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

Abstract

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.

Estimation of empty body and carcass chemical composition of lactating and growing cattle: comparison of imaging, adipose cellularity, and rib dissection methods

“…The DXA scan of half-carcass is the most accurate method tested to predict chemical composition of every chemical component. The DXA technology was calibrated for different breed types (purebred or crossbred) without observing breed effect on relationships for the estimation of carcass tissue composition (i.e., AT, muscles, and bone masses) using anatomical dissection ( López-Campos et al, 2018 ; Segura et al, 2021 ) or computer tomography ( Calnan et al, 2021 ) as a “gold standard” reference. Depending on the considered equations, the external validation performed by López-Campos et al (2018) reached R 2 from 0.93 to 0.99 for muscle mass, 0.74 to 0.98 for AT mass, and 0.60 to 0.94 for bone mass.…”

“…Although the half-carcass DXA scan is the most accurate method to estimate the carcass’ composition, it remains time consuming when dealing with large pieces and requires an expensive device. Still, using DXA in commercial slaughterhouses for beef carcass grading was initiated by Calnan et al (2021) and Segura et al (2021) . The use of such techniques at slaughterhouse line speed was, however, confronted with the technical challenge to develop a DXA device appropriate for a whole beef half-carcass, as realized for sheep ( Gardner et al, 2018 ; Connaughton et al, 2021 ).…”

“…In addition, they cannot be used on larger living animals, such as cow or beef, due to their size in comparison to the device capacity (e.g., bovine has a BW higher than 200 kg), whereas for smaller animals, such as sheep or goat, the need of no movement results in a routine use of anesthesia ( Hunter et al, 2011 ). Still, the use of DXA, in order to estimate the carcass tissue composition in beef cattle, was previously explored ( Lopez-Campos et al, 2018 ; Calnan et al, 2021 ; Segura et al, 2021 ). Recently, interest in the imaging of living beef cattle also increased with the development of in vivo 3D imaging thanks to 3D cameras ( Gomes et al, 2016 ; Miller et al, 2019 ; Cominotte et al, 2020 ).…”

Progress on Nutrient Composition, Meat Standardization, Grading, Processing, and Safety for Different Types of Meat Sources

“…Segura et al [ 8 ] determined the potential of computer vision systems (CVS), namely, the whole-side carcass camera (HCC), to the rib-eye camera (CCC), and the dual-energy X-ray absorptiometry (DXA) technology, to predict the composition of wholesale cuts and carcasses of mature cows. This comparative study [ 8 ] was carried out in Canada where the classification system segregates mature carcasses (for example, cows) as Canada D, with a series of designations (Canada D1 to D4) for the types of carcass that do not count on a method for predicting the cut-out yield before fabrication. The technologies used in the study [ 8 ] provided estimation values of the total amount of tissues and a general description of the composition of the entire carcass and its primal cuts without requiring the destructive procedure of dissection.…”

“…This comparative study [ 8 ] was carried out in Canada where the classification system segregates mature carcasses (for example, cows) as Canada D, with a series of designations (Canada D1 to D4) for the types of carcass that do not count on a method for predicting the cut-out yield before fabrication. The technologies used in the study [ 8 ] provided estimation values of the total amount of tissues and a general description of the composition of the entire carcass and its primal cuts without requiring the destructive procedure of dissection. The DXA technology could be considered the gold standard for estimating carcass composition.…”