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

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FOOD RESEARCH INTERNATIONAL 2025年 203卷 115838页

作者： Lin, Xinyao Yin, Chunling Hu, Leqian Zhao, Liuchuang Chen, Mengyao Hua, Xia Liu, Zhimin Li, Peng Henan Univ Technol Sch Chem & Chem Engn Zhengzhou 450001 Peoples R China Henan Univ Technol Inst Complex Sci Zhengzhou 450001 Peoples R China

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, threedimensional 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 potent

关键词： fluorescent sensor array Green tea Excitation-emission matrix fluorescence spectra Multi-way pattern recognition methods Geographical origin

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Machine learning-driven fluorescent sensor array using aqueous CsPbBr3 perovskite quantum dots for rapid detection and sterilization of foodborne pathogens

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JOURNAL OF HAZARDOUS MATERIALS 2025年 483卷 136655页

作者： Zhang, Shanting Zhu, Weiwei Zhang, Xin Mei, Lianghui Liu, Jian Wang, Fangbin Hefei Univ Technol Hefei 230009 Peoples R China

With the growing global concern over food safety, the rapid detection and disinfection of foodborne pathogens have become critical in public health. This study presents a novel machine learning-driven fluorescent sensor array utilizing aqueous CsPbBr3 perovskite quantum dots (PQDs) for the rapid identification and eradication of foodborne pathogens. The relative signal intensity changes (Delta RGB) generated by the sensor array were analyzed using the machine learning algorithm-Support Vector Machine (SVM). The study achieved the identification and recognition of five pathogens and their mixtures within a concentration range of 1.0 x 10(3) to 1.0 x 107 CFU/ mL with an accuracy rate of 100 %, and the limits of detection (LOD) for the pathogens were found to be low. Additionally, the array also showed excellent performance in the identification of pathogens in tap water, achieving an accuracy rate of 100 %. Furthermore, the fluorescent sensor array was capable of inactivating the pathogens with an efficiency of over 99 % within 30 min post-detection. This development provides an efficient and reliable tool for the field of food safety detection.

关键词： fluorescent sensor array Perovskite quantum dots Machine learning Foodborne pathogens Rapid detection

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A machine learning-assisted fluorescent sensor array utilizing silver nanoclusters for coffee discrimination

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024年 322卷 124760页

作者： Mo, Yidan Xu, Jinming Zhou, Huangmei Zhao, Yu Chen, Kai Zhang, Jie Deng, Lunhua Zhang, Sanjun East China Normal Univ State Key Lab Precis Spect 500Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ Sch Phys & Elect Sci Shanghai Key Lab Magnet Resonance Shanghai 200241 Peoples R China Shanxi Univ Collaborat Innovat Ctr Extreme Opt Taiyuan 030006 Shanxi Peoples R China NYU Shanghai NYU ECNU Inst Phys 3663 North Zhongshan Rd Shanghai 200062 Peoples R China

Coffee is a globally consumed commodity of substantial commercial significance. In this study, we constructed a fluorescent sensor array based on two types of polymer templated silver nanoclusters (AgNCs) for the detection of organic acids and coffees. The nanoclusters exhibited different interactions with organic acids and generated unique fluorescence response patterns. By employing principal component analysis (PCA) and random forest (RF) algorithms, the sensor array exhibited good qualitative and quantitative capabilities for organic acids. Then the sensor array was used to distinguish coffees with different processing methods or roast degrees and the recognition accuracy achieved 100%. It could also successfully identify 40 coffee samples from 12 geographical origins. Moreover, it demonstrated another satisfactory performance for the classification of pure coffee samples with their binary and ternary mixtures or other beverages. In summary, we present a novel method for detecting and identifying multiple coffees, which has considerable potential in applications such as quality control and identification of fake blended coffees.

关键词： Silver nanoclusters fluorescent sensor array Organic acids Coffees Principal component analysis Random forest

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Construction of fluorescent sensor array with nitrogen-doped carbon dots for sensing Sudan Orange G and identification of various azo compounds

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JOURNAL OF COLLOID AND INTERFACE SCIENCE 2024年 667卷 403-413页

作者： Bu, Lutong Li, Shuangying Nie, Linchun Jiang, Liushan Dong, Guangyu Song, Denghao Liu, Wenjing Geng, Xiaodie Meng, Dejing Zhou, Qingxiang China Univ Petr Coll Chem Engn & Environm Beijing 102249 Peoples R China

