Dual-component video image analysis system (VIASCAN) as a predictor of beef carcass red meat yield percentage and for augmenting application of USDA yield grades.

Abstract

An improved ability to quantify differences in the fabrication yields of beef carcasses would facilitate the application of value-based marketing. This study was conducted to evaluate the ability of the Dual-Component Australian VIASCAN to 1) predict fabricated beef subprimal yields as a percentage of carcass weight at each of three fat-trim levels and 2) augment USDA yield grading, thereby improving accuracy of grade placement. Steer and heifer carcasses (n = 240) were evaluated using VIASCAN, as well as by USDA expert and online graders, before fabrication of carcasses to each of three fat-trim levels. Expert yield grade (YG), online YG, VIASCAN estimates, and VIASCAN estimated ribeye area used to augment actual and expert grader estimates of the remaining YG factors (adjusted fat thickness, percentage of kidney-pelvic-heart fat, and hot carcass weight), respectively, 1) accounted for 51, 37, 46, and 55% of the variation in fabricated yields of commodity-trimmed subprimals, 2) accounted for 74, 54, 66, and 75% of the variation in fabricated yields of closely trimmed subprimals, and 3) accounted for 74, 54, 71, and 75% of the variation in fabricated yields of very closely trimmed subprimals. The VIASCAN system predicted fabrication yields more accurately than current online yield grading and, when certain VIASCAN-measured traits were combined with some USDA yield grade factors in an augmentation system, the accuracy of cutability prediction was improved, at packing plant line speeds, to a level matching that of expert graders applying grades at a comfortable rate.

Prediction of Carcass Composition Using Carcass Grading Traits in Hanwoo Steers

“…However, the R 2 value of 23.5% for %RC is lower than the 54% reported by Choy et al (2010). On the other hand, the R 2 values reported for equations for %RC with exotic beef carcasses ranged from 32.2% (Williams et al, 1997) to 75% (Cannell et al, 1999), which are generally higher than the R 2 values from Eq. 3 and Eq.…”

A review of the development and use of video image analysis (VIA) for beef carcass evaluation as an alternative to the current EUROP system and other subjective systems

“…Several studies have investigated how VIA can supplement the visual USDA carcass grading system for assignment of yield grade, (Belk, Scanga, Tatum, Wise, & Smith, 1998;Cannell et al, 1999Cannell et al, , 2002Steiner et al, 2003;Wulf & Page, 2000;Wyle et al, 2003) and quality grade (Wulf & Page, 2000;Wyle et al, 2003). Some of these integrated approaches have resulted in commercial developments as outlined in Section 3.5.…”

“…In general, the accuracy of SMY% prediction increases as the level of fat left on the carcass decreases (Cannell et al, 1999;Vote et al, 2009) because fat trimming is a known source of variation. The lower average amount of variation in SMY% explained by the VIAscan system is largely a result of lower adjusted R 2 values reported by Ferguson, Thompson, Barrettt-Lennard et al (1995) based on four small groups of animals (~30) finished on vastly different diets.…”

Real-time augmentation of USDA yield grade application to beef carcasses using video image analysis1,2

“…On-line grader YG (whole-number), expert grader YG (to the nearest 0.1 of a YG), and VIASCAN-and CVS-augmented YG (to the nearest 0.1 of a YG) accounted for 55, 71, 60, and 63% of the variation in fabricated yields of closely trimmed subprimals, respectively, suggesting that VIA systems can operate at current plant speeds and effectively augment official USDA application of YG to beef carcasses. measure and precisely determine YG factors accurately estimated beef carcass composition, accounting for 70 to greater than 80% of the variation in beef carcass cutability (Abraham et al, 1980;Cannell et al, 1999). However, because beef carcasses are presented to USDA graders at line-speeds of 200 to 450 carcasses per hour, more precise YG assignment using traditional grading techniques is not feasible.…”