J. Anim. Sci. 2003. 81:1488-1498
© 2003 American Society of Animal Science
An evaluation of the lamb vision system as a predictor of lamb carcass red meat yield percentage1
A. S. Brady,
K. E. Belk2,
S. B. LeValley,
N. L. Dalsted,
J. A. Scanga,
J. D. Tatum and
G. C. Smith
Department of Animal Sciences, Colorado State University, Fort Collins 80523-1171
2 Correspondence: phone: 970-491-5826; fax: 970-491-0278; E-mail: keith.belk{at}colostate.edu.
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Abstract
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An objective method for predicting red meat yield in lamb carcasses is needed to accurately assess true carcass value. This study was performed to evaluate the ability of the lamb vision system (LVS; Research Management Systems USA, Fort Collins, CO) to predict fabrication yields of lamb carcasses. Lamb carcasses (n = 246) were evaluated using LVS and hot carcass weight (HCW), as well as by USDA expert and on-line graders, before fabrication of carcass sides to either bone-in or boneless cuts. On-line whole number, expert whole-number, and expert nearest-tenth USDA yield grades and LVS + HCW estimates accounted for 53, 52, 58, and 60%, respectively, of the observed variability in boneless, saleable meat yields, and accounted for 56, 57, 62, and 62%, respectively, of the variation in bone-in, saleable meat yields. The LVS + HCW system predicted 77, 65, 70, and 87% of the variation in weights of boneless shoulders, racks, loins, and legs, respectively, and 85, 72, 75, and 86% of the variation in weights of bone-in shoulders, racks, loins, and legs, respectively. Addition of longissimus muscle area (REA), adjusted fat thickness (AFT), or both REA and AFT to LVS + HCW models resulted in improved prediction of boneless saleable meat yields by 5, 3, and 5 percentage points, respectively. Bone-in, saleable meat yield estimations were improved in predictive accuracy by 7.7, 6.6, and 10.1 percentage points, and in precision, when REA alone, AFT alone, or both REA and AFT, respectively, were added to the LVS + HCW output models. Use of LVS + HCW to predict boneless red meat yields of lamb carcasses was more accurate than use of current on-line whole-number, expert whole-number, or expert nearest-tenth USDA yield grades. Thus, LVS + HCW output, when used alone or in combination with AFT and/or REA, improved on-line estimation of boneless cut yields from lamb carcasses. The ability of LVS + HCW to predict yields of wholesale cuts suggests that LVS could be used as an objective means for pricing carcasses in a value-based marketing system.
Key Words: Carcass Yield • Image Analysis • Lamb
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Introduction
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It has long been apparent and is well documented that the U.S. sheep industry deserves criticism for the production of excessively fat sheep. In a nationwide survey, Tatum et al. (1989) explicitly stated that the majority of U.S. lamb carcasses were excessively fat externally. Ward (1995) suggested that seasonal market price fluctuations in the lamb industry contribute to the feeding of lambs past optimal maturity and slaughter weights to take advantage of optimal prices. Currently, slaughter lambs are priced according to dressing percentage and hot carcass weight, and cutability differences are basically ignored (Purcell, 1995; Ward, 1998). Thus, in order for the lamb industry to move to a leaner end product, there remains a need to develop an objective, accurate method to predict lean meat yield that will also serve as an objective method for assigning values to lamb carcasses.
The U.S. beef industry has explored the use of carcass assessment instrumentation, primarily by evaluating video image analysis (VIA) equipment in an effort to improve accuracy in predicting carcass cutability, and thus carcass value. Cannell et al. (1999; 2002) determined that using VIASCAN and Computer Vision System technology, respectively, could predict fabrication yields more accurately than yield grades assigned by USDA line graders. In a subsequent study, Steiner et al. (2000) sought to employ both instrumentation and on-line grader estimates in an on-line augmentation system to predict carcass yield. They reported that augmentation of the application of yield grades by USDA line graders with VIA in real time could significantly increase the accuracy with which subprimal yields are predicted (Steiner et al., 2000). The objective of this study was to determine if the lamb vision system, a VIA instrumentation system, could be used for accurate prediction of lamb carcass cutability, and therefore carcass value in a commercial setting.
