Publication
In vivo estimation of goat carcass composition and body fat partition by real-time ultrasonography
| dc.contributor.author | Teixeira, Alfredo | |
| dc.contributor.author | Joy, Margalida | |
| dc.contributor.author | Delfa, Rafael | |
| dc.date.accessioned | 2008-09-09T15:59:44Z | |
| dc.date.available | 2008-09-09T15:59:44Z | |
| dc.date.issued | 2008 | |
| dc.description.abstract | The accuracy of ultrasound measurements to assess goat carcass composition and the partition of body fat depots was evaluated. An ultrasound machine with a 5-MHz probe and image analysis was used to assess in vivo fat thickness and muscle depth in 56 Spanish Celtiberica adult goats, in lumbar and breast body regions. The goats were slaughtered and the weight of body fat depots recorded. Measurements corresponding to the in vivo ultrasound fat thickness and muscle depth were taken on carcasses. The left sides of carcasses were completely dissected into their components. The best relationships (r = 0.94, P < 0.01) between in vivo and carcass measurements of fat thickness were obtained when measurements were taken at the sternum, and the best anatomical point was located between the third and fourth sternebrae. The best correlation coefficients (r = 0.84) for muscle depth were found for measurements taken between the third and the fourth lumbar vertebrae at 2 cm from the middle of the vertebral column. Body weight and ultrasound measurements were used to fit the best multiple regression equations to predict carcass composition and the partition of body fat depots. All equations, with the exception of those for muscle quantity, omental, and total body fat depot amounts, were computed after performing a logarithmic transformation. Body weight in association with the ultrasound measurement taken at largest LM muscle depth, between the first and second lumbar vertebrae accounted for 90% of the muscle weight. Body weight was the first variable admitted into the prediction models of muscle, mesenteric fat, and total body fat and accounted for 82, 67, and 79% of the variation in tissue weights, respectively. The ultrasound measurement of fat thickness taken at the third sternebra was the first variable admitted into the prediction models for intermuscular fat, kidney and pelvic fat, and total carcass fat and accounted for by 73, 75, 71, and 79% of the variation in the weight of these fat depots, respectively. The ultrasound measurements taken in the breast region, particularly at the third and fourth sternebrae, were the most suitable for assessing fat thickness. The results of this experiment suggest that BW associated with some in vivo ultrasonic fat measurements allow the accurate prediction of goat carcass composition and body fat depots. | en |
| dc.identifier.citation | Teixeira, Alfredo; Joy, M.; Delfa, R. (2008). In vivo estimation of goat carcass composition and body fat partition by real-time ultrasonography. Jounal of Animal Science. ISSN 0021-8812. 86:9, p. 2369-2376 | en |
| dc.identifier.doi | 10.2527/jas.2007-0367 | |
| dc.identifier.issn | 0021-8812 | |
| dc.identifier.uri | http://hdl.handle.net/10198/792 | |
| dc.language.iso | eng | en |
| dc.peerreviewed | yes | |
| dc.publisher | American Society of Animal Science | en |
| dc.subject | Body fat | en |
| dc.subject | Carcass composition | en |
| dc.subject | Goat | en |
| dc.subject | ultra | en |
| dc.title | In vivo estimation of goat carcass composition and body fat partition by real-time ultrasonography | en |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| person.familyName | Teixeira | |
| person.givenName | Alfredo | |
| person.identifier | 958487 | |
| person.identifier.ciencia-id | 2A1A-FF0C-185B | |
| person.identifier.orcid | 0000-0003-4607-4796 | |
| person.identifier.rid | G-4118-2011 | |
| person.identifier.scopus-author-id | 56195849200 | |
| rcaap.rights | openAccess | |
| rcaap.type | article | en |
| relation.isAuthorOfPublication | 27cc89a2-6661-4d8d-a727-21109c04a74e | |
| relation.isAuthorOfPublication.latestForDiscovery | 27cc89a2-6661-4d8d-a727-21109c04a74e |