In this study, nitrogen-doped carbon dots (N-CDs) were facilely fabricated by one-pot hydrothermal method with levulinic acid and triethanolamine. A fluorescent sensor array was established for identifying azo compounds including Sudan Orange G (SOG), p-diaminoazobenzene, p-aminoazobenzene, azobenzene and quantitative detection of SOG. Experimental results revealed that azo compounds could quench the fluorescent intensity of N-CDs. Owing to various azo compounds showing different affinities to N-CDs, the sensor array exhibited different fluorescence quenching changes, which were further analyzed with principal component analysis to discriminate azo compounds. The sensor array was able to differentiate and recognize diverse concentrations of azo compounds from 0.25 to 2 mg/L. Simultaneously, a variety of factors affecting the detection of SOG were optimized. Under the optimized conditions, the sensor showed excellent stability and sensitivity. The sensor possessed marvelous linearity in the range of 0.1-1 mg/L and 1-4 mg/L and the detection limit was 27.82 mu g/L. Spiked recoveries of 90.8-98.2 % were attained at spiked levels of 0.2 mg/L and 1 mg/L, demonstrating that the constructed fluorescence sensor was dependable and feasible for sensing SOG in environmental water samples.

关键词： Nitrogen-doped carbon dots Azo compounds fluorescent sensor array Sudan Orange G Fluorescence quenching

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Metal-organic frameworks-scaffold gold nanoclusters enabled aggregation-induced enhanced fluorescent sensor array for high-throughput detection of heavy metal ions

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MICROCHEMICAL JOURNAL 2025年 210卷

作者： Hu, Xuemei Mu, Zhaode Li, Yueyuan Bai, Lijuan Qing, Min Chongqing Med Univ Coll Pharm Chongqing Res Ctr Pharmaceut Engn Chongqing 400016 Peoples R China Chongqing Med Univ Affiliated Hosp 1 Dept Resp & Crit Care Med Chongqing 400016 Peoples R China

The ever-expanding set of nanocluster-based sensor array and their programmability exhibit remarkable capacity in sensitive and multiplex quantification of heavy metal ions, especially in complex environments. However, this quantitative performance comes with an ever-present constraint: limited luminescence efficiency of nanoclusters due to the intramolecular vibration and rotation phenomena. Here, we pioneered the development of a fluorescent sensor array utilizing metal-organic framework-scaffold gold nanoclusters (AuNCs@MOFs) as sensing elements, enabling precise identification and sensitive quantification of eight heavy metal ions (Ni2+, Cr3+, Co2+, Pb2+, Cd2+, Ag+, Cu2+, and Zn2+). Three kinds of ligand-protected AuNCs (GSH-AuNCs, MPA-AuNCs, and [2MBA]-AuNCs) embedded in zeolitic imidazolate framework-8 (ZIF-8) exhibit significantly increased luminescence performance. We demonstrate that the fluorescence intensity of the AuNCs@MOFs can be modulated by heavy metal ions, thereby generating distinct fingerprints, which are further analyzed by the statistical analysis method. The developed sensor array identifies eight heavy metal ions accurately across concentrations ranging from 0.5 to 50 mu M. Furthermore, this sensor array recognizes multi-component mixtures of heavy metal ions, exhibiting excellent discriminatory capability in both tap water and traditional Chinese medicine samples. Overall, this study provides a straightforward paradigm to construct a nanocluster-based sensor array for sensitive and multiplex heavy metal ions quantification in a complex environment.

关键词： Aggregation-induced emission Nanocluster fluorescent sensor array Heavy metal ions Traditional Chinese medicine

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Functionalized carbon quantum dots fluorescent sensor array assisted by a machine learning algorithm for rapid foodborne pathogens identification

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MICROCHEMICAL JOURNAL 2024年 201卷

作者： Xiao, Minghui Mei, Lianghui Qi, Jing Zhu, Liang Wang, Fangbin Hefei Univ Technol Sch Food & Biol Engn Hefei 230009 Peoples R China Natl Univ Singapore Dept Chem Singapore 117543 Singapore Hong Kong Polytech Univ Dept Biomed Engn Hong Kong 999077 Peoples R China

Food safety is a global concern, and conventional methods for detecting foodborne bacteria, such as polymerase chain reaction and enzyme-linked immunosorbent assay, often involve time-consuming processes, specialized equipment, or specific recognition of particular bacterial strains. There is an urgent need for more efficient and convenient detection methods for foodborne pathogens. This study addresses this need by introducing an easily constructed fluorescent sensor array for the identification of various foodborne bacteria. The sensor array comprises carbon quantum dots (CQDs) functionalized with ampicillin, polymyxin, and gentamicin, each exhibiting different affinities for binding with specific bacteria. Leveraging machine learning algorithms, the proposed sensor array enables rapid, accurate, and highly sensitive identification of foodborne pathogens. This approach offers a convenient solution for the development of rapid, accurate, and hypersensitive detection methods for multiple bioactive samples. In summary, the fluorescent sensor array in this study holds promise for advancing the field of foodborne bacteria detection.

