## 3 GC\(\times\)GC Separation
### Separation Optimization  
Once the GC\(\times\)GC hardware configuration has been determined, the parameters of this hardware need to be tuned and optimized. Parameters that deserve attention include column pressure, first and second oven temperature programs, and modulation settings. These parameters interact with each other and therefore cannot be optimized separately. The optimization requires statistical experimental design and predictive modeling [36; 51]. The quality of the GC\(\times\)GC separation must be measured quantitatively. The first proposed approach is orthogonality measurement [129]. A number of different metrics were later developed for measuring the orthogonality of 2D separation, such as asterisk equations,bin counting, and convex hull [130; 131; 132; 133]. However, good global orthogonality is not equal to good separation that suits the typical need. Direct measurement distances among the targeted peaks most closely reflect reality. Nearest neighbors' distances (NND) approaches can be modified according to the specific demands of each study [134; 135]. Figure 2 is an example chromatogram of common beverage VOCs obtained at optimized GC\(\times\)GC conditions. Due to the high concentration of ethanol in the sample, it is common to have the early part of the chromatogram overloaded. In practice, a delay phase should be applied for ionization. The principle of k nearest neighbor (kNN) can also be used for the optimization of GC\(\times\)GC chromatographic separation. In order to avoid measurement errors introduced by humans, the separated target standards need to be evenly distributed in the entire chromatogram. As previously mentioned, bidimensional gas chromatography fully exploits its potential in the untargeted analysis of volatiles, which mainly means, for wine and beer, aroma compounds [136]. Even though data processing (which will be extensively discussed in the next paragraph) provides a powerful tool for compound discovery, proper separation is still crucial to avoid signal overlay, which hinders the use of spectral libraries [137]. The following examples are just a snapshot of published GC\(\times\)GC applications since the main application field is compound discovery and minor differences are present in the workflows. In addition, it must be highlighted the role of multivariate analysis, which was essential in these studies where the amount of data was huge and the approach was qualitative or semi-quantitative [138]. An example of application was the comprehensive mapping of volatile compounds in 70 wines from the two main production areas for Italian sparkling wines (48 wineries and 6 vintages involved) by [PERSON] et al. (2016). In this research, HS-SPME was coupled to GC\(\times\)GC-TOF-MS equipped with a thermal modulator and allowed to identify 196 biomarkers within the 1695 compounds detected, which were used to determine the influences of grape cultivar, pedoclimatic conditions, and the metabolomics space of these wines [34]. A similar approach was implemented to determine the effects of various new hybrid yeasts on the aroma profile of beers. In this research, the authors evaluated various yeasts by analyzing the aroma profiles of beers produced with their fermentation [113]. GC\(\times\)GC was used as a complementary tool to LC-based techniques to give a comprehensive snapshot of yeast performance and allowed proper clone selection to be effectively used in brewing to create new products and to eliminate or increase specific traits. Figure 2: Chromatogram of common beverage VOCs obtained at optimized GC\(\times\)GC conditions. Common fermented beverage volatiles (131 compounds), injected at a concentration of 10 mg/L in ethanol, are separated by a polar-semipolar column. A list of VOCs and GC\(\times\)GC setup can be found in a previous study [51]. In addition to untargeted metabolomics, with bidimensional chromatography, it is also possible to solve challenging analytical issues. In a recent publication, [PERSON] et al.