The objectives of the study were: 1) To evaluate computed tomography (CT) as a method to measure carcass composition. 2) Use millimeter radar measurements of live cattle to predict CT-determined carcass composition in real-time.
Cattle (N = 98) of varying of varying sex, live weight, fatness, and muscularity were scanned using a millimeter radar linear body measurement system (SizeR ©, Geissler Corporation, Minneapolis, MN) immediately before harvest at the WSU Meat Laboratory. Carcasses were chilled, and standard USDA Yield Grade and Quality Grade data were obtained. Carcass left sides were separated using reasonably representative industry breaks into 9 primals.
Primals were CT scanned at the WSU Veterinary Teaching Hospital, and CT images were obtained at a cross-sectional thickness of 8 mm. Volumes were calculated from CT images and computed to masses within each primal, and Hounsfield unit (HU) thresholds were established between muscle, fat, and bone.
A subset of rounds (N = 42) was dissected into soft tissue and bone. The mass of fat and fat-free lean (muscle) was determined from a chloroform-methanol extraction method. Chemically determined fat and muscle masses and dissectible bone mass were used to test the performance of CT in measuring tissue composition of the dissected rounds.
Machine learning techniques were used to develop a model predicting carcass muscle percentage from body measurements obtained from the radar system. A series of 70 pre- determined body dimensions were calculated from millimeter radar measurements. A collective subset of body dimensions was identified as being most contributive to percent muscle using a Boruta algorithm. Data were split in to 50% training and 50% testing datasets, and a repeated, cross-validated Naïve Bayesian model was built on the training dataset.
Chemically determined and CT-measured percentages were highly related (R 2 > 0.90) for fat, muscle, and bone of the dissected rounds, indicating a high level of precision in the CT measurement of carcass tissues. Root mean squared error between chemically determined and CT-measured percentages was nearly 1 percentage unit or less for each tissue type, suggesting a high level of accuracy. All relationships appeared to be linear in a nearly 1-to1 ratio across the range of the values.
Although all carcass regions were not dissected, CT-determined and actual primal weights provided the ability to assess the performance of CT in carcass regions other than the round. The mean percentage difference in CT-determined and actual weights in each of 9 primals was not more than 1%, and the RMSE of the percentage difference was not more than 1.5% for any primal. The RMSE of the percentage difference between CT- determined chilled side weight (CSW) and the actual CSW was 0.82%. Since tissue proportions were included to calculate the mass of each primal, it could be deduced that CT is also reasonably accurate at measuring tissue masses within primals, although this was not evaluated in all primals.
Body dimensions selected for their collective relationship to the target variable of percent muscle category using the Boruta algorithm generally included body widths, at high and low locations, on the anterior (heart girth) and posterior (spring of rib) ends of the rib cage.
On the training dataset, the Naïve Bayesian model classified 63% of samples into their correct percent muscle category and 76% of samples within one category (or 2 muscle percentage units) of their correct category. The application of the model to data in the testing dataset essentially replicated the application of the model to “blind” or “unseen” data, just as would happen if the radar system were implemented in commercial practice for carcass composition determination. In the testing dataset, the model classified 24% of samples into their correct percent muscle category, and 49% of samples within one category (or 2 muscle percentage units) of their correct classification.
Differences in radar-predicted and CT-measured muscle percentage values across the entire dataset (only n = 34 cattle used in training the model; n = 62 “unseen” cattle) showed that 64% of all cattle could be predicted within 3 percentage units of their CT-measured muscle percentage. Additionally, the relationships between radar-predicted and CT-measured muscle percent (R 2 = 0.58 to 0.79) indicated that the model had reasonable predictive precision.
This study demonstrated that computed tomography can be used to measure composition of fat, muscle, and bone in beef carcasses and cuts with a high degree of accuracy and precision, such that it might be evaluated as a gold standard method for carcass composition determination.
Body dimensions of live cattle rapidly measured pre-slaughter using millimeter radar could be used to develop a relatively robust model capable of predicting muscle percentage in carcasses at a commercially meaningful accuracy. Further exploration and development of such a model for predicting carcass composition with equal or better accuracy and precision would help the beef industry more adequately quantify and manage its outputs to optimal targets.
These results of this study suggested that computed tomography was reasonably accurate at measuring the fat, muscle, and bone composition of beef carcasses and cuts. Furthermore, using innovative technology like radar, potential exists for feedyards, packers, and researchers to predict carcass composition of beef cattle before harvest quickly, accurately, and precisely from body measurements. If further researched and developed, such prediction would help the beef industry better quantify and manage its outputs to optimal targets.