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Flying Insect Detection and Classification with Inexpensive Sensors
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Artificial Intelligence-Integrated Overcoupled Resonator for Multifunctional Pesticide Spectral Classification and

Dongxiao Li1,2, Ziwei Chen3, Xueyuan Wu3

  • 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore.

ACS Nano
|February 21, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an overcoupled (OC) resonator and artificial intelligence (AI) platform for sensitive pesticide residue detection. The novel system achieves high accuracy in identifying multiple pesticides, even at trace levels, ensuring food and environmental safety.

Keywords:
artificial intelligencemetasurfacemid-infraredovercoupled resonatorpesticide residue detection

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Area of Science:

  • Optoelectronics
  • Spectroscopy
  • Artificial Intelligence

Background:

  • Conventional pesticide detection methods face limitations due to diverse chemicals, overlapping spectral signatures, and low concentrations.
  • Ensuring food safety and environmental health necessitates advanced techniques for accurate pesticide residue analysis.

Purpose of the Study:

  • To develop a multifunctional platform for pesticide analysis using an overcoupled (OC) resonator and artificial intelligence (AI).
  • To overcome the limitations of conventional methods by enhancing detection efficiency, sensitivity, and applicability for various pesticide molecules.

Main Methods:

  • Utilized an OC resonator with an ultrabroadband spectral response (1650-750 cm-1) for resonance-free detection of multiple pesticides.
  • Integrated AI algorithms for spectral classification, concentration prediction, and signal reconstruction to manage complex spectral data.
  • Achieved trace-level detection with a minimum limit of detection of 12.5 ng·μL-1.

Main Results:

  • Demonstrated a 1685-fold bandwidth enhancement in the OC resonator, eliminating the need for resonance tuning.
  • AI algorithms achieved 100% classification accuracy for complex pesticide mixtures.
  • Successfully detected pesticide residues in real-world samples (apple peels, lake water) with high selectivity and interference suppression.

Conclusions:

  • The OC resonator-AI platform offers a versatile and highly sensitive solution for multifunctional pesticide analysis.
  • This approach advances ultratrace molecular diagnostics and provides a framework for developing novel sensors for food safety and environmental monitoring.