Dual Energy X-Ray Absorptiometry as a Rapid and Non-Destructive Method for Determination of Lean, Fat and Bone Content in Livestock
Bibliographic record
Abstract
ObjectivesTo implement dual energy X-ray absorptiometry (DXA) as a platform technology, calibrations and development of robust equations to attain precision and accuracy are required before using for routine predictions of carcass yields in livestock. This manuscript summarized results of ongoing research where DXA has been used to estimate lean, fat, and bone carcass composition in beef, pork and lamb. Materials and MethodsFrom a wide range of carcasses, a total of 334 beef (230 crossbred finished steers and 104 cows), 212 pork and 155 lamb carcasses were used to build calibration equations within each population. Left carcass sides were scanned with a Lunar iDXA unit and then dissected into lean, fat, and bone and weighed. Partial least square regression was used to carry out the prediction equations for lean, fat and bone values from primal cuts scans (independent) and actual lean, fat and bone obtained through the full dissection (dependent). The predictive ability of the models was evaluated in terms of coefficient of determination (R²) and root mean square error of calibration (RMSE). ResultsThe PLSR results between actual and DXA estimated lean and fat values showed high relationship (R² > 0.97) across all the species. Within beef, the present results suggest that DXA capacity to estimate carcass composition is independent of maturity. With regard to the bone predictions, PLSR analyses also improved the relationship for bone predictions compared to simple regression models previously developed at this institution or single pass scans for pork and lamb. Observed R² values for predicting bone were slightly lower than those for lean and fat estimations, particularly in those carcasses with smaller bone sizes such as pork (R² = 0.889) and lamb (R² = 0.870). ConclusionThe results suggest that DXA technology can reliably estimate carcass composition in livestock, particularly for lean and fat estimations. Using PLSR analyses, suitable models for research have been developed from main primal scan data. However, further studies to externally validate the prediction accuracy and to obtain calibration curves for specific retail cuts or carcass cut-outs specifications are needed. Prediction accuracies for industry applications using single pass scans will also be needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".