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Peer-reviewedCC BY-NC 4.0Cell Reports Methods

Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging

  • Xianfeng Lin1,2
  • Lixin Kang1,2
  • Jiaqi Feng1,2
  • Meihua Dong1,2
  • Nuo Duan1,2,3
  • Zhouping Wang1,2,3
  • Shijia Wu1,2,3
  1. 1State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China
  2. 2School of Food Science and Technology, Jiangnan University, Wuxi 214122, China
  3. 3International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China
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Abstract

Mycotoxins pose severe threats to food safety and public health, especially with synergistic toxicity from co-contamination. Herein, a deep learning-driven two-dimensional (2D)-encoded single microbead imaging decoding platform is developed for homogeneous analysis of four mycotoxins. The aptamer-recognition-triggered DNAzyme walker-hybridization chain reaction (Dz-HCR) cascade design enables direct fluorescent signal lighting and amplification on microbeads, avoiding complex separation and signal probe preparation. By leveraging two different-sized microbeads and two fluorophores modified at HCR hairpins, an ingenious color-size 2D-encoding strategy is established for high-throughput analysis. The YOLOv11 deep learning model enables rapid, accurate decoding and analysis of fluorescence images with multi-dimensional information, improving data processing efficiency. This platform exhibits high sensitivity (<1 pg/mL), wide linear ranges, and excellent specificity. The desirable spiked recovery (85.6%–114.0%) in maize and plant-based meat analogs and the consistency with HPLC-MS/MS confirm practical applicability. This method provides a promising strategy for high-throughput mycotoxin detection in food safety monitoring.

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Cite

BibTeXRIS
BibTeX
@article{lin2026deep,
  title = {{Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging}},
  author = {Xianfeng Lin and Lixin Kang and Jiaqi Feng and Meihua Dong and Nuo Duan and Zhouping Wang and Shijia Wu},
  journal = {Cell Reports Methods},
  year = {2026},
  month = aug,
  doi = {10.1016/j.crmeth.2026.101548},
  url = {https://doi.org/10.1016/j.crmeth.2026.101548},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging
AU  - Xianfeng Lin
AU  - Lixin Kang
AU  - Jiaqi Feng
AU  - Meihua Dong
AU  - Nuo Duan
AU  - Zhouping Wang
AU  - Shijia Wu
PY  - 2026
DA  - 2026/08/14
JO  - Cell Reports Methods
DO  - 10.1016/j.crmeth.2026.101548
UR  - https://doi.org/10.1016/j.crmeth.2026.101548
AB  - Mycotoxins pose severe threats to food safety and public health, especially with synergistic toxicity from co-contamination. Herein, a deep learning-driven two-dimensional (2D)-encoded single microbead imaging decoding platform is developed for homogeneous analysis of four mycotoxins. The aptamer-recognition-triggered DNAzyme walker-hybridization chain reaction (Dz-HCR) cascade design enables direct fluorescent signal lighting and amplification on microbeads, avoiding complex separation and signal probe preparation. By leveraging two different-sized microbeads and two fluorophores modified at HCR hairpins, an ingenious color-size 2D-encoding strategy is established for high-throughput analysis. The YOLOv11 deep learning model enables rapid, accurate decoding and analysis of fluorescence images with multi-dimensional information, improving data processing efficiency. This platform exhibits high sensitivity (<1 pg/mL), wide linear ranges, and excellent specificity. The desirable spiked recovery (85.6%–114.0%) in maize and plant-based meat analogs and the consistency with HPLC-MS/MS confirm practical applicability. This method provides a promising strategy for high-throughput mycotoxin detection in food safety monitoring.
SN  - 2667-2375
ER  -