A nanozyme colorimetric sensor combined with cloud-based machine learning algorithm-assisted WeChat mini program for intelligent identification of Chinese green tea.

Journal: Food research international (Ottawa, Ont.)
Published Date:

Abstract

Green tea has become increasingly renowned among consumers by virtue of its exceptional flavor and high nutritional value. There is often a strong correlation between the varieties of green tea, quality and corresponding price. In this work, a simple and smart colorimetric sensor of peroxidase-like activity of AuNPs and cloud-based machine learning algorithm-assisted phone app was developed to rapidly identify Chinese green tea. A well-designed colorimetric sensor was proposed based on the tea component contents differences, and achieved 100 % combined with PLS-DA model in identification of Chinese green tea. The sensor exhibited a linear detection range of 0.01-0.3 mg/mL for tea polyphenols (TP) with a limit of detection (LOD) of 0.0076 mg/mL, demonstrating high sensitivity. Finally, cloud-based machine learning algorithm-assisted phone app was designed to intelligent identification of Chinese green tea. The smartphone app was capable of promptly and accurately recognizing Chinese green tea, achieving an accuracy rate of 97.9 %. This study lays the groundwork for a quick and dependable method to authenticate Chinese green tea, empowering the industry to counteract fraudulent activities more efficiently.

Authors

  • Wanjun Long
    The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
  • Siyu Wang
    School of Nursing, Chengdu University of Traditional Chinese Medicine, Sichuan, Chengdu, 610075, China. Electronic address: 919008390@qq.com.
  • Yangcheng Ling
    The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
  • Yuting Guan
    Guangxi Medical University, Nanning, Guangxi, China.
  • Huiru Zhang
    The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
  • Hengye Chen
    The Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan 430074, PR China.
  • Wei Lan
    School of Computer, Electronics and Information, Guangxi University, 100 Daxue East Road, Nanning, 530004, China.
  • Yuanbin She
    State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310032, PR China. Electronic address: sheyb@zjut.edu.cn.
  • Haiyan Fu
    School of Nursing School of Public Health, Yangzhou University, Yangzhou 225009, China.