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

Animal

Volumn 2, Issue 7, 2008, Pages 1093-1100

  Indurain, G ^a   Goni V ^a   Horcada, A ^b   Insausti, K ^a   Hernandez B ^a   Beriain, M J ^a  

^a PUBLIC UNIVERSITY OF NAVARRE   (Spain)

^b UNIVERSITY OF SEVILLE   (Spain)

Author keywords

Beef; Carcass classification; Colour

Indexed keywords

EID: 44949131831     PISSN: 17517311     EISSN: 1751732X     Source Type: Journal    
DOI: 10.1017/S1751731108002243     Document Type: Article

References (29)

1. Alberti P, Ripoll G, Goyache F, Lahoz F, Olleta JL, Panea B and Sañudo C 2005. Carcass characterisation of seven Spanish beef breeds slaughtered at two commercial weights. Meat Science 71, 514-521.
2. 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.
3. American Meat Science Association 2001. Meat evaluation handbook. American Meat Science Association, Savoy, IL, USA.
4. Association of Official Analytical Chemists 2000. Official method of analysis, 17th edition. AOAC, Washington DC, USA.
5. 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.
6. Cannell RC, Belk KE, Tatum JD, Wise JW, Chapman PL, Scanga JA and 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.
7. 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.
8. de Boer H, Dumont BL, Pomeroy RW and Weniger JH 1974. Manual on EAAP reference methods for the assessment of carcass characteristics in cattle. Livestock Production Science 1, 151-164.
9. Denoyelle C and Berny F 1999. Objective measurement of veal colour for classification purposes. Meat Science 53, 203-209.
10. Díez J, Bahamonde A, Alonso J, López S, del Coz JJ, Quevedo JR, Ranilla J, Luaces O, Alvarez I, Royo LJ and Goyache F 2003. Artificial intelligence techniques point out differences in classification performance between Light and Standard carcasses. Meat Science 64, 249-258.
11. Díez J, Alberti P, Ripoll G, Lahoz F, Fernández I, Olleta JL, Panea B, Sañudo C, Bahamonde A and Goyache F 2006. Using machine learning procedures to ascertain the influence of beef carcasses profile on carcass conformation scores. Meat Science 73, 109-115.
12. Eikelenboom G, Hoving-Bolink AH and Hulsegge B 1992. Evaluation of invasive instruments for assessment of veal colour at time of classification. Meat Science 31, 343-349.
13. 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.htm
14. 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.
15. Goñi MV, Beriain MJ, Indurain G and Insausti K 2007. Predicting longissimus dorsi texture characteristics in beef based on early post-mortem colour measurements. Meat Science 76, 38-45.
16. Gorraiz C, Beriain MJ, Chasco J and 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.
17. Goyache F, Bahamonde A, Alonson J, López S, del Coz JJ, Quevedo JR, Ranilla J, Luaces O, Alvarez I, Royo L and 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.
18. Guignot F, Touraille C, Ouali A, Renerre M and Monin G 1994. Relationships between post-mortem pH changes and some traits of sensory quality in veal. Meat science 37, 315-325.
19. ISO R2917-1974. Measurement of pH (Reference method). International Standards: meat and meat products. International Organization for Standardisation, Geneva, Switzerland.
20. Jurie C, Robelin J, Picard B, Renand G and Geay Y 1995. Inter-animal variation in biological characteristics of muscle tissue in male Limousin cattle. Meat Science 39, 415-425.
21. Klont RE, Barnier VMH, Smulders FJM, Van Dijk A, Hoving-Bolink AH and 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.
22. Lagoda HL, Wilson LL, Henning WR, Flowers SL and Mills EW 2002. Subjective and objective evaluation of veal lean color. Journal of Animal Science 80, 1911-1916.
23. Legras P 1981. The colour of veal. Objective measurement or visual evaluation. Viande et Produits Carnés 2, 17-23.
24. MacDougall DB 1982. Changes in the colour and opacity of meat. Food Chemistry 9, 75-88.
25. Steiner R, Wyle AM, Vote DJ, Belk KE, Scanga JA, Wise JW, Tatum JD and 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.
26. Vote DJ, Belk KE, Tatum JD, Scanga JA and Smith GC 2003. Prediction of beef tenderness using a computer vision system equipped with a BeefCam module. Journal of Animal Science 81, 457-465.
27. Wulf DM and 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.
28. Wulf DM, O'Connor SF, Tatum JD and Smith GC 1997. Using objective measures of muscle color to predict beef longissimus tenderness. Journal of Animal Science 75, 684-692.
29. Wyle AM, Vote DJ, Roebert DL, Cannell RC, Belk KE, Scanga JA, Goldberg M, Tatum JD and 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.