Using Dual Energy X-Ray Absorptiometry (DXA) For A Rapid, Non-Invasive Carcass Fat and Lean Prediction in Beef
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».