The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform
David L. Hopkins - One of the best experts on this subject based on the ideXlab platform.
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An industry applicable model for predicting Lean Meat yield in lamb carcasses
Australian Journal of Experimental Agriculture, 2008Co-Authors: David L. HopkinsAbstract:A wide selection of lamb types (n = 360) of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir. Soft tissue depth at the GR site (thickness of tissue over the 12th rib, 110 mm from the midline) was measured in the chiller, using a GR knife (GR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The predominant industry model for predicting Meat yield in Australia uses hot carcass weight (HCW) and tissue depth at the GR site. A moderate level of accuracy and precision was found when HCW and GR were used to predict Lean Meat yield (R2 = 40.5, r.s.d. = 2.39%), which could be improved markedly when loin muscle cross-sectional area at the 12th rib (EMA) was included in the model (R2 = 54.5, r.s.d. = 2.10%). A better result was achieved when the model included the weight of subcutaneous fat (SLFat) from the shortloin (R2 = 73.8, r.s.d. = 1.59%). A combination of SLFat and the weight of the shortloin muscle (SLMus) negated the need to include either GR or EMA in the model (R2 = 76.1, r.s.d. = 1.52%). The transportability of a model based on HCW, SLFat and SLMus was tested by randomly dividing the dataset and comparing the coefficients and the level of accuracy and precision. Collecting measures of EMA, SLFat and SLMus in boning rooms is potentially feasible. If this can be achieved under commercial conditions, a rigorous method for automatically predicting Lean Meat yield during boning could be applied. Application of the approach to large-scale research programs, where estimates of Lean Meat yield are required, would be possible at a reduced cost compared with alternative systems based on full carcass breakdown. A suitable model is given for this purpose.
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video image analysis in the australian Meat industry precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat Science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
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Video image analysis in the Australian Meat industry – precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
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Predicting the weight of Lean Meat in lamb carcasses and the suitability of this characteristic as a basis for valuing carcasses.
Meat science, 1994Co-Authors: David L. HopkinsAbstract:Abstract The carcasses of 138 lambs were dissected into fat, muscle and bone as the basis for developing a model to estimate the weight of Lean Meat (muscle and intramuscular fat). The lambs represented two sexes (70 wethers, 68 ewes) and three sire genotypes (67 Poll Dorset, 39 Suffolk, 32 Wiltshire Horn) all from Border Leicester × Polwarth × Booroola type ewes. Hot carcass weight (HWT) was found to explain the majority of the variation in the weight of Lean Meat. When measures of subcutaneous fat depth at different sites were used as predictors in addition to HWT, the accuracy with which Lean Meat yield could be estimated was found to increase by a small amount. There was, however, little difference in their individual value as predictors. The area of the M. longissimus thoracis et lumborum at the twelfth-thirteenth rib was found to account for the significant breed type difference between Poll Dorset and Suffolk sired lambs when included in a multiple regression with HWT and the GR measurement (tissue thickness at the twelfth rib 110 mm from the midline). The final model produced an R2 = 0·92 and an RSD = 0·45 kg for the 106 lambs. Using the model for the 106 lambs, the estimated (from the model) and actual values of Lean Meat for the Wiltshire Horn sired lambs were compared. The correlation coefficient between the values was r = 0·97 and the RSD was 0·31 kg. This shows that for second cross lambs as used in this study the fitted model exhibits a degree of general validity and stability. An overall model for the 138 lambs produced an R2 = 0·92 an RSD = 0·43 kg. The potential for pricing Meat on the basis of Lean Meat yield is discussed, with particular emphasis on the current developments in assessment of lamb carcasses in Australia.
Cr Smith - One of the best experts on this subject based on the ideXlab platform.
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video image analysis in the australian Meat industry precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat Science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
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Video image analysis in the Australian Meat industry – precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
Graham E. Gardner - One of the best experts on this subject based on the ideXlab platform.
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Selection for Lean Meat yield in lambs reduces indicators of oxidative metabolism in the longissimus muscle
Meat Science, 2014Co-Authors: K.r. Kelman, L. Pannier, David W. Pethick, Graham E. GardnerAbstract:Selection for increased Lean Meat yield using Australian Sheep Breeding Values for reduced post-weaning c-site fat depth (PFAT) and increased post-weaning eye muscle depth (PEMD) reduces the oxidative capacity of muscle. Isocitrate dehydrogenase (ICDH) activity and myoglobin concentration were measured in 3178 and 5580 lambs, respectively, to indicate oxidative capacity. In the progeny of sires with a reduced PFAT, ICDH activity and myoglobin concentration were reduced by 0.46 μmol/min/g tissue and 0.67 mg/g tissue across the 5 and 6 mm PFAT ranges respectively. In the progeny of sires with an increased PEMD, ICDH activity and myoglobin concentration were reduced by 0.50 μmol/min/g tissue and 0.49 mg/g tissue across the 7 and 6 mm PEMD ranges respectively. However, the sites at which the lambs were raised had a larger impact on oxidative capacity than genetic or other production factors.
