AI-based meat inspection needs reference analytics before it can make robust decisions

Artificial intelligence for food-quality control is progressing most effectively where visual, spectral and chemical assessment are combined rather than treated as competing approaches. A review published on 17 September describes how hyperspectral imaging, machine learning and non-destructive sensing can bring together information on colour, composition, structure and possible spoilage indicators. This matters for meat because decisive attributes—freshness, freeze-thaw damage, oxidation, purge and colour deviation—often change at the same time.

The technical advance is not that a camera can instantly replace laboratory work. The review highlights the limitations of conventional, slow and destructive methods, but it also makes clear that digital models rely on robust reference data. An algorithm can only classify reliably what has first been defined through samples, analytical testing and properly documented quality criteria. A visual correlation alone is not a food-safety decision.

For meat and poultry plants, the practical implication is straightforward: optical tools are particularly promising for repetitive, high-throughput controls, such as separating lots, detecting process changes or prioritising samples for confirmatory analysis. Their value rises when they are linked to lot identity, temperature, time, formulation and analytical results; it falls when they are used as a black box.

Explainable classification therefore becomes increasingly important. A pass-or-fail output is insufficient: quality managers need to identify which signal, spectral range or combination of indicators triggered an alert. Useful digitalisation does not remove sampling plans or validation. It makes them the foundation of faster, traceable and operational quality control.