Using computer vision technology to evaluate the meat tenderness of grazing beef

Food Australia

Volumn 57, Issue 8, 2005, Pages 322-326

  Tian, Yong Q ^a   McCall, David G ^b   Dripps, Weston ^a   Yu, Qian ^c   Gong, Peng ^c  

^a UNIVERSITY OF MASSACHUSETTS BOSTON   (United States)

^b New Zealand Agricultural Research Institute   (New Zealand)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION TECHNOLOGY; MEAT TENDERNESS; MULTIVARIABLE REGRESSION; NEW ZEALAND; RAW MEATS; SURFACE CHARACTERISTICS; SURFACE FEATURE; TEXTURAL CHARACTERISTIC;

FOOD TECHNOLOGY;

MEATS;

ANIMALIA;

EID: 23744511678     PISSN: 10325298     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Review

References (20)

1. Baublits, RT & others. 2004. Carcass and beef color characteristics of three biological types of cattle grazing cool-season forages supplemented with soyhulls. Meat Sci. 68: 297-303.
2. Gao, X & Tan, J. 1996. Analysis of expanded-food texture by image processing, Part I: Geometric properties. J Food Process Eng. 19: 425-444.
3. Gerrard, DE, Gao, X & Tan, J. 1996. Beef marbling and color score determination by image processing. J Food Sci. 61: 145-148.
4. Haralick, RM, Shanmugam, K & Dinstein, I. 1973. Textural features for image classification. IEEE Trans. on Sys. Man and Cybernetics, SMC-3: 610.
5. Harris, JJ, Miller, RK & Savell, JW. 1992. Evaluation of the tenderness of beef top sirloin steaks. J Food Sci. 57 (1): 6-9, 15.
6. Harris, SE & others. 2001. Antioxidant status affects color stability and tenderness of calcium chloride-injected beef. J Animal Sci. 79: 666-677.
7. Jeremiah, LE & Phillips, DM. 2000. Evaluation of a probe for predicting beef tenderness. Meat Sci. 55: 493-502.
8. Kim, ND & others. 1998. Ultrasound image texture analysis for characterizing intramuscular fat content of live beef cattle. Ultrasonic Imaging 20: 191-205.
9. Kosko, B. 1992. Neural Networks and Fuzzy Systems. Prentice Hall, Englewood Cliffs, NJ.
10. Li, J & others. 1999. Image texture features as indicators of beef tenderness. Meat Sci. 53: 17-22.
11. Li, J, Tan J & Shatadal, P. 2001. Classification of tough and tender beef by image texture analysis. Meat Sci. 57: 341-346.
12. Macfarlane, PG & Marer, JM. 1966. An apparatus for determing the tenderness of meat. Food Technol. 20: 134-135.
13. McDonald, TP & Chen,YR. 1991. Visual characterization of marbling in beef ribeyes and its relationship to taste parameters. Trans. ASAE 34: 2499-2504.
14. Park, B & others. 1998. Near-infrared reflectance analysis for predicting beef longissimus tenderness. J Animal Sci. 76: 2115-2120.
15. Savell, JW & others. 1989. National consumer retail beef study: Interaction of trim level price and grade on consumer acceptance of beefsteaks and roasts. J Food Quality 12: 251-274.
16. Silva, JA, Pararata, L & Martins, C. 1999. Influence of ultimate pH on bovine meat tenderness during ageing. Meat Sci. 52: 453-459.
17. Tan, JL. 2004. Meat quality evaluation by computer vision. J Food Eng. 61 (1): 27-35.
18. Vote, DJ & others. 2003. Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module. J Animal Sci. 81 (2): 457-465.
19. Watanabe, A, Daly, CC & Devine, CE. 1996. The effects of the ultimate pH of meat on tenderness changes during ageing. Meat Sci. 42: 67-78.
20. Wulf, DM & others. 1997. Using objective measures of muscle color to predict beef longissimus tenderness. J Animal Sci. 75: 684-692.