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  1. Home
  3. An Intelligent Machine-learning-driven Metalloporphyrin Covalent Organic Framework Sensor Array For Pesticide Discrimination.
  1. Home
  3. An Intelligent Machine-learning-driven Metalloporphyrin Covalent Organic Framework Sensor Array For Pesticide Discrimination.

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An Intelligent Machine-Learning-Driven Metalloporphyrin Covalent Organic Framework Sensor Array for Pesticide

Yanting Liu1, Chenchen Gong2, Rui Bai1

  • 1School of Materials Science and Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, P. R. China.

Analytical Chemistry
|June 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study presents a new colorimetric sensor array using metalloporphyrin covalent organic frameworks (MCOFs) for rapid pesticide detection. The MCOF nanozyme array accurately identifies multiple pesticide residues in environmental and food samples.

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Environmental Science

Background:

  • Pesticide residue detection is crucial for environmental protection and human health.
  • Current methods for rapid and accurate pesticide identification face significant challenges.

Purpose of the Study:

  • To develop a novel colorimetric sensor array for the rapid and accurate discrimination of multiple pesticide residues.
  • To utilize metalloporphyrin covalent organic frameworks (MCOFs) as catalytic sites for enhanced detection.

Main Methods:

  • Fabrication of a colorimetric sensor array using three distinct MCOF nanozymes.
  • Application of Linear Discriminant Analysis (LDA) for pesticide differentiation.
  • Integration of Random Forest (RF) algorithm for analysis in complex matrices.

Main Results:

  • The MCOF nanozyme array showed 2.4-4.5 fold enhanced discrimination activity compared to metal-free COFs.
  • Successfully differentiated five pesticides in the 10-100 nM range with limits of detection as low as 2.19 nM.
  • Achieved 94.1% accuracy in river water and over 95% accuracy in tomato samples for complex pesticide residue identification.

Conclusions:

  • The MCOF-based sensor array offers a simple, convenient, and promising approach for pesticide residue analysis.
  • This technology enables hierarchical screening and prediction of multiple pesticide residues.
  • The developed platform is suitable for environmental and food monitoring applications.