INTRODUCTION
The use of ultrasound for measuring fat and muscling in swine
and other livestock was first introduced in the 1950's. This involved
the use of an A mode ultrasonic unit with a transducer housing
a single crystal for depth readings at one point. Initially ultrasonic
equipment was used to measure fat thickness as a noninvasive substitute
for the ruler probe. Subsequently A-mode readings were made in
which muscle depth was observed in addition to the fat depth.
The next major development in which I was involved was the introduction
of the Scanogram in 1969. The Scanogram produced B-scans by moving
a single transducer over the animal while coordinating the exposure
of a continuous series of intensity modulated A-mode signals on
film with time exposure. Although the images were reasonably accurate,
the technique was too slow and labor intensive to be practical.
The animal had to remain motionless for 10 seconds for a complete
clear scan of a loin eye to be produced.
The introduction of real time ultrasonic equipment in 1984 was
a major development of accurate and practical equipment for use
in live animal evaluation. A linear array transducer had a large
number of elements (100) lined up in a row which were electronically
fired sequentially 30 times per second. A complete cross sectional
image was produced faster than the eye could detect. Therefore,
this technology become known as "real time ultrasound."
CURRENT USE OF REAL TIME ULTRASOUND
The primary application of real time ultrasound for swine at the
present time is for the measurement of fat thickness and loin
eye area at the 10th rib. Depending on the characteristics
of the ultrasonic unit, the operator may make the measurements
on the frozen image at the scanning site or capture the image
on a VCR or digitally on a computer. The captured images are usually
evaluated with computer software and printed for presentation
to breeders or producers. The interpretation of the cross sectional
image requires as much time as it does to scan animals. There
has been a lot of interest in developing technologies for automatic
image interpretation in order to speed up the process and minimize
the subjectivity or human judgment. Attempts have been made to
automate the measurement of loin eye area but that has not been
accomplished as yet.
Another approach is to do a longitudinal scan rather than a cross
sectional scan. Animal Ultrasound Services, Inc. (AUS) has been
involved in this activity for evaluating live hogs and pork carcasses
for several years. Our studies have demonstrated that a longitudinal
scan 5 cm off the midline between the 10th and last
rib provides an accurate and highly repeatable indication of carcass
composition. Another requirement for this scan to be accurate
is for the ultrasonic beam from the linear array transducer to
be perpendicular to the inside or bottom of the intercostal muscle
on the inside of the rib cage. This requires the technician to
have the transducer properly placed and oriented at the time the
image is captured.
An automated and computerized image analysis system was developed
for making multiple measurements of fat and muscle depth from
the longitudinal scan. Results from our participation in the Canadian
1992 hog carcass project demonstrated the accuracy of this technique.
Details of this study were reported in Jones et al. (1994a, b)
and Liu and Stouffer (1995).
The objectives of this study were to develop an automated carcass
value technology (CVT) system using real time ultrasound and computer
technology to remove human judgment from ultrasonic image interpretation,
to establish the accuracy and relationship of longitudinal scans
to traditional cross-sectional scans in predicting carcass leanness,
and to demonstrate the feasibility of using CVT system in modern
slaughter plants.
A total of 325 market barrows and gilts were selected on the rail
in a commercial slaughter plant in Canada. The right side of each
carcass was scanned on the rail longitudinally between the 10th
rib and last rib, five cm lateral to the midline of the carcass,
and cross- sectionally at the 3 / 4 last rib.
The ultrasonic equipment consisted of an ultrasonic unit (Aloka
Model 500V), a 17 cm long, 3.5 MHz linear array transducer, and
a positioning device (U.S. patent 5,316,003, 1994). The computer
hardware and software included a 486/33 MHz IBM-compatible computer
with a Intel 487 math coprocessor, an image analyzer (AUSKey,
Animal Ultrasound Services), an image grabber board (Data Translation
DT2851), and a video monitor (Panasonic TR-930B). A portable video
cassette recorder (Panasonic AG-2400) was used to record ultrasonic
images for later review and manual measurements.
