Meta-analysis of muscle characteristics to predict beef tenderness
The control of beef tenderness is essential for beef producers and retailers to deliver a consistently high quality product to consumers. Being part of the European program ProSafeBeef, my thesis aimed to predict beef tenderness by meta-analysis approaches using biochemical characteristics of muscles. To achieve this goal, we used data available in the BIF-Beef data warehouse which contains animal, carcass, muscle and meat measurements from different research programs. From available data on Longissimus thoracis (LT) and Semitendinosus (ST) muscles, we demonstrated that ST was faster and more glycolytic than LT in both entire males and females but not in steers. With a cluster analysis, we identified muscle biochemical traits associated with meat tenderness. Then, we demonstrated that no specific muscle biochemical characteristic can be a predictor of tenderness for all muscles and animal types. In LT muscle of young bulls, mean muscle fibre area explained 2% of the variability in sensory tenderness score. Mainly in ST muscle, total and insoluble collagen content and enzymatic indicators of glycolytic metabolism each explained about 6% of the variation in shear force. Although we were only able to explain a relatively small proportion of the total variance in tenderness, these results will form an important basis for the design of future experiments and the identification of new genomic markers of tenderness to be combined with muscle biochemistry in order to better predict beef quality.