THE PREDICTION OF PERCENTAGE OF FAT IN PORK CARCASSES'
University of Georgia2, Athens 30602 ABSTRACT
Forty-seven market-weight pigs were slaughtered in order to determine percentage of chemical fat and in an attempt to determine an easily obtainable and inexpensive method to predict this value. The hams and 8-9-10 rib loin sections were removed from the left side of each carcass and dissected into subcutaneous and seam fat, individual muscles, skin and bone. Weights and chemical analysis were determined for each component. Numerous weights, measurements and specific gravity were determined on the carcass, ham and loin section of each pig. Percentage of chemical fat of each ham, loin section and carcasses was determined and correlated with the various weights and measurements taken. Stepwise regression was used to develop prediction equations using carcass data, specific gravity, ham or loin measurements or various combinations of these as dependent variables. The single best indicator of the decimal fraction of chemical fat in the pork carcass was determined to be specific gravity of the carcass half; the prediction equation using this variable had an R-square of .64.By adding loth rib fat thickness to this equation, the Rsquare increased to .72. The best equation using carcass variables included loth rib fat and marbling (R-square = .67). The loin section proved to be an accurate indicator of composition; ham measures were not as accurate as specific gravity and carcass measurements for predicting percentage of carcass fat. This research suggests that the percentage of chemical fat in the pork carcass can be predicted by an easy and inexpensive means. (Key Words: Pork, Carcass Composition, Carcass Yield, Prediction, Fat Percentage.) J. Anim. Sci. 1990. 68:41854192
Introduction
The determination of percentage of fat in pork carcasses through a nondestructive and inexpensive means has long plagued researchers. The most accurate method to determine chemical composition of the carcass is to grind the entire carcass and perform chemical analysis on the resulting ground product (Hankins
'Mention of trade name, propriety product or specific equipment does not constitute a guarantee or warranty of the. product by the Univ. of Georgia and does not imply approval to the exclusion of other products that may also be suitable. 'Dept. of Anim. Sci., Meats and Muscle Biology
Section. Received June 26, 1989. Accepted May 14, 1990.
and Howe, 1946). However, this method is very time-consuming and the resulting product is devalued greatly (Cordray et al., 1978). Therefore, meat researchers have sought methods to predict carcass composition that preserve the value of the product and are easily attainable. The objectives of this study were to establish correlation coefficients between chemical fat content in the carcass and various indices of fat, and to develop regression equations to predict accurately the percentage of carcass fat. The parameters estimated in this study included various carcass measurements, specific gravity of the entire carcass or subcomponents of the carcass, measurements of subcomponents of the carcass, which included the ham and 8-9-10 rib loin section, and various combinations of these.
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L. P. Johnson, M. F. Miller, K. D. Haydon and J. 0. Reagan
4186
JOHNSON ET AL. Materials and Methods
Results and Discussion
The simple statistics, including the ranges, means and standard deviations of the carcass measurements and specific gravity measurements, are shown in Table 1. Identical data are provided in a companion paper (Johnson et al., 1990), but fat characteristics are not discussed therein. Table 2 presents the ranges, means and standard deviations of the measurements taken from the ham and loin sections. There was a great deal of variation in these traits, indicating the diversity of the population measured. The results and simple statistics from the proximate analyses are shown in Table 3. The mean percentage of fat in the total loin section was 40.59, which was quite similar to the percent-
TABLE 1. STATISTICS OF CARCASS AND SPECIFIC GRAVlTY MEASUREMENTS
Variable
Range
Mean
Live wt, kg Chilled carcass side, g 1st Rib fat left, mm 1st Rib fat right, mm Last rib fat left, mm Last rib fat right, mm Last lumb vert left, mma Last lumb vert right, mmb Loin eye area,mm loth Rib fat 1/4 way, mm loth Rib fat 1/2 way, mm 10th Rib fat 3/4 way, mm Ham score (3-pt scale) Length left, mm Lensth right, Depth of chine left, mm Depth of chine right$mm Depth of Gluteus left, mm Depth of Gluteus right,mm MarblingC Dressing percentage, 96 Water temp, 'C Pig temp, 'C Specific pavity
100.70 117.02 31,117 - 37,150 35.00 65.00 30.00 62.00 19.00 37.00 20.00 40.00 20.00 47.00 20.00 42.00 199.99 335.48 25.00 44.00 22.00 46.00 20.00 44.00 1.00 3.00 756.92 825.50 731.52 825.50 65.00 79.00 60.00 78.00 8.00 66.00 10.00 37.00 580.00 110.00 56.90 71.89 -3.33 2.22 -2.22 6.66 1.03 1.05
105.80 34225 46.67 44.53 28.11 28.49 35.47 32.45 272.61
34.06 32.94 31.83 2.02 800.16 796.86 69.84 68.94 20.31 21.64 260.11 64.71
-.ob 1.67 1.04
%ut lumbar vertebrae fat left.
L t lumbar vertebrae fat right. '100 = slight 0,200 = smalt 0, 300 = modest 0. 400 = moderate 0, 500 = slighuy abundant 0.
Standard deviation 3.08 1,493 5.65 6.37 4.31 4.38 5.96 5.54 29.05 4.87 5.77 5.20 25 17.52 22.39 2.93 4.62 927 6.28 127.26 2.49 1.32 2.48 .01
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Measurements and statistics are described in a companion paper with the dependent variable being percentage of chemical fat in the carcass rather than chemical protein (Johnson et al., 1990).
