Evaluation of broiler breast fillets with the woody breast condition using expressible fluid measurement combined with deep learning algorithm

Abstract In this study, the relationship between expressible fluid (EF) measurements and the woody breast (WB) condition in broiler breast fillets (pectoralis major) was investigated and the deep learning algorithm (DLA) was evaluated to predict degrees of the WB condition based on EF images. Fillet samples were collected from a commercial plant and categorized into normal (no WB), moderate WB, and severe WB groups. EF of fresh and frozen samples were measured using the filter paper press method. The features of the images were analyzed using traditional manual method, gray level co-occurrence matrix (GLCM) method and the DLA method, respectively. The results show that there were significant differences in average EF measurements between three WB categories (P