Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies - Siddique, Aftab; Shirzaei, Samira; Smith, Alice E.; Valenta, Jaroslav; Garner, Laura J.; Morey, Amit (Frontiers in Physiology, 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 the bioelectrical impedance analysis (BIA). However, BIA raw data from myopathy affected breast meat is extremely complicated, especially due to 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 categorizing fillets affected with myopathies using BIA. Therefore, more complex data analysis tools can be used such as, support vector machines (SVM) and backpropagation neural network (BPNN) 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 (n=100 x 3 flocks) were analyzed by hand-palpation for WB category (0-normal; 1-mild; 2-moderate; 3-Severe) and SM (presence and absence). BIA data (resistance and reactance) was collected on each breast fillet, the equipment’s algorithm calculates protein and fat index. Data were analyzed using linear discriminant analysis (LDA), SVM, and BPNN with 70:30 :: training : test data set. Compared to LDA analysis, SVM separated WB with a higher accuracy of 71.04% for normal (data for normal and mild merged), 59.99% for moderate, 81.48% for severe WB. Compared to 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.