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Afonso JJ, Almeida M, Batista AC, Guedes C, Teixeira A, Silva S, Santos V. Using Image Analysis Technique for Predicting Light Lamb Carcass Composition. Animals (Basel) 2024; 14:1593. [PMID: 38891640 PMCID: PMC11171010 DOI: 10.3390/ani14111593] [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: 04/19/2024] [Revised: 05/17/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Over the years, numerous techniques have been explored to assess the composition and quality of sheep carcasses. This study focuses on the utilization of video image analysis (VIA) to evaluate the composition of light lamb carcasses (4.52 ± 1.34 kg, mean cold carcass weight ± SD). Photographic images capturing the lateral and dorsal sides of fifty-five light lamb carcasses were subjected to analysis. A comprehensive set of measurements was recorded, encompassing dimensions such as lengths, widths, angles, areas, and perimeters, totaling 21 measurements for the lateral view images and 29 for the dorsal view images. K-Folds stepwise multiple regression analyses were employed to construct prediction models for carcass tissue weights (including muscle, subcutaneous fat, intermuscular fat, and bone) and their respective percentages. The most effective prediction equations were established using data from cold carcass weight (CCW) and measurements from both dorsal and lateral views. These models accounted for a substantial portion of the observed variation in the weights of all carcass tissues (with K-fold-R2 ranging from 0.83 to 0.98). In terms of carcass tissue percentages, although the degree of variation explained was slightly lower (with K-fold-R2 ranging from 0.41 to 0.78), the VIA measurements remained integral to the predictive models. These findings underscore the efficacy of VIA as an objective tool for assessing the composition of light lamb carcasses, which are carcasses weighing ≈ 4-8 kg.
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Affiliation(s)
- João J. Afonso
- Centre for Interdisciplinary Research in Animal Health (CIISA), Faculty of Veterinary Medicine, University of Lisbon, Avenida da Universidade Técnica, 1300-477 Lisboa, Portugal;
| | - Mariana Almeida
- Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (A.C.B.); (C.G.); (S.S.); (V.S.)
| | - Ana Catharina Batista
- Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (A.C.B.); (C.G.); (S.S.); (V.S.)
| | - Cristina Guedes
- Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (A.C.B.); (C.G.); (S.S.); (V.S.)
| | - Alfredo Teixeira
- Mountain Research Centre (CIMO), Escola Superior Agrária, Instituto Politécnico de Bragança, Campus Sta Apolónia Apt 1172, 5301-855 Bragança, Portugal;
| | - Severiano Silva
- Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (A.C.B.); (C.G.); (S.S.); (V.S.)
| | - Virgínia Santos
- Associate Laboratory of Animal and Veterinary Science (AL4AnimalS), Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (A.C.B.); (C.G.); (S.S.); (V.S.)
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Heggli A, Alvseike O, Bjerke F, Gangsei LE, Kongsro J, Røe M, Vinje H. Carcase grading reflects the variation in beef yield - a multivariate method for exploring the relationship between beef yield and carcase traits. Animal 2023; 17:100854. [PMID: 37285649 DOI: 10.1016/j.animal.2023.100854] [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: 02/17/2023] [Revised: 05/03/2023] [Accepted: 05/04/2023] [Indexed: 06/09/2023] Open
Abstract
Beef carcases in Europe are classified as a proxy for the quantity and ratio of tissues, commonly referred to as yield. It is important that proxies accurately measure yield as they contribute to financial transactions between abattoirs and producers. The main purpose of the study was therefore to examine the ability of EUROP carcase classification to explain the variation in yield. Furthermore, the effect of breed, as a confounder, was also examined. A multivariate definition of yield separating the carcase into six product categories was utilised as a response in a linear regression analysis. The conclusion was that EUROP and carcase features explain the majority of yield variation. Breed has an effect on yield beyond what is explained by carcase features including classification. The magnitude of the breed effects varies with breed and product category.
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Affiliation(s)
- A Heggli
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, NO-1432 Ås, Norway; Animalia, P.O. Box 396 - Økern, NO-0513 Oslo, Norway.
