Siddique Aftab, Shirzaei Samira, Smith Alice E, Valenta Jaroslav, Garner Laura J, Morey Amit
Department of Poultry Science, Auburn University, Auburn, AL, United States.
Department of Industrial and Systems Engineering and Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States.
Front Physiol. 2021 Sep 22;12:712649. doi: 10.3389/fphys.2021.712649. eCollection 2021.
Breast meat from modern fast-growing big birds is affected with myopathies such as woody breast (WB), white striping, and spaghetti meat (SM). The detection and separation of the myopathy-affected meat can be carried out at processing plants using technologies such as bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy-affected breast meat are extremely complicated, especially because of the overlap of these myopathies in individual breast fillets and the human error associated with the assignment of fillet categories. Previous research has shown that traditional statistical techniques such as ANOVA and regression, among others, are insufficient in categorising fillets affected with myopathies by BIA. Therefore, more complex data analysis tools can be used, such as support vector machines (SVMs) and backpropagation neural networks (BPNNs), to classify raw poultry breast myopathies using their BIA patterns, such that the technology can be beneficial for the poultry industry in detecting myopathies. Freshly deboned (3-3.5 h post slaughter) breast fillets ( = 100 × 3 flocks) were analysed by hand palpation for WB (0-normal; 1-mild; 2-moderate; 3-Severe) and SM (presence and absence) categorisation. BIA data (resistance and reactance) were collected on each breast fillet; the algorithm of the equipment calculated protein and fat index. The data were analysed by linear discriminant analysis (LDA), and with SVM and BPNN with 70::30: training::test data set. Compared with the LDA analysis, SVM separated WB with a higher accuracy of 71.04% for normal (data for normal and mild merged), 59.99% for moderate, and 81.48% for severe WB. Compared with SVM, the BPNN training model accurately (100%) separated normal WB fillets with and without SM, demonstrating the ability of BIA to detect SM. Supervised learning algorithms, such as SVM and BPNN, can be combined with BIA and successfully implemented in poultry processing to detect breast fillet myopathies.
现代快速生长的大型禽类的胸肉会受到诸如木胸（WB）、白条纹和意大利面状肉（SM）等肌病的影响。受肌病影响的肉的检测和分离可以在加工厂使用生物电阻抗分析（BIA）等技术来进行。然而，来自受肌病影响的胸肉的BIA原始数据极其复杂，特别是因为这些肌病在单个胸肉片中有重叠，以及与肉片类别分配相关的人为误差。先前的研究表明，诸如方差分析和回归等传统统计技术在通过BIA对受肌病影响的肉片进行分类时是不够的。因此，可以使用更复杂的数据分析工具，如支持向量机（SVM）和反向传播神经网络（BPNN），根据其BIA模式对生禽胸肉肌病进行分类，这样该技术在检测肌病方面对家禽业可能是有益的。对刚去骨（屠宰后3-3.5小时）的胸肉片（n = 100×3个鸡群）进行手工触诊，以对WB（0-正常；1-轻度；2-中度；3-重度）和SM（存在和不存在）进行分类。在每个胸肉片上收集BIA数据（电阻和电抗）；设备的算法计算蛋白质和脂肪指数。数据通过线性判别分析（LDA）以及使用70::30:训练::测试数据集的SVM和BPNN进行分析。与LDA分析相比，SVM对正常（正常和轻度数据合并）的WB分离准确率更高，为71.04%，中度为59.99%，重度WB为81.48%。与SVM相比，BPNN训练模型准确地（100%）分离了有和没有SM的正常WB肉片，证明了BIA检测SM的能力。诸如SVM和BPNN等监督学习算法可以与BIA相结合，并成功应用于家禽加工中以检测胸肉片肌病。
