*While the full article for this executive summary is currently under peer review, these initial findings are being made available on BeefResearch.org to enable the industry to act on the research, inform the scientific community of ongoing work, and help prevent duplication of research efforts. Once peer review is complete, a link to the published article will be added to this summary.
BACKGROUND
The beef industry has a considerable need for an improved measure of saleable red meat yield (RMY) for cattle and beef carcasses. Even though traditional camera grading systems do accurately assess muscling and fat measurements used by the USDA yield grade equation, the equation itself may not account for outliers of the cattle being harvested. Technologies with the ability to directly measure or estimate RMY need to be identified and developed so that appropriate market signals can be sent to producers to improve the efficiency, profitability, and sustainability of the beef industry. Utilizing magnetic resonance imaging (MRI) and computed tomography (CT) as a gold standard for measuring total beef composition could potentially add accuracy and precision, while reducing the labor required to determine the saleable yield of meat animals. Therefore, the purpose of this study was to demonstrate the capacity of the data produced by MRI and CT technologies for determining carcass composition on a component basis (muscle, fat, and bone), by performing a carcass dissection and carcass yield test coupled with compositional measurements of lean, fat, and bone.
Methodology
Three (n = 3) market ready, black hided, commercially fed heifers were individually selected and procured to represent a range in red meat yield (i.e., YG 1, YG 3, and YG 5) while maintaining a moderate frame size to ensure compatibility with bore size of CT and MRI machines. Carcasses (n=3) were portioned into hindquarter, chuck/rib, and brisket/plate pieces to accommodate the bore size of the technologies and were imaged. Then, the sides were fabricated into a boneless, industry-standard cutout. Muscle, fat, and bone were calculated from measured weights adjusted by chemical fat analyzed on a whole carcass basis. MRI and CT images were processed and segregated into muscle, fat, and bone, then the proportion of each was calculated.
Findings
MRI was found to be an unsuitable option due to size restrictions and limitations in image post-processing. However, CT had an average difference of -3.8, -8.8, and 12.2% of measuring muscle, fat, and bone, respectively when measured weight proportions were compared to CT volume proportions. The CT underestimated muscle and fat and overestimated bone. The average difference decreased when CT volumes were corrected with reference densities for each respective tissue (muscle = 1.1, fat = 0.9, and bone = 2.2) to -1.5% in muscle, -3.5% in fat, and 4.6% in bone. Estimation errors occurred in the same directions. However, when CT volume proportions were regressed on measured weights and new CT values were calculated from the equations, the average difference between measured weights and calculated CT values for muscle, fat, and bone were -1.2, 0.0, and 0.03%, respectively. Thus, CT demonstrated its viability as a tool to measure total beef carcass composition and should be further pursued and refined.
Implications
MRI, while excellent for segregating soft tissues, is not well suited to be pursued for total beef carcass composition. Contrarily, CT should be further pursued for total beef carcass composition. Its repeatability, ease of processing data, and amenable bore size make it a viable solution. Work should continue in measuring more carcasses to better understand characteristics that allow for better outcomes of gradability for the most accurate assessment of the product.