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Experimental Procedures
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Lamb carcasses (n = 246) were selected by Colorado State University personnel at a commercial packing plant after harvest but before entering the chilling coolers. During each of the 4 wk of the study, carcasses were selected to fit an experimental design on the basis of sex class (ewe and wether), hot carcass weight (light = 
29.48 kg, medium = 29.94 kg to 34.02 kg, and heavy = 
34.47 kg) and USDA yield grade (1 through 5; USDA, 1992). Within-yield grade, an approximately equal number of carcasses were selected to fill two muscling subclasses (light or heavy), with muscling differences included to ensure that cutability differences were not due to fatness alone. Lamb carcasses selected for inclusion in this study reflected the extreme range of variation in carcass traits experienced at the commercial facility each week. Such variability ensured that the lamb vision system (LVS; Research Management Systems USA, Fort Collins, CO) would be tested for accurate prediction of carcass cutability across the extreme range of differences in composition encountered in the present U.S. lamb population. Experimental design and the carcass selection grid are outlined in Table 1
. Immediately following selection, but before carcass chilling, carcasses were scanned using the LVS, and LVS images, hot carcass weight (HCW), and carcass identification numbers were recorded.
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Table 1. Experimental design: Numbers of carcasses included in the study by sex class, weight, and USDA yield grade (YG) category
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The carcass assessment unit of LVS consists of a stationary camera with a lighting processor and a monitor housed in a stainless steel cabinet that contains a computer processor. Following image acquisition, LVS software operates by: 1) recording an image of a background, 2) recording an image of the carcass, and 3) subtracting the carcass image from the background image to provide a defined image of the carcass. In addition, LVS software recognizes all anatomical points that are needed to make a series of total carcass measurements. Measurements made by the LVS software include, but are not limited to, carcass length, groin to right leg length, groin to left leg length, distance from groin to the end of the shank, red color score for shoulder, blue color score for shoulder, red color score for loin, blue color score for loin, distance between the two legs, groin area, carcass area measurements, total carcass width measurements, leg area measurements, leg width measurements, and groin angle measurements. These measurements are utilized as the system output variables that are related to shape and size of the carcass, degree of muscularity, and relative proportions of fat and lean trim.
During each week of the study, an adequate number of carcasses was selected for scanning to ensure that the correct number of carcasses would be available to fill the experimental design. Carcasses with slaughter defects were not selected for the study. These carcasses were then moved to the chilling cooler (-3°C to 1°C) and were held under spray-chill conditions for 24 h, except during weekends, where in the first 2 wk of the study, carcasses were chilled for 72 h. Following chilling, all carcasses were circulated past the grading stand, where, at normal chain-speeds of approximately 480 carcasses/h, a USDA grader (employee of USDA-AMS) assessed and stamped a USDA quality grade and USDA yield grade (USDA, 1992) on the exterior of each carcass. After receiving on-line USDA grades, the selected carcasses were sorted to a static holding rail.
An "expert" USDA grader (a field supervisor of USDA-AMS) then assigned and recorded USDA quality grade and yield grade factors ("Gold Standard" factors), as well as a final USDA quality grade and a final USDA yield grade for each carcass, with the aid of a grading probe and in whatever period of time was necessary to maximize accuracy and precision of grade or grade factor assignments. These "Gold Standard" factors and USDA yield grades were used to determine which carcasses fit the project design. The carcasses remained in the holding cooler (2°C) until the next day when they were moved to the fabrication area. All cutting and trimming steps during fabrication were performed by experienced plant meat-cutters, supervised by Colorado State University personnel.
The foresaddle and hindsaddle of each carcass side were separated between the 12th and 13th ribs. Following this separation, longissimus muscle area (REA) was recorded for 124 of the 246 carcasses to determine, more objectively, muscling variation among carcasses. Each forequarter and each hindquarter of all carcasses was then further divided to generate shoulder, rack, loin, and leg primals. All four primals were split, and one-half of each primal was fabricated into bone-in cuts while the other half was fabricated into boneless cuts following the experimental design for the study as described above. Fabrication into bone-in subprimal/primal cuts was conducted in a manner such that an equal number of right (n = 246) and left (n = 246) sides was fabricated to each cutting style endpoint. Each primal was weighed so that primal weights and side weights could be reconciled for each unit. Primal weights were reconciled by ensuring that all bone-in and boneless cuts, lean trim, fat trim, and bone totaled 99.9% of initial primal weight.
