A prototype rapid dual energy X-ray absorptiometry (DEXA) system can predict the CT composition of beef carcases

Calnan, H.B.; Williams, A.; Peterse, J.; Starling, S. G.; Cook, J. E.; Connaughton, S.L.; Gardner, G.E.

doi:10.1016/j.meatsci.2020.108397

Cited by 20 publications

(17 citation statements)

References 24 publications

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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 ).…”

Section: Discussionmentioning

confidence: 99%

“…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 ).…”

Section: Introductionmentioning

confidence: 99%

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

Xavier

Driesen

Siegenthaler

et al. 2022

Translational Animal Science

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The aim of present study was to compare in vivo and post mortem methods for estimating the empty body (EB) and carcass chemical compositions of Simmental lactating and growing cattle. Indirect methods were calibrated against the direct post mortem reference determination of chemical compositions of EB and carcass, determined after grinding and analyzing the water, lipid, protein, mineral masses, and energy content. The indirect methods applied to 12 lactating cows and 10 of their offspring were ultrasound (US), half-carcass and 11 th rib dual-energy X-ray absorptiometry (DXA) scans, subcutaneous and perirenal adipose cell size (ACS), and dissection of the 11 th rib. Additionally, three-dimensional (3D) images were captured for eight cows. Multiple linear regressions with leave-one-out-cross-validations were tested between predictive variables derived from the methods tested, and the EB and carcass chemical compositions. Partial least square regressions were used to estimate body composition with morphological traits measured on 3D images. Body weight (BW) alone estimated the EB and carcass composition masses with a root mean squared error of prediction (RMSEP) for the EB from 1 kg for minerals to 12.4 kg for lipids, and for carcass from 0.9 kg for minerals to 7.8 kg for water. Subcutaneous adipose tissue thickness measured by US was the most accurate in vivo predictor when associated with BW to estimate chemical composition, with the EB lipid mass RMSEP = 11 kg and R2 = 0.75; carcass water mass RMSEP = 6 kg and R2 = 0.98; and carcass energy content RMSEP = 236 MJ and R2 = 0.91. Post mortem, carcass lipid mass was best estimated by half-carcass DXA scan (RMSEP = 2 kg, R2 = 0.98), 11 th rib DXA scan (RMSEP = 3 kg, R2 = 0.96), 11 th rib dissection (RMSEP = 4 kg, R2 = 0.92), and perirenal ACS (RMSEP = 6 kg, R2 = 0.79) in this respective order. The results obtained by 11 th rib DXA scan were accurate and close to the half-carcass DXA scan with a reduction in scan time. Morphological traits from 3D images delivered promising estimations of the cow EB and carcass chemical component masses with an error less than 13 kg for the EB lipid mass and than 740 MJ for the EB energy. Future research is required to test the 3D imaging method on a larger number of animals to confirm and quantify its interest in estimating body composition in living animals.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Xavier

Driesen

Siegenthaler

et al. 2022

Translational Animal Science

View full text Add to dashboard Cite

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“…Additionally, Gardner et al (2021), also using an online DXA unit installed at a commercial abattoir, reported accurate predictions (R 2 p = 0.63 to 0.95) for several retail cut weights in lambs. In beef, Calnan et al (2021) have recently developed a prototype of a rapid DXA in a shipping container and reported high predictability for lean, fat, and bone of entire carcass sides (R 2 p = 0.85, 0.94, and 0.82, respectively) and forequarters (R 2 p = 0.83, 0.93, and 0.82, respectively) and moderate accuracy for hindquarters (R 2 p = 0.68, 0.80, and 0.73, respectively). It is important to note that, in all studies, CT was used as the standard method for calibration purposes; however, DXA technology is also becoming a reference technology for research studies on growth performance and carcass composition evaluation (Sousa dos .…”

Section: Dual-energy X-ray Absorptiometrymentioning

confidence: 99%

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Leighton

Marcoux

Wei

et al. 2022

Meat and Muscle Biology

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Consumer demand for high-quality healthy food is increasing,  thus meat processors require the means toassess these rapidly, accurately, and inexpensively. Traditional methods forquality assessments are time-consuming, expensive, invasive, and have potentialto negatively impact the environment. Consequently, emphasis has been put onfinding non-destructive, fast, and accurate technologies for productcomposition and quality evaluation. Research in this area is advancing rapidlythrough recent developments in the areas of portability, accuracy, and machinelearning. The present review, therefore, critically evaluates and summarizes developmentsof popular non-invasive technologies (i.e., from imaging to spectroscopicsensing technologies) for estimating beef, pork, and lamb composition andquality, which will hopefully assist in the implementation of thesetechnologies for rapid evaluation/real-timegrading of livestock products in the nearfuture.

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“…For this reason, more objective tools for classifying beef carcasses have been developed in recent years [3][4][5]. These include instrumental methods such as ultrasound [6][7][8][9], computed tomography [10,11], dual energy X-ray absorptiometry (DEXA) [12,13], a bioelectrical impedance analysis (BIA) [14], near-infrared spectroscopy [15,16], microwave systems [17] and, above all, an image analysis [18][19][20]. Artificial intelligence-based techniques for modelling carcass classification parameters by official graders have even been developed [21].…”

Section: Introductionmentioning

confidence: 99%

Predicting Beef Carcass Fatness Using an Image Analysis System

Mendizábal

Ripoll

Urrutia

et al. 2021

Animals

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The amount and distribution of subcutaneous fat is an important factor affecting beef carcass quality. The degree of fatness is determined by visual assessments scored on a scale of five fatness levels (the SEUROP system). New technologies such as the image analysis method have been developed and applied in an effort to enhance the accuracy and objectivity of this classification system. In this study, 50 young bulls were slaughtered (570 ± 52.5 kg) and after slaughter the carcasses were weighed (360 ± 33.1 kg) and a SEUROP system fatness score assigned. A digital picture of the outer surface of the left side of the carcass was taken and the area of fat cover (fat area) was measured using an image analysis system. Commercial cutting of the carcasses was performed 24 h post-mortem. The fat trimmed away on cutting (cutting fat) was weighed. A regression analysis was carried out for the carcass cutting fat (y-axis) on the carcass fat area (x-axis) to establish the accuracy of the image analysis system. A greater accuracy was obtained by the image analysis (R2 = 0.72; p < 0.001) than from the visual fatness scores (R2 = 0.66; p < 0.001). These results show the image analysis to be more accurate than the visual assessment system for predicting beef carcass fatness.

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A prototype rapid dual energy X-ray absorptiometry (DEXA) system can predict the CT composition of beef carcases

Cited by 20 publications

References 24 publications

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

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

Prediction of Carcass Composition and Meat and Fat Quality Using Sensing Technologies: A Review

Predicting Beef Carcass Fatness Using an Image Analysis System

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