age of fat in the carcass (41.32). Fat density has been shown to be less than lean density; therefore, density, or specific gravity, is inversely related to fatness (Berg and Butterfield, 1976; Jones et al., 1978; Shields and Mahan, 1983). The correlations between percentage of carcass chemical fat and the carcass measurements and specific gravity are shown in Table 4. Specifk gravity of the carcass was highly correlated with percentage of carcass fat at -230. This value varies from the value (.86) reported by both Whiteman et al. (1953) and Cordray et al. (1978) and the .95 reported by Adams and Smith (1964). Garrett and Hinman (1969) and Kraybill et al. (1952) also found that specific gravity in beef was highly = correlated to percentage of carcass fat (0 .%). Tenth-rib fat thickness, taken in the same location as 12th-rib fat in the beef yield grading equation (Murphey et al., 1960), also was highly correlated to carcass fat (r = .75). This agrees with the work of Smith and Carpenter (1973) and Cordray et al. (1978) in pork and of Powell and Huffinan (1973),
4187
PREDICTION OF FORK COMPOSITION
TABLE 2. SIMPLE STATISTICS OF HAM AND LOIN MEASUREMENTS
Standard Ham temp, 'C Ham water temp, 'C Specific gravity ham Loin temp, 'C Loin water ternp, 'C Specific gravity loin -Wt,g
Hambonewf,g Hamskinwg Ham soft tissue wt, g Ham SUbQ trim fatwt, g Ham seam fat wt, g Biceps femoris wt, g Semitendinosus wt, g €Jamtip, g Ham top, g Femur wt, g Loinwt,g Loiskiqg Loin bone, g Loin mbQ fat, g Loin seam fat, g b i n u longissimus wt, g Loin other muscles wt, ~t
Range -3.33
M a
-
.a31.03 -2.78 .56 1.02 8,450.00 -
685.60 301.90 4,357.60 1,581.60 336.00 895.40 271.24 714.50 1,192.00291.90 1,032.80 32.80 121.10 273.59 66.94 169.00 206.70 -
3.33 4.44 1.08 8.89 7.22 1.07 10,120.00 1,115.30 575.00 5,957.80 2,718.10 677.30 1,313.50 490.00 1,159.60 1,677.80 444.70 1,600.60 106.24 247.50 593.40 230.50 283.37 363.14
Kauffman et al. (1975)and Miller et al. (1988) in beef; a l l found that 12th rib backfat thickness was highly correlated with percentage of carcass fat. The correlation between chilled carcass weight and percentage of carcass fat was quite low at .13.Other researchers also have found that carcass weight was not indicative of carcass composition (Powell and Huffman, 1968;Smith and Carpenter, 1973;Kauffman et al., 1975). Simple correlations between percentage of carcass fat and measurements taken from the ham and loin and proximate analysis results are shown in Tables 5 and 6, respectively, Percentage of ham fat (Table 6) and loin seam fat weight (Table 5 ) were correlated highly with percentage of carcass fat (.78 and .73, respectively). These and other high correlations between the subcomponents of the carcass and the carcass itself suggest that it may prove feasible to use these subcomponents to predict the composition of the entire carcass. Specific gravity of the ham and the specific gravity of the loin section had correlations of .55 and .53 (not in tabular form), respectively, to the specific gravity of the carcass. These values are lower than values reported by other
-.59 1.38 1.05 -.14 3.48 1.04 9,199.00 879.31 436.06 5,07824 2,155.13 484.92 1,066.84 324.24 852.78 1,387.85 359.93 1.336.48 58.01 72.83 423.57 145.59 226.94 282.29
deviation
1.92 1.26 .01 2.59
2.07 .01 421.57 92.53 64.13 365.55 312.79 88.10 96.08 41.08 80.05 122.45 32.31 123.10 15.93 26.30 77.36 36.09 25.79 37.55
researchers such as Pearson et al. (1956) (.93 and .91, respectively). Brown et al. (1951), Kraybill et al. (1952) and Whiteman et al. (1953) reported results similar to those of Pearson. The regression equations for predicting the decimal fraction of carcass fat using carcass data, specific gravity and their combination are shown in Table 7. All the measurements used in these equations can be attained easily with the proper facilities. In addition, these measurements do not devalue the carcass in any way, with the possible exception of the split between loth and 11th ribs to display the loth rib inkrface. However, this is a minor point, because this method would be used only in research, not commercially, and one can rib the carcass enough to display the needed information without damaging the belly and with the maximum loss of one loin chop. Therefore, the equations in Table 7 can be used in a variety of situations, incorporating specific gravity where the facilities allow and using carcass data done when specific gravity cannot be measured The most statistically valid equation to predict percentage of chemical fat in the carcass using the carcass data alone was Equation 2 flable 7). This two-variable equa-
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Variable
4188
JOHNSON ET AL. TABLE 3. SIMPLE STATISTICS FROM PROXIMATE ANALYSES
Standard Range
MCan
deviation
Ham subQ trim protein, % Ham subQ trim moisture, %
2.98 - 6.84 9.74 - 21.45 71.16 - 86.06 6.71 - 12.65 27.36 - 43.78 45.56 - 65.71 20.29 - 23.68 68.08 - 75.32 2.37 - 9.14 1.43 - 4.51 4.31 - 14.21 77.69 - 92.62 2.69 - 6.64 8.34 - 26.60 66.87 - 89.13 2.13 - 22.42 19.16 - 70.37 9.20 - 73.66 19.31 - 2.5.76 65.28 - 74.48 2.07 - 15.61 31.07 - 51.17 18.07 - 32.06 26.38 - 58.86 14.37 - 19.01 12.23 - 16.03 5.29 - 11.44
4.23 15.94 79.41 9.13 35.63 54.54 22.01 72.13 5.23 2.58 9.74 86.54 4.85 19.82 74.82 18.78 61.53 19.13 23.44 70.91 5.98 41.32 24.49 40.59 16.83 13.58 9.29
.78 2.47 3.45 1.13 4.12 4.90 .75 1.56 1.58 .65 1.99 3.11 .93 3.35 4.43 3.5 1 7.44 9.25 1-48 2.13 2.78 3.94 3.42 6.35 1.06 .67 1.32
~~
HamsubQtrimf*% Ham seam fat protein, % Ham seam fat moisture, % Hamseamfatfat,% Ham lean protein, % Ham lean moisture, % Ham lean fat, % Loin subQ trim protein, % Loin subQ trim moisture, % Loin subQ trim fat, % Loin seam fat protein, % Loin seam fat moisture, % Loin seam fat fat, % Loin other proteiq 56 Loin other moisture, % Loin other fat, % Loin LD' protein, % Loin LD moisture, % Loin LD fat, % Carcass fat, % Ham fat, % Loinfas % Carcass protein, % Ham protein, % Loin proteiq % %D
= M. longissimus muscle.
