A Preliminary Food–AI Readiness Framework for AI-Enabled Hyperspectral Systems in Fungal and Mycotoxin Risk Management: A Critical Narrative Review

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.