Subprimal cuts from the boneless side were trimmed of fat to a denuded (0.32 cm) level, and all parts were weighed and each weight was recorded. Institutional Meat Purchase Specification (IMPS) boneless subprimal cuts (USDA, 1996; cuts were not certified by USDA but closely approximate the IMPS description when appropriate) included: square-cut shoulder, boneless (IMPS 208); rack, ribeye roll (IMPS 204E); boneless loin (IMPS 232C); short tenderloin (IMPS 232D); boneless sirloin (IMPS 234G); boneless leg (IMPS 234A style B); and boneless breast. The neck, foreshank, and hindshank for the boneless side were not fabricated to a boneless endpoint; therefore, weights for those cuts were not included in computations of dependent variables when equations were developed to predict boneless cut yields.
Subprimal/primal cuts from the side designated to be fabricated into bone-in cuts were closely trimmed of external fat (0.64 cm), and all subsequent parts were weighed and recorded. Bone-in subprimals (IMPS cuts were not certified by USDA but closely approximate IMPS description, when appropriate) generated included: square-cut shoulder (IMPS 207); neck; foreshank (IMPS 210); ribs, breast bones off (IMPS 209A); split and chined rack (IMPS 204A); loin short-cut, trimmed, 2.54 cm tail (IMPS 232A); leg (IMPS 233D); and hindshank (IMPS 233G).
The following yields were calculated for carcass sides that were fabricated to boneless cuts: "boneless, saleable meat yield," which refers to the sum of weights of boneless subprimal cuts, plus lean trim from the leg, loin, rack, shoulder, and thin cuts calculated as a percentage of chilled side weight, recorded immediately prior to fabrication; "boneless, subprimal yield," which refers to the sums of weights of boneless subprimal cuts from the leg, loin, rack, and shoulder calculated as a percentage of chilled side weight; "boneless, fat yield," which refers to the sum of trimmable fat weights generated from the production of subprimal cuts and calculated as a percentage of chilled side weight; and "boneless, bone yield," which refers to the summed weight of bones removed during production of subprimal cuts and calculated as a percentage of chilled side weight.
Fabrication yields for bone-in cuts were calculated as a percentage of the weight of the bone-in chilled side weight. The following yields were calculated for carcass sides that were fabricated to yield bone-in cuts: "bone-in, saleable meat yield," which refers to the sum of weights of bone-in subprimal/primal cuts, plus lean trimmings from the leg, loin, rack, shoulder, and thin cuts, calculated as a percentage of chilled side weight; "bone-in, subprimal yield," which refers to the sum of weights of bone-in cuts from the leg, loin, rack, and shoulder calculated as a percentage of chilled side weight; and "bone-in, fat yield," which refers to the sum of trimmable fat weights generated from production of subprimal/primal cuts and calculated as a percentage of chilled side weight.
Statistical Analysis
All statistical analyses, including descriptive statistics, correlation, regression, and ANOVA, were performed using the PROC GLM and PROC REG procedures in SAS (SAS Inst., Inc., Cary, NC). Multiple regression analysis was used to regress dependent carcass yield percentages on the independent variables of LVS output and HCW in an effort to develop a model for the prediction of red meat yield. Regression equations for the prediction of weight of subprimal cuts from each primal were developed using linear methods that were allowed to include LVS output variables and hot carcass weight. Stepwise, forward, and backward selection methods were used to determine which independent variables were common and significant (
= 0.05) for each method of selection. Variables not selected by any of the three selection methods were excluded from the regression analysis, and the three selection methods were performed once more to build models for the various dependent carcass yield percentages. The root mean square error (RMSE) and predicted residual sum of squares (PRESS) statistics were computed to assess precision of red meat yield prediction models. Models were selected based on simplicity, R2 value, PRESS value, and the RMSE value.