tion includes loth rib fat thickness and marbling and had an R-square value of .67 and a C(p) value of 2.81. In a simple regression equation using only one variable (Equation 1, specific gravity), specific gravity alone accounted for 63.3% of the variation. This equation differs from 4. 1 - carcass data and specific gravity in that it is a simple linear regression, as opposed to stepwise regression, which is a multiple regression technique. With the correct facilities, specific gravity is very easy to obtain and could prove quite useful in predicting carcass composition in pork carcasses. By combining the carcass measurements and specific gravity, a more accurate equation can be generated to predict percentage of carcass fat (Equation 2, carcass data and specific gravity). Using specific gravity and loth rib fat thickness at 314 the distance around the M. longissimus, which were the single best indicators in their respective groups, the R-square value was increased to .72 while maintaining an acceptable C@) value. Table 8 presents the regression equations that were generated to predict the decimal
TABLE 4. CORRELATIONS OF PERCENTAGE OF C A R C A S S FAT TO CARCASS DATA AND SPECIFIC GRAVITY ~~
Variable
Correlation ~~~
speclfc gravity loth Rib fat (3/4) loth Rib fat Last lumbar vertebra fat (R) loth Rib Fat (1/4) Last lumbar vertebra fat (L) Last rib fat right Marbling Last rib fat left Depth of chine right Loin eye area 1st Rib fat left 1st Rib fat right Depth of Gluteus left Depth of Gluteus right Live wt Depth of chine left chilled carcass wt Lmgth left Ham score Dressing percentage
(ln)
Lenanrirgt
-.7% .753 .687 .631 .577 .487 .427 .403 .370 -.328 -.314 .291 .264
.225 -.221 .150 -.133 .127 .056 -.045
-.M8 .015
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Variable
4189
PREDICTION OF FORK COMPOSITION TABLE 5. CORRELATIONS OF PERCENTAGE OF CARCASS FAT TO HAM AND LOIN MEASUREMENTS Correlation
Variable
Correlation
.729 .717 .698 .691 .674 -.612 -.608 -501 -.595 -.561 -.547 -.456 -.452 .410 -.359 -.326 ,295 243 -215
Percentage ham fat Percentage loin fat Loin subQ protein Loin seam fat fat Loin seam fat moisture HamsubQtrimfat Ham subQ trim moisture Ham lean fat Loin seam fat protein Loin M. longissimusfat Loin M.longissimusmoisture Loin M.longissimusprotein LoinsubQtrimfat Loin other moisture Loin other fat Ham lean moisture Ham seam fat protein Lom other protein Ham seam fat fat Ham lean protein Loin subQ trim moisture Ham subQ trim protein Ham seam fat moisture
.784 .686 -.a9 .380
~
Loin seam fat Loin fat wt Hamfatwt HamsubQtrimwt LoinsubQtrimwt Loin specific gravity Ham soft tissue wt
Femur wt Ham Bone wt Semitendinosus wt Ham other muscles wt Ham specific gravity Biceps femoris wt Loin wt Ham top wt Ham tip wt Loislrinwt
Ham seam fat wt Loin bone wt Loin other wt Hamwt
-.181 -.120
Loin M.loogissimus wt Hamskinwt
-.lo2 .034
fraction of carcass fat using the subcarcass component measurements of the ham, loin section or the combination of the two. These equations are more difficult to employ because they require more time, money and effort to complete and some require chemical analysis. However, such added work is rewarded by an increase in R-square values; analyzing subcomponents involves much less time, labor and money than analyzing the entire carcass. 'Ihe best equation for predicting the decimal fraction of carcass fat using the ham data (CD = .67,C@) = 3.04) included the variables of ham other muscle weight and percentage of ham fat (Table 8, Fquation 2, ham). The most accurate equation to predict the decimal fraction of carcass fat using variables from the loin section was a five-variable equation that contained loin subcutaneous fat moisture percentage, loin skin weight, loin seam fat weight and the specific gravity of the loin section (Equation 5 , loin). This equation had an Rsquare value of .73,a C@) value of 5.31 and has only one variable that was analyzed chemically. The weights can be determined easily and an apparatus to determine the
-.379 .378
-.350 .328 -319 .297 -.Z8 -.254 .254 -.230 .226 -219 -.173 -.I51 .098 .046 -.035
-.m7 -.024
specific gravity of the loin section can be assembled in any facility using a tub and a set of laboratory scales. Because this equation has an accuracy comparable to that of the twovariable equation in Table 7, which uses loth rib fat and specific gravity of the carcass, it could be implemented in situations in which it is impossible to determine specific gravity on the entire carcass. When the ham and loin section measurements are combined, equations with higher Rsquare values result only at the expense of increasing difficulty, not to mention the devaluing of both of the ham and loin section. Percentage of carcass fat can be predicted using indicators from the carcass without total grinding of the carcass. Specific gravity and carcass measurements are ideal for use in prediction equations; they are highly correlated to composition, easily obtainable and costefficient, because they do not devalue the retail product. The 8-9-10 rib loin section also was an accurate indicator of percentage of carcass fat, although it is more timeconsuming to dissect and some equations utilizing it required chemical analysis. Finally, the ham proved to
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Variable
TABLE 6. CORRELATIONS OF PERCENTAGE OF CARCASS FAT TO HAM AND LOIN PROXIMATE ANALYSES
4190
JOHNSON ET AL.
TABLE 7. EQUATIONS FOR PREDICTING THE DECIMAL FRACTION OF FAT IN PORK CARCASSES USING CARCASS DATA, SPECIFIC GRAVITY MEASUREMENTS OR BOTHa Independent variables
Intercept
loth Rib fat (3/4) loth Rib fat (3/4) + Marbling loth Rib fat (3/4) + Depth of chine (R) + Marbling loth Rib fat (114) + loth Rib fat (3/4) + Depth of chine (R) + Marbling Loin eye area + loth Rib fat (114) + loth Rib fat (314) + Depth of chine (R) + Wbliug
.2129 .1955
Specific gravity Specific Gravity loth Rib fat (3/4) + Specific gravity loth Rib fat (314)
+ Marbling + Specific gravity
.3192
.3090
,2854
. m 21 .00595 .OOolO .0056 1 -.00165 .oOolO .a0190 .00422 -.a0185 .OOo11 .00016 .a0199
R2
C@)
RSD
.563 .672
14.33 2.81
.0262 0230
.709
25
.0219
.726
.05
.a215
.735
1.02
.0215
.w
-.00229 .OOoll Specific gravity 6.8619 -6.17179 .633 Carcass data and specific gravity 6.7811 -6.09356 .641 4.6953 .00301 .717 4.19045 3.8634 .00345 .749
4.1186
chilled carcass wt
4,1704
-3.422 13 .00333 .00026
NDb
8.62 .52
.a235 .0231 .I3207
-1.67
.0198
,762
-1.42
.0195
,775
-1.22
.0192
.m
loth Rib fat (314) +Length right + Mafbhg + Specific gravity
