Incorporating meat quality in sheep breeding programmes: potential of non-invasive technologies

Genetic selection for sheep meat quality is rare, due to difficulties in measuring these traits within practical breeding programmes. Non-invasive methods to predict lamb meat quality in vivo and post-mortem have been investigated, and results indicate scope for their commercial implementation. UK research has examined relationships between meat quality traits and parameters resulting from x-ray computed tomography (CT) scanning, which is routinely implemented in UK terminal sire breeding. CT is poor at predicting mechanical tenderness in vivo (R2 < 15%), across various sheep populations. However, CT can predict intramuscular fat (IMF) with accuracies ranging from 33-70% in live lambs of different breeds, providing a unique in-vivo predictor of meat quality. In vivo CT-predicted IMF is moderately heritable (h2 0.31) and its genetic control differs from total carcass fat (rg 0.68), suggesting potential for its improvement within a multi-trait selection index. Novel methodologies have also been tested using CT and visible and near-infrared spectroscopy (NIR) to analyse chilled and vacuum-packed meat samples post-mortem, allowing their return to the food chain. Lamb loin cuts from commercial carcasses (n=303), varying in fat and conformation grades, were scanned by CT and NIR in vacuum packs, then tested for IMF, tenderness and by a trained UK taste panel. CT parameters predicted IMF with moderate accuracy (R2 36%), but did not accurately predict shear force or sensory traits. NIR predicted IMF in unpackaged meat with moderate accuracy, but predictions were poor in vacuum-packed meat. Samples predicted by CT as having >3% IMF scored significantly higher for sensory eating quality than those predicted as <3% IMF. Work is underway to incorporate CT-predicted IMF, as a proxy for meat quality, into UK breeding programmes for terminal sire sheep, in which IMF is known to be low as a consequence of selection for lean carcasses.