Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module1

Vote, D. J.; Belk, K. E.; Tatum, J. D.; Scanga, J. A.; Smith, G. C.

doi:10.2527/2003.812457x

Cited by 74 publications

(30 citation statements)

References 13 publications

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“…Among all techniques proposed, optical methods have been considered as the most promising candidate for online measurement of beef tenderness [6][7][8]. As light propagates inside muscle tissue, its intensity is attenuated due to absorption from pigments such as myoglobin and its derivatives.…”

Section: Introductionmentioning

confidence: 99%

Distribution of optical scattering properties in four beef muscles

Xia

Weaver

Gerrard

et al. 2008

Sens. & Instrumen. Food Qual.

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The objective of this study was to determine the influence of the mechanisms regulating tenderness on optical scattering properties of beef. Optical scattering properties of four beef muscles with different amounts of connective tissue and varying sarcomere lengths across a range of aging periods were determined. The optical scattering coefficient of beef muscles were measured based on a diffusive fitting of spatially resolved reflectance measurements. As a reference to beef tenderness, WarnerBratzler shear forces were also measured in this study. Variation in muscle composition resulted in a range of optical scattering properties across muscles. Our results implied that connective tissue and sarcomere structure have different effects on the optical scattering properties of muscle. In addition, proteolysis of muscle proteins significantly changes the optical scattering properties. These results provide useful information in understanding the originations of optical scattering in beef.

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Section: Introductionmentioning

confidence: 99%

Distribution of optical scattering properties in four beef muscles

Xia

Weaver

Gerrard

et al. 2008

Sens. & Instrumen. Food Qual.

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“…Of factors considered of primary importance in determining tenderness and overall palatability of cooked beef in the MSA grading system, there is ample US research-study support for use of marbling (Smith et al 1969(Smith et al , 2007Savell et al 1987Savell et al , 1989George et al 1999;Wyle 2000;Platter et al 2003bPlatter et al , 2005Gruber et al 2006), maturity (Berry et al 1974a(Berry et al , 1974bSmith et al 1982Smith et al , 1988Smith et al , 2008Hilton et al 1998), amount of B. indicus genetics (McKeith et al 1985a(McKeith et al , 1985bSherbeck et al 1995Sherbeck et al , 1996O'Connor et al 1997), sex (Choat et al 2006;Tatum et al 2007), tenderstretch carcass suspension (Smith et al , 1979(Smith et al , 2007Orts et al 1971;Hostetler et al 1975), ultimate pH (Smulders et al 1990;Tatum 1991a, 1994;Eilers et al 1996;Wulf et al 1997), meat colour (Jeremiah et al 1972;Wulf et al 1997;Cannell et al 2000;Wyle et al 2003;Vote et al 2003), fat colour (Hilton et al 1998;Wyle et al 1998Wyle et al , 2003Wyle 2000;Vote et al 2003) and subcu...…”

Section: Advantages Of the Tqm Approach To Assessing Beef Qualitymentioning

confidence: 99%

International perspective: characterisation of United States Department of Agriculture and Meat Standards Australia systems for assessing beef quality

Smith

Tatum

Belk

2008

Aust. J. Exp. Agric.

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Abstract. The intent, in this manuscript, is to characterise the United States Department of Agriculture (USDA) and Meat Standards Australia (MSA) systems for assessing beef quality and to describe the research evidence that supports the principles involved in grade application. USDA beef quality grading standards rely on carcass-trait-only assessments of approximate age of the animal at harvest and amount of intramuscular fat (as marbling) inside the muscles. USDA beef quality grading started 82 years ago. Then, as now, because no traceability system was in place, each animal's history (exact age, feeding regimen, management practices, etc.) was incomplete; those who assigned quality grades used indicators of age (physiological maturity) and plane of nutrition (amount of marbling), and they do so still. Since 1926, research studies have identified a multitude of palatability-determining live-animal factors (e.g. genetics, use of hormonal growth promotants, high-energy diet finishing) and carcass-treatment factors (e.g. electrical stimulation, tenderstretch carcass suspension, postmortem aging) that cannot be incorporated into a carcass-trait-only quality assessment system. The USA beef industry has depended on development of more than 100 beef brands -some using palatability assurance critical control point plans, total quality management (TQM) philosophies, USDA certification and process verification programs, or combinations of live-animal factors, carcass-treatment factors and carcass-trait constraints -to further differentiate fresh beef products. The MSA grading system is a TQM grading approach that incorporates animal-specific traits (e.g. genetics, sex, age), control of certain pre-harvest and post-harvest processes in the beef chain, cut-specific quality differences and consumer preferences, into a beef pricing system. A unique aspect of the MSA grading system is that the grades are assigned to cuts or muscles, not carcasses; cuts or muscles from the same carcass are assigned individual (and in many cases, different) grades that reflect differences in expected eating quality performance among the various cuts of beef further adjusted to reflect the influence of cut or muscle aging and alternative cooking methods. The MSA grading system is still being modified and refined (using results of an extensive, ongoing consumer testing program), but it represents the best existing example of a TQM grading approach for improving beef quality and palatability. Research studies have shown that the accuracy of palatability-level prediction by use of the two systems -USDA quality grades for US customers and consumers and MSA grades for Australian customers and consumers -is sufficient to justify their continued use for beef quality assessment.

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“…Meanwhile, video image analysis (VIA) and hyperspectral imaging, which uses a combination of VIA and NIRS, are becoming popular in meat classification systems (Naganathan et al, 2015). In VIA, digital video images of carcass sides or cuts are taken and processed in software to generate output variables, which are then evaluated for their relationship to meat quality attributes (Vote et al, 2003). The best opportunities for improving computer vision solutions lie in hyperspectral imaging (HIS), which provides additional information about meat composition and structure.…”

Section: Opportunities For Improving Beef Carcass Grading and Classifmentioning

confidence: 99%

Towards a regional beef carcass classification system for Southern Africa

Chingala¹,

Raffrenato²,

Dzama³

et al. 2017

SA J. An. Sci.

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Southern Africa is home to over 64 million cattle, of which 75% are raised on natural pasture in smallholder farming areas. Indigenous cattle breeds Bos indicus (zebu), B. taurus africanus (Sanga type) and their crosses with B. taurus (European and British) are the most dominant. Despite their dominance, indigenous cattle breeds in smallholder farming areas are commonly marketed through informal markets, and their contribution to formal national economies is therefore limited. This is partly because the current beef carcass grading and classification systems used in the region value inappropriately carcasses from slow-maturing indigenous cattle breeds that are ideally suited to being marketed off natural pasture. The existing systems use carcass yield and quality attributes, but do not predict eating quality at consumer level. Moreover, the principal criteria used to estimate carcass yield and quality, namely age, fat cover and conformation, are assessed indirectly and subjectively. The objective of the current review is to provide an overview of beef carcass grading and classification systems in Southern Africa and analyse their shortcomings in valuing carcasses from indigenous breeds and local production systems. In addition, the review highlights opportunities for improving these systems in Southern Africa and makes suggestions towards developing a regional beef carcass classification system.

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Online prediction of beef tenderness using a computer vision system equipped with a BeefCam module1

Cited by 74 publications

References 13 publications

Distribution of optical scattering properties in four beef muscles

Distribution of optical scattering properties in four beef muscles

International perspective: characterisation of United States Department of Agriculture and Meat Standards Australia systems for assessing beef quality

Towards a regional beef carcass classification system for Southern Africa

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