+ loth Rib fat (314) +Length right + Marblim
bValues
.00004 -3 36170
+ specificgravity
-.m .00365 .OOo39
.00004 -3.90076
%pations were developed using decimal fractions rather than percentages for all input and output values. bNot detectable. % best equation, weighing R-square and C@) values. Example:
US@
Specificgravity = 1.04689 loth rib fat thickness (3/4) = 22 mm 4 . 2 Carcass data and specific-vi&, X 4.6953 + (.00301 X 22) - (4.19045 x 1.04689) = 4.6953 + .06622 - 4.38694a2 = .3745798 = 37.46%fat
-
be the least accurate indicator of percentage of carcass fat that was investigated. Conclusions
The most important discovery in this research project was the development of the two-variable equation to predict percentage of
carcass fat using specific gravity of the carcass and loth rib fat thickness. The high coefficient of determination for this prediction equation, the ease of obtaining the independent variables and the preservation of the retail value attest to the importance of this information. Standal (1965) and Cordray et al. (1978) found specific gravity to be the single best indicator of pork
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Equation
4191
PREDICTION OF PORK COMPOSITION
TABLE 8. EQUATIONS FOR PREDICTING THE DECCMAL FRACTION OP CHEMICAL FAT IN PORK CARCASSES USING INDICATORS FROM THE HAM, LOIN OR B O m Independent variables Percentage Ham Fat
Intercept
+biceps femoris wt
94894 -.m7 31727 .00593 -.m7 96652 ,00629 -.m5
+HamOthlZwt
-.m5
Ham other wt
+ Percentage ham fat
Ham lean moisture
.1790 .3136
b-Values
-.1538
+HamOthCTwt
+ Percentage ham fat Ham lean moisture
-.1374
+ Percentage ham fat
Ham lean moisture
0)
RSD
.616 .674
8.27 3.04
.m9
.716
-.29
.a219
R2
0232
.729
.11
.a217
,743
.31
.a214
.531 .640
26.35 12.75
.a273 .m2
.688
7.86
St228
.714
6.18
.0221
.734
5.31
.MI6
.616 .727
1.87 -8.19
.0249 .0213
,772
-11.15
.0916
.812
-11.48
.OM9
315
-1 1.83
.os12
94358 -.2359
-.00730 .oooO8
+Hamskinwt +Biceps femoris wt
-.m -.m
+Ham O t h e r w t +Percentage ham fat
.95404 Loin
Loin seam fat wt Loin subQfat wt + Loin seam fat wt Loin subQ fat wt
.2845 .2357 .2998
1.1781
1.3844
+Loinskinwt +LoinsubQwt +Loinseamfatwt + Specifc gravity loin Percentage ham fat Percentage ham fat +Loin scam fat wt
Ham soft tissue wt
.00016
.00049 -1.06870 .17W .BOO
+HamsubQwt
+ Loin seam fat wt
+ Specific gravity loin
Ham and loin 94894 .65 133 .OOO49
. a 7
-.00004 .00004
.4970
,00004 .00004
.ooo49
+Hamfatwt +Loin subQ protein +Loinseamfatwt
Ham lean protein + Ham soft tissue wt
-.m5 .00018 .OOO51 -.O0022 -.82426 .00336 -.00065
+Hamfatwt +Loin seam fat wt
Ham soft tissue wt
.oom4 .00054
+Loinsesmfatwt + Loin other wt + Specifii gravity loin
Loin s u m moisture
.O0020 .ooo61
+Loinseamfatwt + Loin other wt
Loin s u m fat wt
.00086
1.3781
-.01072 .00043 -.03 -.m3
.m .00048
-.71407
%quatiom were generated using decimal fractions rather than percentages for all input and output values. best equation, weighing R-square and C@) values.
%e
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mation
4192
JOHNSON ET AL.
Implications
Prediction equations to determine carcass composition potentially could save investigators time and money. Further research is needed with more numbers and more diverse animals, especially lean ones, because the accuracy of specific gravity works best for fat carcasses. Literature Cited
Adams, J. L. and W. C. Smith. 1964. The use of specific gravity and its reciprocal in predicting the carcass composition of pigs slaughtered at three weights. Anim. Prod. 697. Berg, R. T.and R. M. Butterfield. 1976. New concepts of cattle p w t h . Sydney Univ. Press, Australia. Brown, C. J., J. C. Hiller and J. A. Wbatley. 1951. Specific gravity as a measure of Ute fat content of the pork carcass. J. Anim. Sci. 1097. Cordray, J. C., D. L. Hu&nan and J. A. McGuire. 1978. F'redictive equations for estimating protein and fat in the pork carcass. J. Anim. Sci. 46:666. Garrett, W. N. and N . Hinman.1969. Re-evaluation of the relationship between carcass density and body composition of steers. J. Anim. Sci. 28(1):1. Hankins, 0. G. and P.E. Howe. 1946. Estimation of tbc composition of beef carcasses and cuts. USDA Tech. Bull. 926. Washington, DC. Johnson, L. P.. M.F.Miller, K.D. Haydon and J. 0.Reagan.
1990. The prediction of percentage of protein in pork carcasses. J. Anim. Sci. 68:4176. Jones,SD.M.,M.A.PriceandR.T.Berg. 1978.Areviewof carcass density, its measurements and relationship with bovine carcass fatness. J. Auim. Sci. 46:1151. Kauffman, R. G., M. E. Vantes, R A. Long and D. M. Schaefer. 1975. Marbling: Its use in predicting beef carcass composition, J. Anim. Sci. 40:235. Kraybill, F.P., H.L. Biller and 0. G. Hankjns. 1952. Body compositionof cattle ll: DetCrmination offat and HzO content frommeasurementsof body specificgravity. I. Appl. Physiol. 4575. Miller, M. F., H. R. Cross, J. F.Black, F. M. Byers and H. A. Recio. 1988. Evaluation of live and carcass techdques for predicting beef carcass composition. J. Meat Sci. 23:111. Murphey, C. G., D. K.Hallef W. E. Tyler and J. C. Pierce. 1960. Estimating yields of retail cats from beef carcasses. J. Anim. Sci. 191240(Abstr.). Peanon, A. M,L. J. Bratzler, R J. Deans, J. F.Price,I . A. Hoefer, E. P. Reineke and R W. Luecke. 1956. The use of specific gravity of Certainuntrimmedpork cuts as a measure of m a s s value. J. Anim. Sci. 15:86. Powell, W. E. and D. L. Huffman. 1%8. An evaluationof the quautitative estimates of beef carcass composition. J. Anim. Sci. 271554. Powell, W. E. and D. L. Huffman. 1973. Predictbgchcmical composition of beef carcasses from easily obtainable mcnss variables. J. Anim. Sci. 36:1069. Shields, R. G.,Jr. and D. C.Mahan. 1983. Evaluation of ground carcass, sawdust residue and specific gravity methodsfor estimathg body compositionof reproducing swine. J. Anim. Sci. 57:604. Smith, G. C. and Z. L. Carpenter. 1973. Evaluation of the fxtors associated with the composition of pork carcasses. J. Anim. Sci. 36493. Standal, N. 1965. Specific gravity and other carcass measurement as a predictor of percent lean and fat in hams. Acta Agric. Scand. 1565. Whiteman, J. V., J. A. Whatley and J. C. Hiller. 1953. A furtherinvestigationof specificgravity as a measure of pork carcass value. J. Anim. Sci. 12:859.
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carcass composition. Kraybill et al. (1952), Whiteman et al. (1953), Adams and Smith (1964) and Jones et al. (1978) all discussed the high utility of specific gravity in predicting carcass composition.