| | - O Alvseike
- Animalia, P.O. Box 396 - Økern, NO-0513 Oslo, Norway
| | - F Bjerke
- Animalia, P.O. Box 396 - Økern, NO-0513 Oslo, Norway
| | - L E Gangsei
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, NO-1432 Ås, Norway; Animalia, P.O. Box 396 - Økern, NO-0513 Oslo, Norway
| | - J Kongsro
- Animalia, P.O. Box 396 - Økern, NO-0513 Oslo, Norway
| | - M Røe
- Animalia, P.O. Box 396 - Økern, NO-0513 Oslo, Norway
| | - H Vinje
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), P.O. Box 5003, NO-1432 Ås, Norway
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Alempijevic A, Vidal-Calleja T, Falque R, Quin P, Toohey E, Walmsley B, McPhee M. Lean meat yield estimation using a prototype 3D imaging approach. Meat Sci 2021; 181:108470. [DOI: 10.1016/j.meatsci.2021.108470] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 02/12/2021] [Accepted: 02/15/2021] [Indexed: 11/29/2022]
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Visual Image Analysis for a new classification method of bovine carcasses according to EU legislation criteria. Meat Sci 2021; 183:108654. [PMID: 34419789 DOI: 10.1016/j.meatsci.2021.108654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 08/05/2021] [Accepted: 08/09/2021] [Indexed: 11/22/2022]
Abstract
In the European Community, conformation and fat cover of bovine carcasses is assessed using the SEUROP grading system. In this study we pursued the development of an application software (App) based on Visual Image Analysis, useful for SEUROP and Fat Cover grading of bovine carcasses using a smartphone. The App was trained using 500 bovine carcasses. Carcass conformation and Fat Cover classes were assessed in parallel by expert evaluators and by App. Overall, a high correspondence was found between the measurements of carcasses parameters by operators and by the App, as high as 84.2% for SEUROP and 86.4% for the Fat Cover. In the 15.8% of samples with discordant SEUROP evaluation, and in the 13.6% of samples with discordant Fat Cover evaluation, the operators' and App measurements deviated by only one class. All values also aligned with the requirements expected by the current legislation for the use of automated and/or semi-automated systems able to determine the market value of carcasses.
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Segura J, Aalhus JL, Prieto N, Larsen IL, Juárez M, López-Campos Ó. Carcass and Primal Composition Predictions Using Camera Vision Systems (CVS) and Dual-Energy X-ray Absorptiometry (DXA) Technologies on Mature Cows. Foods 2021; 10:foods10051118. [PMID: 34070040 PMCID: PMC8158109 DOI: 10.3390/foods10051118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 05/11/2021] [Accepted: 05/13/2021] [Indexed: 11/29/2022] Open
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.
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Addis AH, Blair HT, Morris ST, Kenyon PR, Schreurs NM. Prediction of the Hind-Leg Muscles Weight of Yearling Dairy-Beef Steers Using Carcass Weight, Wither Height and Ultrasound Carcass Measurements. Animals (Basel) 2020; 10:ani10040651. [PMID: 32283750 PMCID: PMC7222711 DOI: 10.3390/ani10040651] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 04/03/2020] [Accepted: 04/06/2020] [Indexed: 11/25/2022] Open
Abstract
Simple Summary Carcass classification and grading systems are typically inadequate for young cattle processed for beef production. Conformation of the hindquarter region of cattle has been used to classify and grade the whole carcass from older beef cattle. This study was initiated with the objective of providing a carcass classification and grading system based on hind-leg muscles weight. Prediction equations for the indirect prediction of saleable meat yield using hind-leg muscles weight from young dairy-origin steers were developed, and could be used for their carcass classification and grading. These equations avoid the need to isolate and track boneless subprimal cuts to establish the saleable meat yield of individual animals. Abstract Prediction equations have been widely utilized for carcass classification and grading systems in older beef cattle. However, the equations are mostly relevant for common beef breeds and 18 to 24 month old animals; there are no equations suitable for yearling, dairy-origin cattle. Therefore, this study developed prediction models using 60 dairy-origin, 8 to 12 month old steers to indicate saleable meat yield from hind-legs, which would assist with carcass classification and grading. Fat depth over the rump, rib fat depth, and eye muscle area between the 12th and 13th ribs were measured using ultrasound, and wither height was recorded one week prior to slaughter. The muscles from the hind-leg were retrieved 24 h after slaughter. Prediction equations were modeled for the hind-leg muscles weight using carcass weight, wither height, eye muscle area, rump, and rib fat depths as predictors. Carcass weight explained 61.5% of the variation in hind-leg muscles weight, and eye muscle area explained 39.9% (p < 0.05). Their combination in multivariate analysis explained 63.5% of the variation in hind-leg muscles weight. The R2 of the prediction in univariate and multivariate analyses was improved when data were analyzed per age group. Additional explanatory traits for yearling steers, including body length, hearth girth, and muscle depth and dimensions measured using video image analysis scanning (VIAscan), could improve the prediction ability of saleable meat yield from yearling dairy beef steers across the slaughter age groups.
