Analysis of image-based measurements and USDA characteristics as predictors of beef lean yield

Lu, Wei; Tan, Jinglu

doi:10.1016/s0309-1740(03)00139-6

Cited by 19 publications

(5 citation statements)

References 15 publications

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“…In the literature, most of the studies considering the use of CVS in beef carcass classification have focused on the quantification of LMY, RCY, and/or the total amount of fat, lean, and bone using CCC systems. Among others, Farrow et al [ 31 ], Lu and Tan [ 32 ], McEvers et al [ 10 ], and Shackelford et al [ 33 ] used several variables obtained from the analysis of rib-eye images to define different regression equations to improve the accuracy, precision, and robustness of total tissue amount, LMY, or RCY estimations. The results reported by these authors (R 2 = 0.43–0.91) are within the range of those observed in the present study.…”

Section: Discussionmentioning

confidence: 99%

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

Segura

Aalhus

Prieto

et al. 2021

Foods

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This study determined the potential of computer vision systems, namely the whole-side carcass camera (HCC) compared to the rib-eye camera (CCC) and dual energy X-ray absorptiometry (DXA) technology to predict primal and carcass composition of cull cows. The predictability (R2) of the HCC was similar to the CCC for total fat, but higher for lean (24.0%) and bone (61.6%). Subcutaneous fat (SQ), body cavity fat, and retail cut yield (RCY) estimations showed a difference of 6.2% between both CVS. The total lean meat yield (LMY) estimate was 22.4% better for CCC than for HCC. The combination of HCC and CCC resulted in a similar prediction of total fat, SQ, and intermuscular fat, and improved predictions of total lean and bone compared to HCC/CCC. Furthermore, a 25.3% improvement was observed for LMY and RCY estimations. DXA predictions showed improvements in R2 values of 26.0% and 25.6% compared to the HCC alone or the HCC + CCC combined, respectively. These results suggest the feasibility of using HCC for predicting primal and carcass composition. This is an important finding for slaughter systems, such as those used for mature cattle in North America that do not routinely knife rib carcasses, which prevents the use of CCC.

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Section: Discussionmentioning

confidence: 99%

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

Segura

Aalhus

Prieto

et al. 2021

Foods

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show abstract

“…Both meat yield and distribution are the primary determinant of carcass value, which affect the value of meat cuts [2]. Studies have been conducted to examine the characteristics affecting carcass yield grade and retail product weight [3][4][5][6][7] and the retail product weight is mainly predicted based on the carcass weight (CW), longissimus dosi area, and carcass fat thickness (FT) [8][9][10]. A few studies have focused on the relationship between carcass scores and carcass composition values.…”

Section: Introductionmentioning

confidence: 99%

Relationship between body size traits and carcass traits with primal cuts yields in Hanwoo steers

Seo

Seong

et al. 2021

Anim Biosci

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Objective: This study was conducted to evaluate the correlation between body size traits, carcass traits, and primal cuts in Hanwoo steers.Methods: Sixty-one beef carcasses were classified for conformation and primal cut weight. Additionally, carcass weight, fat thickness, carcass dimensions, and <i>longissimus</i> muscle area were determined to complement the grading.Results: The average live weight and cold carcass weight were 759 and 469 kg, respectively. The mean carcass meat, fat, and bone proportions were 551, 298, and 151 g/kg, respectively. Primal cuts weights showed significant positive correlations (p<0.001) of 0.42 to 0.82 with live weight, carcass weight, and longissimus muscle area and a significant negative correlation with carcass fat (without shank, –0.38 to –0.10). Primal cut weights were positively correlated (p<0.01) with carcass length (0.41 to 0.77), forequarter length (0.33 to 0.57), 6th lumbar vertebrae–heel length (0.33 to 0.59), 7th cervical vertebrae carcass breadth (0.35 to 0.58), 5th to 6th thoracic vertebrae breadth (0.36 to 0.65), 7th to 8th thoracic vertebrae girth (0.38 to 0.63), and coxae girth (0.34 to 0.56) and non-significantly related to cervical vertebrae length and coxae thickness.Conclusion: There was a high correlation among live weight, carcass weight, <i>longissimus</i> muscle area, carcass length, 7th cervical vertebrae carcass breadth, 5th to 6th thoracic vertebrae breadth, and 7th to 8th thoracic vertebrae girth of the primal cuts yield. The correlation between fat and primal cut yields was highly significant and negative. Carcass length and 7th to 8th thoracic vertebrae girth, appear to be the most important traits affecting primal cut yields.

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“…In regard to carcass classification of domestic mammals, the research was mainly focused on either improving or replacing methods that are currently used. Many studies were carried out on classification or carcass quality evaluation in bovine carcasses (Borggaard et al, 1996;Hwang et al, 1997;Díez et al, 2003;Hatem et al, 2003;Lu & Tan, 2004), but also for lamb (Chandraratne et al, 2007) or goat (Peres et al, 2010). In these species the principle of grading is similar and consists of visual notes given by the classifier, which are the indicators of lean meat quantity.…”

Section: Application Of Ann For Carcass Quality or Classificationmentioning

confidence: 99%

“…In these species the principle of grading is similar and consists of visual notes given by the classifier, which are the indicators of lean meat quantity. In these cases the aim was either to predict carcass lean meat content (Hwang et al, 1997;Berg et al, 1998;Lu & Tan, 2004) or to replace the classifier using automated grading (Borggaard et al, 1996 Other studied applications were interested in prediction of fat depots based on in vivo measurements (Peres et al, 2010) or prediction of carcass maturity (Hatem et al, 2003). In the case of pig classification, studies using ANN are rare (Berg et al, 1998), probably because the current classification methods are based on objective measurements on the carcass which are well correlated to lean meat content thus providing sufficient accuracy using standard regression methods.…”

Section: Application Of Ann For Carcass Quality or Classificationmentioning

confidence: 99%

Application of Artificial Neural Networks in Meat Production and Technology

Prevolnik¹,

Škorjanc²,

Čandek-Potokar³

et al. 2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Analysis of image-based measurements and USDA characteristics as predictors of beef lean yield

Cited by 19 publications

References 15 publications

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows

Relationship between body size traits and carcass traits with primal cuts yields in Hanwoo steers

Application of Artificial Neural Networks in Meat Production and Technology

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