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Electronic Tongue Generating Continuous Recognition Patterns for Protein Analysis

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Published on: September 16, 2014

Machine Learning Algorithms Enabled Visual On-Site Intelligent Sensing of Bioactive Components by

Zemin Ren¹, Yatong Zhang¹, Feifei Xu¹

¹Tianjin Key Laboratory of Industrial Microbiology, College of Biotechnology, Tianjin University of Science and Technology, No. 29 of 13th Street, TEDA, Tianjin 300457, PR China.

|June 18, 2026

Summary

This summary is machine-generated.

This study introduces a novel histidine-functionalized nanozyme for enhanced detection of bioactive components (BCs) in food. Machine learning integration achieved 100% precision in identifying BCs, improving food safety and health analysis.

Keywords:

TA-Cu-His nanozyme bioactive components laccase-like machine learning sensor array

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Area of Science:

Biochemistry
Materials Science
Analytical Chemistry

Background:

Identifying bioactive components (BCs) in food is crucial for understanding health benefits.
Existing nanozyme sensor arrays for BC detection need performance improvements for practical applications.
Developing highly active nanozymes is key to enhancing sensor array performance and biosensing capabilities.

Purpose of the Study:

To develop an advanced nanozyme with superior catalytic activity for improved BC detection.
To leverage machine learning and pH-dependent activity for precise BC identification.
To create an intelligent sensing platform for efficient and practical BC analysis.

Main Methods:

A histidine-functionalized trimesic acid-copper (TA-Cu-His) nanozyme was synthesized using defect engineering.
Recursive Feature Elimination (RFE) was used to select optimal pH sensing channels.
Machine learning algorithms, including ResNet-50, were integrated with the sensor array for data analysis.

Main Results:

The TA-Cu-His nanozyme exhibited enhanced laccase-like (LAC) activity compared to the original TA-Cu material.
The sensor array achieved 100% precision in classifying BCs, a significant improvement from 58.62%.
Blind samples were successfully recognized, demonstrating the system's robustness.

Conclusions:

The developed TA-Cu-His nanozyme offers enhanced catalytic activity for biosensing applications.
The integration of RFE and ML algorithms enables highly accurate and efficient identification of multiple BCs.
This work presents a novel strategy for intelligent and practical BC identification using ML-assisted sensing platforms.