V.TEC.2500 - Sheep meat eating quality prediction from dual energy x-ray absorptionmetry

Summary

Eating quality is an important attribute to consumers who express a willingness to pay for a high-quality product. The current Meat Standards Australia sheepmeat eating quality prediction model incorporates carcass measures such as hot carcass weight, loin intramuscular fat (IMF) percentage and carcass lean percentage. This enables processing plants to sort carcasses prior to bone out for better utilisation and allow product segregation into quality grades. The carcass lean % input value can be predicted by abattoirs that have installed on-line Dual Energy Xray Absorptiometry (DXA) systems, with these DXA devices now accredited to predict computed tomography fat, lean and bone percentage. Previous studies have demonstrated a relationship between eating quality and the R values of carcass bone from these DXA images (Anderson et al., 2021). The relationship between DXA and eating quality in this experiment varied across the cuts assessed and appeared to be independent of loin intramuscular fat and carcass lean perentage (Anderson et al., 2021). Since this earlier study the methodology used to define DXA bone pixels has been improved, warranting further investigation into the relationship between DXA bone R and eating quality of cuts across the carcass.

Objectives

• Explore the relationship between DXA bone R and eating quality traits across a range of cuts and flocks to and the inclusion of DXA bone R in the Meat standards Australia eating quality prediction model
• Evaluate whether there are differences in the predicted composition of sheep when they are scanned hot versus cold using in-line DXA scanning – completed, with further work required to investigate this research question
• Determine and outline the next steps required to develop a DXA related eating quality trait in collaboration with the Industry Calibration Working Group and Meat Standards Australia.

Key findings

DXA bone R demonstrated a negative association with the MQ4 score of a range of cuts across the carcass. The relationship remained even when models were corrected for IMF%, carcase lean percentage, and carcass weight, indicating that DXA bone R describes MQ4 variation that is independent of these other traits. This is a crucial finding given that all of these traits are included in the existing MSA eating quality model. The results were most consistent in the grilled loin where there was a reduction in MQ4 within 3 of the 4 flocks analysed. There was only 1 flock in this study where eating quality was not influenced by DEXA bone R, however in this case the flock had no range in animal age, supporting the assertion that DEXA bone R may be a proxy for the effect of maturity on eating quality.

Benefits to industry

This research provides 3 keys benefits: 

1. Enables supply chains to underpin elite brands with an enhanced eating quality claim.
2. Will allow accelerated adoption for any processors utilising a hot DXA system.
3. Enhances the MSA cuts-based model, with this complexity setting Australia apart from other nations with eating quality prediction systems in lamb.

MLA action

MLA plans to continue investments in this technology to further unlock the potential for premium eating quality lamb and sheep brands.

Future research

To robustly determine the coefficients for this trait, a carefully constructed experimental design is required. This should test a variety of cuts and cooking methods sampled from animals of a diverse age range that are slaughtered as one group (removing the kill group effect). This should be done across a series of sites where DXA systems are available, with at least multiple reps undertaken at each site.

Furthermore, the biology of the trait should be investigated as this will help to ensure the proper integration of this trait into the MSA model and enhance the credibility of its implementation both nationally and internationally.