University of Georgia2, Athens 30602 ABSTRACT
Forty-seven market-weight pigs were slaughtered in order to determine percentage of chemical fat and in an attempt to determine an easily obtainable and inexpensive method to predict this value. The hams and 8-9-10 rib loin sections were removed from the left side of each carcass and dissected into subcutaneous and seam fat, individual muscles, skin and bone. Weights and chemical analysis were determined for each component. Numerous weights, measurements and specific gravity were determined on the carcass, ham and loin section of each pig. Percentage of chemical fat of each ham, loin section and carcasses was determined and correlated with the various weights and measurements taken. Stepwise regression was used to develop prediction equations using carcass data, specific gravity, ham or loin measurements or various combinations of these as dependent variables. The single best indicator of the decimal fraction of chemical fat in the pork carcass was determined to be specific gravity of the carcass half; the prediction equation using this variable had an R-square of .64.By adding loth rib fat thickness to this equation, the Rsquare increased to .72. The best equation using carcass variables included loth rib fat and marbling (R-square = .67). The loin section proved to be an accurate indicator of composition; ham measures were not as accurate as specific gravity and carcass measurements for predicting percentage of carcass fat. This research suggests that the percentage of chemical fat in the pork carcass can be predicted by an easy and inexpensive means. (Key Words: Pork, Carcass Composition, Carcass Yield, Prediction, Fat Percentage.) J. Anim. Sci. 1990. 68:41854192
Introduction
The determination of percentage of fat in pork carcasses through a nondestructive and inexpensive means has long plagued researchers. The most accurate method to determine chemical composition of the carcass is to grind the entire carcass and perform chemical analysis on the resulting ground product (Hankins
'Mention of trade name, propriety product or specific equipment does not constitute a guarantee or warranty of the. product by the Univ. of Georgia and does not imply approval to the exclusion of other products that may also be suitable. 'Dept. of Anim. Sci., Meats and Muscle Biology
Section. Received June 26, 1989. Accepted May 14, 1990.
and Howe, 1946). However, this method is very time-consuming and the resulting product is devalued greatly (Cordray et al., 1978). Therefore, meat researchers have sought methods to predict carcass composition that preserve the value of the product and are easily attainable. The objectives of this study were to establish correlation coefficients between chemical fat content in the carcass and various indices of fat, and to develop regression equations to predict accurately the percentage of carcass fat. The parameters estimated in this study included various carcass measurements, specific gravity of the entire carcass or subcomponents of the carcass, measurements of subcomponents of the carcass, which included the ham and 8-9-10 rib loin section, and various combinations of these.
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L. P. Johnson, M. F. Miller, K. D. Haydon and J. 0. Reagan
4186
JOHNSON ET AL. Materials and Methods
Results and Discussion
The simple statistics, including the ranges, means and standard deviations of the carcass measurements and specific gravity measurements, are shown in Table 1. Identical data are provided in a companion paper (Johnson et al., 1990), but fat characteristics are not discussed therein. Table 2 presents the ranges, means and standard deviations of the measurements taken from the ham and loin sections. There was a great deal of variation in these traits, indicating the diversity of the population measured. The results and simple statistics from the proximate analyses are shown in Table 3. The mean percentage of fat in the total loin section was 40.59, which was quite similar to the percent-
TABLE 1. STATISTICS OF CARCASS AND SPECIFIC GRAVlTY MEASUREMENTS
Variable
Range
Mean
Live wt, kg Chilled carcass side, g 1st Rib fat left, mm 1st Rib fat right, mm Last rib fat left, mm Last rib fat right, mm Last lumb vert left, mma Last lumb vert right, mmb Loin eye area,mm loth Rib fat 1/4 way, mm loth Rib fat 1/2 way, mm 10th Rib fat 3/4 way, mm Ham score (3-pt scale) Length left, mm Lensth right, Depth of chine left, mm Depth of chine right$mm Depth of Gluteus left, mm Depth of Gluteus right,mm MarblingC Dressing percentage, 96 Water temp, 'C Pig temp, 'C Specific pavity
100.70 117.02 31,117 - 37,150 35.00 65.00 30.00 62.00 19.00 37.00 20.00 40.00 20.00 47.00 20.00 42.00 199.99 335.48 25.00 44.00 22.00 46.00 20.00 44.00 1.00 3.00 756.92 825.50 731.52 825.50 65.00 79.00 60.00 78.00 8.00 66.00 10.00 37.00 580.00 110.00 56.90 71.89 -3.33 2.22 -2.22 6.66 1.03 1.05
105.80 34225 46.67 44.53 28.11 28.49 35.47 32.45 272.61
34.06 32.94 31.83 2.02 800.16 796.86 69.84 68.94 20.31 21.64 260.11 64.71
-.ob 1.67 1.04
%ut lumbar vertebrae fat left.
L t lumbar vertebrae fat right. '100 = slight 0,200 = smalt 0, 300 = modest 0. 400 = moderate 0, 500 = slighuy abundant 0.
Standard deviation 3.08 1,493 5.65 6.37 4.31 4.38 5.96 5.54 29.05 4.87 5.77 5.20 25 17.52 22.39 2.93 4.62 927 6.28 127.26 2.49 1.32 2.48 .01
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Measurements and statistics are described in a companion paper with the dependent variable being percentage of chemical fat in the carcass rather than chemical protein (Johnson et al., 1990).
