Abstract
As freeze/thaw procedure leads to inevitable drip loss, elucidation of mechanism on dynamic changes in water holding capacity (WHC) of muscle is urgently needed. In this study, the proteomic profile by DIA-based strategy, muscle microstructure, water mobility, and WHC indices of bovine longissimus dorsi muscles were investigated under different freezing conditions as well as the correlations among them. Results indicated that slow freezing (SF) sample exhibited significantly higher water mobility, thaw loss, total loss, and shear force value than the samples subjected to fast freezing (FF) and non-frozen control (CON). According to the protein profile, we have identified 272 differential abundance proteins (DAPs), in which more significant proteome changes were found in SF/CON samples as compared with FF/CON. Among the 132 DAPs in FF/SF comparison, correlation analysis revealed that MYL3, DES, SYNE2, EXR, RPL35A, RPS6, and Hsp40 were closely correlated with T23 , thaw loss, and total loss. Accordingly, we considered those seven proteins as potential biomarkers related to WHC of frozen muscle. Our study should give a further understanding on mechanisms behind the various WHC of muscle when subjected to different freezing conditions. PRACTICAL APPLICATIONS: Freezing plays a key role in the preservation method for meat and meat products. However, the drip loss during freezing and subsequent thawing procedure causes considerable economic and nutritional losses. To minimize the losses, elucidation of mechanism on the mechanism of thaw loss formation is urgently needed. DIA-based proteomics is a novel, robust method that provides further understanding on the mechanisms behind the dynamic changes in water holding capacity of muscle. The screened protein biomarkers in frozen muscle would play key roles in the development of WHC, especially for the thaw loss formation. Through this perspective, we can explain the origin of thaw loss and the variation under different freezing conditions, which should provide the meat industries with theoretical basis for reducing losses.
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