AI freshness systems need biological reference data before they can make reliable meat decisions

Artificial-intelligence systems for assessing meat freshness should not be judged by a single laboratory accuracy figure. A review published in *Food Bioscience* argues that industrial value depends on linking sensor signals to meaningful biological and chemical markers—and on a model continuing to work beyond the sample series on which it was trained. The paper brings together approaches using spectroscopy, imaging, gas sensors, impedance and humidity data, among others. Their purpose may be to classify freshness states or estimate remaining shelf life. Yet colour, odour, pH, microbiology and metabolites do not develop identically across species, cuts, packaging atmospheres and cold chains. A technically distinct signal, therefore, does not automatically amount to a robust decision on suitability or shelf life. The authors particularly caution against sample-level data splits where samples from the same animal or batch are distributed between training and test sets. That procedure can overstate an algorithm’s real-world capability. Sensor drift, fouling, variation in the meat matrix and calibration transfer between plants are also identified as major obstacles. For processors and equipment suppliers, the practical implication is straightforward: AI must be validated against reference analytics, with independent batches and real processing and logistics conditions. It has strong potential as decision support—for prioritising checks or managing inventory, for example. But before product-release decisions are automated, each model must demonstrate uncertainty, repeatability and its defined limits of use.