Effect of machine and carcase factors on CT prediction of carcase composition in lamb and beef carcases
Did you know that medical computed tomography (CT) can be used to accurately determine carcase composition?
| Publication date: | 06 April 2022 |
| Project status: | Completed |
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Summary
This work was undertaken to establish the immediate repeatability of CT estimates of lamb and beef carcase composition, the impact of changes to scanning methodology such as carcase sectioning and freeze/thaw protocols, and the effect of machine scanning voltage and CT scan slice width.
Objectives
The aim of this project was to confirm that medical CT is a robust system for determining lean meat yield and the best method to provide the 'gold standard' for training, calibration and accreditation of measurement technologies.
Key findings
This work demonstrated several outcomes including:
1. CT estimates of carcase composition are almost perfectly repeatable.
2. Carcase sectioning has a small impact on CT estimates of carcase composition, therefore a standardised carcase sectioning method should be used when CT is used as the reference standard for calibrating other measurement technologies.
3. Slice width and scan voltage will impact results therefore these should be standardised when CT is used as the reference standard for calibrating other measurement technologies.
4. When the CT methodology is standardised, it demonstrates substantially better repeatability than determining carcase composition using chemical methods.
Benefits to industry
Industry requires a robust 'gold standard' against with lean meat yield measurement technologies can be calibrated and approved grading in the Australian meat language. The outcomes of the research provide industry with confidence that medical CT is appropriate for this function.
MLA action
The outcomes of this project, and planned future work, will underpin development of lean meat yield with the intent of it being adopted in the AUS-MEAT language.
Future research
Future research will complement this project by developing a robust methodology to account for variations that occur between CT machines.
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For more information Contact Project Manager: Richard Apps |
