Accuracy and repeatability of beef carcass longissimus muscle area measurements

Objective grading of rib eye traits using the Q-FOM™ camera in Australian beef carcasses

The objective of this study was to develop calibration models against rib eye traits and independently validate the precision, accuracy, and repeatability of the Frontmatec Q-FOM™ Beef grading camera in Australian carcasses. This study compiled 12 different research datasets acquired from commercial processing facilities and were comprised of a diverse range of carcass phenotypes, graded by industry identified expert Meat Standards Australia (MSA) graders and sampled for chemical intramuscular fat (IMF%). Calibration performance was maintained when the device was independently validated. For continuous traits, the Q-FOM™ demonstrated precise (root mean squared error of prediction, RMSEP) and accurate (coefficient of determination, R2) prediction of eye muscle area (EMA) (R2 = 0.89, RMSEP = 4.3 cm2, slope = 0.96, bias = 0.7), MSA marbling (R2 = 0.95, RMSEP = 47.2, slope = 0.98, bias = -12.8) and chemical IMF% (R2 = 0.94, RMSEP = 1.56%, slope = 0.96, bias = 0.64). For categorical traits, the Q-FOM™ predicted 61%, 64.3% and 60.8% of AUS-MEAT marbling, meat colour and fat colour scores equivalent, and 95% within ±1 classes of expert grader scores. The Q-FOM™ also demonstrated very high repeatability and reproducibility across all traits.

Association between visual marbling score and chemical intramuscular fat with camera marbling percentage in Australian beef carcasses

This study assessed the precision and accuracy in the prediction of chemical intramuscular fat (IMF%), Meat Standards Australia (MSA) marbling score and AUS-MEAT eye-muscle area (EMA) using Meat Imaging Japan (MIJ) prototype camera systems. Eleven carcass datasets from the Beef Information Nucleus (BIN) project were compiled with carcass grading, IMF% and camera data. Camera prediction of IMF%, MSA marbling score and EMA was assessed using a leave-one-out cross validation method. There was an association between MIJ mirror and MIJ-30 camera traits and IMF%, MSA marbling score and EMA. However, for both prototypes precision varied for IMF% (R² = 0.4-0.5, RMSECV = 1.5-1.6%), MSA marbling (R² = 0.3-0.5, RMSECV = 57.5-59.3) and EMA (R² = 0.7-0.6, RMSECV = 4.1-5.8 cm²). Accuracy also fluctuated with average bias values of 1.7-1.8%, 45.8-40.0 units and 3.8-4.1 cm² for IMF%, MSA marbling score and EMA respectively. Key differences between carcass and camera traits and processing factors affecting the grading site are likely to have contributed to this variation.

Objective grading of eye muscle area, intramuscular fat and marbling in Australian beef and lamb

The objective of this study was to test the performance of a prototype vision system in phenotypically diverse beef and lamb carcasses against visual grading of eye muscle area (EMA), marbling and chemical intramuscular fat (IMF%). Validation in beef demonstrated that the camera prototype in combination with analytical techniques enabled prediction of EMA (r² = 0.83, RMSEP = 6.4 cm²), MSA marbling (r² = 0.76, RMSEP = 66.1), AUS-MEAT marbling (r² = 0.70, RMSEP = 0.74) and chemical IMF% (r² = 0.78, RMSEP = 1.85%). Accuracy was also maintained on validation with all four traits displaying minimal bias of -3.6, 6.3, 0.07 and - 0.01, for EMA, MSA marbling, AUS-MEAT marbling and IMF% respectively. Preliminary analysis in lamb indicates potential of the system for the prediction of EMA (r² = 0.41, RMSEP = 1.87) and IMF% (r² = 0.28, RMSEP = 1.10), however further work to standardise image acquisition and environmental conditions is required.

