Intelligent nanozyme-based colorimetric sensor array for identifying six major teas and quantifying adulteration
Xiaoyan Wang1, An Zhao1, Zhen Cao1
1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou, 311300, China.
Talanta
|July 20, 2026
Summary
A new nanozyme sensor array accurately identifies tea types and detects adulteration using machine learning. This technology offers rapid, reliable tea quality assessment for food safety and market supervision.
Area of Science:
- Analytical Chemistry
- Materials Science
- Food Science
Background:
- Rapid tea variety discrimination and adulteration detection are crucial for food safety and quality control.
- Existing methods may lack the speed, sensitivity, or specificity required for comprehensive tea analysis.
- Tea polyphenols, key compounds influencing tea quality, exhibit structural diversity impacting their interactions with sensing materials.
Purpose of the Study:
- To develop a novel colorimetric sensor array for distinguishing tea polyphenols and assessing tea quality.
- To investigate the potential of nanozyme-based sensor arrays combined with machine learning for tea analysis.
- To establish a reliable method for detecting tea adulteration and quantifying specific tea polyphenols.
Main Methods:
- Construction of a three-channel colorimetric sensor array using MIL100(Fe), gold nanoclusters (Au NCs), and MIL100(Fe)/Au NCs.
- Exploitation of the differential inhibition of peroxidase-like activity by tea polyphenols to generate unique fingerprint patterns.
- Application of machine learning algorithms, including K-Nearest Neighbors (KNN) with data fusion, for data analysis and classification.
Main Results:
- The sensor array achieved 100% internal cross-validation accuracy for distinguishing five tea polyphenols within a dynamic range of 10 nM to 10 mM.
- Optimized machine learning models demonstrated 100% accuracy in classifying six major tea types.
- A linear quantitative model for Longjing tea adulteration (0-100%) was developed, alongside ultrasensitive detection of specific tea polyphenols at the nanomolar level.
Conclusions:
- Machine learning-assisted nanozyme sensor arrays show significant potential for rapid tea quality assessment.
- The developed sensor array provides a robust platform for distinguishing tea varieties and detecting adulteration.
- This approach offers a promising tool for market supervision and ensuring food safety in the tea industry.
Related Concept Videos
01:24Automated Microbial Diagnostics
Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
01:17Microbial Biosensors
Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...