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Machine learning assisted multi-signal nanozyme sensor array for the antioxidant phenolic compounds intelligent

Jiahao Xu¹, Yu Wang¹, Ziyuan Li¹

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.

|January 11, 2025

Summary

A novel nanozyme sensor array identifies antioxidant phenolic compounds (APs) in food. This dual-mode system uses colorimetric and photothermal detection with artificial neural networks for accurate analysis in beverages.

Keywords:

Antioxidant phenolic compounds Colorimetric Machine learning Nanozyme sensor array Photothermal

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Area of Science:

Analytical Chemistry
Materials Science
Biochemistry

Background:

Antioxidant phenolic compounds (APs) are vital for health benefits, necessitating accurate food analysis.
Current methods for AP identification can be complex and time-consuming.
Developing rapid and reliable detection techniques for APs is crucial.

Purpose of the Study:

To develop a novel bifunctional nanozyme sensor for identifying and quantifying antioxidant phenolic compounds (APs).
To construct a dual-mode colorimetric and photothermal sensor array for discriminant analysis of APs.
To integrate artificial neural network (ANN) algorithms for precise AP identification in food and beverages.

Main Methods:

Preparation of a bifunctional Cu-1,3,5-benzenetricarboxylic acid (Cu-BTC) nanozyme with laccase-like and peroxidase-like activities.
Construction of a dual-mode sensor array utilizing colorimetric changes and photothermal signals.
Application of artificial neural network (ANN) algorithms for data analysis and AP prediction.
Development of a smartphone-assisted portable detection system.

Main Results:

The Cu-BTC nanozyme effectively catalyzed AP oxidation, producing colored quinone imines.
The nanozyme's peroxidase-like activity was inhibited by APs, enabling dual-mode detection.
The sensor array achieved discriminant analysis of APs.
Precise identification and prediction of APs in black tea, coffee, and wine were accomplished using ANN integration.
A portable detection method for APs was successfully developed using smartphones.

Conclusions:

The bifunctional Cu-BTC nanozyme enables a sensitive and selective dual-mode sensor for AP detection.
The integration of ANN algorithms enhances the accuracy and predictive power of the sensor array.
The developed smartphone-based system offers a portable and practical solution for AP analysis in real-world food samples.