Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

Aftab Siddique; Samira Shirzaei; Alice E Smith; Jaroslav Valenta; Laura J Garner; Amit Morey

doi:10.3389/fphys.2021.712649

Acceptability of Artificial Intelligence in Poultry Processing and Classification Efficiencies of Different Classification Models in the Categorisation of Breast Fillet Myopathies

Front Physiol. 2021 Sep 22:12:712649. doi: 10.3389/fphys.2021.712649. eCollection 2021.

Authors

Aftab Siddique¹, Samira Shirzaei², Alice E Smith², Jaroslav Valenta³, Laura J Garner¹, Amit Morey¹

Affiliations

¹ Department of Poultry Science, Auburn University, Auburn, AL, United States.
² Department of Industrial and Systems Engineering and Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, United States.
³ Department of Animal Science, Czech University of Life Sciences, Prague, Czechia.

Abstract

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 (n = 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.

Keywords: artificial intelligence; backpropagation neural networking; bioelectrical impedance analysis; machine learning; meat myopathies; spaghetti meat; support vector machines; woody breast.