Caldas-Cueva Juan P, Mauromoustakos A, Sun X, Owens Casey M
Department of Poultry Science, University of Arkansas, Fayetteville, AR 72701, USA.
Agricultural Statistics Laboratory, University of Arkansas, Fayetteville, AR 72701, USA.
Poult Sci. 2021 Apr;100(4):100890. doi: 10.1016/j.psj.2020.12.003. Epub 2020 Dec 9.
Woody breast (WB) condition causes significant economic losses to the global poultry industry, and the lack of an objective and fast tool to identify this myopathy is a contributing factor. The aim of this study was to determine if there are broiler carcass conformation changes that can be used to identify WB characteristics using image analysis. Images of 8-wk-old male broiler carcasses (n = 544) of high breast-yielding strains were captured before evisceration, which were processed and analyzed using ImageJ software. Measurements were as follows: M0, breast length; M1, breast width in the cranial region; M2, one-fifth of the breast length starting at the tip of keel; M3, breast width at the end of M2; M4, angle formed at the tip of keel and extending to outer points of M3; M5, area of the triangle formed by M3 and lines generated by M4; M6, area of the breast above M3; and M7, M6 minus M5. Ratios of these measurements were also considered. Whole breast fillets were scored for WB severity based on tactile assessment and compression analysis to correlate them. Spearman's correlation coefficient (r) between WB scores and compression force was highly significant (r = 0.83, P < 0.01). Measurements M4 and M3 as well as ratios M9 (M3/M2) and M11 (M1/M0) had the highest correlation to the WB score (r ≥ 0.70; P < 0.01) and compression force (r ≥ 0.64; P < 0.01). The best validated model (generalized [Gen.] R = 0.60) to predict WB included M1, M2, and M3. Using this model, 84% of broiler carcasses were correctly classified as WB or normal with a sensitivity of 82% to detect affected samples. Alternatively, M4 and M6 as well as ratios M9 and M11 could be considered as predictors in different models (Gen. R ≥ 0.56). The same predictors were significant to estimate compression force (Gen. R ≥ 0.49). These data support the use of image analysis to predict WB condition in broiler carcasses. The potential integration of these image measurements into commercial in-line vision grading systems would allow processors to sort broiler carcasses by WB severity.
木胸（WB）病症给全球家禽业造成了巨大经济损失，而缺乏一种客观快速的工具来识别这种肌病是一个促成因素。本研究的目的是确定是否存在可用于通过图像分析识别WB特征的肉鸡胴体形态变化。在去内脏之前，采集了高胸肉产量品系的8周龄雄性肉鸡胴体（n = 544）的图像，使用ImageJ软件对其进行处理和分析。测量如下：M0，胸长；M1，头部区域的胸宽；M2，从龙骨尖端开始的胸长的五分之一；M3，M2末端的胸宽；M4，在龙骨尖端形成并延伸到M3外点的角度；M5，由M3和M4生成的线形成的三角形的面积；M6，M3上方的胸部面积；以及M7，M6减去M5。还考虑了这些测量值的比率。根据触觉评估和压缩分析对整个胸肉进行WB严重程度评分，以将它们关联起来。WB评分与压缩力之间的斯皮尔曼相关系数（r）非常显著（r = 0.83，P < 0.01）。测量值M4和M3以及比率M9（M3/M2）和M11（M1/M0）与WB评分（r≥0.70；P < 0.01）和压缩力（r≥0.64；P < 0.01）的相关性最高。用于预测WB的最佳验证模型（广义[Gen.]R = 0.60）包括M1、M2和M3。使用该模型，84%的肉鸡胴体被正确分类为WB或正常，检测受影响样本的灵敏度为82%。或者，M4和M6以及比率M9和M11可被视为不同模型中的预测指标（Gen. R≥0.56）。相同的预测指标对于估计压缩力也很显著（Gen. R≥0.49）。这些数据支持使用图像分析来预测肉鸡胴体的WB状况。将这些图像测量值潜在地整合到商业在线视觉分级系统中，将使加工者能够根据WB严重程度对肉鸡胴体进行分类。
