Metabolomics: The latest technology to predict meat quality potential
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abstract
Metabolomics. The term metabolomics is simply defined as 'the study of as many small metabolites as possible' (Cevallos-Cevallos et al., 2009). More specifically, Goldansaz et al. (2017) referred to metabolites as the 'canaries' of the genome, just as canaries for coal miners served as sensitive indicators of problems in coal mines, metabolites can be exquisitely sensitive indicators of problems in the genome. In fact, Pearson (2007) reported that metabolites have already proven their worth in cholesterol and glucose as canaries for heart disease and diabetes, respectively. Metabolites are effectively the end of very complex interactions occurring inside the cell (in the genome) and those occurring outside the cell or organism (in the environment; Gondansaz et al., 2017). In other words, metabolomics allows researchers to obtain a highly sensitive and more complete description of the phenotype (Bouatra et al., 2013; Monteiro et al., 2013). Recent advances in analytical chemistry and metabolite data analysis techniques are making metabolomics much more common in mainstream research. As a result, the field of metabolomics has experienced exponential growth with just two papers published on the subject in 1999 to more than 2,400 in 2015 (Goldansaz et al., 2017) and today a simple database search produced almost 5,000 research articles involving metabolomics. Unfortunately, of those 2,400 reported by Goldansaz et al. (2017), just 16 involved beef animal products.Untargeted metabolomics focuses on the detection of as many groups of metabolites as possible to obtain patterns or fingerprints of biological phenomena such as disease, without necessarily identifying nor quantifying a specific compound (Cevallos-Cevallos et al. 2009). Hundreds or even thousands of metabolites can be measured utilizing advance chemistry detection techniques such as high-performance liquid chromatography - quadrapole time of flight (HPLC-qTOF). These non-volatile flavor metabolites are then analyzed using discriminate analyses like multi-variate discriminate (MVDA) and principal components (PCA) analyses. When combined with trained descriptive sensory and consumer panels as well as volatile aroma compounds, it is plausible that a fingerprint of good and(or) bad flavor descriptors can be formed. Our objective is to develop the flavor potential of a product.The overall aim of this project is to determine the impact of USDA Quality Grade, aging time, and degree of doness on the trained sensory panel flavor analyses, volatile aroma compounds, and non-volatile flavor precursors.
