AI-powered programmable wetting-delamination μPAD for point-of-care food safety detection.

Journal: Biosensors & bioelectronics
Published Date: Oct 11, 2025

Abstract

The widespread use of pesticides and genetically modified (GM) crops has greatly improved agricultural productivity and food security. However, excessive pesticide application and the presence of exogenous proteins from GM crops pose significant risks to ecosystems and public health. Conventional detection methods, while sensitive, often require complex instrumentation and trained personnel, limiting their applicability for rapid, on-site analysis. Here, we present a low-cost, point-of-care microfluidic paper-based analytical device (μPAD) enhanced with a programmable wetting-delamination timer (PWDT) for rapid visual detection of food contaminants within 10-30 min and at a cost of less than $1 per test. The PWDT leverages the differential water-induced delamination between ink-treated paper and adhesive tape to achieve programmable fluid delays. A precutting-assisted dip-dyeing strategy enables precise timer fabrication with improved reproducibility and delay control, increasing stability by over 50 %. The modular "toy brick" design supports both 2D and 3D configurations for flexible reagent release. Phoxim is a broad-spectrum organophosphate pesticide widely used in agriculture to control pests in vegetable crops and livestock. Using phoxim as a target for pesticide residue detection, MnO2 nanozymes were incorporated to convert traditional color attenuation (CA) signals into color enhancement (CE), significantly improving naked-eye sensitivity. The dual-mode CA/CE system demonstrated 100 % sensitivity, 95 % specificity, and 97.5 % accuracy across 40 real samples. Cry1Ab/Ac is a common fusion Bt protein used in transgenic crops. For transgenic protein detection, we developed a PWDT-assisted lateral flow assay (LFA) that, combined with signal amplification reagents, improves the detection limit for Cry1Ab/Ac protein by 5.56-fold over conventional LFAs. Integration with deep learning-based image analysis enabled automated result interpretation under varying lighting conditions, achieving 100 % sensitivity and 98.6 % accuracy. Altogether, the PWDT-μPAD platform provides a versatile, scalable, and cost-effective solution for the simultaneous detection of pesticide residues and transgenic proteins, paving the way for user-friendly food safety diagnostics in low-resource and at-home settings.

Authors

  • Chenxi Dai
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Hao Huang
    School of Information Science and Engineering, Xinjiang University, Shangli Road, Urumqi 830046, China.
  • Yunhao Zhang
    State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • Yingying Liu
    Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
  • Yu Tao
    Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, People's Republic of China.
  • Tucan Chen
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Chao Wan
    The First Affiliated Hospital, Wannan Medical College, Wuhu, Anhui, China.
  • Shunji Li
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Zeyu Miao
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
  • Yiwei Li
    New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
  • Peng Chen
  • Bi-Feng Liu
    The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics and Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.

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