age of fat in the carcass (41.32). Fat density has been shown to be less than lean density; therefore, density, or specific gravity, is inversely related to fatness (Berg and Butterfield, 1976; Jones et al., 1978; Shields and Mahan, 1983). The correlations between percentage of carcass chemical fat and the carcass measurements and specific gravity are shown in Table 4. Specifk gravity of the carcass was highly correlated with percentage of carcass fat at -230. This value varies from the value (.86) reported by both Whiteman et al. (1953) and Cordray et al. (1978) and the .95 reported by Adams and Smith (1964). Garrett and Hinman (1969) and Kraybill et al. (1952) also found that specific gravity in beef was highly = correlated to percentage of carcass fat (0 .%). Tenth-rib fat thickness, taken in the same location as 12th-rib fat in the beef yield grading equation (Murphey et al., 1960), also was highly correlated to carcass fat (r = .75). This agrees with the work of Smith and Carpenter (1973) and Cordray et al. (1978) in pork and of Powell and Huffinan (1973),
4187
PREDICTION OF FORK COMPOSITION
TABLE 2. SIMPLE STATISTICS OF HAM AND LOIN MEASUREMENTS
Standard Ham temp, 'C Ham water temp, 'C Specific gravity ham Loin temp, 'C Loin water ternp, 'C Specific gravity loin -Wt,g
Hambonewf,g Hamskinwg Ham soft tissue wt, g Ham SUbQ trim fatwt, g Ham seam fat wt, g Biceps femoris wt, g Semitendinosus wt, g €Jamtip, g Ham top, g Femur wt, g Loinwt,g Loiskiqg Loin bone, g Loin mbQ fat, g Loin seam fat, g b i n u longissimus wt, g Loin other muscles wt, ~t
Range -3.33
M a
-
.a31.03 -2.78 .56 1.02 8,450.00 -
685.60 301.90 4,357.60 1,581.60 336.00 895.40 271.24 714.50 1,192.00291.90 1,032.80 32.80 121.10 273.59 66.94 169.00 206.70 -
3.33 4.44 1.08 8.89 7.22 1.07 10,120.00 1,115.30 575.00 5,957.80 2,718.10 677.30 1,313.50 490.00 1,159.60 1,677.80 444.70 1,600.60 106.24 247.50 593.40 230.50 283.37 363.14
Kauffman et al. (1975)and Miller et al. (1988) in beef; a l l found that 12th rib backfat thickness was highly correlated with percentage of carcass fat. The correlation between chilled carcass weight and percentage of carcass fat was quite low at .13.Other researchers also have found that carcass weight was not indicative of carcass composition (Powell and Huffman, 1968;Smith and Carpenter, 1973;Kauffman et al., 1975). Simple correlations between percentage of carcass fat and measurements taken from the ham and loin and proximate analysis results are shown in Tables 5 and 6, respectively, Percentage of ham fat (Table 6) and loin seam fat weight (Table 5 ) were correlated highly with percentage of carcass fat (.78 and .73, respectively). These and other high correlations between the subcomponents of the carcass and the carcass itself suggest that it may prove feasible to use these subcomponents to predict the composition of the entire carcass. Specific gravity of the ham and the specific gravity of the loin section had correlations of .55 and .53 (not in tabular form), respectively, to the specific gravity of the carcass. These values are lower than values reported by other
-.59 1.38 1.05 -.14 3.48 1.04 9,199.00 879.31 436.06 5,07824 2,155.13 484.92 1,066.84 324.24 852.78 1,387.85 359.93 1.336.48 58.01 72.83 423.57 145.59 226.94 282.29
deviation
1.92 1.26 .01 2.59
2.07 .01 421.57 92.53 64.13 365.55 312.79 88.10 96.08 41.08 80.05 122.45 32.31 123.10 15.93 26.30 77.36 36.09 25.79 37.55
researchers such as Pearson et al. (1956) (.93 and .91, respectively). Brown et al. (1951), Kraybill et al. (1952) and Whiteman et al. (1953) reported results similar to those of Pearson. The regression equations for predicting the decimal fraction of carcass fat using carcass data, specific gravity and their combination are shown in Table 7. All the measurements used in these equations can be attained easily with the proper facilities. In addition, these measurements do not devalue the carcass in any way, with the possible exception of the split between loth and 11th ribs to display the loth rib inkrface. However, this is a minor point, because this method would be used only in research, not commercially, and one can rib the carcass enough to display the needed information without damaging the belly and with the maximum loss of one loin chop. Therefore, the equations in Table 7 can be used in a variety of situations, incorporating specific gravity where the facilities allow and using carcass data done when specific gravity cannot be measured The most statistically valid equation to predict percentage of chemical fat in the carcass using the carcass data alone was Equation 2 flable 7). This two-variable equa-
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Variable
4188
JOHNSON ET AL. TABLE 3. SIMPLE STATISTICS FROM PROXIMATE ANALYSES
Standard Range
MCan
deviation
Ham subQ trim protein, % Ham subQ trim moisture, %
2.98 - 6.84 9.74 - 21.45 71.16 - 86.06 6.71 - 12.65 27.36 - 43.78 45.56 - 65.71 20.29 - 23.68 68.08 - 75.32 2.37 - 9.14 1.43 - 4.51 4.31 - 14.21 77.69 - 92.62 2.69 - 6.64 8.34 - 26.60 66.87 - 89.13 2.13 - 22.42 19.16 - 70.37 9.20 - 73.66 19.31 - 2.5.76 65.28 - 74.48 2.07 - 15.61 31.07 - 51.17 18.07 - 32.06 26.38 - 58.86 14.37 - 19.01 12.23 - 16.03 5.29 - 11.44
4.23 15.94 79.41 9.13 35.63 54.54 22.01 72.13 5.23 2.58 9.74 86.54 4.85 19.82 74.82 18.78 61.53 19.13 23.44 70.91 5.98 41.32 24.49 40.59 16.83 13.58 9.29
.78 2.47 3.45 1.13 4.12 4.90 .75 1.56 1.58 .65 1.99 3.11 .93 3.35 4.43 3.5 1 7.44 9.25 1-48 2.13 2.78 3.94 3.42 6.35 1.06 .67 1.32
~~
HamsubQtrimf*% Ham seam fat protein, % Ham seam fat moisture, % Hamseamfatfat,% Ham lean protein, % Ham lean moisture, % Ham lean fat, % Loin subQ trim protein, % Loin subQ trim moisture, % Loin subQ trim fat, % Loin seam fat protein, % Loin seam fat moisture, % Loin seam fat fat, % Loin other proteiq 56 Loin other moisture, % Loin other fat, % Loin LD' protein, % Loin LD moisture, % Loin LD fat, % Carcass fat, % Ham fat, % Loinfas % Carcass protein, % Ham protein, % Loin proteiq % %D
= M. longissimus muscle.
