Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-15T07:30:28.043Z Has data issue: false hasContentIssue false
  • English
  • Français

The ability of video image analysis to predict lean meat yield and EUROP score of lamb carcasses

Published online by Cambridge University Press:  07 May 2014

E. Einarsson ,
E. Eythórsdóttir ,
C. R. Smith  and
J. V. Jónmundsson
E. Einarsson
Affiliation:
The Agricultural University of Iceland, Hvanneyri, 311 Borgarnes, Iceland
E. Eythórsdóttir*
Affiliation:
The Agricultural University of Iceland, Hvanneyri, 311 Borgarnes, Iceland
C. R. Smith
Affiliation:
Cedar Creek Company Unit, 10/11 62 Bishop St, Kelvin Grove, Brisbane, QLD 4059, Australia
J. V. Jónmundsson
Affiliation:
The Farmers Association of Iceland, Hagatorgi 1, 107 Reykjavik, Iceland
*
†E-mail: [email protected]
Get access

Abstract

A total of 862 lamb carcasses that were evaluated by both the VIAscan® and the current EUROP classification system were deboned and the actual yield was measured. Models were derived for predicting lean meat yield of the legs (Leg%), loin (Loin%) and shoulder (Shldr%) using the best VIAscan® variables selected by stepwise regression analysis of a calibration data set (n=603). The equations were tested on validation data set (n=259). The results showed that the VIAscan® predicted lean meat yield in the leg, loin and shoulder with an R2 of 0.60, 0.31 and 0.47, respectively, whereas the current EUROP system predicted lean yield with an R2 of 0.57, 0.32 and 0.37, respectively, for the three carcass parts. The VIAscan® also predicted the EUROP score of the trial carcasses, using a model derived from an earlier trial. The EUROP classification from VIAscan® and the current system were compared for their ability to explain the variation in lean yield of the whole carcass (LMY%) and trimmed fat (FAT%). The predicted EUROP scores from the VIAscan® explained 36% of the variation in LMY% and 60% of the variation in FAT%, compared with the current EUROP system that explained 49% and 72%, respectively. The EUROP classification obtained by the VIAscan® was tested against a panel of three expert classifiers (n=696). The VIAscan® classification agreed with 82% of conformation and 73% of the fat classes assigned by a panel of expert classifiers. It was concluded that VIAscan® provides a technology that can directly predict LMY% of lamb carcasses with more accuracy than the current EUROP classification system. The VIAscan® is also capable of classifying lamb carcasses into EUROP classes with an accuracy that fulfils minimum demands for the Icelandic sheep industry. Although the VIAscan® prediction of the Loin% is low, it is comparable to the current EUROP system, and should not hinder the adoption of the technology to estimate the yield of Icelandic lambs as it delivered a more accurate prediction for the Leg%, Shldr% and overall LMY% with negligible prediction bias.

Keywords

video image analysislean meat yieldlamb carcass qualityEUROP classificationcarcass conformation
Type
Full Paper
Information
animal , Volume 8 , Issue 7 , July 2014 , pp. 1170 - 1177
Copyright
© The Animal Consortium 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, P and McGeehin, B 2001. Measuring the lean content of carcasses using TOBEC. The National Food Centre, Teagasc, Dublin, Ireland, 24pp., http://www.teagasc.ie/research/reports/foodprocessing/4896/eopr-4896.pdf Google Scholar
Berg, EP, Forrest, JC, Thomas, DL, Nusbaum, N and Kauffman, RG 1994. Electromagnetic scanning to predict lamb carcass composition. Journal of Animal Science 72, 1728–1736.Google Scholar
Brady, AS, Belk, KE, LeValley, SB, Dalsted, NL, Scanga, JA, Tatum, JD and Smith, GC 2003. An evaluation of the lamb vision system as a predictor of lamb carcass red meat yield percentage. Journal of Animal Science 81, 1488–1498.Google 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.Google Scholar
Einarsdóttir, ÓB 1998. The use of probes to predict the composition of Icelandic lamb carcasses. MSc thesis, Publication No. 76, Swedish University of Agricultural Sciences, Department of Food Science, Uppsala, Sweden.Google Scholar
European Community 1992. Council Regulation (EEC) No 2137/92 of 23 July 1992. Official Journal of the European Communities L 214, 1–5.Google Scholar
Hopkins, DL 1996. The relationship between muscularity, muscle: bone ratio and cut dimensions in male and female lamb carcasses and the measurement of muscularity using image analysis. Meat Science 44, 307–317.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 ScholarPubMed
Horgan, GW, Murphy, SV and Simm, G 1995. Automatic assessment of sheep carcasses by image analysis. Animal Science 60, 197–202.Google Scholar
Johansen, J, Aastveit, AH, Egelandsdal, B, Kvaal, K and Røe, M 2006. Validation of the EUROP system for lamb classification in Norway; repeatability and accuracy of visual assessment and prediction of lamb carcass composition. Meat Science 74, 497–509.CrossRefGoogle ScholarPubMed
Kempster, AJ, Cuthbertson, A and Harrington, G 1982. The relationship between conformation and the yield and distribution of lean meat in the carcasses of British pigs, cattle and sheep: a review. Meat Science 6, 37–53.Google Scholar
Kongsro, J, Røe, M, Aastveit, AH, Kvaal, K and Egelandsdal, B 2008. Virtual dissection of lamb carcasses using computer tomography (CT) and its correlation to manual dissection. Journal of Food Engineering 88, 86–93.Google Scholar
Nsoso, SJ, Young, MJ and Beatson, PR 2000. A review of carcass conformation in sheep: assessment, genetic control and development. Small Ruminant Research 35, 89–96.Google Scholar
Pálsson, H 1939. Meat qualities in the sheep with special reference to Scottish breeds and crosses. I. Journal of Agricultural Science Cambridge 29, 544–625.Google Scholar
Purchas, RW and Wilkin, GH 1995. Characteristics of lamb carcasses of contrasting subjective muscularity. Meat Science 41, 357–368.Google 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.Google Scholar
SAS Institute, Inc. 2004. SAS OnlineDoc® 9.1.3. SAS Institute Inc, Cary, NC.Google Scholar
Stanford, K, Richmond, RJ, Jones, SDM, Robertson, WM, Price, MA and Gordon, AJ 1998. Video image analysis for on-line classification of lamb carcasses. Animal Science 67, 311–316.Google Scholar
The Ministry of Fisheries and Agriculture Reykjavík. 2010. Reglugerð um gæðamat, flokkun og merkingu sláturafurða nr. 882/2010, The Ministry of Fisheries and Agriculture Reykjavík, Iceland.Google Scholar
Thorgeirsson, S and Thorsteinsson, SS 1986. Carcass measurements and grading as indicators of tissue composition in Icelandic lambs. Paper presented at the 37th Annual Meeting of the European Association for Animal Production, Budapest, Hungary, 1–4 September.Google Scholar
Supplementary material: File

Einarsson supplementary material

Einarsson supplementary material

Download Einarsson supplementary material(File)
File 44.5 KB
Supplementary material: File

Einarsson Supplementary Material

Table S1 and Table S2

Download Einarsson Supplementary Material(File)
File 14.6 KB