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Machine Learning-Assisted Concentration-Independent Recognition of Neonicotinoids Based on Multienzyme-like
Qingluan Li1,2, Zhizhong Sun1,2,3, Min Chen4
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
ACS Applied Materials & Interfaces
|April 1, 2026
Summary
A novel nanozyme sensor using iron-copper dual-atom sites offers accurate identification of neonicotinoid insecticides (NEOs). This technology overcomes concentration-dependent limitations, ensuring agricultural product safety.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Materials Science
Background:
- Neonicotinoid insecticides (NEOs) residues pose significant risks to ecosystems and human health.
- Conventional nanozyme sensors face challenges with limited catalytic diversity and concentration-dependent responses, leading to misclassification.
- Developing selective and accurate detection methods for NEOs is crucial for environmental and food safety.
Purpose of the Study:
- To develop a novel nanozyme sensor for accurate and concentration-independent identification of neonicotinoid insecticides (NEOs).
- To address the limitations of existing sensors, such as signal homogenization and cross-concentration misclassification.
- To establish a robust platform for rapid NEO detection in real-world samples.
Main Methods:
- Fabrication of a Fe-Cu dual-atom nanozyme (FeCu DAzyme) with enhanced oxidase, peroxidase, and laccase activities.
- Construction of a colorimetric sensor array utilizing the FeCu DAzyme to capture real-time inhibition kinetics.
- Integration with a machine learning classifier for pattern recognition and pesticide identification.
Main Results:
- The FeCu DAzyme exhibited synergistic catalytic effects and specific coordination with NEOs, enabling distinct concentration-dependent inhibition responses.
- The sensor array generated unique multidimensional response patterns, achieving 92.50% accuracy in discriminating five NEO structural analogs.
- The platform demonstrated high accuracy in identifying NEOs in spiked real-world samples (lake water, agricultural products), validating its practical utility.
Conclusions:
- The developed FeCu DAzyme-based sensor array provides a promising approach for concentration-independent identification of NEOs.
- This technology overcomes limitations of conventional sensors, offering enhanced catalytic diversity and specific response mechanisms.
- The platform is critical for ensuring agricultural product safety and environmental protection through rapid NEO detection.