National Beef Quality Audit - 2016: Survey of carcass characteristics through instrument grading assessments

The instrument grading assessment portion of the National Beef Quality Audit (NBQA) - 2016 allows the unique opportunity to evaluate beef carcass traits over the course of a year. One week of instrument grading data was collected each month from 5 beef processing corporations encompassing 18 facilities from January 2016 through December 2016 ( = 4,544,635 carcasses). Mean USDA yield grade (YG) was 3.1 with 1.37 cm fat thickness (FT), 88.9 cm LM area, 393.6 kg HCW, and 2.1% KPH. Frequency distribution of USDA YG was 9.5% YG 1, 34.6% YG 2, 38.8% YG 3, 14.6% YG 4, and 2.5% YG 5. Increases in HCW and FT since the NBQA-2011 were major contributors to differences in mean YG and the (numerically) increased frequency of YG 3, 4, and 5 carcasses found in the current audit. Mean marbling score was Small, and the distribution of USDA quality grades was 4.2% Prime, 71.4% Choice, 21.7% Select, and 2.7% other. Frequency of carcasses grading Prime on Monday (6.43%) was numerically higher than the average frequency of carcasses grading Prime overall (4.2%). Monthly HCW means were 397.6 kg in January, 397.2 kg in February, 396.5 kg in March, 389.3 kg in April, 384.8 kg in May, 385.0 kg in June, 386.1 kg in July, 394.1 kg in August, 399.1 kg in September, 403.9 kg in October, 406.5 kg in November, and 401.9 kg in December. Monthly mean marbling scores were Small in January, Small in February, Small in March, Small in April, Small in May, Small in June, Small in July, Small in August, Small in September, Small in October, Small in November, and Small in December. Both mean HCW and mean marbling score declined in the months of May and June. The month with the greatest numerical frequency of dark cutters was October (0.74%). Comparison of overall data from in-plant carcass and instrument grading assessments revealed close alignment of information, especially for YG (3.1 for in-plant assessment versus 3.1 for instrument grading) and marbling (Small for in-plant assessment versus Small for instrument grading). These findings allow the beef industry access to the greatest volume of beef value-determining characteristics for the U.S. fed steer and heifer population than ever reported, resulting in potentially more precise targeting of future quality and consistency efforts.

Quantification of saleable meat yield using objective measurements captured by video image analysis technology

Video image analysis (VIA) images from grain-finished beef carcasses [n = 211; of which 63 did not receive zilpaterol hydrochloride (ZIL) and 148 received ZIL before harvest] were analyzed for indicators of muscle and fat to illustrate the ability to improve methodology to predict saleable meat yield of cattle fed and not fed ZIL. Carcasses were processed in large commercial beef processing facilities and were fabricated into standard subprimals, fat, and bone. Images taken by VIA technology were evaluated using computer image analysis software to quantify fat and lean parameters which were subsequently used in multiple-linear regression models to predict percentage of saleable meat yield for each carcass. Prediction models included variables currently quantified by VIA technology such as LM area (LMA), subcutaneous (SC) fat thickness at 75% the length of the LM (SFT75), and intramuscular fat score (IMF). Additional distance and area measures included LM width (LW), LM depth (LD), iliocostalis muscle area (IA), SC fat thickness at 25, 50, and 100% the length of the LM (SFT25, SFT50, SFT100), SC fat area from 25 to 100% the length of the LM (SCFA), and SC fat area adjacent to the 75% length of the LM from the spinous processes (SCFA75). Multiple ratio and product variables were also created from distance and area measures. For carcasses in this investigation, a 6 variable equation (Adj. R(2) = 0.62, MSE = 0.022) was calculated which included coefficients for ZIL treatment, SCFA75, LW, SCFA, SCFA/HCW, and SFT100/HCW. Use of parameters in the U.S. (Adj. R(2) = 0.39, MSE = 0.028) and Canadian [Adj. R(2) = 0.10, root mean square error (MSE) = 0.034] yield grade equations lack the predictability of the newly adapted equations developed for ZIL-fed and non-ZIL-fed cattle. Prediction equations developed in this study indicate that the use of VIA technology to quantify measurements taken at the 12th/13th rib separation could be used to predict saleable meat yield more accurately than those currently in use by U.S. and Canadian grading systems. Improvement in saleable meat yield prediction has the potential to decrease boxed beef variability via more homogeneous classification of carcass fabrication yield.

