# Enlarging Knowledge on Lager Beer Volatile Metabolites Using Multidimensional Gas Chromatography
## 3 Results and Discussion  
### Chromat Contour Plot Analysis  
In order to characterize the volatile metabolites present in beer, eight chemical families were selected according to the available literature [3; 4; 5] (Figure 1): acids, alcohols, esters, monoterpenic compounds, norisoprenoids, sesquiterpene compounds, sulfur compounds, and volatile phenols. From these chemical families, monoterpenic and sesquiterpene compounds were previously analyzed in our laboratory [28]. This previous targeted metabolomics approach [28] was focused on the in-depth coverage of beer terpenic compounds, in which a beer terpen-typing was achieved due to samples' category clustering. Nevertheless, as this current study intends to characterize all the volatile metabolites present in lager beer, these chemical families (monoterpenic and sesquiterpene compounds) could not be ignored, and therefore, they were also included in this study, being key elements of the beer volatile composition. This targeted metabolomics approach, achieved by the high throughput and sensitive methodology based on HS-SPME/GC\(\times\)GC-ToFMS, allowed the detection of 329 volatile metabolites from the 8 selected chemical families previously enumerated. As monoterpenic and sesquiterpene compounds were previously reported [28], the remaining detected volatile metabolites were identified, being possible the putative identification of 181 volatile metabolites, from which 96 are reported for the first time in the lager beer volatile composition, which corresponds to an increase of ca. 53% of new chemical information. The putative metabolites' identification was accomplished through a set of parameters that included the standards' co-injection (when available), mass spectra comparison (home-made and commercial databases), calculation of retention index (RI\({}_{\text{calc}}\)) and their comparison with retention index from literature (RI\({}_{\text{lit}}\)) for columns of 5% phenylpolysilphenylene-siloxane (or equivalent), and analysis of the metabolites retention times according to the structured chromatogram principle (i.e., similar chemical structures are displayed in the same 2D chromatographic space, being a unique and distinctive criteria for metabolites identification by GC\(\times\)GC-ToFMS) (Supplementary Table S1). The distribution of the retention time' coordinates of the 329 volatile metabolites is illustrated in the peak apex representation, which can be observed in Figure 2. This figure shows a practical example of the structured chromatogram, in which each metabolite is displayed in the chromatographic space according its physicochemical properties. Indeed, the orthogonal separation achieved by non-polar/polar (NP/P) set of columns that were employed, allows the volatile metabolites' separation through their volatility (\({}^{1}\)D) and polarity (\({}^{2}\)D). For instance, esters tend to be the least polar compounds, presenting the global lower retention time for the second dimension (\({}^{2}t_{\text{R}}\)); while acids, which present higher polarity, registered higher \({}^{2}t_{\text{R}}\) value (Figure 2 and Supplementary Table S1). The analytical variability of HS-SPME/GC\(\times\)GC-ToFMS analysis was evaluated based on the data reproducibility, expressed through relative standard deviation (% RSD) of each analyte. DTIC GC\(\times\)GC area was used to estimate its relative content. The % RSD of the detected volatile metabolites was examined, being achieved a median value of 20.9% for all metabolites, whereas the % RSD varied between 0.1% and 157%, minimum and maximum (Supplementary Table S1), respectively, considering each metabolite by itself. Furthermore, 52.4% of the peaks had % RSD lower than 20% (recommended limit for analytical variability of target bioanalysis by FDA [32]), and % RSD higher than 50% was only verified for 8.7% of the peaks, which indicated a good reproducibility.