Use of digital images to predict carcass cut yields in cattle
Use of digital images to predict carcass cut yields in cattle
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DOI：
10.1016/j.livsci.2010.10.012
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发表时间：
2011-05-01
影响因子：
1.8
通讯作者：
Berry, D. P.
中科院分区：
文献类型：
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作者：
Pabiou, T.;Fikse, W. F.;Berry, D. P.
本研究的目的是评估视频图像分析 (VIA) 在预测牛的各种批发屠体切割方面的潜力。视频图像分析和切肉重量可从两个不同的来源获得：实验数据集（n = 346）和商业数据集（n = 281）。实验数据集中使用的牛是杂交阉牛（主要品种是比利时蓝牛、安格斯牛、弗里斯兰牛、夏洛莱牛、荷斯坦牛、利木赞牛和西门塔尔牛），商业数据集中使用的牛是杂交小母牛（主要品种是利木赞牛、比利时蓝牛、夏洛莱牛和西门塔尔牛）。在这两个数据集中，根据零售价值将肉类切割分为四组：低价值切割 (LVC)、中价值切割 (MVC)、高价值切割 (HVC) 和极高价值切割 (VHVC)；总肉重计算为各个切肉重量的总和。此外，实验数据集中还提供了总骨重和总脂肪重。在这两个数据集中，为每个屠体切割组创建了校准和验证子数据集。对每个校准数据集应用多元回归分析，以根据预测变量使用三组不同的模型来预测切割量：1) 仅胴体重量，2) 胴体重量加上 EUROP 胴体分类，以及 3) 胴体重量加上 VIA 参数。预测分割产量的准确性优于预测分割产量占胴体重量的比例。在实验数据集和商业数据集中，使用胴体重量作为唯一预测因子，验证数据集中批发分割产量的变化比例 (R-2) 范围从 0.33（实验数据集中的总脂肪重量）到 0.91（实验数据集中的总肉类重量）。当使用胴体重量加上 VIA 变量作为预测变量时，R-2 增加到 0.65（商业数据集中的 LVC）和 0.97（实验数据集中的总肉重）之间。在对实验数据和商业数据的分析中，包含 VIA 变量的模型具有最低的跨性状预测均方根误差。残差和预测值之间的平均偏差和相关性通常不为零。这项研究的结果表明，使用结合胴体重量和 VIA 变量的多元回归模型可以准确预测阉牛和小母牛的批发屠宰量。常规存储的屠体图像为牛肉育种计划提供了强大的工具，以选择更有价值的屠体。 (C) 2010 Elsevier B.V. 保留所有权利。
The objective of this study was to assess the potential of video image analysis (VIA) in predicting various wholesale carcass cuts in cattle. Video image analysis and meat cut weights were available from two different sources: an experimental (n = 346) and a commercial dataset (n = 281). The cattle used were crossbred steers (predominant breeds were Belgian Blue, Angus, Friesian, Charolais, Holstein, Limousin, and Simmental) in the experimental dataset, and crossbred heifers (predominant breeds were Limousin, Belgian Blue, Charolais, and Simmental) in the commercial dataset. In both datasets, the meat cuts were grouped into four groups based on retail value: Low Value Cuts (LVC), Medium Value Cuts (MVC), High Value Cuts (HVC), and Very High Value Cuts (VHVC); total meat weight was calculated as the sum of the individual meat cut weights. In addition, total bone weight and total fat weight were available in the experimental dataset. In both datasets, a calibration and a validation sub-dataset were created for each of the carcass cut groups. Multiple regression analyses were applied to each calibration dataset to predict the cuts from using three different sets of models based on the predictors: 1) carcass weight only, 2) carcass weight plus EUROP carcass classification, and 3) carcass weight plus VIA parameters. The accuracy of predicting yields of cuts was superior to prediction of cut yields as a proportion of the carcass weight. Across both the experimental and the commercial datasets, the proportion of variation of wholesale cut yields in the validation dataset explained (R-2) ranged from 0.33 (total fat weight in the experimental dataset) to 0.91 (total meat weight in the experimental dataset) using carcass weight as the sole predictor. The R-2 increased to between 0.65 (LVC in the commercial dataset) and 0.97 (total meat weight in the experimental dataset) when carcass weight plus VIA variables were used as predictors. In the analyses of both the experimental and the commercial data, models that included the VIA variables had the lowest root mean square error of prediction across traits. Mean bias and correlations between the residuals and predicted values were generally not different from zero. Results from this study show that wholesale cuts in steers and heifers can be accurately predicted using multiple regression models incorporating carcass weight and VIA variables. The carcass images routinely stored provide a powerful tool for use in a beef breeding program to select for more valuable carcasses. (C) 2010 Elsevier B.V. All rights reserved.