School of Agricultural Sciences, Murdoch University, Western Australia 6150, Australia; Advanced Livestock Measurement Technologies Project, Murdoch, Australia.
School of Agricultural Sciences, Murdoch University, Western Australia 6150, Australia; Advanced Livestock Measurement Technologies Project, Murdoch, Australia.
Meat Sci. 2024 Oct;216:109556. doi: 10.1016/j.meatsci.2024.109556. Epub 2024 Jun 1.
The value of precise dual energy X-ray absorptiometry (DEXA) cut weight predictions to lamb allocation to cut plans is unknown. Lambs (n = 191) varying in carcase weight (HSCW) and GR (tissue depth over the 12th rib) were DEXA scanned and boned out to weigh retail cuts. Cut weights were predicted using HSCW; HSCW + GR; HSCW + DEXA and HSCW + DEXA image components in GLM models. DEXA improved cut weight predictions in most cuts (P < 0.05). A dataset of 10,000 carcases was then simulated using the associations between HSCW, GR and cut weights, before being truncated to 4500 lambs representing onel day's HSCW distribution. A lamb Carcase Optimisation Tool scenario was developed with 2-3 cut options per carcase section and cut weight thresholds applied to several cuts. Processing costs, market values and actual cut weights were input into the Optimiser to determine carcase allocation to cut options for optimised profits. This scenario was repeated using the predicted cut weights to determine the cut misallocations caused. DEXA-predicted cut weights produced 16.7% and 8.0% less misallocations than HSCW and GR. DEXA produced 20.8% and 14.3% less misallocations than HSCW and GR in shortloins, and 25.5% and 12.9% less in hindquarters. While cut misallocations have little direct impact on total profits, as product is over and under-valued when misallocated, reducing cut misallocations will improve processor compliance when sorting carcases into cut plans- reducing their need to retrim, downgrade and repackage product or the erosion of customer confidence caused by supplying product not meeting market specifications.
精确双能 X 射线吸收法（DEXA）对羔羊分配到切割计划的体重预测的价值尚不清楚。对体重（HSCW）和 GR（第 12 肋上方的组织深度）不同的羔羊（n=191）进行 DEXA 扫描并去骨以称重零售切块。使用 HSCW、HSCW+GR、HSCW+DEXA 和 HSCW+DEXA 图像组件在 GLM 模型中预测切块重量。DEXA 提高了大多数切块的切块重量预测（P<0.05）。然后，使用 HSCW、GR 和切块重量之间的关联模拟了一个包含 10000 具尸体的数据集，然后将其截断为代表 1 天 HSCW 分布的 4500 只羔羊。开发了一个羔羊胴体优化工具方案，每个胴体部分有 2-3 个切块选项，并对多个切块应用切块重量阈值。将加工成本、市场价值和实际切块重量输入优化器，以确定胴体分配到切块选项以实现优化利润。使用预测的切块重量重复此方案，以确定造成的切块分配错误。与 HSCW 和 GR 相比，DEXA 预测的切块重量导致 16.7%和 8.0%的切块分配错误更少。在短肋肉中，DEXA 比 HSCW 和 GR 导致 20.8%和 14.3%的切块分配错误更少，在后躯中导致 25.5%和 12.9%的切块分配错误更少。虽然切块分配错误对总利润的直接影响很小，但由于切块分配错误时产品被高估或低估，减少切块分配错误将改善处理器在将胴体分类到切割计划时的合规性-减少他们重新修剪、降级和重新包装产品的需求，或因供应不符合市场规格的产品而导致客户信心受损。
