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Segura J, Aalhus JL, Prieto N, Zawadski S, Scott H, López-Campos Ó. Prediction of primal and retail cut weights, tissue composition and yields of youthful cattle carcasses using computer vision systems; whole carcass camera and/or ribeye camera. Meat Sci 2023; 199:109120. [PMID: 36791485 DOI: 10.1016/j.meatsci.2023.109120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/29/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023]
Abstract
The application of two computer vision systems (CVS) was evaluated to predict primal and retail cut composition in youthful beef carcasses. Left carcass sides from a total of 634 animals were broken down into primal cuts, scanned using dual-energy x-ray absorptiometry for the prediction of tissue composition and fabricated into retail cuts. Cold carcass camera (CCC) images led to higher R2 values than hot carcass camera (HCC) images. The CVS coefficients of prediction for the primal cut weights ranged from 0.61 to 0.97. For the primal cut tissue composition predictions, R2 values ranged from 0.09 for Brisket HCC bone prediction to 0.82 for Chuck CCC fat prediction. Retail cut weight estimations had similar R2 values, ranging from 0.10 for IMPS 112 (Ribeye roll-denuded ribeye) to 0.99 for IMPS 113C (semi-boneless chuck) both using CCC. The results suggest the feasibility of CVS technologies to predict beef primal and retail cuts weights together with tissue composition, and yield percentages.
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Affiliation(s)
- José Segura
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada
| | - Jennifer L Aalhus
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada
| | - Nuria Prieto
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada
| | - Sophie Zawadski
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada
| | - Haley Scott
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada
| | - Óscar López-Campos
- Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, 6000 C&E Trail, Lacombe, Alberta T4L 1W1, Canada.
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Xavier C, Morel I, Dohme-Meier F, Siegenthaler R, Le Cozler Y, Lerch S. Estimation of carcass chemical composition in beef-on-dairy cattle using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut. J Anim Sci 2023; 101:skad380. [PMID: 37950488 PMCID: PMC10718802 DOI: 10.1093/jas/skad380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 11/08/2023] [Indexed: 11/12/2023] Open
Abstract
The aim of the present study was to estimate the chemical composition (water, lipid, protein, mineral, and energy contents) of carcasses measured postmortem using dual-energy X-ray absorptiometry (DXA) scans of cold half-carcass or 11th rib cut. One hundred and twenty beef-on-dairy (dam: Swiss Brown, sire: Angus, Limousin, or Simmental) bulls (n = 66), heifers (n = 42), and steers (n = 12) were included in the study. The reference carcass composition measured after grinding, homogenization, and chemical analyses was estimated from DXA variables using simple or multiple linear regressions with model training on 70% (n = 84) and validation on 30% (n = 36) of the observations. In the validation step, the estimates of water and protein masses from the half-carcass (R2 = 0.998 and 0.997; root mean square error of prediction [RMSEP], 1.0 and 0.5 kg, respectively) and 11th rib DXA scans (R2 = 0.997 and 0.996; RMSEP, 1.5 and 0.5 kg, respectively) were precise. Lipid mass was estimated precisely from the half-carcass DXA scan (R2 = 0.990; RMSEP = 1.0 kg) with a slightly lower precision from the 11th rib DXA scan (R2 = 0.968; RMSEP = 1.7 kg). Mineral mass was estimated from half-carcass (R² = 0.975 and RMSEP = 0.3 kg) and 11th rib DXA scans (R2 = 0.947 and RMSEP = 0.4 kg). For the energy content, the R2 values ranged from 0.989 (11th rib DXA scan) to 0.996 (half-carcass DXA scan), and the RMSEP ranged from 36 (half-carcass) to 55 MJ (11th rib). The proportions of water, lipids, and energy in the carcasses were also precisely estimated (R2 ≥ 0.882) using either the half-carcass (RMSEP ≤ 1.0%) or 11th rib-cut DXA scans (RMSEP ≤ 1.3%). Precision was lower for the protein and mineral proportions (R2 ≤ 0.794, RMSEP ≤ 0.5%). The cattle category (sex and breed of sire) effect was observed only in some estimative models for proportions from the 11th rib cut. In conclusion, DXA imaging of either a cold half-carcass or 11th rib cut is a precise method for estimating the chemical composition of carcasses from beef-on-dairy cattle.
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Affiliation(s)
- Caroline Xavier
- Ruminant Nutrition and Emissions, Agroscope, 1725 Posieux, Switzerland
- PEGASE INRAE-Institut Agro Rennes-Angers, 16 Le Clos, 35590 Saint-Gilles, France
| | - Isabelle Morel
- Ruminant Nutrition and Emissions, Agroscope, 1725 Posieux, Switzerland
| | | | | | - Yannick Le Cozler
- PEGASE INRAE-Institut Agro Rennes-Angers, 16 Le Clos, 35590 Saint-Gilles, France
| | - Sylvain Lerch
- Ruminant Nutrition and Emissions, Agroscope, 1725 Posieux, Switzerland
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Xavier C, Driesen C, Siegenthaler R, Dohme-Meier F, Le Cozler Y, Lerch S. Estimation of Empty Body and Carcass Chemical Composition of Lactating and Growing Cattle: Comparison of Imaging, Adipose Cellularity, and Rib Dissection Methods. Transl Anim Sci 2022; 6:txac066. [PMID: 35702177 PMCID: PMC9186311 DOI: 10.1093/tas/txac066] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/18/2022] [Indexed: 11/21/2022] Open
Abstract
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 11th rib dual-energy X-ray absorptiometry (DXA) scans, subcutaneous and perirenal adipose cell size (ACS), and dissection of the 11th rib. Additionally, three-dimensional (3D) images were captured for 8 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), 11th rib DXA scan (RMSEP = 3 kg, R2 = 0.96), 11th 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 11th 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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Affiliation(s)
- Caroline Xavier
- Ruminants Research Group, Agroscope, Posieux, Switzerland
- PEGASE INRAE-Institut Agro, Le Clos, Saint Gilles, France
| | - Charlotte Driesen
- Ruminants Research Group, Agroscope, Posieux, Switzerland
- Empa, Laboratory for Advanced Analytical Technologies, Überlandstrasse, Dübendorf, Switzerland
| | - Raphael Siegenthaler
- Agroscope, Research Contracts Animals, Route de la Tioleyre, Posieux, Switzerland
| | | | | | - Sylvain Lerch
- Ruminants Research Group, Agroscope, Posieux, Switzerland
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Leighton PL, Segura JD, Lam SD, Marcoux M, Wei X, Lopez-Campos OD, Soladoye P, Dugan ME, Juarez M, PRIETO NURIA. Prediction of carcass composition and meat and fat quality using sensing technologies: A review. MEAT AND MUSCLE BIOLOGY 2021. [DOI: 10.22175/mmb.12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
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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Huerta-Leidenz N. Progress on Nutrient Composition, Meat Standardization, Grading, Processing, and Safety for Different Types of Meat Sources. Foods 2021; 10:foods10092128. [PMID: 34574238 PMCID: PMC8469319 DOI: 10.3390/foods10092128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 09/07/2021] [Indexed: 11/17/2022] Open
Affiliation(s)
- Nelson Huerta-Leidenz
- Department of Animal and Food Sciences, Texas Tech University, P.O. Box 42141, Lubbock, TX 79409-2141, USA
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