ReviewFoods (Basel, Switzerland)2026
A Preliminary Food-AI Readiness Framework for AI-Enabled Hyperspectral Systems in Fungal and Mycotoxin Risk Management: A Critical Narrative Review.
Review in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fungal contamination and mycotoxins remain important hazards in cereal, nut, oilseed, and processed-food chains. For grain handlers, processors, storage facilities, quality-assurance laboratories, and technology developers, the practical challenge is not only detection but determining when an AI-enabled optical output is sufficiently documented to support accepting, holding, sorting, or referring material for confirmatory analysis. This critical narrative review, supported by structured evidence appraisal, examines artificial intelligence (AI)-enabled hyperspectral imaging (HSI) for fungal and mycotoxin risk management. The focused evidence map comprises a combined 20-study evidence map, including 17 spectral-imaging studies and three contextual non-imaging near-infrared studies addressing aflatoxins, fumonisins, deoxynivalenol (DON), zearalenone (ZEN), ochratoxin A (OTA), fungal status, and co-contamination; no patulin-specific AI-HSI study met the defined corpus criteria. Most evidence remains laboratory-based, with controlled contamination, limited independent validation, incomplete transfer testing, or no line-level demonstration. The review proposes the preliminary Food-AI Deployment-Readiness Framework (F-AIDRF), a non-regulatory appraisal tool covering analytical validity, algorithmic robustness, explainability and auditability, transferability and maintenance, operational feasibility, and decision integration. Applied descriptively, F-AIDRF is intended to reveal missing evidence, not to certify technologies. For food-industry readers, it offers a practical vocabulary for comparing screen-sort-confirm systems before investment, pilot testing, or regulatory-facing use.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.