Use of carcass density for determining carcass composition in beef cattle

Johnson, ER; Chant, D. C.

doi:10.1080/00288233.1998.9513317

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…This is a novelty. On the other hand, contrary to our research, the investigations conducted in [ 19 , 20 , 21 , 22 , 23 ] did not identify different types of meat.…”

Section: Introductioncontrasting

confidence: 99%

“…It worth noticing that, recently some attempts have been undertaken to explore density measurements to investigate the properties of meat materials [ 19 , 20 , 21 , 22 , 23 ]. However, these works are mainly focused on the investigation of the physicochemical properties and chemical composition of meat materials, not on the discrimination of various types of MSM.…”

Section: Introductionmentioning

confidence: 99%

“…However, the methods proposed in [ 19 , 20 , 21 , 22 , 23 ] exhibit also some disadvantages. For example, in [ 23 ], meat samples were fabricated artificially with a specified fat content, the density of which was then evaluated.…”

Section: Introductionmentioning

confidence: 99%

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Application of Density Measurements for Discrimination and Evaluation of Chemical Composition of Different Types of Mechanically Separated Meat (MSM)

et al. 2022

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At present, the problem of identifying and controlling different types of Mechanically Separated Meat (MSM) is a very important practical issue in the meat industry. To address this challenge, the authors propose a new, analytical method for the discrimination and characterization of MSM that uses density measurements. The method proposed by the authors, in contrast to the analytical methods existing so far, is rapid, non-destructive, relatively simple and can be computerized. The density measurements of meat samples were conducted with a modified pycnometric method. Statistically significant (p<0.01) differences were found in the evaluated mean values of density for all investigated types of meat. Subsequently, the density measurements were correlated with the physicochemical properties of meat samples. A high correlation coefficient was found between the density of meat samples and the content of protein, sodium and fat. The authors have proven that density measurements allow for rapid discrimination of various types of MSM, and can also be effectively used to determine the chemical composition of MSM samples, e.g., the content of protein, fat and sodium.

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“…This is a novelty. On the other hand, contrary to our research, the investigations conducted in [ 19 , 20 , 21 , 22 , 23 ] did not identify different types of meat.…”

Section: Introductioncontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Density Measurements for Discrimination and Evaluation of Chemical Composition of Different Types of Mechanically Separated Meat (MSM)

et al. 2022

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“…Moreover, with a reported average cortical thickness of 2.3mm (± 0.13 mm) and Bone Mineral Density of 66 , bovine ribs are frequently employed in most bone simulation experiments used in agricultural as well as general surgical sciences. Finally, considering the grease, muscle and fat content of typical domestic bovine individuals 67 , alongside the general size of P. pardus teeth, it was concluded that the use of rib elements for this sample was the closest possible analogy to the tooth marks collected from other animals.…”

Section: Methodsmentioning

confidence: 96%

Developments in data science solutions for carnivore tooth pit classification

Courtenay

Herranz‐Rodrigo

González‐Aguilera

et al. 2021

Sci Rep

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Competition for resources is a key question in the study of our early human evolution. From the first hominin groups, carnivores have played a fundamental role in the ecosystem. From this perspective, understanding the trophic pressure between hominins and carnivores can provide valuable insights into the context in which humans survived, interacted with their surroundings, and consequently evolved. While numerous techniques already exist for the detection of carnivore activity in archaeological and palaeontological sites, many of these techniques present important limitations. The present study builds on a number of advanced data science techniques to confront these issues, defining methods for the identification of the precise agents involved in carcass consumption and manipulation. For the purpose of this study, a large sample of 620 carnivore tooth pits is presented, including samples from bears, hyenas, jaguars, leopards, lions, wolves, foxes and African wild dogs. Using 3D modelling, geometric morphometrics, robust data modelling, and artificial intelligence algorithms, the present study obtains between 88 and 98% accuracy, with balanced overall evaluation metrics across all datasets. From this perspective, and when combined with other sources of taphonomic evidence, these results show that advanced data science techniques can be considered a valuable addition to the taphonomist’s toolkit for the identification of precise carnivore agents via tooth pit morphology.

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“…According to Gardner et al (1997), the evaluation technique used to predict meat yield must be able to function online in a commercial setting without disrupting the normal product flow. Johnson and Chant (1998) noted that research has used very expensive technologies to improve the accuracy of carcass composition prediction, whereas Shackelford et al (1995) reported that, to their knowledge, equations to predict boneless and totally trimmed retail cut yields have not been published.…”

Section: Introductionmentioning

confidence: 99%

Predicting beef carcass meat, fat and bone proportions from carcass conformation and fat scores or hindquarter dissection

Conroy

Drennan

McGee

et al. 2010

Animal

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Equations for predicting the meat, fat and bone proportions in beef carcasses using the European Union carcass classification scores for conformation and fatness, and hindquarter composition were developed and their accuracy was tested using data from 662 cattle. The animals included bulls, steers and heifers, and comprised of Holstein-Friesian, early-and late-maturing breeds 3 HolsteinFriesian, early-maturing 3 early-maturing, late-maturing 3 early-maturing and genotypes with 0.75 or greater late-maturing ancestry. Bulls, heifers and steers were slaughtered at 15, 20 and 24 months of age, respectively. The diet offered before slaughter includes grass silage only, grass or maize silage plus supplementary concentrates, or concentrates offered ad libitum plus 1 kg of roughage dry matter per head daily. Following the slaughter, carcasses were classified mechanically for conformation and fatness (scale 1 to 15), and the right side of each carcass was dissected into meat, fat and bone. Carcass conformation score ranged from 4.7 to 14.4, 5.4 to 10.9 and 2.0 to 12.0 for bulls, heifers and steers, respectively; the corresponding ranges for fat score were 2.7 to 11.5, 3.2 to 11.3 and 2.8 to 13.3. Prediction equations for carcass meat, fat and bone proportions were developed using multiple regression, with carcass conformation and fat score both included as continuous independent variables. In a separate series of analyses, the independent variable in the model was the proportion of the trait under investigation (meat, fat or bone) in the hindquarter. In both analyses, interactions between the independent variables and gender were tested. The predictive ability of the developed equations was assed using cross-validation on all 662 animals. Carcass classification scores accounted for 0.73, 0.67 and 0.71 of the total variation in carcass meat, fat and bone proportions, respectively, across all 662 animals. The corresponding values using hindquarter meat, fat and bone in the model were 0.93, 0.87 and 0.89, respectively. The bias of the prediction equations when applied across all animals was not different from zero, but bias did exist among some of the genotypes of animals present. In conclusion, carcass classification scores and hindquarter composition are accurate and efficient predictors of carcass meat, fat and bone proportions.

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Use of carcass density for determining carcass composition in beef cattle

Cited by 3 publications

References 20 publications

Application of Density Measurements for Discrimination and Evaluation of Chemical Composition of Different Types of Mechanically Separated Meat (MSM)

Application of Density Measurements for Discrimination and Evaluation of Chemical Composition of Different Types of Mechanically Separated Meat (MSM)

Developments in data science solutions for carnivore tooth pit classification

Predicting beef carcass meat, fat and bone proportions from carcass conformation and fat scores or hindquarter dissection

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