The estimation of sheep carcass composition from fat and muscle thickness measurements taken by probes
Three trials, involving a total of 290 lambs, were carried out to examine the precision of probed fat and muscle measurements for estimating carcass composition in classification and grading schemes. The measurement positions and probes were not always the same in different trials but common measure...
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Veröffentlicht in: | Meat science 1986, Vol.16 (2), p.113-126 |
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Zusammenfassung: | Three trials, involving a total of 290 lambs, were carried out to examine the precision of probed fat and muscle measurements for estimating carcass composition in classification and grading schemes. The measurement positions and probes were not always the same in different trials but common measurements provided the basis for comparison between trials. Residual standard deviations (sd) for the prediction of carcass lean percentage averaged over trials are referred to in this Summary.
Residual standard deviation for prediction from carcass weight was 3·5. The visual fat assessment currently used in the national Sheep Carcase Classification Scheme, operated by MLC, contributed significantly to the prediction (residual standard deviation = 3·0) but was less precise than a visual assessment of carcass subcutaneous fat content to the nearest percentage unit (SF
e) (residual standard deviation = 2·6).
Fat thickness measurements taken over the
M. longissimus at the 12th rib with the Danish optical probe, a simple steel rule or the pig version of the Hennessy Grading Probe (HGP), showed similar precision to the classification fat class. An
M. longissimus thickness measurement taken by the HGP did not add significantly to the precision.
Probe fat measurements added significantly to the precision achieved with visual fat assessments (residual standard deviation with classification fat class = 2·8; residual standard deviation with SF
e = 2·5). There would be advantages, therefore, in using both a visual fatness assessment and fat measurements in classification. |
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ISSN: | 0309-1740 1873-4138 |
DOI: | 10.1016/0309-1740(86)90070-7 |