Multi-Component Analysis of Five Beers

Introduction

Sensory test methods that present flavor and taste in numerical terms 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. Combining comprehensive analytical data with use of multivariate analysis has become an effective option for elucidating the differences in a wide range of constituents. This combination of multivariate analysis and metabolomics promises to see application in areas of quality evaluation and quality improvement, as well as in the development of foods with improved functional properties. In this article, we describe the simultaneous analysis of five commercially available beers with a triple quadrupole mass spectrometer (LCMS-8060) using a primary metabolite method package, and performing principal component analysis on the data set obtained using Traverse MS software. We classified the five beers based on differences in constituent compounds, and confirmed characteristic components in each beer. We then performed hierarchical clustering analysis to show how these five beers are classified.

July 21, 2016 GMT