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

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.

Classification and Feature Extraction Using Supervised and Unsupervised Machine Learning Approach for Broiler Woody Breast Myopathy Detection

“…SVM aims to discover a hyperplane that can separate two classes of provided data with a maximum margin while still offering the best generalization capability for a two-class linearly separable learning assignment. It provides highly accurate results on the training dataset and high predicting accuracy for the new dataset from the same population as the training dataset [ 30 ]. A detailed working principle for the SVM algorithm can be found in Siddique et al [ 30 ].…”

“…It provides highly accurate results on the training dataset and high predicting accuracy for the new dataset from the same population as the training dataset [ 30 ]. A detailed working principle for the SVM algorithm can be found in Siddique et al [ 30 ].…”

“…In the food industry, BIA parameters can be adjusted to specific species, and it has been utilized in fish for quick detection of proximate composition and pre-harvest conditions to improve fish quality [ 26 , 27 , 28 , 29 ]. Morey et al [ 7 ] and Siddique et al [ 30 ] have reported that BIA may be used to successfully detect WB fillets as an alternative to hand-palpation, reducing hand classification mistakes and increasing the accuracy of detecting WB.…”

“…Furthermore, BIA can detect other overlapping breast muscle myopathies, such as spaghetti meat, which is a myopathic condition impacting the chicken industry. Classification using BIA data can be improved using advanced data analytic technologies such as machine learning (ML), which includes data mining, artificial neural networks (ANN), deep learning (DL), and artificial intelligence [ 30 ]. Siddique et al [ 30 ] reported that machine learning techniques such as Support Vector Machines (SVM) and backpropagation neural network (BPNN) classification algorithms could be another approach to classify the myopathic fillets based on severity levels [ 30 ].…”

Multi-Omics Analysis of the Microbiome and Metabolome Reveals the Relationship Between the Gut Microbiota and Wooden Breast Myopathy in Broilers

“…Wooden breast (WB) is a breast myopathy that has recently begun to occur in the pectoral muscles of broiler chickens at slaughter age. Wooden breast-affected filets are characterized by a dull appearance and tough texture ( 1 ). It has been reported that birds with wooden breast were even up to 79% ( 2 ), which implies chicken breast filets with low quality has become one of the major problems nowadays, and it has been estimated to cause annual economic losses up to one billion dollars worldwide ( 3 ).…”