{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "State Key Laboratory of Food Science and Resources, Jiangnan University, Wuxi 214122, China",
    "School of Food Science and Technology, Jiangnan University, Wuxi 214122, China",
    "International Joint Laboratory on Food Safety, Jiangnan University, Wuxi 214122, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Xianfeng Lin"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Lixin Kang"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Jiaqi Feng"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Meihua Dong"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Nuo Duan"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Zhouping Wang"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Shijia Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0mey8td0qw696.bib",
  "cited_by": 0,
  "doi": "10.1016/j.crmeth.2026.101548",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13721751/",
  "id": "0mey8td0qw696",
  "issns": [
    "2667-2375"
  ],
  "landing_url": "https://doi.org/10.1016/j.crmeth.2026.101548",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "1312|Advanced biosensing and bioanalysis techniques"
    ]
  ],
  "openalex": "W7203455798",
  "pdf_url": null,
  "pmcid": "PMC13721751",
  "pmid": "42600617",
  "published": "2026-08-14",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/0mey8td0qw696.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:22:23Z",
      "native_id": "PMC13721751",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.crmeth.2026.101548"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Advanced biosensing and bioanalysis techniques",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging",
  "title_text": "Deep learning-driven multiplexed mycotoxin detection via color-size encoded microbead imaging",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:23Z",
  "url": "https://research.draconitic.com/paper/0mey8td0qw696",
  "venue": "Cell Reports Methods",
  "versions": []
}