Nondestructive Assessment of Woody Breast Myopathy in Chicken Fillets Using Optical Coherence Tomography Imaging with Machine Learning

Abstract

Woody breast (WB) myopathy is a major muscle abnormality in chicken fillets, causing excessive hardness and chewiness. The WB condition can potentially cause big economical losses in the poultry industry by decreasing meat quality, increasing waste, degrading nutritional content, and reducing customer satisfaction. A histological technique using a light microscope has been the gold standard to characterize the sub-surface properties of the muscle with the WB condition, which is destructive, costly, time-consuming, and limited to analyzing only small sample areas. It is currently very challenging to assess the degree of WB myopathy objectively and rapidly in individual fillets. There is a need to develop an effective sensing technology for rapidly characterizing the WB condition by measuring the sub-surface cross-sections of the entire fillet at a high resolution. In this study, we utilized optical coherence tomography (OCT) to image the sub-surface microstructure of chicken muscle tissue along the entire fillet with a micrometer resolution. The OCT images provided valuable microstructural features, which were further analyzed using machine learning models to classify chicken fillets based on the WB severity. The results demonstrated a detection accuracy of up to 100% in detecting severe WB samples. The machine learning models achieved a classification accuracy of 93.3% in distinguishing normal from WB fillets. Overall, the successful application of large-scale OCT imaging demonstrated its effectiveness as a non-invasive method for evaluating WB in chicken meat. Furthermore, the study suggests that OCT imaging holds the potential for evaluating other agricultural and food products.