrograms. We have also tested typical blind samples by concentration-dependent PCA, and an accuracy of 100% was obtained. In addition, the response mechanisms of the Ln-MOFs to the compounds were also studied. Compared with traditional methods using liquid chromatography-mass spectrometry, the developed fluorescent sensor array provides a more efficient and economic strategy to discriminate various active/toxic ingredients in natural medicine.

Multi-variable selection strategy based on near-infrared spectra for the rapid description of dianhong black tea quality

The main objectives of the study are to understand and explore critical feature wavelengths of the obtained near-infrared (NIR) data relating to dianhong black tea quality categories, we propose a multi-variable selection strategy based on the variable space optimization from big to small which is the kernel idea of a variable combination of the improved genetic algorithm (IGA) and particle swarm optimization (PSO) in this study. A rapid description based on the NIR technology is implemented to assess black tea tenderness and rankings. First, 700 standard samples from dianhong black tea of seven quality classes are scanned using a NIR system. The raw spectra acquired are preprocessed by Savitzky-Golay (SG) filtering coupled with standard normal variate transformation (SNV). Then, the multi-variable selection algorithm (IGA-PSO) is applied to compare with the single method (the IGA and PSO) and search the optimal characteristic wavelengths. Finally, the identification models are developed using a decision tree (DT), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM) based on different kernel functions combined with the effective features from the above variables screening paths for the discrimination of black tea quality. The results show that the IGA-PSO-SVM model with a radial basis function achieves the best predictive results with the correct discriminant rate (CDR) of 95.28% based on selected four characteristic variables in the prediction process. The overall results demonstrate that NIR combined with a multi-variable selection method can constitute a potential tool to understand the most important features involved in the evaluation of dianhong black tea quality helping the instrument manufacturers to achieve the development of low-cost and handheld NIR sensors.

96-Well Microtiter Plate Made of Paper: A Printed Chemosensor Array for Quantitative Detection of Saccharides

Simple, rapid, and accurate detection methods for saccharides are potentially applicable to various fields such as clinical and food chemistry. However, the practical applications of on-site analytical methods are still limited. To this end, herein, we propose a 96-well microtiter plate made of paper as a paper-based chemosensor array device (PCSAD) for the simultaneous classification of 12 saccharides and the quantification of fructose and glucose among 12 saccharides. The mechanism of the saccharide detection relied on an indicator displacement assay (IDA) on the PCSAD using four types of catechol dyes, 3-nitrophenylboronic acid, and the saccharides. The design of the PCSAD and the experimental conditions for the IDA were optimized using a central composite design. The chemosensors exhibited clear color changes upon the addition of saccharides on the paper because of the competitive boronate esterification. The color changes were employed for the subsequent qualitative, semiquantitative, and quantitative analyses using an automated algorithm combined with pattern recognition for digital images. A qualitative linear discrimination analysis offered discrimination of 12 saccharides with a 100% classification rate. The semiquantitative analysis of fructose in the presence of glucose was carried out from the viewpoint of food analysis utilizing a support vector machine, resulting in clear discrimination of the various concentrations of fructose. Most importantly, the quantitative detection of fructose in two types of commercial soft drinks was also successfully carried out without sample pretreatments. Thus, the proposed PCSAD can be a powerful method for on-site food analyses that can meet the increasing demand from consumers for sensors of saccharides.