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Machine Learning-Based Nanozyme Sensor Array as an Electronic Tongue for the Discrimination of Endogenous Phenolic
Wenjie Jing1, Yajun Yang1, Qihao Shi1
1Key Laboratory of Industrial Fermentation Microbiology, Ministry of Education, Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, No.29 of 13th Street, TEDA, Tianjin 300457, PR China.
Analytical Chemistry
|September 26, 2024
Summary
A new vanillic acid-copper (VA-Cu) nanozyme sensor array detects endogenous phenolic compounds (EPs) in food. This method uses artificial neural networks and smartphones for portable EPs identification.
Area of Science:
- Analytical Chemistry
- Biochemistry
- Materials Science
Background:
- Endogenous phenolic compounds (EPs) in food are crucial for understanding bioactivity and health impacts.
- Accurate detection of EPs is essential for food quality and safety assessment.
- Existing methods for EPs detection can be complex and time-consuming.
Purpose of the Study:
- To develop a novel bifunctional nanozyme sensor for detecting endogenous phenolic compounds (EPs) in food.
- To construct a multi-channel sensor array utilizing enzyme-mimic activities for enhanced EPs analysis.
- To create a portable and accurate method for EPs identification in various food matrices.
Main Methods:
- Preparation of a bifunctional vanillic acid-copper (VA-Cu) nanozyme with peroxidase-like and laccase-like activities.
- Construction of a six-channel nanozyme sensor array based on differential enzyme-mimic catalysis and time-resolved measurements.
- Application of artificial neural network (ANN) algorithms for discriminant analysis and prediction of EPs.
- Integration of the sensor array with a smartphone for portable food analysis.
Main Results:
- The VA-Cu nanozyme exhibited both peroxidase-like activity (inhibited by EPs) and laccase-like activity (catalyzed EPs oxidation).
- The six-channel sensor array successfully achieved discriminant analysis of nine different EPs.
- ANN algorithms combined with the sensor array accurately identified and predicted nine EPs in black tea, honey, and grape juice.
- A portable method for EPs identification in food was successfully demonstrated using a smartphone.
Conclusions:
- The developed VA-Cu nanozyme sensor array offers a sensitive and selective platform for EPs detection.
- The combination of nanozyme technology, sensor arrays, and AI provides a powerful tool for food analysis.
- This approach enables rapid, accurate, and portable identification of endogenous phenolic compounds in complex food samples.
