Ultrasonic, needle, and carcass measurements for predicting chemical composition of lamb carcasses1,2

Ramsey, C. B.; Kirton, A. H.; Hogg, B. W.; Dobbie, J. L.

doi:10.2527/1991.6993655x

Cited by 19 publications

(23 citation statements)

References 4 publications

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“…These results agree with those found by Delfa et al (1996), Fernández et al (1998), Hopkins (1990), Mendizabal et al (2003) and Silva et al (2005) and Teixeira and Delfa (1997). On the other hand, Ramsey, Kirton, Hogg, and Dobbie (1991) were of the opinion that the improvements in accuracy of prediction due to live weight were not sufficient to warrant the use of this variable in multiple regression equations, which is not in agreement with the results obtained in the present work.…”

Section: Resultscontrasting

confidence: 57%

In vivo estimation of lamb carcass composition by real-time ultrasonography

et al. 2006

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The objective of this study was to evaluate the accuracy of ultrasonography to measure fat thickness in live lambs and predict carcass composition to find a system that can assess carcass composition easily and accurately, without damage to the product and can improve carcass classification.Fat thickness (FTh) between the 12th and 13th ribs and between the 3rd and 4th lumbar vertebrae, was measured in vivo using realtime ultrasound, in 67 Churra Galega Bragançana males (mean weight 36.0 kg, range 21.5-47.0 kg), using an ALOKA SSD-500V equipped with two probes (5 and 7.5 MHz) in order to predict carcass composition. The most satisfactory correlation between carcass and ultrasound measurements was between the 3rd and 4th lumbar vertebrae fat thickness (FTh). The first variable admitted in the models to predict carcass composition (live weight, LW) explained between 63% and 96% of the total variation of the weight of the components of the carcass. In fact, 96% of the variation in total amount of muscle was accounted for by live weight. The inclusion of the ultrasound measures with the 7.5 MHz probe improved, in all prediction equations, the coefficient of determination (R 2 ) with a substantial reduction in the residual standard deviation (RSD). In relation to the subcutaneous fat of the carcass, 85% of the variation was explained by live weight and the FTh 12-13 7.5 measurement. In the prediction model for total carcass fat weight, live weight and ultrasound fat thickness measurement explained 88% of the variation. The results indicate that in vivo ultrasound fat thickness measures in association with live weight can be used to predict carcass composition in Churra Galega Bragançana lambs.

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Section: Resultscontrasting

confidence: 57%

In vivo estimation of lamb carcass composition by real-time ultrasonography

et al. 2006

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“…Tschirhart et al (2002) reported that carcass body wall thickness is often superior to BF as a predictor of percentage lean yield. In New Zealand, ultrasonic estimates of the rib tissue thickness (GR), defined as the total tissue depth between the surface of the carcass and the rib at a point 11 cm from the midline in the region of the 12th rib , were shown to account for 64 and 49% of the variation in percentage of carcass fat and protein, respectively, and were highly correlated (r = 0.87) to the corresponding carcass measurement (Ramsey et al, 1991). In a recent Canadian study, Thériault et al (2009) reported a correlation between ultrasound and carcass measurements at the GR site of 0.83, slightly greater than their reported correlations of 0.78 to 0.82 between ultrasound and carcass measurements of BF.…”

Section: Correlation Analysis Of Ultrasound and Carcass Measurementsmentioning

confidence: 99%

Validation of live animal ultrasonic measurements of body composition in market lambs

Emenheiser

Greiner

Lewis

et al. 2010

Journal of Animal Science

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Market lambs from the state fair of Virginia (n = 172) were ultrasonically evaluated by 4 scan technicians and 3 image interpreters to determine accuracy of ultrasonic estimates of loin muscle area (ULMA), backfat thickness (UBF), and body wall thickness (UBW). Lambs were initially scanned at the preferred magnification setting of each technician; 2 chose 1.5× and 2 chose 2.0×. Lambs were then scanned a second time for ULMA and UBF with machine magnification settings changed from 1.5 to 2.0×, or vice versa, midway through the second scan. Lambs were then slaughtered, and analogous measurements [carcass loin muscle area, carcass backfat thickness, and carcass body wall thickness (CBW)] were recorded on chilled carcasses. Pooled, residual correlation coefficients within technicians and interpreters between ultrasonic measurements from the first scan and carcass measurements were 0.66 for loin muscle area, 0.78 for backfat thickness, and 0.73 for body wall thickness, but were reduced to 0.43, 0.69, and 0.50, respectively, by inclusion of linear effects of carcass weight in the model. Mean bias for technicians and interpreters ranged from −1.30 to −2.66 cm 2 for loin muscle area, −0.12 to −0.17 cm for backfat thickness, and 0.14 to −0.03 cm for body wall thickness; prediction errors ranged from 1.86 to 2.22 cm 2 , 0.12 to 0.14 cm, and 0.35 to 0.38 cm, respectively. Pooled correlations between repeated measures were 0.67 for ULMA, 0.79 for UBF, and 0.68 for UBW at the same magnification and 0.73 for ULMA and 0.76 for UBF across different magnification settings. Mean differences between repeated measures were more variable among technicians and interpreters than statistics comparing ultrasound to carcass measures. Standard errors of repeatability ranged from 1.61 to 2.45 cm 2 for ULMA, 0.07 to 0.11 cm for UBF, and 0.36 to 0.42 cm for UBW. The effect of changing magnification setting on technician and interpreter repeatability was small for UBF and ULMA. The accuracy of prediction of CBW from UBW was similar to that achieved for backfat thickness; further assessment of the value of ultrasonic measurements of body wall thickness in lambs is warranted. These results indicate that ultrasound scanning can reliably predict carcass loin muscle area and backfat thickness in live lambs and, accordingly, has value in selection programs to improve composition. Development of certification standards for US lamb ultrasound technicians based on results of this study and others is proposed.

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“…Over the years, several reports have shown that RTU is a suitable technique for predicting carcass composition both in sheep (Kempster et al ., 1982;McEwan et al ., 1989;Ramsey et al ., 1991;Young and Deaker, 1994;Silva et al ., 2005Silva et al ., , 2006aTeixeira et al ., 2006;Hopkins et al ., 2007;Ripoll et al ., 2009) and goats (Delfa et al ., 1995a(Delfa et al ., , 1996Teixeira et al ., 2008). In general, these studies develop models which are able to explain the variation in carcass composition in terms of muscle, fat and bone content.…”

Section: Use Of Rtu To Predict Carcass Composition and Meat Traits Inmentioning

confidence: 99%

Real-time ultrasound (RTU) imaging methods for quality control of meats

Silva¹,

Cadavez²

2012

Computer Vision Technology in the Food and Beverage Industries

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Abstract:In this chapter the use of real-time ultrasonography to predict in vivo carcass composition and meat traits will be reviewed. The chapter begins by discussing background and principles of ultrasound. Then aspects affecting the suitability of realtime ultrasonography and image analysis for predicting carcass composition and meat traits of meat producing species and fish will be presented. This chapter also provides an overview of the present and future trends in the application of real-time ultrasonography in the meat industry.

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Ultrasonic, needle, and carcass measurements for predicting chemical composition of lamb carcasses1,2

Cited by 19 publications

References 4 publications

In vivo estimation of lamb carcass composition by real-time ultrasonography

In vivo estimation of lamb carcass composition by real-time ultrasonography

Validation of live animal ultrasonic measurements of body composition in market lambs

Real-time ultrasound (RTU) imaging methods for quality control of meats

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