## 5 Conclusions and Perspectives  
This review provides an overview of the analytical approach of GC\(\times\)GC in beer and wine VOC profiling. GC\(\times\)GC-MS is a well-developed technique at this stage. Although physics and materials scientists are still devoting efforts to innovate instrumental techniques such as 3-dimensional gas chromatography [201], high-resolution mass spectrometry, and higher detection sensitivity, the bottleneck of multidimensional gas chromatography applications is still sample preparation and data analysis. Regarding the sample preparation techniques, we describe their working mechanisms and provide information that is essential for the selection of the appropriate technique and the optimization of their parameters in the experiments. New sample preparation techniques are rapidly evolving, such as adsorbents that can adsorb a wider range of VOCs, assist sample preparation by physical means, and alter the geometry of the adsorbent to increase sample capacity and mass transfer rate. Based on the guidance of green analytical chemistry, organic solvent-free is an important direction. Besides, automation definitely has high demands. Extracted beverage peaks of interest must be well separated by the GC\(\times\)GC system. Based on previous studies, there is no doubt about the GC\(\times\)GC separation hardware setup in wine and beer VOC analysis. A thermal modulator and polar-semipolar column combination with the same diameter are recommended for beer and wine VOC analysis. Optimization of the GC\(\times\)GC parameter requires a statistical design of the experiment. To have an optimized separation that suits the typical need, defining the peaks of interest and directly measuring distances among them would be a good option. According to our observation, the peak shape of the eluates in the GC\(\times\)GC analysis is not simply two-dimensional Gaussian [157]. It remains for chromatography to further refine the separation theory of GC\(\times\)GC. Mining valuable information from a large amount of high-dimensional GC\(\times\)GC data is not an easy task. Analysts need a certain background in signal processing and statistics to choose the appropriate data processing methods and adjust the parameters. There is some vendor-supported software for processing GC\(\times\)GC data. However, they are not open source. Users do not have a high degree of freedom to customize the data processing process according to their needs. Also, some software does not fully disclose the parameter selection algorithm of the data processing process. Even analysts with relevant backgrounds do not have full control over the data processing. This problem is particularly acute in the case of fermented beverages, where both high and trace analytes are present on the chromatogram. In the future, a tool package based on an open-source platform that integrates published mainstream algorithms and provides a freely customizable process will undoubtedly be favored by most analysts. It has been shown that the application of a sample-specific VOC database can significantly improve the sensitivity of peak detection and the accuracy of analyte identification. Considering the complexity of VOCs in beer and wine, using the same database to train the AI classifier to assist in peak annotation would be a trend. In addition, more and more research is applying AI to the feature selection process of omics studies. Therefore, the analyst should not only understand the beer and wine chemistry and the GC\(\times\)GC hardware employed but also have sufficient knowledge to perform chemometric and AI learner training. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/analytica4030026/sl](https://www.mdpi.com/article/10.3390/analytica4030026/sl)]([https://www.mdpi.com/article/10.3390/analytica4030026/sl](https://www.mdpi.com/article/10.3390/analytica4030026/sl)). Table S1 acronyms, Table S2 physicochemical properties of common VOCs in the beverages.