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S. D. Shackelford2, T. L. Wheeler, and M. Koohmaraie - page 9 / 10

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IMAGE ANALYSIS OF BEEF

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al., 1996). In the present experiment, image analysis more accurately predicted carcass cutability than did calculated yield grade.

Many image analysis systems have attempted to mimic the USDA yield grading process. That is, those systems have attempted to measure s.c. fat thickness and longissimus area. Our approach to estimating carcass cutability was much simpler and seems to be more effective. We simply measured the entire area of lean and fat in the surface of the 12th13th rib steak (Figure 1) and calculated percentage lean. By itself, percentage lean as determined by image analysis accounted for as much of the variation in RPYD as did yield grade.

Because we measured total fat area in the surface of the steak rather than s.c. fat thickness, our measurement of fat included s.c. and inter- and intramuscular fat depots. Inclusion of inter- and intramuscular fat probably increased our ability to predict carcass cutability (Jones et al., 1990; Shackel- ford et al., 1995).

The precision of our system was likely increased by the size and composition of the steak sampled. Rather than limiting the steak to the length of the longissi- mus muscle, we left an 8-cm long tail on the steak (Figure 1). The tail, which is commonly referred to as the lower rib region of carcasses, is frequently

Belk et al. (1998) proposed a system in which an instrument would be used to measure longissimus area, on-line USDA graders would assess carcass fatness, and yield grade would be calculated using a computer. Our system was quite accurate at predict- ing longissimus area and, thus, could serve as the instrumental method of obtaining longissimus area for such a system. Even if fat tears and other slaughter defects interfere with this system’s ability to assess carcass fatness under commercial conditions, this system would still be quite accurate at predicting longissimus area because LEAN, by itself, accounted for 85% of the variation in longissimus area.

The present data set did not contain sufficient variation in marbling or lean color to adequately investigate the ability of this technology to predict those traits. Given the economic importance of those traits to the beef industry, further research using cattle more typical of the U.S. fed-cattle slaughter population is needed.

We have developed a macro computer program that could be used in conjunction with the system we have described in this paper. Less than 9 s is required to capture the image, conduct image analysis, and output the data to a computer data base. Thus, this technology could be used to evaluate up to 400 carcasses per hour.

evaluated by graders when adjusting fat thickness.

Frequently, when carcasses are dressed, a portion of the s.c. fat cover is torn or removed. Thus, a concern with the practical use of image analysis to predict carcass cutability is that fat tears will lead to erroneous results. Indeed, in reviewing a series of experiments designed to evaluate image analysis systems, Belk et al. (1996) concluded that the major limitation to current image analysis systems was that they were much less accurate than USDA graders at assessing carcass fatness because of errors induced by fat tears and other slaughter defects. Even though the carcasses analyzed in the present data set were not void of fat tears, the incidence and severity of fat tears on our carcasses was probably less than those typical of large-scale commercial packing plants. Therefore, one might speculate that the accuracy of this technol- ogy may decline in practical application. However, the inclusion of the 8-cm-long tail in the image analysis steak and measuring the combined area of all fat depots rather than s.c. fat thickness should help to minimize the error-inducing effects of fat tears.

Belk et al. (1998) determined that the biggest limitation to on-line determination of USDA yield g r a d e w a s t h a t g r a d e r s c a n n o t a c c u r a t e l y ( R 2 = . 2 3 estimate longissimus area at chain speeds. Belk et al. (1998) demonstrated that yield grade could be ) d e t e r m i n e d a c c u r a t e l y ( R 2 = . 9 3 ) b y c o m b i n i n g o n - l i USDA graders’ assessments of fat thickness and kidney, pelvic, and heart fat with “gold standard” measurements of longissimus area and carcass weight. n e

Implications

The technology described herein could be used by the beef industry in combination with tenderness classification to accurately characterize beef for car- cass cutability, longissimus area, subprimal cut weights, and tenderness. These tools should help facilitate the development of value-based marketing systems.

Literature Cited

Belk, K. E., J. A. Scanga, J. D. Tatum, J. W. Wise, and G. C. Smith. 1998. Simulated instrument augmentation of USDA yield grade application to beef carcasses. J. Anim. Sci. 76:522527. Belk, K. E., J. D. Tatum, H. G. Dolezal, J. B. Morgan, and G. C. Smith. 1996. Status of applied research on instrument assess- ment of composition since completion of the 1994 National Beef Instrument Assessment planning symposium. Proc. Recip. Meat Conf. 49:172174. Boleman, S. L., S. J. Boleman, W. W. Morgan, D. S. Hale, D. B. Griffin, J. W. Savell, R. P. Ames, M. T. Smith, J. D. Tatum, T. G. Field, G. C. Smith, B. A. Gardner, J. B. Morgan, S. L. Northcutt, H. G. Dolezal, D. R. Gill, and F. K. Ray. 1998. National Beef Quality Audit-1995: Survey of producer-related defects and carcass quality and quantity attributes. J. Anim. Sci. 76:96103. Casas, E., J. W. Keele, S. D. Shackelford, M. Koohmaraie, T. S. Sonstegard, T.P.L. Smith, S. M. Kappes, and R. T. Stone. 1998. Association of the muscle hypertrophy locus with carcass traits in beef cattle. J. Anim. Sci. 76:468473.

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