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Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

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Published on: June 1, 2012

A microneedle-based integrated three-electrode system for pesticide detection using machine learning.

Kang-Xun Zhao^1,2, Tian-Qi Ma^3,4, Yu-Lian Li^1,5

¹School of Ocean Information Engineering, Jimei University, Xiamen 361021, China. youxueqiu@jmu.edu.cn.

|October 2, 2025

Summary

This study introduces a novel method for detecting pesticide residues using 3D-printed microneedle arrays and deep learning. The technology accurately identifies various pesticides, enhancing food safety and public health protection.

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Area of Science:

Electrochemistry
Biosensing
Agricultural Science

Background:

Pesticide residues in crops pose significant health risks.
Current detection methods are often insufficient for persistent residues.
Accurate pesticide detection is crucial for agricultural safety.

Purpose of the Study:

To develop a novel method for sensitive and accurate pesticide classification and recognition.
To integrate 3D-printed microneedle arrays with differential pulse voltammetry (DPV) and deep learning (DL).
To enhance electrochemical sensing performance for pesticide analysis.

Main Methods:

Fabrication of 3D-printed microneedle arrays (MNs).
Modification of working electrodes with carbon nanotubes for improved surface area and current response.
Application of differential pulse voltammetry (DPV) for electrochemical signal capture.
Utilizing deep learning (DL) algorithms, including convolutional neural networks (CNNs), for pesticide classification based on electrochemical fingerprints.

Main Results:

Successful classification and identification of six predefined pesticide samples (MS222, p-nitrophenol, crystal violet, malachite green, vanillin, and nitrofurazone).
Average classification accuracies exceeding 90% across all DL algorithms.
Convolutional neural network (CNN) achieved 100% classification accuracy.
Demonstrated satisfactory performance on an extended dataset.

Conclusions:

The integrated DPV and DL approach provides an effective and accurate method for pesticide discrimination.
This innovative technology offers a promising pathway for advancing agricultural safety and public health.
The developed system demonstrates high potential for real-world application in pesticide monitoring.