Dependent carcass yield percentages also were regressed on USDA on-line and expert yield grades, with the USDA yield grades serving as the sole independent variable in the model. Root mean square error and PRESS statistics were calculated for each regression equation to determine the precision of the red meat yield model predictions. These regression equations were compared to the best-fit equations developed using LVS output and HCW as independent variables.
In addition, ANOVA was employed to test for differences in means of boneless subprimal cut yield and bone-in subprimal/primal cut yields among carcasses in expert whole-number USDA yield grade classes, as well as among those in on-line whole-number USDA yield grade classes. Tukey’s mean separation procedures were used when ANOVA F-tests indicated statistical differences existed among means (P < 0.05).
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Results and Discussion
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Descriptive statistics for the 246 carcasses in the sample population are presented in Table 2. Inasmuch as the sample population was selected to represent an extreme range of variability in carcass traits present in a commercial facility each week, the large standard deviations for hot carcass weight, fat thickness, and USDA yield grade were due to this intentional selection for carcass variation.
Regression equations were developed to predict fabrication yields using LVS output plus HCW as independent variables. Values for R2, RMSE, and PRESS statistics for the best regression equations are presented in Table 3. Hot carcass weight independently accounted for 10, 8, 14, and 15% of the observed variability in boneless lean meat yield, boneless subprimal yield, bone-in lean meat yield, and bone-in subprimal yield, respectively. Inclusion of HCW as an independent variable to predict red meat yield supported the findings of Garrett et al. (1992) and Jones et al. (1992), who also reported that carcass weight was a significant factor for prediction of red meat yields. It is likely that HCW was an important determinant of percentage yields of trimmed cuts from lamb carcasses because increased weight is indicative of increased carcass fatness in ways (e.g., as seam fat) not accounted for by use of subcutaneous fat thickness measurements.
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Table 3. Independent variables, R2 and root mean square error (RMSE) values, and predicted residual sum of squares (PRESS) statistics for best-fit regression equations developed to predict percentage carcass side yields using lamb vision system output plus hot carcass weight, expert whole-number USDA yield grades, expert nearest-tenth USDA yield grades, and on-line whole-number USDA yield grades
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The best regression equation developed to predict boneless, saleable meat yield accounted for 60% of the observed variability, whereas the best regression equation to predict bone-in, saleable meat yield accounted for approximately 62% of the observed variability in actual red meat yields (Table 3
). Predictive accuracy of LVS plus HCW exceeded that achieved in previous instrumentation studies with lamb carcasses. It was reported that the use of bioelectrical impedance generated R2 values of 0.296 (Berg et al., 1996) and 0.258 (Berg et al., 1997) in the prediction of percentage boneless, closely trimmed primal cuts. Other technology that has been evaluated to predict saleable meat yield includes the Hennessy grading probe, ultrasound, and electromagnetic scanning. Garrett et al. (1992) and Berg et al. (1997) reported that use of a Hennessy grading probe to predict saleable meat yield only resulted in R2 values of 0.52 and 0.486, respectively. Ultrasound is a poor predictor of lean meat yield, as reported by Puntilla (1986), Edwards et al. (1989), and Berg et al. (1997), generating R2 values of 0.17, 0.31, and 0.142, respectively. Berg et al. (1997) reported a R2 value of 0.371 when using electromagnetic scanning to predict red meat yield. Hence, the LVS plus HCW performed more accurately than previously evaluated instrumentation methods.
Use of LVS plus HCW also was capable of predicting carcass fat yields accurately. Best-fit LVS plus HCW models for fat trim yields explained 74 and 71% of the observed variability in boneless and bone-in fat yields, respectively. These coefficients were of importance since a large proportion of lamb cutability variation is due to differences in external fat between carcasses; thus, LVS plus HCW was capable of accurately predicting carcass cutability in terms of either red meat yield or percentage fat trim.