关键词： Carbon quantum dots fluorescent sensor array Pathogen identification Machine learning

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fluorescent sensor array based on Janus silica nanoflakes to realize pattern recognition of multiple aminoglycoside antibiotics and heavy metal ions

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sensorS AND ACTUATORS B-CHEMICAL 2023年第1期378卷

作者： Tan, Lei Li, Yuling Wu, Xiaotong Liu, Wanqiong Peng, Zhihua Dong, Yanshan Huang, Zeyu Zhang, Lin Liang, Yong South China Normal Univ Sch Chem Guangzhou 510006 Peoples R China Guangzhou Ctr Dis Control & Prevent Guangzhou 510440 Peoples R China Foshan Sanshui Foshui Water Supply Co Ltd Foshan 528100 Peoples R China

The development of highly sensitive and selective chemical sensor arrays that can be used for the recognition of multiple organic and inorganic compounds has been a pressing demand and persistent challenge. We combined the Pickering emulsion and quantum dots (QDs) doping technology to fabricate Janus silica nanoflakes-based fluorescence sensor arrays for the pattern recognition of multiple heavy metal ions and aminoglycoside antibi-otics. Janus silica nanoflakes were embedded with green and red light-emitting CdTe QDs as signal sensing units. The aptamer and sulfhydryl-specific recognition elements were modified and attached to the sides for the simultaneous recognition of heavy metal ions and aminoglycosides. The thiol groups chelated the target metal ions, resulting in the fluorescence quenching of the QDs. The aptamers provided specific recognition sites for aminoglycosides, and the fluorescence intensities of the QDs were enhanced in the presence of aminoglycosides. The parameters characterizing the fluorescence sensor array were optimized, and by analyzing the fluorescence response patterns following the linear discriminant analysis method, we observed that the sensor array could discriminate between the target compounds successfully. Finally, Cu2+ and Co2+ were detected in crayfish samples, and the results were confirmed following the inductively coupled plasma atomic emission spectroscopy technique.

关键词： Janus aptamer fluorescent sensor array Metal ions Aminoglycoside antibiotics

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fluorescent sensor array constructed by functionalized carbon nanodots for qualitative and quantitative analysis of urinary organic acids biomarkers

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sensorS AND ACTUATORS B-CHEMICAL 2022年 350卷 130825-130825页

作者： Du, Na Chen, Manling Cui, Yunmiao Liu, Xue Li, Yang Liaoning Univ Liaoning Prov Key Lab Green Synth & Preparat Chem Shenyang 110036 Liaoning Peoples R China China Med Univ Key Lab Med Cell Biol Minist Publ Hlth Dept Cell BiolKey Lab Cell BiolMinist Educ Shenyang 110122 Liaoning Peoples R China

Adequate assessment of the level of organic acids (OA) in human urine is significant for non-invasive diagnosis and monitoring of OA-related diseases. However, the common lock-and-key sampling method for specific OA analysis is dramatically perturbed by the complicated chemical components in urine. A comprehensive qualitative and quantitative analysis of OAs in urine samples remains a significant challenge. Herein, a cross-reactive fluorescent sensor array based on amino acid-functionalized carbon nanodots (CDs) was designed for simultaneous qualitative and quantitative analysis of seven kinds of OAs, including homovanillic acid (HVA), vanillylmandelic acid (VMA), 3-hydroxyisovaleric acid (3-HA), lactic acid (LA), pyruvic acid (PA), glutaric acid (GA), and methylmalonic acid (MMA). The array system can specifically recognize multiple OAs in urine samples over a wide concentrations range (0.1-1000 mu M) with 100% accuracy. The wide concentration range and high accuracy of the CD array system for OA analysis depend on a distinct fluorescence response from the differential binding affinity of various OAs to the CDs. The excellent recognition ability and selectivity of the sensing system were further confirmed by the double-blind and interference experiments. The sensor array achieved satisfactory distinguishing effects of separate OAs and binary OA samples (HVA/VMA) in urine samples. In particular, the quantitative analysis of the one-OA system or two-OA system (HVA/VMA) can be realized by the array system. These results confirm that the sensor array based on the CDs is an efficient platform for OA analysis and a convenient tool for the non-invasive diagnosis of OA-related diseases.

