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.