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

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 < 0.05) regardless of fillet state (Fresh or Frozen). The DLA feature, instead of EF ratios, showed a close relationship between the WB grade and Water-holding capacity (WHC) in broiler breast fillets directly based on EF images. The correct classification rate of WB grades could be as high as 93.3% for fresh and 92.3% for frozen fillets in independent validation set. Data suggest that the WB condition significantly affects the meat WHC measured by the EF method. The deep learning algorithm provides a useful reference for the assessment of the EF images.

Automated summary

The study explored the correlation between EF measurements and WB condition in broiler breast fillets, utilizing a deep learning algorithm to predict WB grades based on EF images. Results indicated significant differences in EF measurements among WB categories, with the DLA method achieving an accuracy rate of up to 93.3% in predicting WB grades for fresh fillets.