Descriptions of all dependent and independent variables used in
this study were given as footnotes in Table 1. All data were analyzed
using SAS (1988).
| Variable a | N | Mean | SD | Min | Max |
| HCWT | 325 | 81.29 | 6.59 | 65.60 | 94.80 |
| AUTOFD | 325 | 19.68 | 7.02 | 5.40 | 40.50 |
| AUTOMD | 325 | 53.82 | 5.36 | 37.90 | 68.00 |
| LRFD | 324 | 17.05 | 6.15 | 5.90 | 37.40 |
| R10FD | 324 | 23.92 | 7.53 | 7.20 | 45.90 |
| R34FD | 138 | 20.91 | 6.69 | 6.90 | 41.70 |
| R34MD | 138 | 49.45 | 4.99 | 38.30 | 66.30 |
| R34LMA | 138 | 37.11 | 4.67 | 27.17 | 50.48 |
| LEAN1 | 322 | 32.00 | 3.37 | 24.25 | 40.31 |
| GRLEAN | 322 | 42.42 | 3.92 | 33.95 | 53.37 |
| LOIN_L | 323 | 9.20 | 1.09 | 6.46 | 11.98 |
| HAM_L | 322 | 12.59 | 1.41 | 9.63 | 16.20 |
| SHLDR_L | 323 | 10.21 | 1.09 | 7.41 | 13.17 |
| LEAN1P | 312 | 39.37 | 3.19 | 31.02 | 48.87 |
| GRLEANP | 312 | 52.17 | 2.78 | 44.00 | 60.33 |
| LOIN_LP | 313 | 11.31 | 1.06 | 8.01 | 14.31 |
| HAM_LP | 312 | 15.50 | 1.45 | 11.71 | 19.98 |
| SHLDR_LP | 313 | 12.56 | 1.04 | 9.82 | 15.21 |
Table 2. Regression equations predicting weight (kg) of carcass lean, grade lean, and primal cuts using hot carcass weight and automatic fat depth and muscle depth (n=312)
| Dependent Variable | Intercept | HCWT | AUTOFD | AUTOMD | RSD | R² x 100 |
| LEAN1 | -.2030 | .3513a | -.2836 a | .1705 a | 1.19 | 87.64 |
| GRLEAN | -1.8112 c | .5038 a | -.2436 a | .1491 a | 1.09 | 92.29 |
| LOIN_L | -1.2368 b | .1009 a | -.0770 a | .0694 a | .51 | 78.63 |
| HAM_L | .2672 | .1263 a | -.1131 a | .0792 a | .67 | 77.45 |
| SHLDR_L | .7596 | .1241 a | -.0934 a | .0220 a | .52 | 77.95 |
Table 3. Regression equations predicting weight (kg) of carcass
lean, grade lean, and primal cuts using hot carcass weight, manual
fat depth, muscle depth, and loin muscle area measured between
the 3rd and 4th last rib of the carcass right sides (n=131)
| Dependent Variable | Intercept | HCWT | R34FD | R34MD | R34LMA | RSD | R² x 100 |
| LEAN1 | .1953 | .3814 a | -.3426 a | .1516 a | --- | 1.34 | 84.95 |
| 2.3066 | .3593 a | -.3183 a | --- | .1800 a | 1.31 | 85.58 | |
| GRLEAN | -1.8404 | .5452 a | .2947 a | .1145 a | --- | 1.21 | 90.94 |
| -.4943 | .5229 a | .2738 a | --- | .1536 a | 1.15 | 91.77 | |
| LOIN_L | -1.3165 b | .1208 a | -.0972 a | .0545 a | --- | .52 | 77.98 |
| -.6371 | .1111 a | -.0876 a | --- | .0705 a | .50 | 79.86 | |
| HAM_L | .0389 | .1299 a | -.1373 a | .0941 a | --- | .70 | 76.06 |
| 1.5345 | .1204 a | -.1241 a | --- | .0986 a | .72 | 75.06 | |
| SHLDR_L | 1.4579 b | .1308 a | -.1081 a | .0031 | --- | .53 | 76.76 |
| 1.3953 b | .1280 a | -.1066 a | --- | .0111 | .53 | 76.92 |
Table 4. Regression equations predicting percentage (%) of carcass
lean, grade lean, and primal cuts using hot carcass weight and
automatic fat and muscle depth measurements (n=312)
| Dependent Variable | Intercept | AUTOFD | AUTOMD | RSD | R² x 100 |
| LEAN1P | 36.6616 a | -.3640 a | .1829 a | 1.50 | 78.24 |
| GRLEANP | 48.9019 a | -.3065 a | .1723 a | 1.35 | 76.43 |
| LOIN_LP | 9.0095 a | -.0986 a | .0788 a | .63 | 64.52 |
| HAM_LP | 14.1802 a | -.1495 a | .0789 a | .85 | 65.49 |
| SHLDR_LP | 13.4752 a | -.1159 a | -.0252 a | .63 | 63.82 |
Table 5. Regression equations predicting percentage (%) of carcass
lean, grade lean, and primal cuts using manual fat depth, muscle
depth, and loin muscle area measured at the 3rd AND 4th last rib
of the carcass right sides (n=131)
| Dependent Variable | Intercept | R34FD | R34MD | R34LMA | RSD | R² x 100 |
| LEAN1P | 38.6268 a | -.4241 a | .1835 a | --- | 1.62 | 76.46 |
| 39.9075 a | -.4031 a | --- | .1981 a | 1.61 | 76.93 | |
