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Prediction of foal carcass composition and wholesale cut yields by using video image analysis

Published online by Cambridge University Press:  11 July 2017

J. M. Lorenzo ,
C. M. Guedes ,
R. Agregán ,
M. V. Sarriés ,
D. Franco  and
S. R. Silva
J. M. Lorenzo*
Affiliation:
Centro Tecnológico de la Carne de Galicia, Rua Galicia No. 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
C. M. Guedes
Affiliation:
CECAV–Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
R. Agregán
Affiliation:
Centro Tecnológico de la Carne de Galicia, Rua Galicia No. 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
M. V. Sarriés
Affiliation:
EscuelaTécnica Superior de Ingenieros Agrónomos, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Spain
D. Franco
Affiliation:
Centro Tecnológico de la Carne de Galicia, Rua Galicia No. 4, Parque Tecnológico de Galicia, San Cibrao das Viñas, 32900 Ourense, Spain
S. R. Silva
Affiliation:
CECAV–Universidade de Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801 Vila Real, Portugal
*
†E-mail: jmlorenzo@ceteca.net
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Abstract

This work represents the first contribution for the application of the video image analysis (VIA) technology in predicting lean meat and fat composition in the equine species. Images of left sides of the carcass (n=42) were captured from the dorsal, lateral and medial views using a high-resolution digital camera. A total of 41 measurements (angles, lengths, widths and areas) were obtained by VIA. The variation of percentage of lean meat obtained from the forequarter (FQ) and hindquarter (HQ) carcass ranged between 5.86% and 7.83%. However, the percentage of fat (FAT) obtained from the FQ and HQ carcass presented a higher variation (CV between 41.34% and 44.58%). By combining different measurements and using prediction models with cold carcass weight (CCW) and VIA measurement the coefficient of determination (k-fold-R2) were 0.458 and 0.532 for FQ and HQ, respectively. On the other hand, employing the most comprehensive model (CCW plus all VIA measurements), the k-fold-R2 increased from 0.494 to 0.887 and 0.513 to 0.878 with respect to the simplest model (only with CCW), while precision increased with the reduction in the root mean square error (2.958 to 0.947 and 1.841 to 0.787) for the hindquarter fat and lean percentage, respectively. With CCW plus VIA measurements is possible to explain the wholesale value cuts yield variation (k-fold-R2 between 0.533 and 0.889). Overall, the VIA technology performed in the present study could be considered as an accurate method to assess the horse carcass composition which could have a role in breeding programmes and research studies to assist in the development of a value-based marketing system for horse carcass.

Keywords

carcass compositionvideo image analysispredictionfoalcommercial cutting
Type
Research Article
Information
animal , Volume 12 , Issue 1 , January 2018 , pp. 174 - 182
Copyright
© The Animal Consortium 2017 

