Abstract
Untargeted metabolomics focuses on the detection of as many groups of metabolites as possible to obtain patterns or fingerprints of biological phenomena. Hundreds or even thousands of metabolites can be measured, utilizing advanced chemistry detection techniques such as high-performance liquid chromatography – quadrupole time of flight (HPLC-qTOF). Statistical analyses are utilized to narrow the number of metabolites from thousands to just those that are statistically impactful on the applied treatments. This smaller number of compounds (100 to 200) may then be analyzed statistically using various multivariate approaches to then identify those influenced by applied treatments. When analyzing serum from either Angus or Brahman cattle that were segregated into three temperament treatments based on chute scores, differential upregulation of metabolites was found between the two breed types and the temperament scores. It appears that in Angus cattle, the most temperamental cattle had significantly upregulated metabolites according to the heatmap, while Brahman cattle showed downregulated metabolites in individual animals in both the Intermediate and Temperamental temperament scores. In a separate study to illustrate the impact of metabolomics on meat quality, 18 USDA Select and 18 USDA Choice strip loins were collected and then aged for either 10 or 20 d post-processing. Furthermore, steaks from each quality grade and aging treatment were then cooked to either 63, 71, or 80(o)C internal temperature. Samples from the interior of each steak that excluded the exterior seared surface were analyzed. In a Principal Components Discriminant Analysis of the significant metabolites, all treatment combinations within 10 or 20 d aging were evenly split on either side of the vertical centerline with Principal Component 1 (50.5%) pulling the metabolites apart. In particular, Select steaks cooked to the two higher internal temperatures were pulled the furthest apart. Metabolomics is a relatively new but effective technology in determining cause and effect for commonly-known factors that impact meat quality.