## 3 Results and Discussions
### Chemometrics  
Development of an adequate protocol to evaluate BR and ENG beers was accomplished by MPCA. The main idea was to extract data information by using a multiway exploratory method, which benefits from all the available data dimensions and results in useful and easily understood plots. Principal components (PC) are generated based on the explained information extracted from the original data, with the first PC explaining most of the data variance and subsequent components explaining progressively less of the remaining variance [24]. In this pixel-based chemometric analysis four replicates from the same BR beer sample and four replicates from the same ENG beer sample form the sample group, and each variable refers to a pixel of the chromatogram's data obtained after raw data handling in MATLAB environment. MPCA basically decomposes the data into what is called scores and loadings. Through the scores information it is possible to identify the patterns within the samples, while the loadings can be used to pinpoint the chromatographic peaks responsible for such patterns. The scores graph of total ion chromatogram (TIC) from PC1 and PC2, seen in Figure 4, reveals important information for pattern recognition. Its analysis reveals the presence of distinct traits between the VOC profile of BR and ENG beer samples. In this case, the information within PC1 is able to distinguish the samples groups (BR and ENG). The loading's graph can be used to elucidate the variables/pixels that contributed to the pattern evidenced in Figure 4. For additional detail on template matching refer to Reference [32]. This information enables targeting the analytes that are responsible for beer differentiation and establishing the contributions the unique _Brazilian Ale 02_ yeast strain brought to the VOC profile of the beer. As the BR samples are found in the negative part of PC1, loadings with negative values cover the VOC that are present in higher intensities in BR samples in comparison with ENG. By selecting the negative weights from the loadings of PC1, it was possible to plot a virtual chromatogram, which highlighted the peaks that differentiated BR and ENG samples. This virtual chromatogram was then used to create a template using GC Image software, Figure 5a. After creating the template, it was matched and applied onto the real sample chromatograms of BR samples. Hence, the analytes that varied the most between BR and ENG beer were identified, as shown in Figure 5b. Figure 4: Scores plot of pixel-based two component Multiway principal components analysis (MPCA) from beer samples evaluated (BR and ENG) using the four-way data from GC\(\times\)GC-QMS. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Formula**} & \multirow{2}{*}{**CAS**} & \multirow{2}{*}{**Match**} & \multicolumn{4}{c}{**LTPR1**} & \multicolumn{4}{c}{**Detection**} & \multicolumn{4}{c}{**Template**} & \multirow{2}{*}{**Odor**} & \multirow{2}{*}{**Flavor**} \\ \cline{3-3} \cline{5-12}  & & & & **Exp** & **NIST** & **BR** & & **ENG** & **BR** & & **ENG** \\ \hline isoamyl acetate & C\({}_{2}\)H\({}_{4}\)O\({}_{2}\) & 123-92-2 & 885 & 841 & 876 & x & x & x & \ \begin{tabular}{c} sweet, frinity, banana \\ solvent \\ \end{tabular}  & \begin{tabular}{c} sweet, frinity, banana \\ a given ripe nuance \\ \end{tabular}  \\ \hline