关键词： fluorescent sensor array Organic acids Carbon nanodots Urine

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Catalyst-integrated dual-fluorescent sensor array for highly efficient detection of triacetone triperoxide via simplified quadrantal pattern recognition

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sensorS AND ACTUATORS B-CHEMICAL 2023年 385卷

作者： Zhang, Li Yang, Li Han, Ji-Min Beijing Inst Technol State Key Lab Explos Sci & Technol 5 Zhongguancun South St Beijing 100081 Peoples R China Beijing Inst Technol Explos Protect & Emergency Disposal Technol Engn R Minist Educ 5 Zhongguancun South St Beijing 100081 Peoples R China

A catalyst-integrated dual-fluorescent sensor array for efficient vapor detection of triacetone triperoxide (TATP) was designed and realized. The selected loading fluorescence sensors were catalog compounds of substituted 1,8naphthalimides (C6NI), where boronate substituted C6NIB as H2O2 sensors and azetidine substituted C6NIN as polarity quenching sensors for TATP/acetone sensing. These two sensors were well characterized. The catalyst was integrated into middle of the array, helping detect TATP in site directly by before-catalyst sensors and indirectly by post-catalyst sensors. Different catalysts' decomposition performance was verified. The array's accurate selectivity for TATP was confirmed to be unaffected by TATP signature compounds, humidity change or ambient interference via simplified quadrantal pattern recognition (QPR) method, making TATP data points conspicuous without excessive data processing. The array also performed comparable low detection limit and high sensitivity. The array's ability to detect both directly and indirectly, along with the design of proper sensing materials and catalyst setup, allow for the sensitive and selective identification of TATP in a variety of gaseous, in-situ, and real-time scenarios. With fewer sensing materials and simplified data processing, the array offers a promising future for numerous applications with low false positives.

关键词： Peroxide explosive Triacetone triperoxide Catalyst-integrated fluorescent sensor array Direct-indirect vapor detection Quadrantal pattern recognition

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Tree-based machine learning models assisted fluorescent sensor array for detection of metal ions based on silver nanocluster probe

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023年第1期297卷 122738页

作者： Chen, Xihang Xu, Jinming Zhou, Huangmei Zhao, Yu Wu, Ying Zhang, Jie Zhang, Sanjun East China Normal Univ State Key Lab Precis Spect 500 Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ State Key Lab Estuarine & Coastal Res 500 Dongchuan Rd Shanghai 200241 Peoples R China East China Normal Univ Sch Phys & Elect Sci Shanghai Key Lab Magnet Resonance Shanghai 200241 Peoples R China Shanxi Univ Collaborat Innovat Ctr Extreme Opt Taiyuan 030006 Shanxi Peoples R China NYU Shanghai NYU ECNU Inst Phys 3663 North Zhongshan Rd Shanghai 200062 Peoples R China

The growing concern over heavy metal pollution and its impact on the environment and human health has led to a proliferation of research on the detection and differentiation of heavy metal ions. A novel fluorescent sensor array utilizing only one single Ag-nanoclusters (Ag NCs) was developed for the efficient detection of six metal ions. The Ag NCs probe was prepared by using poly(methyl vinyl ether-alt-maleic acid) (PMVEM) as the ligand and has different fluorescence properties in water and dimethyl sulfoxide (DMSO). The interaction between metal ions and Ag NCs resulted in a characteristic fluorescence variation pattern which was subsequently analyzed using various tree-based machine learning models. We have compared different combinations of classification models and pre-processing methods of which the K-Nearest Neighbors Classifier with the first five linear discriminants has the highest accuracy. Through the integration of concentration models within a treebased pipeline optimization framework, six unique concentration regression models were selected for each metal ion. In addition, the developed sensor array could identify metal ions in binary mixtures. And it still kept high accuracy for the classification of six target metal ions in river water. In conclusion, the proposed framework was found to be effective in the detection of heavy metal ions in environmental samples, thus providing a promising approach for addressing heavy metal pollution.

关键词： Heavy metal ion detection Silver nanocluster Machine learning fluorescent sensor array Tree-based pipeline optimization tool

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