A greater proportion of the variation in boneless cut yields was explained with more accuracy and precision with the LVS plus HCW best-fit regression equations than with any of the USDA yield grade values (on-line or expert, whole-number or nearest-tenth) (Table 3). Furthermore, bone-in, saleable meat yield predictions (using LVS plus HCW) also could be useful in a commercial setting since the LVS plus HCW prediction model had a higher R2 value compared with equations using USDA yield grades, as well as RMSE and PRESS values that were similar to those for prediction models using USDA yield grades.
To further investigate potential use of LVS in a commercial setting, regression models to predict weights rather than percentages of wholesale, major-primal cuts from lamb carcasses were developed; R2 and RMSE values for these equations are presented in Table 4. The high accuracy with which these regression models (Table 4) were able to predict weights of wholesale cuts partially resulted, particularly for the shoulder and rack, from autocorrelation that existed between HCW and some wholesale cut weights in regression models. Partial R2 values for the independent variables in the regression models indicated that a strong relationship existed between HCW and weights of shoulder and rack when fabricated to either the boneless or bone-in style, and between HCW and bone-in weights of leg. Garrett et al. (1992) and Jones et al. (1992) previously reported that HCW is highly correlated to carcass yields of fabricated cuts, and thus is an important factor to be included in lamb carcass cutability prediction models. The R2 values for the prediction of boneless wholesale cut weights exceeded the prediction accuracy of electromagnetic scanning as reported by Berg et al. (1997). Prediction accuracy was increased by 18, 9, 11, and 3 percentage points when using LVS plus HCW to predict the weight of boneless shoulders, racks, loins, and legs, respectively, compared with electromagnetic scanning.
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Table 4. Values of R2, root mean square error (RMSE), and partial R2 for regression equations using lamb vision system output variables and hot carcass weight (HCW) to predict the weight of wholesale cuts from lamb carcasses
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The LVS plus HCW models presented were accurate in sorting carcasses based on cutability differences and based on expected weights of wholesale cuts. Whereas those models have potential to aid the commercial lamb industry by helping packers to assess and sort carcasses more effectively based on differences in cutability. It is possible that the ability of LVS to sort carcasses into percent yield classes that parallel industry-recognized USDA yield grades might be beneficial, which might ease the process of using such technology for purposes of applying official USDA grades to lamb carcasses.
Boneless, saleable meat yields from carcasses classified by on-line USDA graders as yield grade 1 vs. 2 did not differ (P > 0.05), whereas the yields from carcasses of the other yield grade classes (yield grades 3, 4, and 5) were lower (P < 0.05) than those of yield grade 1 and 2 carcasses, and differed (P < 0.05) from each other (yield grades 3 vs. 4 and 4 vs. 5) (Table 5). Furthermore, means of boneless, saleable meat yields from carcasses determined to be yield grade 1 vs. 2 by expert whole-number USDA yield grades were numerically different, but did not differ (P > 0.05). However, yields of boneless, saleable meat from carcasses of the other yield grade classes (yield grades 3, 4, and 5) were lower (P < 0.05) from those of yield grade 1 and 2 carcasses, and decreased with increasing yield grade numbers from 3 to 5 (P < 0.05). Means for bone-in, saleable meat yields decreased with increasing yield grade numbers (P < 0.05) among all five expert whole-number USDA yield grades.
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Table 5. Means and standard deviations for boneless and bone-in saleable meat yields of lamb carcasses arrayed by on-line whole-number USDA yield grades (YG) and expert whole-number USDA yield grades
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Figure 1 provides the frequency distribution for boneless saleable meat yields of carcasses sorted by on-line whole-number USDA yield grades. Considerable overlap in actual yields occurred between carcasses in adjacent on-line whole-number or expert whole-number USDA yield grades and clearly suggested that there is a need to improve the accuracy with which yield grades sort carcasses into appropriate cutability classes. Heaton et al. (1993) reported that yield grades 1 and 2 did not differ (P > 0.05) from each other and that yield grades 4 and 5 did not differ (P > 0.05) from each other in mean lean meat yield.
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Figure 1. Frequency distribution of boneless, saleable meat yield for carcasses sorted by on-line whole-number USDA yield grades.
