In this study, we report the combination of comprehensive two-dimensional gas chromatography (GC×GC) with multivariate pattern recognition through template matching for the assignment of the contribution of Brazilian Ale 02 yeast strain to the aroma profile of beer compared with the traditional Nottingham yeast. Volatile organic compounds (VOC) from two beer samples, which were fermented with these yeast strains were sampled using headspace solid-phase microextraction (HS-SPME). The aroma profiles from both beer samples were obtained using GC×GC coupled to a fast scanning quadrupole mass spectrometer. Data processing performed through multiway principal components analysis succeeded in separating both beer samples based on yeast strain. The execution of a simple and reliable procedure succeeded and identified 46 compounds as relevant for sample classification. Furthermore, the bottom-up approach spotted compounds found exclusively in the beer sample fermented with the Brazilian yeast, highlighting the bioaromatization properties introduced to the aroma profile by this yeast strain.

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This article describes a compendium of chemometrics applications in separation science to demonstrate the importance and synergy of data handling. We review the main points that must be raised when using multivariate techniques and list references for further reading. With examples in food science, the case studies comprise of applications of pattern recognition (principal component analysis, PCA), regression (partial least squares, PLS), and classification methods (soft independent modelling of class analogies, SIMCA).

This work has the purpose of optimizing the analysis of serum samples by comprehensive two-dimensional gas chromatography coupled to mass spectrometry, using derivatization for sample preparation, and aiming at further application in metabolomics studies of bipolar disorder.