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References

Allen, P and Finnerty, N 2000. Objective beef carcass classification. Report of a trial of three VIA classification systems. The National Food Centre, Dublin, Ireland.Google Scholar
Argo, CM, Dugdale, AHA, Curtis, GC and Morrison, PK 2014. Evaluating body composition in living horses: where are we up to? In Farm animal imaging Copenhagen (ed. CA Maltin, C Craigie and L Bunger), pp. 12–17. Quality Meat Scotland, Ingliston, UK.Google Scholar
Craigie, CR, Navajas, EA, Purchas, RW, Maltin, CA, Bünger, L, Hoskin, SO, Ross, DW, Morris, ST and Roehe, R 2012. A review of the development and use of video image analysis (VIA) for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems. Meat Science 92, 307–318.CrossRefGoogle Scholar
Cross, HR, Gilliland, DA, Durland, PR and Seideman, S 1983. Beef carcass evaluation by use of a video image analysis system. Journal of Animal Science 57, 908–917.CrossRefGoogle Scholar
Cunha, BCN, Belk, KE, Scanga, JA, LeValley, SB, Tatum, JD and Smith, GC 2004. Development and validation of equations utilizing lamb vision system output to predict lamb carcass fabrication yields. Journal of Animal Science 82, 2069–2076.CrossRefGoogle ScholarPubMed
Dugdale, AHA, Curtis, GC, Cripps, P, Harris, PA and Argo, CM 2010. Effect of dietary restriction on body condition, composition and welfare of overweight and obese pony mares. Equine Veterinary Journal 42, 600–610.CrossRefGoogle ScholarPubMed
FAOSTAT 2014. Online database of the Food and Agriculture Organization of the United Nations. Retrieved on 4 May 2016 from http://faostat.fao.org: Production.livestock primary and Trade. TradeSTAT. Crops and livestock products.Google Scholar
Ferguson, DM, Thompson, JM, Barrett-Lennard, D and Sorrensen, B 1995. Prediction of beef carcass yield using whole carcass VIAscan. In 41st Annual International Congress of Meat Science and Technology, 20 to 25 August, San Antonio, TX, USA, pp. 183–184.Google Scholar
Franco, D, Crecente, S, Vázquez, JA, Gómez, M and Lorenzo, JM 2013. Effect of cross breeding and amount of finishing diet on growth parameters, carcass and meat composition of foals slaughtered at 15 months of age. Meat Science 93, 547–556.CrossRefGoogle ScholarPubMed
Franco, D and Lorenzo, JM 2014. Effect of muscle and intensity of finishing diet on meat quality of foals slaughtered at 15 months. Meat Science 96, 327–334.CrossRefGoogle Scholar
Franco, D, Rodríguez, E, Purriños, L, Crecente, S, Bermúdez, R and Lorenzo, JM 2011. Meat quality of “Galician Mountain” foals breed. Effect of sex, slaughter age and livestock production system. Meat Science 88, 292–298.CrossRefGoogle ScholarPubMed
Hopkins, DL, Gardner, GE and Toohey, ES 2015. Australian view on lamb carcass and meat quality – the role of measurement technologies in the Australian sheep industry. In Farm animal imaging (ed. CA Maltin, C Craigie and L Bünger), pp. 17–21. Edinburgh, UK.Google Scholar
Hopkins, DL, Safari, E, Thompson, JM and Smith, CR 2004. Video image analysis in the Australian meat industry–precision and accuracy of predicting lean meat yield in lamb carcasses. Meat Science 67, 269–274.CrossRefGoogle Scholar
Lambe, NR, Navajas, EA, Bünger, L, Fisher, AV, Roehe, R and Simm, G 2009. Prediction of lamb carcass composition and meat quality using combinations of post-mortem measurements. Meat Science 81, 711–719.CrossRefGoogle ScholarPubMed
Lorenzo, JM, Crecente, S, Franco, D, Sarriés, MV and Gómez, M 2014a. The effect of livestock production system and concentrate level on carcass traits and meat quality of foals slaughtered at 18 months of age. Animal 8, 494–503.CrossRefGoogle ScholarPubMed
Lorenzo, JM, Fuciños, C, Purriños, L and Franco, D 2010. Intramuscular fatty acid composition of “Galician Mountain” foals breed: effect of sex, slaughtered age and livestock production system. Meat Science 86, 825–831.CrossRefGoogle ScholarPubMed
Lorenzo, JM, Sarriés, MV and Franco, D 2013. Sex effect on meat quality and carcass traits of foals slaughtered at 15 months of age. Animal 7, 1199–1207.CrossRefGoogle ScholarPubMed
Lorenzo, JM, Sarriés, MV, Tateo, A, Polidori, P, Franco, D and Lanza, M 2014b. Carcass characteristics, meat quality and nutritional value of horsemeat: a review. Meat Science 96, 1478–1488.CrossRefGoogle ScholarPubMed
MacNeil, MD 1983. Choice of a prediction equation and the use of the selected equation in subsequent experimentation. Journal of Animal Science 57, 1328–1336.CrossRefGoogle Scholar
Ngo, L, Ho, H, Hunter, P, Quinn, K, Thomson, A and Pearson, G 2016. Post-mortem prediction of primal and selected retail cut weights of New Zealand lamb from carcass and animal characteristics. Meat Science 112, 39–45.CrossRefGoogle ScholarPubMed
Oliver, A, Mendizabal, JA, Ripoll, G, Albertí, P and Purroy, A 2010. Predicting meat yields and commercial meat cuts from carcasses of young bulls of Spanish breeds by the SEUROP method and an image analysis system. Meat Science 84, 628–633.CrossRefGoogle Scholar
Pabiou, T, Fikse, WF, Cromie, AR, Keane, MG, Näsholm, A and Berry, DP 2011. Use of digital images to predict carcass cut yields in cattle. Livestock Science 137, 130–140.CrossRefGoogle Scholar
Rius-Vilarrasa, E, Bünger, L, Maltin, C, Matthews, KR and Roehe, R 2009. Evaluation of video image analysis (VIA) technology to predict meat yield of sheep carcasses on-line under UK abattoir conditions. Meat Science 82, 94–100.CrossRefGoogle ScholarPubMed
Rossel, RA, McGlynn, RN and McBratney, AB 2006. Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137, 70–82.CrossRefGoogle Scholar
Sarriés, MV and Beriain, MJ 2005. Carcass characteristics and meat quality of male and female foals. Meat Science 70, 141–152.CrossRefGoogle ScholarPubMed
Sarriés, MV, Murray, BE, Troy, D and Beriain, MJ 2006. Intramuscular and subcutaneous lipid fatty acid profile composition in male and female foals. Meat Science 72, 475–485.CrossRefGoogle ScholarPubMed
Silva, SR, Afonso, J, Guedes, CM, Gomes, MJ, Santos, VA, Azevedo, JMT and Dias-da-Silva, A 2016. Ewe whole body composition predicted in vivo by real-time ultrasonography and image analysis. Small Ruminant Research 136, 173–178.CrossRefGoogle Scholar
Sørensen, SE 1983. Possibilities for application of video image analysis in beef carcass classification. In Vivo measurement of body composition in meat animals (ed. N Apellido), pp. 113–122. Elsevier Applied Science Publishers, London, UK.Google Scholar
Sørensen, SE, Klastrup, S and Petersen, F 1988. Classification of bovine carcasses by means of video image analysis and reflectance probe measurements. In Proceedings of the 34th International Congress of Meat Science and Technology, 29 August to 2 September. Brisbane, Australia, pp. 635–638.Google Scholar
Wassenberg, RL, Allen, DM and Kemp, KE 1986. Video image analysis prediction of total kilograms and percent primal lean and fat yield of beef carcasses. Journal of Animal Science 62, 1609–1616.CrossRefGoogle Scholar
Znamirowska, A 2005. Prediction of horse carcass composition using linear measurements. Meat Science 69, 567–570.CrossRefGoogle ScholarPubMed