National Beef Quality Audit-2011: Survey of instrument grading assessments of beef carcass characteristics

The instrument grading assessments for the 2011 National Beef Quality Audit evaluated seasonal trends of beef carcass quality and yield attributes over the course of the year. One week of instrument grading data, HCW, gender, USDA quality grade (QG), and yield grade (YG) factors, were collected every other month (n = 2,427,074 carcasses) over a 13-mo period (November 2010 through November 2011) from 4 beef processing corporations, encompassing 17 federally inspected beef processing facilities, to create a "snapshot" of carcass quality and yield attributes and trends from carcasses representing approximately 8.5% of the U.S. fed steer and heifer population. Mean yield traits were YG (2.86), HCW (371.3 kg), fat thickness (1.19 cm.), and LM area (88.39 cm(2)). The YG distribution was YG 1, 15.7%; YG 2, 41.0%; YG 3, 33.8%; YG 4, 8.5%; and YG 5, 0.9%. Distribution of HCW was <272.2 kg, 1.6%; 272.2 to 453.6 kg, 95.1%; and ≥453.6 kg, 3.3%. Monthly HCW means were November 2010, 381.3 kg; January 2011, 375.9 kg; March 2011, 366.2 kg; May 2011, 357.9 kg; July 2011, 372.54 kg; September 2011, 376.1 kg; and November 2011, 373.5 kg. The mean fat thickness for each month was November 2010, 1.30 cm; January 2011, 1.22 cm; March 2011, 1.17 cm; May 2011, 1.12 cm; July 2011, 1.19 cm; September 2011, 1.22 cm; and November 2011, 1.22 cm. The overall average marbling score was Small(49). The USDA QG distribution was Prime, 2.7%; Top Choice, 22.9%; Commodity Choice, 38.6%; and Select, 31.5%. Interestingly, from November to May, seasonal decreases (P < 0.001) in HCW and fat thicknesses were accompanied by increases (P < 0.001) in marbling. These data present the opportunity to further investigate the entire array of factors that determine the value of beef. Data sets using the online collection of electronic data will likely be more commonly used when evaluating the U.S. fed steer and heifer population in future studies.

Establishing an appropriate mode of comparison for measuring the performance of marbling score output from video image analysis beef carcass grading systems

A beef carcass instrument grading system that improves accuracy and consistency of marbling score (MS) evaluation would have the potential to advance value-based marketing efforts and reduce disparity in quality grading among USDA graders, shifts, and plants. The objectives of this study were to use output data from the Video Image Analysis-Computer Vision System (VIA-CVS, Research Management Systems Inc., Fort Collins, CO) to develop an appropriate method by which performance of video image analysis MS output could be evaluated for accuracy, precision, and repeatability for purposes of seeking official USDA approval for using an instrument in commerce to augment assessment of quality grade, and to use the developed standards to gain approval for VIA-CVS to assist USDA personnel in assigning official beef carcass MS. An initial MS output algorithm was developed (phase I) for the VIA-CVS before 2 separate preliminary instrument evaluation trials (phases II and III) were conducted. During phases II and III, a 3-member panel of USDA expert graders independently assigned MS to 1,068 and 1,242 stationary carcasses, respectively. Mean expert MS was calculated for each carcass. Additionally, a separate 3-member USDA expert panel developed a consensus MS for each carcass in phase III. In phase II, VIA-CVS stationary triple-placement and triple-trigger instrument repeatability values (n = 262 and 260, respectively), measured as the percentage of total variance explained by carcasses, were 99.9 and 99.8%, respectively. In phases II and III, 95% of carcasses were assigned expert MS for which differences between individual expert MS, and for which the consensus MS in phase III only, was < or = 96 MS units. Two differing approaches to simple regression analysis, as well as a separate method-comparability analysis that accommodates error in both dependent and independent variables, were used to assess accuracy and precision of instrument MS predictions vs. mean expert MS. Method-comparability analysis was more appropriate in assessing the bias and precision of instrument MS predictions. Ether-extractable fat percentages (n = 257; phase II) differed among MS (P < 0.05) but were not suitable to predict or validate assigned MS. The performance and reproducibility of expert MS assignment in future evaluations was considered, and an official USDA performance standard was established, to which an instrument must conform to be approved for official on-line MS assessment. The VIA-CVS subsequently was approved to assign MS to carcasses on-line after completion of a 2006 USDA instrument approval trial conducted according to methods developed during completion of this study.