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Does selection for Lean Meat yield reduce the sensory scores of Australian lamb
2013Co-Authors: L. Pannier, A.j. Ball, Graham E. Gardner, David W. PethickAbstract:Sensory enjoyment is one of the key drivers that influences the consumer demand for lamb in Australia. One of the key factors that determines consumer satisfaction of lamb is intramuscular fat (IMF). Yet the challenge is to balance this against the industry aim of selecting for Lean Meat yield using Australian Sheep Breeding values for post-weaning eye muscle depth (PEMD) and subcutaneous fat depth at the c-site (PFAT), as these have been shown to decrease IMF levels. Hence, we hypothesised that selection for reduced PFAT and increased PEMD will reduce the sensory scores of lamb and that this relationship is driven through reduced LMF levels. Sensory scores were generated on both the longissimus thoracis et lumborum (loin) and semimembranosus (topside) muscle from 1,434 lambs. Five day aged grilled steaks were tasted by untrained consumers who scored (1-100 score) the samples for tenderness, juiciness, flavour, odour and overall liking. Increasing PEMD was associated with 5.3, 3.6 and 3.1 lower sensory scores for tenderness, overall liking and flavour for both the loin and topside samples. Decreasing PFAT was associated with a 3.1 score reduction for tenderness within the loin samples only All sensory scores increased with higher IMF levels, most strongly for juiciness and flavour, however in this analysis variation in IME levels did not appear to explain the impact of either PEMD or PFAT. This illustrates that the associations seen between PEMD and PFAT with the sensory scores are not solely driven through the phenotypic impact of IMF, in contrast to our initial hypothesis. Yet in support of our hypothesis, selection for more muscular and Leaner animals did reduce the sensory score, confirming our growing concerns that selecting for Lean Meat yield would reduce consumer eating quality. This highlights the need for careful monitoring of selection programs to maintain the eating quality of lamb.
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using australian sheep breeding values to increase Lean Meat yield percentage
Animal Production Science, 2010Co-Authors: A.j. Ball, Graham E. Gardner, A Williams, J P Siddell, S I Mortimer, R H Jacob, K L PearceAbstract:This study describes the impact of Australian Sheep Breeding Values (ASBV) for post-weaning weight (PWWT), C-site fatness (PFAT) and eye muscle depth (PEMD) on lamb carcasses within the Australian Sheep Industry CRC Information Nucleus Flock. These results are taken from the 2007 drop progeny, consisting of ~2000 lambs slaughtered at a target weight of 21.5 kg. These lambs were the progeny of sires selected to ensure genetic diversity across various production traits. As expected, the PWWT ASBV increased weight at slaughter, and hot standard carcass weight. Dressing percentage was markedly improved by increasing PEMD ASBV, thus prime lamb producers will be maintaining an animal of similar weight on farm, but delivering a markedly larger carcass at slaughter. Lean Meat yield % (LMY%) was highest in the progeny of sires with low PFAT ASBV, which decreased whole carcass fatness and increased muscularity. PWWT ASBV affected carcass composition but had little impact on LMY%, as the decreased fatness was largely offset by increased bone, with relatively little change in muscle content. Lastly, PEMD ASBV had little impact on whole carcass LMY%, but did appear to cause some level of muscle redistribution to the higher value loin cuts, in turn increasing the value of the carcass Lean.
E Safari - One of the best experts on this subject based on the ideXlab platform.
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video image analysis in the australian Meat industry precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat Science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
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Video image analysis in the Australian Meat industry – precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
John Mitchell Thompson - One of the best experts on this subject based on the ideXlab platform.
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video image analysis in the australian Meat industry precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat Science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.
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Video image analysis in the Australian Meat industry – precision and accuracy of predicting Lean Meat yield in lamb carcasses
Meat science, 2004Co-Authors: David L. Hopkins, E Safari, John Mitchell Thompson, Cr SmithAbstract:Abstract A wide selection of lamb types of mixed sex (ewes and wethers) were slaughtered at a commercial abattoir and during this process images of 360 carcasses were obtained online using the VIAScan® system developed by Meat and Livestock Australia. Soft tissue depth at the GR site (thickness of tissue over the 12th rib 110 mm from the midline) was measured by an abattoir employee using the AUS-Meat sheep probe (PGR). Another measure of this thickness was taken in the chiller using a GR knife (NGR). Each carcass was subsequently broken down to a range of trimmed boneless retail cuts and the Lean Meat yield determined. The current industry model for predicting Meat yield uses hot carcass weight (HCW) and tissue depth at the GR site. A low level of accuracy and precision was found when HCW and PGR were used to predict Lean Meat yield ( R 2 =0.19, r.s.d.=2.80%), which could be improved markedly when PGR was replaced by NGR ( R 2 =0.41, r.s.d.=2.39%). If the GR measures were replaced by 8 VIAScan® measures then greater prediction accuracy could be achieved ( R 2 =0.52, r.s.d.=2.17%). A similar result was achieved when the model was based on principal components (PCs) computed from the 8 VIAScan® measures ( R 2 =0.52, r.s.d.=2.17%). The use of PCs also improved the stability of the model compared to a regression model based on HCW and NGR. The transportability of the models was tested by randomly dividing the data set and comparing coefficients and the level of accuracy and precision. Those models based on PCs were superior to those based on regression. It is demonstrated that with the appropriate modeling the VIAScan® system offers a workable method for predicting Lean Meat yield automatically.