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Colour differences among carcasses graded with similar score for conformation and fatness

Published online by Cambridge University Press:  01 July 2008

G. Indurain ,
V. Goñi ,
A. Horcada ,
K. Insausti ,
B. Hernández  and
M. J. Beriain
G. Indurain
Affiliation:
Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Pública de Navarra, Campus de Arrosadía 31006, Pamplona, Spain
V. Goñi
Affiliation:
Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Pública de Navarra, Campus de Arrosadía 31006, Pamplona, Spain
A. Horcada
Affiliation:
Escuela Universitaria de Ingeniería Técnica Agrícola, Universidad de Sevilla, Carretera de Utrera Km 15, 41013, Sevilla, Spain
K. Insausti
Affiliation:
Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Pública de Navarra, Campus de Arrosadía 31006, Pamplona, Spain
B. Hernández
Affiliation:
Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Pública de Navarra, Campus de Arrosadía 31006, Pamplona, Spain
M. J. Beriain*
Affiliation:
Escuela Técnica Superior de Ingenieros Agrónomos, Universidad Pública de Navarra, Campus de Arrosadía 31006, Pamplona, Spain
*
E-mail: [email protected]
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Abstract

In a population of 268 yearling bulls, those carcasses graded as U−, U0 or U+ for beef carcass conformation (n = 240) and those graded as 2−, 20 or 2+ for beef carcass fatness (n = 213) were selected to study the efficiency of carcass weight, carcass dimensions and instrumental colour of latissimus dorsi, rectusabdominis and subcutaneous fat, to discriminate among these carcass grades, in a population of high-muscled and very lean carcasses from young bulls. The increase in conformation grade meant an increase in carcass weight and perimeter of the leg. Classifiers use attributes characterizing muscular development and carcass profiles from a general impression of the whole carcass. There were no significant differences for carcass weight or carcass dimensions, among the carcasses classified according to the three fat classes. The a* and b* coordinate values for the latissimus dorsi muscle were observed to decrease significantly as the carcass conformation score increased (P < 0.05). However, muscle and subcutaneous fat of fatter carcasses showed higher a*, b* colour coordinates and chroma (C*) values than leaner carcasses. The CIE (Commission International de l’Éclairage) L*, a* and b* colour coordinate measurements taken on the carcasses 45 min post mortem varied significantly from the readings taken after hanging for 24 h (P < 0,001). The higher a* and b* values on the carcasses chilled for 24 h could be caused by oxygenation of both subcutaneous fat, and latissimusdorsi and rectusabdominis muscles in the time elapsing after slaughter and after carcass exposition to circulating air in the cooler for 24 h. Lightness of the latissimus dorsi muscle underwent a decrease, compared with an increase in the rectusabdominis muscle. Hardening of the subcutaneous fat during cold storage may exert an influence on the decrease in lightness observed. These differences in carcass colour during chilling storage would suggest that the relationship between carcass colour and conformation grades was higher shortly after slaughter. Both L* colour coordinate of fat colour (P < 0.01) and a*, b* and C* colour coordinates of latissimus dorsi muscle (P < 0.05) were related to conformation classification. Colour was more efficient to differentiate conformation than fat cover classes. Sixty-two percent of carcasses were correctly classified for conformation by colour differences but only 37% of carcasses were correctly classified for fatness by colour.

Keywords

beefcarcass classificationcolour
Type
Full Paper
Information
animal , Volume 2 , Issue 7 , July 2008 , pp. 1093 - 1100
Copyright
Copyright © The Animal Consortium 2008

