Streamlining Metabolomic Data Acquisition and Multivariate Analysis for Characterization of Five Beer Brands

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Sensory tests that numerically present flavour and taste are widely used techniques for the quantitative evaluation of food quality. Recently, attempts have been made to obtain a greater amount of data by combining the results of sensory tests with metabolomics data that provides a comprehensive analysis of food constituents. With increasing amount of data, effective data processing and multivariate analysis become the key part of analytical workflow.

In this application note, we describe the metabolomic analysis of five commercially available beers using the LCMS-8060 and conditions given in the ‘LC/MS/MS Primary Metabolite Method Package’. Using the dataset obtained, efficient data review and principal component analysis was performed by Traverse™ MS Software. Hierarchical clustering demonstrated the relative similarities of the beer brands and identified the responsible components. For example, Lager Brand A was more similar to Ale Beer than to another Lager Brand B, and this was largely attributed to adenosine, proline and guanosine content.

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