Enhancements in muscle quality as well as quantity
of muscle are essential for improved consumer acceptance of pork.
Specific factors influencing pork quality, therefore must be addressed.
The presence of marbling or lipids is said to be
of importance to meat quality and sensory properties (Garcia et
al., 1968; and Essen-Gustavsson et al., 1994). Numerous studies
have suggested that marbling influences the palatability of cooked
meat (Judge et al., 1959; Blumer, 1963; Field et al., 1966; Romans
et al., 1969; and Cameron and Enser, 1991).
The trend in modern pork production has been toward
producing leaner pork. Increased selection for lean has caused
considerable concern regarding the possibility of a reduction
of intramuscular fat in pigs (Schworer et al., 1995). Lower intramuscular
fat levels would have detrimental effects on eating quality of
pork (Barton-Gade, 1990).
Heritability estimates for intramuscular fat range
from 0.49 to 0.81 (Duniec et al., 1961; Barton-Gade, 1990; and
Schworer et al., 1995). The genetic correlation between intramuscular
fat and meat content is low (rg = -0.30). A breeding
program emphasizing simultaneous selection for higher levels of
intramuscular fat and a higher meat content, therefore should
be possible (Barton-Gade, 1990).
At present breeding programs for improving muscle
quality require the collection of measurements on the carcass.
Direct selection, therefore cannot be performed, and breeding
programs for these traits must be based on progeny tests.
Real-time ultrasound offers an alternative to traditional
swine breeding programs for muscle quality, because it allows
noninvasive measurement of a trait on the live animal at a reasonable
Ultrasound has been used to predict tenth - eleventh
rib subcutaneous fat and loin muscle area of live swine with a
correlation near .9 and .8, respectively (Moeller, 1994). Few
studies have been conducted to evaluate the accuracy of ultrasound
in predicting intramuscular fat. Dion et al. (1996) predicted
marbling score with real-time ultrasonic cross-sectional and longitudinal
scans. The accuracy of prediction was essentially zero.
The objective of this study, therefore, was to investigate
the feasibility of predicting intramuscular fat in the longissimus
muscle of live swine from a single real-time ultrasonic longitudinal
image, and to assess the merit of using ultrasound predicted IMF
values to classify animals into specific IMF groups.
MATERIALS AND METHODS
Data utilized for this project (N=756) were collected
as a part of the 1995 National Barrow Show® (NBS) Progeny
Test (N=348) and the 1996 Livestock Producer Assistance Program
(LPAP) test (N=408) conducted at the Northeast Iowa Swine Improvement
Association Station located near New Hampton, Iowa. Animals represented
in the NBS test were of the eight major breeds and crossbreds,
while only crossbreds were included in the LPAP test.
Pigs were weighed and scanned off-test on an individual
basis at weekly intervals upon reaching a weight > 240 lbs.
Scanning was 24 hours prior to slaughter and accomplished with
an ALOKA 500V (Corometrics Medical Systems, Wallingford, Connecticut)
real-time ultrasonic machine fitted with a 12.5 cm, 3.5 MHz linear
array transducer. Ultrasonic images were digitized on-site using
a personal computer equipped with a frame-grabber board and controlling
software. The images were stored as digitized files for later
Two ultrasonic images were taken on each pig. A cross
sectional image of the loin muscle and subcutaneous fat overlying
the loin muscle on the right hand side of the pig at the tenth
rib was acquired using a sound emitting transducer guide which
fitted the natural contour of the pig's back. A longitudinal image
was taken approximately 2.5 in. off the midline and parallel to
the spine. The image included the 9th, 10th and 11th ribs.
Digitized cross-sectional images were interpreted
using Quality Evaluation and Prediction (Iowa State University,
Ames, Iowa), a computer software package developed specifically
to measure linear distance and area of digitized images and matriculate
these to a data file. BF10 was measured as the distance from the
outer edge of the skin to the start of the fascia layer in the
center of the longissimus muscle at a point approximately
2.5 in. lateral to the spine.
Longitudinal images were subjectively evaluated for
image quality. Images with less than ideal image quality were
removed from this analysis. Two 40*80 pixel regions of interest
(ROI) were then selected from the image above the 10th and 11th
ribs. Image-processing parameters were then determined for the
selected ROI. Image-processing parameters were calculated using
texture analysis. Texture parameters provide information about
the image patterns generated in part by ultrasound scattering.
Upon completion of the test, pigs were transported
to Hormel Co. in Austin, Minnesota, for carcass evaluation. Carcass
measurements were taken by Iowa State University personnel following
a 2-hour rapid chill. Standard carcass collection procedures were
followed, as outlined in Procedures to Evaluate Market Hogs (NPPC,
1991, 3rd ed.). A sample was excised from the loin muscle at the
10th rib. The intramuscular lipids from the excised sample were
measured according to the total lipid extraction method of Bligh
and Dyer (1959).
Of the 756 images collected, 300 images had ideal
image quality and were used for this analysis. Image variables,
live animal measures including 10th rib backfat and sex of the
animal, IMF values and marbling score were statistically analyzed
to select a set of parameters for regression model development.
Stepwise regression procedures were used for selection of variables
to determine the prediction models.
Linear regression (PROC REG; SAS, 1985) was used
to predict IMF and marbling score. Of the 300 images used, 200
were randomly chosen for model development and the remaining 100
were used for validation of the model.
Bias and mean absolute difference were calculated
between predicted IMF and marbling score and actual IMF and marbling
score. Regression equations containing eight ultrasound image
variables, sex and BF10 were utilized to obtain predicted IMF
and marbling score values. Bias was defined as:
The mean absolute difference was defined as:
= ( ô
Chemical - Ultrasound ô
Standard errors of prediction (SEP), widely considered
as the standard measure of the ability of RTU to precisely evaluate
differences between carcass and ultrasound measurements, were
computed using the formula:
Mean absolute differences were sorted into one of
five groups: 0-.25%, .26-.50%, .51-.75%, .76-1.0% and > 1.0%.
The frequency of observations in each group was determined.
Actual IMF and predicted IMF values were grouped
in one of three classes: < 1%, 1-2%, 2-3%, 3-4%, 4-5% and >
5%. The frequency of observations in each class was determined.
RESULTS AND DISCUSSION
The pearson and spearman correlations between actual and predicted IMF were .70 and .69, respectively. The mean absolute difference was .66 between actual and predicted IMF and .50 between actual and predicted marbling score. The SEP was .83 for IMF and .61 for marbling score. Bias for IMF prediction was -0.005, while bias for prediction of marbling score was 0.004.
Figure 1 shows the classification of mean absolute
differences by group. Over 77% of the mean absolute differences
were less than 1%.
Figure 2 illustrates the grouping of actual and predicted
IMF values. Over 70% of the predicted values were correctly classified.
All misclassifications fell into an adjacent class.
Figure 1. Classification of mean
absolute differences between actual and predicted intramuscular
Figure 2. Classification of actual
and predicted intramuscular fat values.
The results of this study indicate that objective
measurement of IMF of the live animal with real-time ultrasound
is feasible. This will allow the identification of breeding animals
that are superior for IMF. Selection based on muscle quality measurements
taken on the live animal will, therefore, be possible. The use
of real-time technology to predict IMF may also be applicable
for carcass evaluation. Image collection and procedures for image
analysis must be further refined to enhance accuracy of IMF prediction.
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