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References

Alberti, P, Ripoll, G, Goyache, F, Lahoz, F, Olleta, JL, Panea, B, Sañudo, C 2005. Carcass characterisation of seven Spanish beef breeds slaughtered at two commercial weights. Meat Science 71, 514–521.CrossRefGoogle ScholarPubMed
Allen P and Finnerty N 2001. Mechanical grading of beef carcasses. En-of Porject Reports Teagasc ISBN 1 84170 626 5. The Irish Agriculture and Food Development Authority, Dublin, Ireland.Google Scholar
American Meat Science Association 2001. Meat evaluation handbook. American Meat Science Association, Savoy, IL, USA.Google Scholar
Association of Official Analytical Chemists 2000. Official method of analysis, 17th edition. AOAC, Washington DC, USA.Google Scholar
Barnier VMH, Klont RE, Van Dijk A, Eikelenboom G, Hoving-Bolink AH and Smulders FJM 1998. Post mortem variation in pH, temperature and colour profiles of electrically stimulated veal carcasses in relation to preslaughter blood haemoglobin content. Proceedings of the 44th International Congress of Meat Science and Technology, Barcelona, Spain, pp. 496–497.Google Scholar
Cannell, RC, Belk, KE, Tatum, JD, Wise, JW, Chapman, PL, Scanga, JA, Smith, GC 2002. Online evaluation of a commercial video image analysis system (Computer Vision System) to predict beef carcass red meat yield and for augmenting the assignation of USDA yield grade. Journal of Animal Science 80, 1195–1201.CrossRefGoogle Scholar
Commission International de l’Éclairage 1976. Official recommendation on uniform color space. Color difference equations and metric color terms, Suppl. 2. CIE Publication no. 15: Colorimetry. CIE, Paris, France.Google Scholar
de Boer, H, Dumont, BL, Pomeroy, RW, Weniger, JH 1974. Manual on EAAP reference methods for the assessment of carcass characteristics in cattle. Livestock Production Science 1, 151–164.CrossRefGoogle Scholar
Denoyelle, C, Berny, F 1999. Objective measurement of veal colour for classification purposes. Meat Science 53, 203–209.CrossRefGoogle ScholarPubMed
Díez, J, Bahamonde, A, Alonso, J, López, S, del Coz, JJ, Quevedo, JR, Ranilla, J, Luaces, O, Alvarez, I, Royo, LJ, Goyache, F 2003. Artificial intelligence techniques point out differences in classification performance between Light and Standard carcasses. Meat Science 64, 249–258.CrossRefGoogle ScholarPubMed
Díez, J, Alberti, P, Ripoll, G, Lahoz, F, Fernández, I, Olleta, JL, Panea, B, Sañudo, C, Bahamonde, A, Goyache, F 2006. Using machine learning procedures to ascertain the influence of beef carcasses profile on carcass conformation scores. Meat Science 73, 109–115.CrossRefGoogle ScholarPubMed
Eikelenboom, G, Hoving-Bolink, AH, Hulsegge, B 1992. Evaluation of invasive instruments for assessment of veal colour at time of classification. Meat Science 31, 343–349.CrossRefGoogle ScholarPubMed
European Commission 2007. Quality products catch the eye: PDO, PGI and TSG. Retrieved May 23, 2007 from http://ec.europa.eu/agriculture/foodquality/quali1_en.htmGoogle Scholar
Goñi V, Mendizabal JA, Beriain JM, Alberti P, Arana A, Aguinoa AP and Purroy A 1999. Marbrure de la viande de veaux de sept races á viande espagnoles determinée par analyse d’image. Proceedings of the 6th Meeting Rencontres Recherches Ruminants, Paris, France, 278pp.Google Scholar
Goñi, MV, Beriain, MJ, Indurain, G, Insausti, K 2007. Predicting longissimus dorsi texture characteristics in beef based on early post-mortem colour measurements. Meat Science 76, 38–45.CrossRefGoogle ScholarPubMed
Gorraiz, C, Beriain, MJ, Chasco, J, Insausti, K 2002. Effect of aging time on volatile compounds odor, and flavor of cooked beef from Pirenaica and Friesian bulls and heifers. Journal of Food Science 67, 916–922.CrossRefGoogle Scholar
Goyache, F, Bahamonde, A, Alonson, J, López, S, del Coz, JJ, Quevedo, JR, Ranilla, J, Luaces, O, Alvarez, I, Royo, L, Díez, J 2001. The usefulness of Artificial Intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science and Technology 12, 370–381.CrossRefGoogle Scholar
Guignot, F, Touraille, C, Ouali, A, Renerre, M, Monin, G 1994. Relationships between post-mortem pH changes and some traits of sensory quality in veal. Meat science 37, 315–325.CrossRefGoogle ScholarPubMed
ISO R2917-1974. Measurement of pH (Reference method). International Standards: meat and meat products. International Organization for Standardisation, Geneva, Switzerland.Google Scholar
Jurie, C, Robelin, J, Picard, B, Renand, G, Geay, Y 1995. Inter-animal variation in biological characteristics of muscle tissue in male Limousin cattle. Meat Science 39, 415–425.CrossRefGoogle ScholarPubMed
Klont, RE, Barnier, VMH, Smulders, FJM, Van Dijk, A, Hoving-Bolink, AH, Eikelenboom, G 1999. Post-mortem variation in pH, temperature and colour profiles of veal carcasses in relation to breed, blood haemoglobin content and carcass characteristics. Meat Science 53, 195–202.CrossRefGoogle ScholarPubMed
Lagoda, HL, Wilson, LL, Henning, WR, Flowers, SL, Mills, EW 2002. Subjective and objective evaluation of veal lean color. Journal of Animal Science 80, 1911–1916.CrossRefGoogle ScholarPubMed
Legras, P 1981. The colour of veal. Objective measurement or visual evaluation. Viande et Produits Carnés 2, 17–23.Google Scholar
MacDougall, DB 1982. Changes in the colour and opacity of meat. Food Chemistry 9, 75–88.CrossRefGoogle Scholar
Steiner, R, Wyle, AM, Vote, DJ, Belk, KE, Scanga, JA, Wise, JW, Tatum, JD, Smith, GC 2003. Real-time augmentation of USDA yield grade application to beef carcasses using video image analysis. Journal of Animal Science 81, 2239–2246.CrossRefGoogle ScholarPubMed
Vote, DJ, Belk, KE, Tatum, JD, Scanga, JA, Smith, GC 2003. Prediction of beef tenderness using a computer vision system equipped with a BeefCam module. Journal of Animal Science 81, 457–465.CrossRefGoogle ScholarPubMed
Wulf, DM, Page, JK 2000. Using measurements of muscle color, pH, and electrical impedance to augment the current USDA beef quality grading standards and improve the accuracy and precision of sorting carcasses into palatability groups. Journal of Animal Science 78, 2595–2607.CrossRefGoogle ScholarPubMed
Wulf, DM, O’Connor, SF, Tatum, JD, Smith, GC 1997. Using objective measures of muscle color to predict beef longissimus tenderness. Journal of Animal Science 75, 684–692.CrossRefGoogle ScholarPubMed
Wyle, AM, Vote, DJ, Roebert, DL, Cannell, RC, Belk, KE, Scanga, JA, Goldberg, M, Tatum, JD, Smith, GC 2003. Effectiveness of the SmartMV prototype BeefCam System to sort beef carcasses into expected palatability groups. Journal of Animal Science 81, 441–448.CrossRefGoogle ScholarPubMed