## 3 Results and Discussion  
One of the crucial steps in the analysis of hop varieties and their volatile compounds is to achieve an acceptable separation of peaks, leading to the increase in selectivity and improvement in the identification of substances due to reduced overlapping interferences. The complexity of hop samples can be represented by the single separation dimension for the Cascade hop GC-MS result shown in Figure 2A, for which the given GC column is apparently of conventional separation quality with a peak capacity for the 32 min analysis of approximately 300; this indicates responses that have considerable peak overlap. For example, \"d\" (15.4 min) is a linalool with a cluster of poorly resolved peaks, and peak \"e\" at 22.0 min (geraniol) has an evident shoulder before a peak on the geraniol tail. The molecular composition of such regions will be indeterminate. The complexity becomes immediately clear when GC-MS (Figure 2A) is compared with GC\(\times\)GC-MS (Figure 2B) with retention times on the DB-5 ms UI columns arranged to show the same peaks aligned vertically in each panel. The existence of significantly more compounds is confirmed in the latter GC\(\times\)GC result, simply through the addition of the second dimension (\({}^{2}\)D) separation. Being a more polar column (wax-type), the \({}^{2}\)D retention proceeds from less to more polar along this axis, and so this indicates the relative polarity of compounds that coeluted on the \({}^{1}\)D column, and can serve as a check for possible chemical class. This leads to the notion of molecular structure-retention relationships in GC\(\times\)GC [48]. It is evident that the 2D presentation of GC\(\times\)GC with most peaks resolved covers a large proportion of the volatile (headspace) composition of the sample, leading to the suggestion that this embodies the requirements for comprehensive metabolite profiling [49] and is a quintessential untargeted method for volatile compounds [50]. The GC\(\times\)GC-MS configuration scenario for metabolite analysis, generating a greater number of peaks, should also include better identification by the MS through more intense peaks that improve minor constituent detectability, fewer matrix interferences, and less phase bleeding. Here GC-MS analysis was similar to that in the literature [22]. For GC\(\times\)GC-MS, \({}^{1}\)D was a nonpolar DB-5 ms UI phase column (5% phenyl methylpolysiloxane), and two \({}^{2}\)D phase columns were tested: an intermediate polarity BPX50 phase (50% phenyl polysilphenylene-siloxane) and a polar phase (SUPELCOWAX 10; polyethylene glycol), both of the same dimensions. The results (Figure S1) displayed an enhanced separation with the polar \({}^{2}\)D phase, better resolution of peaks, and a larger number of recorded metabolites. The modulation period (\(P_{\text{M}}\)) used for GC\(\times\)GC corresponds to the time available for completion of the \({}^{2}\)D analysis before the wrap-around might occur. Times of 4, 5, 6, and 7 s were tested, with the best separation for 6 s, and a little wrap-around. As for the choice of the SPME fibre, the higher number of peaks, which we correlate with better coverage of total metabolites, was the pink fibre (Figure S2). The SPME method presented in this work is advantageous for the study of volatiles in hop. Compared to other methods previously applied (Table 1), SPME has the advantages of being easier, requiring fewer resources, being solventless, and using mild temperatures and small amounts of sample. This limits compound degradation, while being environmentally friendly with less energy requirements and generating less waste. This sample preparation method is established in the literature, the fibres are commercially available, and automation is possible, which can facilitate and reduce costs in operation. The intra-day and inter-day precision were evaluated using Cascade hop with HS-SPME-GC\(\times\)GC-MS selected for 22 compounds and tabulated in Table S2.