Discrimination of Chinese green tea according to tea polyphenols using fluorescence sensor array based on Tb (III) and Eu (III) doped Zr (IV) metal-organic frameworks

Tracing the geographical origin of Chinese green tea based on fluorescent sensor array combined with multi-way chemometrics analyses

Fluorescent sensor arrays are becoming a hot topic in many fields because they can simultaneously detect multiple targets in complex research systems. However, most researches have only collected two-dimensional fluorescence spectral data from fluorescent sensor array interacting with target analytes. In contrast, three-dimensional fluorescence spectra can provide richer information than two-dimensional fluorescence spectra. Based on the hypothesis that collecting three-dimensional fluorescence spectra can obtain more abundant information of green tea samples from different regions, which can improve the accuracy and reliability of origin identification. This study aimed to explore the feasibility of using three-dimensional fluorescent sensor array combined with multi-way pattern recognition methods for the origin discrimination of green tea based on the differences in the contents and types of metal ions in green tea. To investigate this, we first designed a fluorescent sensor array based on amino acid-derived carbon dots and examined its ability to recognize common metal ions in green tea. Excitation-emission matrix spectra of green tea extracts from different geographical origins after interaction with the fluorescent sensor array were collected. Several multi-way pattern recognition methods were used to analyze the three-dimensional fluorescent array data of 100 green tea samples from five origins. The overall classification results of green tea from the five geographical origins were satisfactory, with the best prediction accuracy reaching 96.88%. In comparison, multilinear partial least squares discriminant analysis could make full use of the information of three-dimensional fluorescence data. And its correct identification results for green tea were superior to those of unfold partial least squares discriminant analysis. These results sufficiently demonstrated that the fluorescent sensor array integrated with multi-way pattern recognition, has promising potential for tracing the origin of Chinese green tea.

The design, synthesis and application of metal-organic framework-based fluorescence sensors

Fluorescence-based chemical sensors have garnered significant attention due to their rapid response, high sensitivity, cost-effectiveness and ease of operation. Recently, metal-organic frameworks (MOFs) have been extensively utilized as platforms for constructing fluorescence sensors, owing to their ultra-high porosity, flexible tunability, and excellent luminescent properties. This feature article summarizes the progress made mainly by our research group in recent years in the construction strategies, principles, and types of MOF sensors, as well as their applications in quantitative sensing, qualitative identification analysis, and multimodal/multifunctional analysis. In addition, the challenges and an outlook on the future progression of MOF-based sensors are discussed, highlighting how these studies can contribute to addressing these issues. Hopefully, this feature article can provide some valuable guidance for the construction and application of MOFs in fluorescence sensing, thereby broadening their practical applications.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Ensuring food safety by artificial intelligence-enhanced nanosensor arrays

Current analytical methods utilized for food safety inspection requires improvement in terms of their cost-efficiency, speed of detection, and ease of use. Sensor array technology has emerged as a food safety assessment method that applies multiple cross-reactive sensors to identify specific targets via pattern recognition. When the sensor arrays are fabricated with nanomaterials, the binding affinity of analytes to the sensors and the response of sensor arrays can be remarkably enhanced, thereby making the detection process more rapid, sensitive, and accurate. Data analysis is vital in converting the signals from sensor arrays into meaningful information regarding the analytes. As the sensor arrays can generate complex, high-dimensional data in response to analytes, they require the use of machine learning algorithms to reduce the dimensionality of the data to gain more reliable outcomes. Moreover, the advances in handheld smart devices have made it easier to read and analyze the sensor array signals, with the advantages of convenience, portability, and efficiency. While facing some challenges, the integration of artificial intelligence with nanosensor arrays holds promise for enhancing food safety monitoring.

A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea

Green tea is popular among consumers because of its high nutritional value and unique flavor. There is often a strong correlation among the type of tea, its quality level and the price. Therefore, the rapid identification of tea types and the judgment of tea quality grades are particularly important. In this work, a novel sensor array based on nanozyme with polyphenol oxidase (PPO) activity is proposed for the identification of tea polyphenols (TPs) and Chinese green tea. The absorption spectra changes of the nanozyme and its substrate in the presence of different TPs were first investigated. The feature spectra were scientifically selected using genetic algorithm (GA), and then a sensor array with 15 sensing units (5 wavelengths × 3 time) was constructed. Combined with the support vector machine (SVM) discriminative model, the discriminative rate of this sensor array was 100% for different concentrations of typical TPs in Chinese green tea with a detection limit of 5 μM. In addition, the identification of different concentrations of the same tea polyphenols and mixed tea polyphenols have also been achieved. Based on the above study, we further developed a facile and efficient new method for the category differentiation and adulteration identification of green tea, and the accuracy of this array was 96.88% and 100% for eight types of green teas and different adulteration ratios of Biluochun, respectively. This work has significance for the rapid discrimination of green tea brands and adulteration.

A Recent Advancement in Food Quality Assessment: Using MOF-Based Sensors: Challenges and Future Aspects

Monitoring food safety is crucial and significantly impacts the ecosystem and human health. To adequately address food safety problems, a collaborative effort needed from government, industry, and consumers. Modern sensing technologies with outstanding performance are needed to meet the growing demands for quick and accurate food safety monitoring. Recently, emerging sensors for regulating food safety have been extensively explored. Along with the development in sensing technology, the metal-organic frameworks (MOF)-based sensors gained more attention due to their excellent sensing, catalytic, and adsorption properties. This review summarizes the current advancements and applications of MOFs-based sensors, including colorimetric, electrochemical, luminescent, surface-enhanced Raman scattering, and electrochemiluminescent sensors. and also focused on the applications of MOF-based sensors for the monitoring of toxins such as heavy metals, pesticide residues, mycotoxins, pathogens, and illegal food additives from food samples. Future trends, as well as current developments in MOF-based materials.

Protective effects and molecular mechanisms of tea polyphenols on cardiovascular diseases

Aging is the most important factor contributing to cardiovascular diseases (CVDs), and the incidence and severity of cardiovascular events tend to increase with age. Currently, CVD is the leading cause of death in the global population. In-depth analysis of the mechanisms and interventions of cardiovascular aging and related diseases is an important basis for achieving healthy aging. Tea polyphenols (TPs) are the general term for the polyhydroxy compounds contained in tea leaves, whose main components are catechins, flavonoids, flavonols, anthocyanins, phenolic acids, condensed phenolic acids and polymeric phenols. Among them, catechins are the main components of TPs. In this article, we provide a detailed review of the classification and composition of teas, as well as an overview of the causes of aging-related CVDs. Then, we focus on ten aspects of the effects of TPs, including anti-hypertension, lipid-lowering effects, anti-oxidation, anti-inflammation, anti-proliferation, anti-angiogenesis, anti-atherosclerosis, recovery of endothelial function, anti-thrombosis, myocardial protective effect, to improve CVDs and the detailed molecular mechanisms.