Volatomics, brewing and multivariate statistical analysis: new insights into the production of beer

Beer is one of the most popular beverages worldwide. The production of craft beer has increased dramatically in recent year, however, despite volatilomics [1] plays a significant role in food quality and authenticity assessment, only a reduced number of studies have afforded a rigorous evaluation of the aromatic profile of craft beer. The complex volatile profile of this product is influenced by both raw materials and brewing procedures with a variety of volatile compounds, including alcohols, esters, carbonyl compounds, sulfur compounds, and terpenes able to affect the flavor and taste of beer. In this study, a solid-phase microextraction (SPME)-GC-MS method was developed to study the volatile profile of four different craft beers, namely American IPA (AI) and Cream Ale (CA) (high fermentation beers), Vienna Lager (VL) and Bohemian Pilsner (BP) (low fermentation beers). The use of a 50/30 μm DVB/CAR/PDMS fiber exposed in the headspace above the sample for 20 min at 60 °C, allowed for the extraction of more than 59 compounds. Compound annotation was carried out by comparing: i) the experimental spectra with those stored in the NIST library, ii) the calculated Kovats indices with those reported in literature or stored in proprietary databases [2], iii) the injection of pure standards. Multivariate statistical analysis, namely principal component analysis proved to be useful in differentiating the beer samples (Fig. 1a and b) being the 74% of the variance explained by the first two PCs. As shown in Fig. 1, AI beers could be differentiated according to PC1, whereas PC2 was useful in discriminating CA from BP beers.