| GRLEANP | 51.3618 a | -.3539 a | .1559 a | --- | 1.47 | 73.37 |
| 51.5657 a | -.3346 a | --- | .1915 a | 1.40 | 76.05 | |
| LOIN_LP | 10.1547 a | -.1167 a | .0723 a | --- | .65 | 62.56 |
| 10.3596 a | -.1079 a | --- | .0859 a | .62 | 65.79 | |
| HAM_LP | 13.8101 a | -.1762 a | .1035 a | --- | .88 | 66.78 |
| 15.1122 a | -.1653 a | --- | .0966 a | .91 | 64.56 | |
| SHLDR_LP | 14.6654 a | -.1314 a | -.0077 | --- | .65 | 65.01 |
| 14.4400 a | -.1301 a | --- | .0156 | .64 | 65.34 |
The results of this study indicate that a computer based real
time ultrasound system can be used to accurately evaluate multiple
fat and lean measurements on a longitudinal scan between the tenth
and last ribs. These measurements can accurately predict the composition
of pork carcasses and major cuts.
Similar results have been accomplished on live hogs (Cisneros
et al., 1996). The same technology can be used for selecting breeding
stock. This technology has been used at major barrow shows to
produce carcass evaluation on live hogs in lieu of carcass measurements
in a packing plant because of the increasing difficulty to collect
the latter.
POTENTIAL APPLICATION
Real time ultrasound technology will continue to be improved and
this will provide for advancement in current applications. Improved
image quality should result in greater accuracy in the measurement
of fat and muscling. There will be expanded opportunities for
automation and computerization and real time ultrasound for live
animal and carcass evaluation.
Another application of real time ultrasound is to evaluate primal
cuts to decide how they could best be utilized. External fat and
amount of seam fat are important in deciding which hams should
be processed for specialty markets. Belly thickness and ratio
of fat to lean would be desirable information in sorting for different
markets. Techniques for determining these characteristics could
be developed for scanning live animals as well as carcasses and
primal cuts.
The role of quality in value based marketing is becoming more
important in the distribution of pork both domestically and for
export. Measurements of intramuscular fat in live hogs and pork
carcasses with real time ultrasound is very likely. The detection
of soft and exudative muscle in pork carcasses and major boneless
cuts is another area that real time ultrasound may be utilized
with computer and software development.
The universal adoption of electronic identification in the swine
and meat packing industry should be a driving force for the adoption
of further automated and computerized real time ultrasonic systems.
This could lead to pork carcass evaluation prior to splitting
which is more accurate and easier to automate.
Cisneros, F., M. Ellis, K. D. Miller, J. Novakofski, E. R. Wilson,
and F. K. McKeith. 1996. Comparison of transverse and longitudinal
real-time ultrasound scans for prediction of lean cut yields and
fat-free lean content in live pigs. J. Anim. Sci. 74:2566-2576.
Jones, S. D. M., A. K. W. Tong, W. M. Robertson, and P. Skoczylas.
1994a. National Pork Carcass Cutout Project (1992) Part 1: A comparison
of the 1978 and 1992 cut outs. Canadian Pork Council. 75 Albert
Street, Suite 1101, Ottawa, Canada.
Jones, S. D. M., A. K. W. Tong, A. F. Fortin, C. Campbell, and
W. M. Robertson. 1994b. National Pork Carcass Cutout Project (1992)
Part 3: Evaluation of new technology for grading pork carcasses.
Canadian Pork Council. 75 Albert Street, Suite 1101, Ottawa, Canada.
Liu, Y and J. R. Stouffer, 1995. Pork carcass evaluation with an automated and computerized ultrasonic system. J. Anim. Sci. 73:29-38.