Prediction of composition traits of young Charolais bull carcasses using a morphometric method

Laville, Élisabeth; Martin, Victor; Bastien, Olivier

doi:10.1016/s0309-1740(96)00087-3

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…In the literature, carcass composition was less well predicted by dierent carcass traits and in vivo measurements than in the present work (Laville, Martin & Bastien, 1996;Renand & Fisher, 1997). The correlations between morphological traits as single predictors of carcass composition obtained by Laville et al, (1996) were moderate or low (r=0.69 for muscle weight, r=0.48 for fat weight).…”

Section: Resultscontrasting

confidence: 79%

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Estimation of the carcass composition of yearling bulls of “Asturiana de los Valles” breed from the dissection of a rib joint

et al. 2001

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To evaluate dierent methods of estimating bovine carcass composition, seventy yearling bulls of the``Asturiana de los Valles'' beef breed were slaughtered and their carcass composition estimated by a commercial dissection of the right half-carcass and by tissular dissection of the 6th and 10th rib of the left half-carcass. Correlation and regression analyses were used to identify the most accurate predictors of carcass composition. In simple regression, the percentage of lean in the 10th rib was the most accurate predictor (r=0.88; P<0.001) of the lean proportion in the carcass and the percentage of fat in the 6th rib was the best predictor (r=0.90; P<0.001) of the carcass fat content. The correlation coecients for estimating the bone percentage in the carcass from the bone proportion in the ribs (6th and 10th) were low (r=0.63 and 0.51 respectively), although signi®cant (P<0.001) and the coecient improved when the proportion of lean in the ribs was used as predictor (r=0.78 and 0.70 for the 6th and the 10th rib respectively). In multiple regression, the addition of more regressors of the rib composition and carcass traits, such as carcass hot weight, the carcass fatness score and the weight of the kidney knob and channel fat, led to an improvement (P<0.05) in accuracy for some predictions. The results in general show that the accuracy of the predictions for the carcass composition obtained from dissection of both the 6th and 10th ribs was similar, although the dissection of the 10th rib tended to overestimate the proportion of fat in the carcass. Therefore, considering the carcass quartering method for the extraction of the ribs, it is proposed that dissection of the 6th rib is more appropriate than dissection of the 10th rib so as not to reduce the carcass value and obtain a good estimate of the carcass composition. #

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Section: Resultscontrasting

confidence: 79%

“…The correlations between morphological traits as single predictors of carcass composition obtained by Laville et al, (1996) were moderate or low (r=0.69 for muscle weight, r=0.48 for fat weight). Only when using several carcass traits as predictors did they obtain a high ®t for the prediction of muscle weight (R 2 = 0.98).…”

Section: Resultsmentioning

confidence: 89%

Estimation of the carcass composition of yearling bulls of “Asturiana de los Valles” breed from the dissection of a rib joint

et al. 2001

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“…The first five PCs explain more than 85% of total variation for pH, chemical, and technological parameters and higher than 86% for textural parameters. Analyzing 76 morphometric variables from young Charolais bull carcasses, Laville et al [36] found that the first 10 PCs explained 80% of the total variability of those measurements. However, in rabbits, Hernández et al [37] reported the four first PCs for meat quality explained 62% of the total variation.…”

Section: Results and Discusionmentioning

confidence: 99%

Application of principal component analysis method for characterization chemical, technological, and textural parameters of farmed and pastured red deer

Šnirc

Král

Ošťádalová

et al. 2016

International Journal of Food Properties

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“…This multivariate statistical technique is used to find a smaller set of measurements explaining most of the observed variability in the measurements taken, but also helps in examining the relationships between traits and the differences between the groups of animals compared (Hernández et al, 2000). PC analysis has been used before to describe carcass characteristics (Laville et al, 1996;Hernández et al, 2000;Cañeque et al, 2004) and meat quality (Karlsson, 1992;Naes et al, 1996;Hernández et al, 1997;Hernández et al, 1998Hernández et al, , 2000Destefanis et al, 2000;Albertí et al, 2005;Cañeque et al, 2004) of several species and seems to be a very useful tool to visualize and interpret the data. Furthermore, other multivariate statistical methodologies, such as discriminant analysis (DA) can also be suitable Oliete et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

The use of multivariate analysis to characterize carcass and meat quality of goat kids protected by the PGI “Cabrito de Barroso”

et al. 2008

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Fifty five suckling kids from three genotypes and two sexes protected by the PGI "Cabrito de Barroso" European quality label were used in this experiment. Carcass quality was assessed using indices from carcass measurements, dressing percentages, refrigerated losses, higher priced joints proportion and tissue composition of the carcass. Meat pH, colour, total pigment, fat, dry matter, collagen determinations (total and soluble), cooking losses and shear force estimated in longissimus thoracis et lumborum (LTL) and gluteobiceps (GB) muscles were used to characterize meat quality. Principal component (PC) analysis was performed in order to examine carcass quality traits (n = 16) and meat quality (n = 16) traits. The five first principal components (PCs) explained about 86% of the total variability for carcass quality and 75% of the total variability for meat quality. Compactness indices of carcass and leg, carcass weight and subcutaneous fat were the most effective variables for the PC1, whereas the higher priced joints proportion, muscle proportions of the higher priced joints and of the carcass and the muscle and bone ratio were useful to define the PC2. The first PC of the meat quality parameters was characterized by colour traits (L ⁎ , b ⁎ , a ⁎ , C ⁎ , H ⁎ and total pigment) whereas collagen determinations (total collagen and collagen solubility) defined the second PC. When the carcass quality data were projected on the plane defined by the first two PCs, two separate groups of points appeared, corresponding to the animals with slaughter live weight higher or lower than 10 kg. The distribution of the meat quality data on the plane defined by the first two PCs allowed the identification of two separate groups, corresponding to the muscles GB and LTL. The differences between genotypes tend to be small and related to slaughter live weight, which implies certain constancy in carcass and meat quality of the PGI "Cabrito de Barroso".

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Prediction of composition traits of young Charolais bull carcasses using a morphometric method

Cited by 16 publications

References 13 publications

Estimation of the carcass composition of yearling bulls of “Asturiana de los Valles” breed from the dissection of a rib joint

Estimation of the carcass composition of yearling bulls of “Asturiana de los Valles” breed from the dissection of a rib joint

Application of principal component analysis method for characterization chemical, technological, and textural parameters of farmed and pastured red deer

The use of multivariate analysis to characterize carcass and meat quality of goat kids protected by the PGI “Cabrito de Barroso”

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