Fluorescent enhanced endogenous carbon dots derived from green tea residue for multiplex detection of heavy metal ions in food

被引:1
|
作者
Zhang, Lei [1 ]
Cai, Zhenli [1 ]
Liu, Yaqi [1 ]
Fan, Yao [1 ]
She, Yuanbin [1 ]
机构
[1] Zhejiang Univ Technol, Coll Chem Engn, State Key Lab Breeding Base Green Chem Synth Techn, Hangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
green tea residue; carbon dots; fluorescence enhanced markers; heavy metal ions detection; chemometrics; QUANTUM DOTS; SELECTIVE DETECTION; SENSOR;
D O I
10.3389/fsufs.2024.1431792
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Introduction The presence of excessive heavy metal content in food poses potential risks to human health.Methods This paper presents the fabrication of a novel type of Carbon Dots (CDs) using green tea residue as a carbon source, which can be applied for simultaneous detection of Hg2+, Pb2+, Fe3+, and Cu22+ through particle swarm optimization (PSO) based optimized variable-weighted least-squares support vector machine (VWLS-SVM) model and the partial least squares discriminant analysis (PLSDA) method.Results and Discussion The utilization of PSO-VWLS-SVM model discovered and verified two fluorescence enhancement markers of CDs, namely isoquercitrin and 5-methyl furfural in green tea residues. By employing PLSDA, simultaneous qualitative and quantitative determination of these four metal ions was achieved. These CDs are capable of detecting four types of metal ions at low concentrations even when there are high concentrations of other metal ions and amino acids. More importantly, the CDs were successfully applied for the detection of Hg2+, Pb2+, Fe3+, and Cu2+ in real food samples. The recovery rates of four metal ions spiked into five different matrices were found to be the range of 99.1-101.3%, while both intra-day and inter-day relative standard deviations remained below 0.5% for all samples. This study on chemometrics-assisted exploration into formation mechanisms of endogenous CDs provides theoretical guidance for enhancing their fluorescence properties and expanding their application in heavy metal detection in food.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Ratiometric detection of heavy metal ions using fluorescent carbon dots
    Yarur, Francisco
    Macairan, Jun-Ray
    Naccache, Rafik
    ENVIRONMENTAL SCIENCE-NANO, 2019, 6 (04) : 1121 - 1130
  • [2] Controllable synthesis of biosourced blue-green fluorescent carbon dots from camphor for the detection of heavy metal ions in water
    Gaddam, Rohit Ranganathan
    Vasudevan, D.
    Narayan, Ramanuj
    Raju, K. V. S. N.
    RSC ADVANCES, 2014, 4 (100) : 57137 - 57143
  • [3] Heavy metal ion detection using green precursor derived carbon dots
    Landa, Simei Darinel Torres
    Bogireddy, Naveen Kumar Reddy
    Kaur, Inderbir
    Batra, Vandana
    Agarwal, Vivechana
    ISCIENCE, 2022, 25 (02)
  • [4] Highly fluorescent carbon dots derived from Mangifera indica leaves for selective detection of metal ions
    Singh, Jagpreet
    Kaur, Sukhmeen
    Lee, Jechan
    Mehta, Akansha
    Kumar, Sanjeev
    Kim, Ki-Hyun
    Basu, Soumen
    Rawat, Mohit
    SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 720
  • [5] Application of green tea residue-derived carbon dots in the detection of Fe3+ions and imaging of Caco-2 cells
    He, Yu
    Liu, Suqing
    Xie, Feng
    Zhou, Yan
    Yang, Xiaosheng
    JOURNAL OF FOOD COMPOSITION AND ANALYSIS, 2024, 132
  • [6] Detection of heavy metal ions using green fluorescent protein biosensors by quenching FRET from endogenous aromatic amino acids
    Beranek, Amanda
    Hicks, Barry
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2015, 249
  • [7] Endogenous Fluorescence Carbon Dots Derived from Food Items
    Wang, Haitao
    Su, Wentao
    Tan, Mingqian
    INNOVATION, 2020, 1 (01):
  • [8] Quantum Dots Based Fluorescent Probe for the Selective Detection of Heavy Metal Ions
    Akshaya Biranje
    Namrah Azmi
    Abhishekh Tiwari
    Atul Chaskar
    Journal of Fluorescence, 2021, 31 : 1241 - 1250
  • [9] Quantum Dots Based Fluorescent Probe for the Selective Detection of Heavy Metal Ions
    Biranje, Akshaya
    Azmi, Namrah
    Tiwari, Abhishekh
    Chaskar, Atul
    JOURNAL OF FLUORESCENCE, 2021, 31 (05) : 1241 - 1250
  • [10] Machine-Learning-Enhanced Fluorescent Nanosensor Based on Carbon Quantum Dots for Heavy Metal Detection
    Tian, Changyu
    Lee, Yullim
    Song, Youngho
    Elmasry, Mohamed R.
    Yoon, Minyeong
    Kim, Dong-Hwan
    Cho, Soo-Yeon
    ACS APPLIED NANO MATERIALS, 2024, 7 (05) : 5576 - 5586