Using Dual Energy X-Ray Absorptiometry (DXA) For A Rapid, Non-Invasive Carcass Fat and Lean Prediction in Beef
Bibliographic record
Abstract
ObjectivesThe objective of this study was to evaluate the potential use of the dual energy X-ray absorptiometry (DXA) for estimating total lean, fat and bone content of beef carcasses and main primal cuts.Materials and MethodsTo represent the majority of commercial cattle, the animal population used (n = 316) to develop and validate the prediction models was selected within a grid based on live weight at slaughter and grade fat. Following normal commercial slaughter and chilling, left carcass sides were fabricated using the Institutional Meat Purchase Specifications (IMPS) for Fresh Beef Products, Series 100. The primals collected included the chuck (IMPS #113), rib (IMPS #103), brisket (IMPS #118), flank (IMPS #193, non-trimmed), foreshank (IMPS #117), loin (IMPS #172A), round (IMPS #158A) and plate (IMPS #121). Each primal cut was scanned with a Lunar iDXA unit (GE Lunar Lunar Prodigy Advance, General Electric, Madison, WI, USA) using the whole body scan option on standard mode to estimate DXA fat, lean and bone tissues. After scanning, all left primal cuts were fully dissected into subcutaneous fat, intermuscular fat, body cavity fat, lean, and bone and then weighed.ResultsRegression and partial least square regression (PLSR) were used to evaluate the relationship of DXA lean, fat and bone values from primal cut scans to cut-out values from manual dissection. Using linear regression, R2 (i.e., the % of variation accounted for by the model) for total carcass lean, fat and bone were 0.88, 0.95 and 0.53, respectively. Within individual primals, R2 for lean, fat and bone ranged from 0.26 to R2 = 0.90, with the highest R2 generally for fat, and the lowest predictions generally for bone. Industry applications of this technology for total lean, fat and bone would be expected to achieve these levels of prediction robustness.Using PLSR analyses, which consider all the DXA scan information from all primals as independent variables, overall R2 for total carcass lean and fat were both 0.98. PLSR improved the lean prediction for most of primal cuts yielding R2 over 0.94 for the brisket, foreshank and flank. The highest R2 were observed for the chuck (R2 = 0.99), round (R2 = 0.98) and loin (R2 = 0.97). Similar to lean, PLSR increased the R2 for fat predictions; with all being higher than 0.91, excluding the foreshank (R2 = 0.77). The highest R2 for fat were observed for the flank (R2 = 0.98), rib, plate and loin (R2 = 0.97).ConclusionDXA was able to be used to develop robust equations for estimating total lean and fat in carcasses normally encountered in the market. Both linear and PLSR equations will provide realistic estimations of the potential utility of DXA within an industry environment, to improve estimations of lean meat yield. In research, DXA coupled with PLSR will also be able to replace manual cut-outs which are destructive, time consuming and costly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".