Genetic parameters for carcass composition and performance data in crossbred lambs measured by Video Image Analysis

A total of 7074 crossbred lambs, produced by mating crossbred Mule ewes with terminal sire rams were used in this study. Of these, 630 were scanned using a Video Image Analysis (VIA) to estimate carcass quality traits. Genetic parameters for average daily gain (ADG), scanning live weight (SW), ultrasonic measures of muscle (UMD) and fat (UFD) depths, cold carcass weight (CCW) and VIA measurements of primal carcass joint weights (LEG, CHUMP, LOIN, BREAST and SHOULDER) were estimated using multivariate animal models. Additionally, VIA traits were evaluated under a repeatability model, considering the primal joints as repeated measures of the same trait. Direct heritability estimates were low to moderate (0.08-0.26) for VIA measurements of primal joints. Repeatability estimates for VIA traits were high (>0.90). Moderate to high heritability estimates (0.25-0.55) were found for performance traits (ADG, SW, UMD and UFD) and CCW. Genetic correlations between VIA traits and ADG were strong (0.75-0.93). Most of the VIA traits were highly correlated to SW (0.60-0.97). UFD was significantly negatively correlated with UMD (-0.22), ADG (-0.18) and CCW (-0.18). The results of this study suggest that selection on performance and carcass traits, measured by VIA, could possibly improve primal meat yield of carcass cuts without increasing the overall carcass fatness. High repeatability estimates of VIA traits and moderate heritabilities of the most valuable carcass joints suggests that including VIA information in breeding programs would be useful in order to improve carcass quality.

Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi

An automatic method for estimating the content of intramuscular fat (IMF) in beef M. longissimus dorsi (LD) was developed using a sequence of image processing algorithm. To extract IMF particles within the LD muscle from structural features of intermuscular fat surrounding the muscle, three steps of image processing algorithm were developed, i.e. bilateral filter for noise removal, kernel fuzzy c-means clustering (KFCM) for segmentation, and vector confidence connected and flood fill for IMF extraction. The technique of bilateral filtering was firstly applied to reduce the noise and enhance the contrast of the beef image. KFCM was then used to segment the filtered beef image into lean, fat, and background. The IMF was finally extracted from the original beef image by using the techniques of vector confidence connected and flood filling. The performance of the algorithm developed was verified by correlation analysis between the IMF characteristics and the percentage of chemically extractable IMF content (P<0.05). Five IMF features are very significantly correlated with the fat content (P<0.001), including count densities of middle (CDMiddle) and large (CDLarge) fat particles, area densities of middle and large fat particles, and total fat area per unit LD area. The highest coefficient is 0.852 for CDLarge.

Development and validation of equations utilizing lamb vision system output to predict lamb carcass fabrication yields1

This study was performed to validate previous equations and to develop and evaluate new regression equations for predicting lamb carcass fabrication yields using outputs from a lamb vision system-hot carcass component (LVS-HCC) and the lamb vision system-chilled carcass LM imaging component (LVS-CCC). Lamb carcasses (n = 149) were selected after slaughter, imaged hot using the LVS-HCC, and chilled for 24 to 48 h at -3 to 1 degrees C. Chilled carcasses yield grades (YG) were assigned on-line by USDA graders and by expert USDA grading supervisors with unlimited time and access to the carcasses. Before fabrication, carcasses were ribbed between the 12th and 13th ribs and imaged using the LVS-CCC. Carcasses were fabricated into bone-in subprimal/primal cuts. Yields calculated included 1) saleable meat yield (SMY); 2) subprimal yield (SPY); and 3) fat yield (FY). On-line (whole-number) USDA YG accounted for 59, 58, and 64%; expert (whole-number) USDA YG explained 59, 59, and 65%; and expert (nearest-tenth) USDA YG accounted for 60, 60, and 67% of the observed variation in SMY, SPY, and FY, respectively. The best prediction equation developed in this trial using LVS-HCC output and hot carcass weight as independent variables explained 68, 62, and 74% of the variation in SMY, SPY, and FY, respectively. Addition of output from LVS-CCC improved predictive accuracy of the equations; the combined output equations explained 72 and 66% of the variability in SMY and SPY, respectively. Accuracy and repeatability of measurement of LM area made with the LVS-CCC also was assessed, and results suggested that use of LVS-CCC provided reasonably accurate (R2 = 0.59) and highly repeatable (repeatability = 0.98) measurements of LM area. Compared with USDA YG, use of the dual-component lamb vision system to predict cut yields of lamb carcasses improved accuracy and precision, suggesting that this system could have an application as an objective means for pricing carcasses in a value-based marketing system.