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Araújo J, Lima A, Nunes M, Sousa M, Serrão G, Morais E, Daher L, Silva A. Relationships among carcass shape, tissue composition, primal cuts and meat quality traits in lambs: A PLS path modeling approach. Small Rumin Res 2020. [DOI: 10.1016/j.smallrumres.2019.106024] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Savoia S, Albera A, Brugiapaglia A, Di Stasio L, Ferragina A, Cecchinato A, Bittante G. Prediction of meat quality traits in the abattoir using portable and hand-held near-infrared spectrometers. Meat Sci 2019; 161:108017. [PMID: 31884162 DOI: 10.1016/j.meatsci.2019.108017] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2019] [Revised: 11/19/2019] [Accepted: 11/19/2019] [Indexed: 01/29/2023]
Abstract
The use of near-infrared spectrometers (NIRS) for predicting meat quality traits directly in the abattoir was tested with three trials. For the calibration trial, spectra were acquired from the cross-cut surface of the Longissimus thoracis muscle on 1166 carcasses of Piemontese young bulls with a portable visible-near-infrared spectrometer (Vis-NIRS) and with a small hand-held instrument (Micro-NIRS). A sample of the same muscle was analyzed to provide the reference. Validation statistics of the two instruments were similar. Predictabilities of meat color and purge loss were good, whereas for the other traits they were less promising. The repeatability trial showed that post-slaughter factors, not predictable by NIR spectra collected in the abattoir, affect reference meat quality values. A trial under operative conditions showed that both spectrometers were able to capture the major sources of variation in most of the meat quality traits. Overall, NIRS could be used to predict the animals' "native" characteristics exploitable for genetic improvement of meat quality traits.
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Affiliation(s)
- Simone Savoia
- Associazione Nazionale Allevatori dei Bovini di Razza Piemontese, Carrù, CN, Italy; Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro, PD, Italy.
| | - Andrea Albera
- Associazione Nazionale Allevatori dei Bovini di Razza Piemontese, Carrù, CN, Italy
| | - Alberto Brugiapaglia
- Department of Agricultural, Forest and Food Science, University of Torino, Via L. Da Vinci 44, 10095 Grugliasco, TO, Italy
| | - Liliana Di Stasio
- Department of Agricultural, Forest and Food Science, University of Torino, Via L. Da Vinci 44, 10095 Grugliasco, TO, Italy
| | - Alessandro Ferragina
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro, PD, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro, PD, Italy
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE) University of Padova (Padua), viale dell'Università 16, 35020 Legnaro, PD, Italy
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Nogalski Z, Pogorzelska-Przybyłek P, Sobczuk-Szul M, Purwin C. The effect of carcase conformation and fat cover scores (EUROP system) on the quality of meat from young bulls. ITALIAN JOURNAL OF ANIMAL SCIENCE 2019. [DOI: 10.1080/1828051x.2018.1549513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Zenon Nogalski
- Faculty of Animal Bioengineering, Katedra Hodowli Bydła i Oceny Mleka, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Paulina Pogorzelska-Przybyłek
- Faculty of Animal Bioengineering, Katedra Hodowli Bydła i Oceny Mleka, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Monika Sobczuk-Szul
- Faculty of Animal Bioengineering, Katedra Hodowli Bydła i Oceny Mleka, University of Warmia and Mazury in Olsztyn, Olsztyn, Poland
| | - Cezary Purwin
- Faculty of Animal Bioengineering, Katedra Żywienia Zwierząt i Paszoznawstwa, University of Warmia and Mazury, Olsztyn, Poland
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Toohey ES, van de Ven R, Hopkins DL. The value of objective online measurement technology: Australian red meat processor perspective. ANIMAL PRODUCTION SCIENCE 2018. [DOI: 10.1071/an17775] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
In the past, the adoption of online measurement technologies for measuring carcass and meat quality traits objectively has been low among Australian red meat processors. The aim of the present work was to obtain a greater understanding of Australian processor views on the value of objective online measurement technologies. This was achieved through consultation with 65 Australian processors, to understand which carcass and meat quality traits they considered important to objectively measure and what they thought of current and future technologies. It was shown that beef processors ranked meat colour and tenderness as the most important traits (P < 0.001) to objectively measure online. Sheep processors ranked tenderness, pH, age, meat colour, total tissue depth at the 12th rib 110 mm from the midline (GR) and saleable meat yield percentage as the most important traits (P < 0.001) to objectively measure online. The overall processor responses indicated that there is support for online measurement technologies, with 80% of processors stating that online objective grading systems have a role in the Australian meat processing sector now and 88% considered these to have a role in the future. Much can be learned from the implementation of previous online objective measurement technologies by processors in terms of commercialisation and adoption strategies. The development and adoption of objective online measurement technologies is challenging and complex. However, increased adoption of online measurement technologies has the potential to achieve benefits to the whole of industry and needs continued support, coupled with new approaches to enhance adoption.
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