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CC BY-NC-ND V4.0, Open Access
Journal article
Dynamic correction of dual energy X-ray absorptiometry images improves chain speed prediction of lamb composition in abattoirs
Animal (Cambridge, England), Vol.18(6), 101171
2024
Abstract
A prototype, on-line Dual Energy X-ray Absorptiometer (DXA) has shown high precision of prediction of carcass composition for the purpose of improved sheep meat grading in the Australian lamb supply chain, albeit with small inaccuracies over time. These inaccuracies were present across hours, and more significantly across days, which were unacceptable for any accreditation of this device as an objective carcass measurement tool in Australia. This inaccuracy demanded the creation of a novel image processing algorithm for the prototype DXA. This DXA was tested for repeatability of predictions of lamb carcass composition over minutes, hours, and days, using two developed image processing algorithms. There was high immediate repeatability for both algorithms when predicting lean muscle % in 40 lamb carcasses, with a maximum coefficient of variation of 0.65% over five repeated scans. There was a decrease in the coefficient of variation of the prediction of lean muscle % of 30 lambs scanned three times over a 48-hour period from 5.93% to 1.19% when the superior algorithm was used. The inaccuracies of lean muscle % predictions were associated with increases in the unattenuated space pixel values in DXA images. Improvements of the current algorithm is required to demonstrate repeatability over time for the purpose of accreditation within the Australian sheep meat industry, and for possible expansion of this technology into international supply chains.
Details
- Title
- Dynamic correction of dual energy X-ray absorptiometry images improves chain speed prediction of lamb composition in abattoirs
- Authors/Creators
- S.L. ConnaughtonA. WilliamsF. Anderson - Murdoch University, Centre for Animal Production and HealthK.R. Kelman - Murdoch University, Centre for Animal Production and HealthG.E. Gardner - Murdoch University, Centre for Animal Production and Health
- Publication Details
- Animal (Cambridge, England), Vol.18(6), 101171
- Publisher
- Elsevier B.V. on behalf of The Animal Consortium.
- Identifiers
- 991005672969307891
- Copyright
- © 2024 The Authors.
- Murdoch Affiliation
- School of Agricultural Sciences
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 5 Physics
- 5.221 Nuclear Instruments
- 5.221.1230 Mass Attenuation Coefficient
- Web Of Science research areas
- Agriculture, Dairy & Animal Science
- Veterinary Sciences
- ESI research areas
- Plant & Animal Science