ABSTRACT: Video image analysis (VIA) images
from grain-fi nished beef carcasses [n = 211; of which
63 did not receive zilpaterol hydrochloride (ZIL) and
148 received ZIL before harvest] were analyzed for
indicators of muscle and fat to illustrate the ability to
improve methodology to predict saleable meat yield of
cattle fed and not fed ZIL. Carcasses were processed in
large commercial beef processing facilities and were
fabricated into standard subprimals, fat, and bone.
Images taken by VIA technology were evaluated using
computer image analysis software to quantify fat and
lean parameters which were subsequently used in
multiple-linear regression models to predict percentage
of saleable meat yield for each carcass. Prediction
models included variables currently quantifi ed by VIA
technology such as LM area (LMA), subcutaneous (SC)
fat thickness at 75% the length of the LM (SFT75),
and intramuscular fat score (IMF). Additional distance
and area measures included LM width (LW), LM depth
(LD), iliocostalis muscle area (IA), SC fat thickness at
25, 50, and 100% the length of the LM (SFT25, SFT50,
SFT100), SC fat area from 25 to 100% the length of
the LM (SCFA), and SC fat area adjacent to the 75%
length of the LM from the spinous processes (SCFA75).
Multiple ratio and product variables were also created
from distance and area measures. For carcasses in
this investigation, a 6 variable equation (Adj. R² =
0.62, MSE = 0.022) was calculated which included
coeffi cients for ZIL treatment, SCFA75, LW, SCFA,
SCFA/HCW, and SFT100/HCW. Use of parameters in
the U.S. (Adj. R² = 0.39, MSE = 0.028) and Canadian
[Adj. R² = 0.10, root mean square error (MSE) = 0.034]
yield grade equations lack the predictability of the
newly adapted equations developed for ZIL-fed and
non-ZIL-fed cattle. Prediction equations developed
in this study indicate that the use of VIA technology
to quantify measurements taken at the 12th/13th rib
separation could be used to predict saleable meat
yield more accurately than those currently in use by
U.S. and Canadian grading systems. Improvement
in saleable meat yield prediction has the potential to
decrease boxed beef variability via more homogeneous
classifi cation of carcass fabrication yield.
Key words: β–agonist, beef, prediction, saleable meat yield, video image analysis,
Quantifi cation of Saleable Meat Yield Using Objective
Measurements Captured by Video Image Analysis Technology1
T. J. McEvers,* J. P. Hutcheson,† and T. E. Lawrence*2
*Beef Carcass Research Center—Department of Agricultural Sciences, West Texas A&M University, Canyon 79016; and
†Intervet Schering Plough Animal Health, DeSoto, KS 66018
The U.S. beef industry relies on a merit-based
system for determination of carcass value when cattle
are sold using discounts and premiums. In most of these
marketing arrangements, producers are paid based on a
true carcass value derived from HCW, yield grade (YG)
[either offi cial USDA stamped or video image analysis
(VIA) assessed], quality grade (which may or may not
be augmented using VIA-assessed intramuscular fat
content, IMF), and other quality defects (Hopkins and
Several advances in VIA technology have allowed
for improved repeatability in evaluation of beef
carcasses for yield and quality. Specifi cally, the VIA
technology used in this investigation (VBS, 2000)
was jointly developed by the German company E+V
Technology GmbH (Oranienburg, Germany) and
researchers at the USDA meat animal research center
© 2012 American Society of Animal Science. All rights reserved.
J. Anim. Sci. 2012.90:3294–3300
1Supported by funding from Intervet Schering Plough Animal
Health, DeSoto, KS 66018.
2Corresponding author: firstname.lastname@example.org
Received April 29, 2011.
Accepted February 28, 2012.
Published January 20, 2015
McEvers et al.
predict saleable meat yield resulted in low (Adjusted R2 =
0.39; USDA) to poor (Adjusted R2 = 0.10; Canadian)
predictive ability. These data are similar to Cannell et
al. (1999), who reported that estimates derived from
the dual component VIASCAN system accounted for
46% of the variation in commodity-trimmed subprimals.
Moreover, investigations of the ability of the USDA
YG equation to predict red meat yield (Lawrence et al.,
2010) support the fi ndings of this study, indicating that
calculated YG accounted for only 40% of the variation
in subprimal yield.
Improved saleable meat yield prediction using new
parameters discussed in our models may allow for a
greater emphasis to be placed on actual saleable meat
yield. For beef producers, an implication of an improved
prediction system could include more fi nite categories of
yield value. Carcass yield could be valued into an infi nite
number of groups and account for much more variation
than the standard 5 category system currently in place.
For beef processors, improved VIA yield determination
could allow beef processors to sort carcasses into
refi ned groups to create more homogenous boxed beef.
Moreover, the use of VIA calculation of saleable meat
yield may further level the playing fi eld among producers,
which may create a more competitive market place when
marketing fed cattle using value-added formulas.
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