A comprehensive multi-omics analysis, integrating proteomics and metabolomics, was employed to elucidate tea-induced stewed beef quality change mechanisms.

[Display omitted] • Joint multi-omics analysis to explain the mechanism of tea effect on the quality of stewed beef. • Tea affected metabolic pathways such as oxidative phosphorylation and fatty acid degradation in stewed beef. • Four proteins have been identified as potential biomarkers. • Key proteins associated with differential metabolite expression were identified. To better understand the functional mechanism of four types of tea (green tea, black tea, jasmine tea, and dark tea) on the quality of stewed beef, changes in quality characteristics, proteomics, and metabolomics were investigated. Adding these four tea types decreased the pH value, L* value, shear force, and hardness of the stewed beef. Among these groups, black tea (BT) significantly improved the tenderness of the stewed beef. They have substantially impacted pathways related to protein oxidative phosphorylation, fatty acid degradation, amino acid degradation, and peroxisomes in stewed beef. The study identified that Myosin-2, Starch binding domain 1, Heat shock protein beta-6, and Myosin heavy chain four are significantly correlated with the quality characteristics of tea-treated stewed beef, making them potential biomarkers. Green tea (GT), black tea (BT), jasmine tea (JT), and dark tea (DT) led to the downregulation of 20, 36, 38, and 31 metabolites, respectively, which are lipids and lipid-like molecules in the stewed beef. The co-analysis of proteomics and metabolomics revealed that differential proteins significantly impacted metabolites associated with carbohydrates, amino acids, lipids, and other nutrients. This study determined the effects of four types of tea on the quality of stewed beef and their underlying mechanisms, providing valuable insights for applying of tea in meat products. At the same time, it can offer new ideas for developing fresh meat products. [ABSTRACT FROM AUTHOR]