Your browser doesn't support javascript.
loading
A sensor array based on a nanozyme with polyphenol oxidase activity for the identification of tea polyphenols and Chinese green tea.
Yang, Xiaoyu; Zou, Bin; Zhang, Xinjian; Yang, Jie; Bi, Zhichun; Huang, Hui; Li, Yongxin.
Affiliation
  • Yang X; College of Food Science and Engineering, Jilin University, Changchun, 130025, PR China.
  • Zou B; College of Food Science and Engineering, Jilin University, Changchun, 130025, PR China.
  • Zhang X; College of Food Science and Engineering, Jilin University, Changchun, 130025, PR China.
  • Yang J; College of Food Science and Engineering, Jilin University, Changchun, 130025, PR China.
  • Bi Z; College of Food Science and Engineering, Jilin University, Changchun, 130025, PR China.
  • Huang H; College of Food Science and Engineering, Jilin University, Changchun, 130025, PR China. Electronic address: huanghui@jlu.edu.cn.
  • Li Y; Key Lab of Groundwater Resources and Environment of Ministry of Education, Key Lab of Water Resources and Aquatic Environment of Jilin Province, College of New Energy and Environment, Jilin University, Changchun, 130021, PR China.
Biosens Bioelectron ; 250: 116056, 2024 Apr 15.
Article in En | MEDLINE | ID: mdl-38271889
ABSTRACT
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.
Subject(s)
Key words

Full text: 1 Database: MEDLINE Main subject: Biosensing Techniques / Camellia sinensis Type of study: Diagnostic_studies Country/Region as subject: Asia Language: En Journal: Biosens Bioelectron / Biosens. bioelectron / Biosensors and bioelectronics Year: 2024 Type: Article

Full text: 1 Database: MEDLINE Main subject: Biosensing Techniques / Camellia sinensis Type of study: Diagnostic_studies Country/Region as subject: Asia Language: En Journal: Biosens Bioelectron / Biosens. bioelectron / Biosensors and bioelectronics Year: 2024 Type: Article