Journal article
Dual energy x-ray absorptiometry predicts lamb carcass composition at abattoir chain speed with high repeatability across varying processing factors
Meat Science, Vol.181, Article 108413
2021
Abstract
An on-line Dual Energy X-ray Absorptiometry (DXA) scanner was used in an Australian abattoir to predict computed tomography (CT) determined composition % of fat, lean muscle and bone in lamb carcasses at chain speed. This study assessed the effect of spray-chilling on these estimates, as well as their repeatability over a 10-min period, and over a 72 h period. There was no prediction bias between the 15 spray-chilled and 15 non-spray-chilled carcasses. When repeat DEXA scans were undertaken across a 10-min period, there was a high level of repeatability for the prediction of CT Fat %. When repeat scans were conducted at 6 time points across a 72 h period the precision of the DXA prediction of CT Fat % of 30 carcasses remained high (R2 = 0.94, RMSEP = 1.20%), although small biases existed between time points (P < 0.01). These biases were minimised when the DXA scanner had been operational prior to experimentation, suggesting a ‘warm-up’ effect.
Details
- Title
- Dual energy x-ray absorptiometry predicts lamb carcass composition at abattoir chain speed with high repeatability across varying processing factors
- Authors/Creators
- S.L. Connaughton (Author/Creator) - Murdoch UniversityA. Williams (Author/Creator) - Murdoch UniversityF. Anderson (Author/Creator) - Murdoch UniversityK.R. Kelman (Author/Creator) - Murdoch UniversityJ. Peterse (Author/Creator) - Murdoch UniversityG.E. Gardner (Author/Creator) - Murdoch University
- Publication Details
- Meat Science, Vol.181, Article 108413
- Publisher
- Elsevier
- Identifiers
- 991005544229107891
- Copyright
- © 2020 Elsevier Ltd
- Murdoch Affiliation
- School of Agricultural Sciences
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 3 Agriculture, Environment & Ecology
- 3.51 Dairy & Animal Sciences
- 3.51.206 Meat Quality
- Web Of Science research areas
- Food Science & Technology
- ESI research areas
- Agricultural Sciences