{
  "abstract": "Graphical overview of explainable artificial intelligence (XAI) for farm-to-fork postharvest preservation. Postharvest deterioration accumulates across orchard, packhouse, refrigerated transportation, warehouse, and distribution stages under fluctuating temperature, humidity, atmosphere, and mechanical stress. Multimodal data streams, including host omics, microbiome profiles, environmental sensing, RGB/hyperspectral/thermal imaging, spectroscopy, key genes, and logistics records, are integrated through a data lakehouse and analyzed by postharvest XAI models. Explainable modules, including SHapley Additive exPlanations (SHAP)/local attribution, graph neural network (GNN) explanation, pathway-constrained models, counterfactual reasoning, and stability/faithfulness auditing, convert black-box spoilage-risk prediction into interpretable biological mechanisms. These mechanisms guide actionable interventions such as antioxidant coating, elicitor spray, biocontrol consortia, packaging optimization, and gene-targeted strategies. Validation through storage trials, sensory evaluation, microbial testing, and sequencing closes the loop from prediction to explanation, intervention, and validated decision support.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Key Laboratory of Agro‐products Processing Ministry of Agriculture and Rural Affairs Beijing China",
    "State Key Laboratory of Plant Diversity and Specialty Crops, Institute of Botany Chinese Academy of Sciences Beijing China",
    "Beijing Key Laboratory of Growth and Developmental Regulation for Protected Vegetable Crops, College of Horticulture China Agricultural University Beijing China",
    "School of Agricultural Economics and Rural Development Renmin University of China Beijing China",
    "Department of Nutrition and Food Science, College of Agriculture and Natural Resources University of Maryland, College Park College Park Maryland USA",
    "College of Food Science and Engineering Nanjing University of Finance and Economics Nanjing China",
    "Laboratory of Food Process Engineering Wageningen University & Research Wageningen The Netherlands",
    "School of Chemistry, Chemical Engineering and Biotechnology Nanyang Technological University Singapore Singapore",
    "Department of Plant Science, School of Agriculture and Biology Shanghai Jiao Tong University Shanghai China"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Peihua Ma",
      "orcid": "0000-0002-5041-0361"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaoxue Jia"
    },
    {
      "affs": [
        0
      ],
      "name": "Bei Fan"
    },
    {
      "affs": [
        1
      ],
      "name": "Boqiang Li",
      "orcid": "0000-0001-7787-8303"
    },
    {
      "affs": [
        2
      ],
      "name": "Tao Lin",
      "orcid": "0000-0003-3647-0488"
    },
    {
      "affs": [
        3
      ],
      "name": "Jiping Sheng",
      "orcid": "0000-0003-1259-6350"
    },
    {
      "affs": [
        4
      ],
      "name": "Cheng–I Wei",
      "orcid": "0000-0001-9764-3181"
    },
    {
      "affs": [
        5
      ],
      "name": "Yingjian Lu",
      "orcid": "0000-0001-6425-2774"
    },
    {
      "affs": [
        6
      ],
      "name": "Yizhou Ma"
    },
    {
      "affs": [
        7
      ],
      "name": "L Chen",
      "orcid": "0000-0002-5506-5835"
    },
    {
      "affs": [
        8
      ],
      "name": "Songtao Jiu",
      "orcid": "0000-0001-7437-111X"
    },
    {
      "affs": [
        0
      ],
      "name": "Fengzhong Wang",
      "orcid": "0000-0001-7533-0344"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8y29vpqr7wz1b.bib",
  "cited_by": 5,
  "doi": "10.1002/imt2.70139",
  "fulltext_url": null,
  "id": "8y29vpqr7wz1b",
  "issns": [
    "2770-596X",
    "2770-5986"
  ],
  "landing_url": "https://doi.org/10.1002/imt2.70139",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1110|Postharvest Quality and Shelf Life Management"
    ]
  ],
  "openalex": "W7164127919",
  "pdf_url": null,
  "pmcid": "PMC13377414",
  "pmid": "42491397",
  "published": "2026-06-01",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/8y29vpqr7wz1b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:26:50Z",
      "native_id": "W7164127919",
      "source": "openalex",
      "url": "https://doi.org/10.1002/imt2.70139"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "openalex:1110|Postharvest Quality and Shelf Life Management",
      "subfield": "plant-biology"
    }
  ],
  "title": "From prediction to actionable mechanisms: Explainable multi‑omics AI for farm‑to‑fork postharvest preservation",
  "title_text": "From prediction to actionable mechanisms: Explainable multi‑omics AI for farm‑to‑fork postharvest preservation",
  "type": "peer-reviewed",
  "updated_at": "2026-11-09T13:21:09Z",
  "url": "https://research.draconitic.com/paper/8y29vpqr7wz1b",
  "venue": "iMeta",
  "versions": []
}