tion includes loth rib fat thickness and marbling and had an R-square value of .67 and a C(p) value of 2.81. In a simple regression equation using only one variable (Equation 1, specific gravity), specific gravity alone accounted for 63.3% of the variation. This equation differs from 4. 1 - carcass data and specific gravity in that it is a simple linear regression, as opposed to stepwise regression, which is a multiple regression technique. With the correct facilities, specific gravity is very easy to obtain and could prove quite useful in predicting carcass composition in pork carcasses. By combining the carcass measurements and specific gravity, a more accurate equation can be generated to predict percentage of carcass fat (Equation 2, carcass data and specific gravity). Using specific gravity and loth rib fat thickness at 314 the distance around the M. longissimus, which were the single best indicators in their respective groups, the R-square value was increased to .72 while maintaining an acceptable C@) value. Table 8 presents the regression equations that were generated to predict the decimal
TABLE 4. CORRELATIONS OF PERCENTAGE OF C A R C A S S FAT TO CARCASS DATA AND SPECIFIC GRAVITY ~~
Variable
Correlation ~~~
speclfc gravity loth Rib fat (3/4) loth Rib fat Last lumbar vertebra fat (R) loth Rib Fat (1/4) Last lumbar vertebra fat (L) Last rib fat right Marbling Last rib fat left Depth of chine right Loin eye area 1st Rib fat left 1st Rib fat right Depth of Gluteus left Depth of Gluteus right Live wt Depth of chine left chilled carcass wt Lmgth left Ham score Dressing percentage
(ln)
Lenanrirgt
-.7% .753 .687 .631 .577 .487 .427 .403 .370 -.328 -.314 .291 .264
.225 -.221 .150 -.133 .127 .056 -.045
-.M8 .015
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Variable
4189
PREDICTION OF FORK COMPOSITION TABLE 5. CORRELATIONS OF PERCENTAGE OF CARCASS FAT TO HAM AND LOIN MEASUREMENTS Correlation
Variable
Correlation
.729 .717 .698 .691 .674 -.612 -.608 -501 -.595 -.561 -.547 -.456 -.452 .410 -.359 -.326 ,295 243 -215
Percentage ham fat Percentage loin fat Loin subQ protein Loin seam fat fat Loin seam fat moisture HamsubQtrimfat Ham subQ trim moisture Ham lean fat Loin seam fat protein Loin M. longissimusfat Loin M.longissimusmoisture Loin M.longissimusprotein LoinsubQtrimfat Loin other moisture Loin other fat Ham lean moisture Ham seam fat protein Lom other protein Ham seam fat fat Ham lean protein Loin subQ trim moisture Ham subQ trim protein Ham seam fat moisture
.784 .686 -.a9 .380
~
Loin seam fat Loin fat wt Hamfatwt HamsubQtrimwt LoinsubQtrimwt Loin specific gravity Ham soft tissue wt
Femur wt Ham Bone wt Semitendinosus wt Ham other muscles wt Ham specific gravity Biceps femoris wt Loin wt Ham top wt Ham tip wt Loislrinwt
Ham seam fat wt Loin bone wt Loin other wt Hamwt
-.181 -.120
Loin M.loogissimus wt Hamskinwt
-.lo2 .034
fraction of carcass fat using the subcarcass component measurements of the ham, loin section or the combination of the two. These equations are more difficult to employ because they require more time, money and effort to complete and some require chemical analysis. However, such added work is rewarded by an increase in R-square values; analyzing subcomponents involves much less time, labor and money than analyzing the entire carcass. 'Ihe best equation for predicting the decimal fraction of carcass fat using the ham data (CD = .67,C@) = 3.04) included the variables of ham other muscle weight and percentage of ham fat (Table 8, Fquation 2, ham). The most accurate equation to predict the decimal fraction of carcass fat using variables from the loin section was a five-variable equation that contained loin subcutaneous fat moisture percentage, loin skin weight, loin seam fat weight and the specific gravity of the loin section (Equation 5 , loin). This equation had an Rsquare value of .73,a C@) value of 5.31 and has only one variable that was analyzed chemically. The weights can be determined easily and an apparatus to determine the
-.379 .378
-.350 .328 -319 .297 -.Z8 -.254 .254 -.230 .226 -219 -.173 -.I51 .098 .046 -.035
-.m7 -.024
specific gravity of the loin section can be assembled in any facility using a tub and a set of laboratory scales. Because this equation has an accuracy comparable to that of the twovariable equation in Table 7, which uses loth rib fat and specific gravity of the carcass, it could be implemented in situations in which it is impossible to determine specific gravity on the entire carcass. When the ham and loin section measurements are combined, equations with higher Rsquare values result only at the expense of increasing difficulty, not to mention the devaluing of both of the ham and loin section. Percentage of carcass fat can be predicted using indicators from the carcass without total grinding of the carcass. Specific gravity and carcass measurements are ideal for use in prediction equations; they are highly correlated to composition, easily obtainable and costefficient, because they do not devalue the retail product. The 8-9-10 rib loin section also was an accurate indicator of percentage of carcass fat, although it is more timeconsuming to dissect and some equations utilizing it required chemical analysis. Finally, the ham proved to
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Variable
TABLE 6. CORRELATIONS OF PERCENTAGE OF CARCASS FAT TO HAM AND LOIN PROXIMATE ANALYSES
4190
JOHNSON ET AL.
TABLE 7. EQUATIONS FOR PREDICTING THE DECIMAL FRACTION OF FAT IN PORK CARCASSES USING CARCASS DATA, SPECIFIC GRAVITY MEASUREMENTS OR BOTHa Independent variables
Intercept
loth Rib fat (3/4) loth Rib fat (3/4) + Marbling loth Rib fat (3/4) + Depth of chine (R) + Marbling loth Rib fat (114) + loth Rib fat (3/4) + Depth of chine (R) + Marbling Loin eye area + loth Rib fat (114) + loth Rib fat (314) + Depth of chine (R) + Wbliug
.2129 .1955
Specific gravity Specific Gravity loth Rib fat (3/4) + Specific gravity loth Rib fat (314)
+ Marbling + Specific gravity
.3192
.3090
,2854
. m 21 .00595 .OOolO .0056 1 -.00165 .oOolO .a0190 .00422 -.a0185 .OOo11 .00016 .a0199
R2
C@)
RSD
.563 .672
14.33 2.81
.0262 0230
.709
25
.0219
.726
.05
.a215
.735
1.02
.0215
.w
-.00229 .OOoll Specific gravity 6.8619 -6.17179 .633 Carcass data and specific gravity 6.7811 -6.09356 .641 4.6953 .00301 .717 4.19045 3.8634 .00345 .749
4.1186
chilled carcass wt
4,1704
-3.422 13 .00333 .00026
NDb
8.62 .52
.a235 .0231 .I3207
-1.67
.0198
,762
-1.42
.0195
,775
-1.22
.0192
.m
loth Rib fat (314) +Length right + Mafbhg + Specific gravity