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Previous studies evaluating the use of VIA instrumentation in the beef industry to predict carcass yields suggested that this technology can be used, in combination with USDA yield grade factors, to predict red meat yield (Belk et al., 1998; Cannell et al., 1999; Steiner et al., 2000). Table 6 presents R2 and RMSE values for regression equations that included LVS output variables, HCW, and carcass measurements provided by expert graders (adjusted fat thickness [AFT] and REA) to predict yields of cuts from lamb carcasses fabricated to boneless and bone-in cutting endpoints. Longissimus muscle area measurements were obtained on 124 of the carcasses included in the study. As a result, the regression equations presented in Table 6 were developed using only data from those 124 carcasses to determine if the addition of AFT and/or REA to LVS output variables would explain a greater proportion of the observed variability, compared with use of LVS plus HCW alone.
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Table 6. Values for R2, root mean square error (RMSE), and predicted residual sum of squares (PRESS) statistics for regression equations using lamb vision system factors, HCW, and REA and/or AFT measurements provided by expert graders, to predict saleable meata and subprimalb yields from lamb carcass sides (n = 124)
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Addition of REA to LVS output variables and HCW explained an additional 4.4 and 7.5 percentage points of the observed variability in boneless and bone-in saleable meat yield, respectively, as well as 4.7 and 8.6 percentage points more of the observed variation in boneless and bone-in subprimal yields, respectively, compared with the best-fit model (Table 6) that did not include REA as an independent variable. The models that included the addition of REA were also more precise since values for RMSE and PRESS statistics were numerically reduced in these models compared with the best-fit models (Table 6).
Addition of an AFT measurement to LVS prediction equations explained 3 and 7 percentage points more of the observed variation in boneless and bone-in saleable meat yields, respectively, and 3 and 5 percentage points more of the observed variation in boneless and bone-in subprimal yields, respectively, compared with the best-fit model (Table 6) that did not utilize AFT assigned by an expert USDA grader.
Regression equations that included LVS output, HCW, and both AFT and REA measurements improved predictive accuracy of the observed variability explained in boneless and bone-in saleable meat yield by 5 and 10 percentage points, respectively, and improved (by 8 and 10 percentage points) the prediction of boneless and bone-in subprimal yields, respectively, compared with the best-fit model (Table 6). In addition, the LVS equations that included AFT and REA were also more precise since values for RMSE and PRESS statistics were numerically reduced using these equations compared with the best-fit model (Table 6).
Although the best-fit LVS plus HCW models in Table 3 explained a greater proportion of variability in boneless red meat yield compared with expert whole-number USDA yield grades, expert nearest-tenth USDA yield grades, and on-line whole-number USDA yield grades in the prediction of carcass yields; results presented in Table 6 suggest that adding AFT and REA to the independent variables of LVS output and HCW further enhanced the predictive ability of LVS.
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Implications
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Use of the on-line lamb vision system, combined with hot carcass weight, explained a greater proportion of the observed variation in yields of boneless cuts from carcasses and predicted bone-in cut yields with accuracy and precision similar to USDA yield grades. Commercially, the lamb vision system combined with hot carcass weight can be used to accurately sort carcasses into cutability classes. Packers would benefit from the use of the lamb vision system by having tighter control on inventories, and producers would benefit by receiving feedback regarding lamb carcass data. Use of such technology could facilitate development of a value-based pricing system in the U.S. sheep industry, with pricing based on lean yield values rather than on carcass weight, as is currently the case. It is recommended that validation studies be completed to confirm these findings.
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Footnotes
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1 The authors acknowledge the cooperation of ConAgra Foods and B. Rosen & Sons for providing access to their facilities during the completion of this project. 
Received for publication May 6, 2002.
Accepted for publication February 18, 2003.
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Literature Cited
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B. C. N. Cunha, K. E. Belk, J. A. Scanga, S. B. LeValley, J. D. Tatum, and G. C. Smith
Development and validation of equations utilizing lamb vision system output to predict lamb carcass fabrication yields
J Anim Sci,
July 1, 2004;
82(7):
2069 - 2076.
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Abstract
Introduction
