Prediction of beef carcass composition using linear measurements obtained by DXA scan.
The third most popular meat in the world, behind chicken and veal, is beef. This emphasizes how important it is to precisely determine your market value to ensure that producers are paid fairly. There is a need for more precise methodologies to analyze the composition of the carcass because the USDA's current method for determining yield has its limitations. In this study, lineal measurements were taken using DXA scans, and models were developed to predict sub primal performance as well as fat and bone percentage. The highlighted model for subprime yield achieved an R2 of 0.97 using just five predictors. Using three predictors, the model for fat content recorded an R2 of 0.94, whilst the prediction for bone content was established at an R2 of 0.81. Although 11 potential predictors were identified, the models were refined to only use the five most significant predictors. The study indicates a promising path for improving the assessment of the carcass composition by multiple linear regression in order to obtain more accurate valuations in the beef industry.
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| Format: | Thesis biblioteca |
| Language: | eng |
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Zamorano: Escuela Agrícola Panamericana
2023
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| Subjects: | USDA, DXA (dual energy X ray absorptiometry), sub primal performance, fat percentage, bone percentage, |
| Online Access: | https://hdl.handle.net/11036/7745 |
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