+ loth Rib fat (314) +Length right + Marblim
bValues
.00004 -3 36170
+ specificgravity
-.m .00365 .OOo39
.00004 -3.90076
%pations were developed using decimal fractions rather than percentages for all input and output values. bNot detectable. % best equation, weighing R-square and C@) values. Example:
US@
Specificgravity = 1.04689 loth rib fat thickness (3/4) = 22 mm 4 . 2 Carcass data and specific-vi&, X 4.6953 + (.00301 X 22) - (4.19045 x 1.04689) = 4.6953 + .06622 - 4.38694a2 = .3745798 = 37.46%fat
-
be the least accurate indicator of percentage of carcass fat that was investigated. Conclusions
The most important discovery in this research project was the development of the two-variable equation to predict percentage of
carcass fat using specific gravity of the carcass and loth rib fat thickness. The high coefficient of determination for this prediction equation, the ease of obtaining the independent variables and the preservation of the retail value attest to the importance of this information. Standal (1965) and Cordray et al. (1978) found specific gravity to be the single best indicator of pork
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Equation
4191
PREDICTION OF PORK COMPOSITION
TABLE 8. EQUATIONS FOR PREDICTING THE DECCMAL FRACTION OP CHEMICAL FAT IN PORK CARCASSES USING INDICATORS FROM THE HAM, LOIN OR B O m Independent variables Percentage Ham Fat
Intercept
+biceps femoris wt
94894 -.m7 31727 .00593 -.m7 96652 ,00629 -.m5
+HamOthlZwt
-.m5
Ham other wt
+ Percentage ham fat
Ham lean moisture
.1790 .3136
b-Values
-.1538
+HamOthCTwt
+ Percentage ham fat Ham lean moisture
-.1374
+ Percentage ham fat
Ham lean moisture
0)
RSD
.616 .674
8.27 3.04
.m9
.716
-.29
.a219
R2
0232
.729
.11
.a217
,743
.31
.a214
.531 .640
26.35 12.75
.a273 .m2
.688
7.86
St228
.714
6.18
.0221
.734
5.31
.MI6
.616 .727
1.87 -8.19
.0249 .0213
,772
-11.15
.0916
.812
-11.48
.OM9
315
-1 1.83
.os12
94358 -.2359
-.00730 .oooO8
+Hamskinwt +Biceps femoris wt
-.m -.m
+Ham O t h e r w t +Percentage ham fat
.95404 Loin
Loin seam fat wt Loin subQfat wt + Loin seam fat wt Loin subQ fat wt
.2845 .2357 .2998
1.1781
1.3844
+Loinskinwt +LoinsubQwt +Loinseamfatwt + Specifc gravity loin Percentage ham fat Percentage ham fat +Loin scam fat wt
Ham soft tissue wt
.00016
.00049 -1.06870 .17W .BOO
+HamsubQwt
+ Loin seam fat wt
+ Specific gravity loin
Ham and loin 94894 .65 133 .OOO49
. a 7
-.00004 .00004
.4970
,00004 .00004
.ooo49
+Hamfatwt +Loin subQ protein +Loinseamfatwt
Ham lean protein + Ham soft tissue wt
-.m5 .00018 .OOO51 -.O0022 -.82426 .00336 -.00065
+Hamfatwt +Loin seam fat wt
Ham soft tissue wt
.oom4 .00054
+Loinsesmfatwt + Loin other wt + Specifii gravity loin
Loin s u m moisture
.O0020 .ooo61
+Loinseamfatwt + Loin other wt
Loin s u m fat wt
.00086
1.3781
-.01072 .00043 -.03 -.m3
.m .00048
-.71407
%quatiom were generated using decimal fractions rather than percentages for all input and output values. best equation, weighing R-square and C@) values.
%e
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mation
4192
JOHNSON ET AL.
Implications
Prediction equations to determine carcass composition potentially could save investigators time and money. Further research is needed with more numbers and more diverse animals, especially lean ones, because the accuracy of specific gravity works best for fat carcasses. Literature Cited
Adams, J. L. and W. C. Smith. 1964. The use of specific gravity and its reciprocal in predicting the carcass composition of pigs slaughtered at three weights. Anim. Prod. 697. Berg, R. T.and R. M. Butterfield. 1976. New concepts of cattle p w t h . Sydney Univ. Press, Australia. Brown, C. J., J. C. Hiller and J. A. Wbatley. 1951. Specific gravity as a measure of Ute fat content of the pork carcass. J. Anim. Sci. 1097. Cordray, J. C., D. L. Hu&nan and J. A. McGuire. 1978. F'redictive equations for estimating protein and fat in the pork carcass. J. Anim. Sci. 46:666. Garrett, W. N. and N . Hinman.1969. Re-evaluation of the relationship between carcass density and body composition of steers. J. Anim. Sci. 28(1):1. Hankins, 0. G. and P.E. Howe. 1946. Estimation of tbc composition of beef carcasses and cuts. USDA Tech. Bull. 926. Washington, DC. Johnson, L. P.. M.F.Miller, K.D. Haydon and J. 0.Reagan.
1990. The prediction of percentage of protein in pork carcasses. J. Anim. Sci. 68:4176. Jones,SD.M.,M.A.PriceandR.T.Berg. 1978.Areviewof carcass density, its measurements and relationship with bovine carcass fatness. J. Auim. Sci. 46:1151. Kauffman, R. G., M. E. Vantes, R A. Long and D. M. Schaefer. 1975. Marbling: Its use in predicting beef carcass composition, J. Anim. Sci. 40:235. Kraybill, F.P., H.L. Biller and 0. G. Hankjns. 1952. Body compositionof cattle ll: DetCrmination offat and HzO content frommeasurementsof body specificgravity. I. Appl. Physiol. 4575. Miller, M. F., H. R. Cross, J. F.Black, F. M. Byers and H. A. Recio. 1988. Evaluation of live and carcass techdques for predicting beef carcass composition. J. Meat Sci. 23:111. Murphey, C. G., D. K.Hallef W. E. Tyler and J. C. Pierce. 1960. Estimating yields of retail cats from beef carcasses. J. Anim. Sci. 191240(Abstr.). Peanon, A. M,L. J. Bratzler, R J. Deans, J. F.Price,I . A. Hoefer, E. P. Reineke and R W. Luecke. 1956. The use of specific gravity of Certainuntrimmedpork cuts as a measure of m a s s value. J. Anim. Sci. 15:86. Powell, W. E. and D. L. Huffman. 1%8. An evaluationof the quautitative estimates of beef carcass composition. J. Anim. Sci. 271554. Powell, W. E. and D. L. Huffman. 1973. Predictbgchcmical composition of beef carcasses from easily obtainable mcnss variables. J. Anim. Sci. 36:1069. Shields, R. G.,Jr. and D. C.Mahan. 1983. Evaluation of ground carcass, sawdust residue and specific gravity methodsfor estimathg body compositionof reproducing swine. J. Anim. Sci. 57:604. Smith, G. C. and Z. L. Carpenter. 1973. Evaluation of the fxtors associated with the composition of pork carcasses. J. Anim. Sci. 36493. Standal, N. 1965. Specific gravity and other carcass measurement as a predictor of percent lean and fat in hams. Acta Agric. Scand. 1565. Whiteman, J. V., J. A. Whatley and J. C. Hiller. 1953. A furtherinvestigationof specificgravity as a measure of pork carcass value. J. Anim. Sci. 12:859.
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carcass composition. Kraybill et al. (1952), Whiteman et al. (1953), Adams and Smith (1964) and Jones et al. (1978) all discussed the high utility of specific gravity in predicting carcass composition.
