D4—Lancaster Farming, Saturday, Novambar 26,1983
Video image analyzer used
to evaluate beef carcasses
CLAY CENTER, NB. -
Predicting lean and fat compostion
of beef carcasses has been
achieved more accurately and
faster with a video a camera and a
computer than with conventional
measurements applied in
predictive equations for USDA
grading standards.
Video image analysis should still
be considered experimental at this
point, says food technologist H.
Russell Cross of USDA’s
Agricultural Research Service
(ARS). A larger scale study in
commercial settings will be
needed before the automated
procedure can become considered
for official yield grading. It ap
pears that the video image
analysis may be the wave of the
future, providing an efficient way
for the meat industry to assure
consumers that they will get the
leanness in beef that they desire.
In laboratory tests, the
technique was estimated to be 93.6
percent accurate in its
measurements of the lean and fat
content of the 9th, 10th and 11th
ribs of the carcass which is the
indicator of the carcass leanness.
USDA meat graders, in the
current system, measure visible
areas of lean and fat of the 12th and
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13th ribs and then apply an
equation to predict the total con
tent. The conventional way of
determining the same
measurement was 84 percent
accurate.
Accuracy differences between
the two methods conceivably could
have been more striking than they
were if the methods had been
compared in a typical grading
situation rather than in the
laboratory, Cross says.
That’s because determining
yield grades by using the con
ventional measurements in the
standard equation was done
painstakingly and took longer in
the experiment than in typical
grading conditions.
“Under typical grading con
ditions, USDA meat graders are
expected to quality and yield grade
more than 250 carcasses per
hour,” says Cross.
Engineers of Kansas State
University developed the in
strument under a contract with
ARS and also cooperated in the
latest research.
The video image analyzer
consists of a video camera, video
monitor, data terminal and
computer. The computer converts
what the camera sees into
numerical codes for fat, lean and
marbled portions of the meat
section.
Cross said the analyzer “did an
excellent job” of duplicating lean
and fat measurements. It was
precise 97 percent of the time in
measuring total fat areas and 98
percent for total lean areas. For
counting marbling pieces and
measuring subcutaneous fat
thickness, results suggest that
repeated handling of carcasses
may reduce the precision.
Not including subcutaneous fat
measurements in a yield-grading
prediction equation, however, does
not make the instrument’s
equation less accurate than con
ventional predictions that are
complicated by adjustments for fat
trimming prior to chilling, Cross
says.
As few or as many kinds of
measurements as necessary may
be made to formulate an accurate
prediction equation for instrument
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grading, Cross says, while adding I LIVESTOCK
measurements to conventional | LATEST ]
prediction equations may increase
the time needed for yield grading.
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