## 4 Data Processing  
At present, the biggest limitation of GC\(\times\)GC applications is data processing and data mining [140]. When a beverage sample is analyzed by GC\(\times\)GC in combination with a multi-channel detector such as a time-of-flight mass spectrometer (TOF-MS), a massive amount of information (which takes up hundreds of megabits of disk space per measurement) is obtained. When many sample groups are included in a study, and considering that each measurement requires technical replication to ensure accuracy, the amount of information to be processed is tremendous. As a matter of fact, considering the amount of information contained in a trilinear GC\(\times\)GC data file [141], the extraction and verification of this information cannot be solved manually but requires the application of signal processing and statistical analysis techniques [142].  
The analysis of chromatographic data can be performed in different ways: at the pixel level, on a peak list or peak area basis, by commercial software, or by laboratory-developed code [143, 144]. The extraction of useful chemical information from these experimental data requires image signal processing approaches that have to be coupled with linear algebra and statistical concepts to perform a multivariate analysis of the data. These data processing methods can be loosely referred to as \"chemometric-based approaches\". A regular data processing flow for GC\(\times\)GC-MS data and the user input impact for each step are summarized in Figure 3. The complete data processing workflow starts with the experiment design, extracting the accurate peak information from the GC\(\times\)GC-MS data, multivariate analysis, and statistical model validation. The user has a high impact on the validation of peak information and the statistical model [145]. Among the different aspects of the workflow, many new ideas are under development, particularly in the steps of data preprocessing, peak detection, peak deconvolution, and annotation. However, as often happens, there is no agreement on a \"standard\" procedure, and there is no clear evidence that one approach is clearly better than others. In addition, even with the most popular commercial GC\(\times\)GC software, many data processing parameters need to be set by the analyst based on the experimental data and optimized for the matrix. For all these reasons, it is of paramount importance for the analyst to have a basic understanding of these processes. Besides, with a proper understanding of the principles of data processing, an advanced user can customize the workflow according to their needs [146].  
Figure 3: General data processing workflow for GC\(\times\)GC-MS data and the user input impact for each step. As quoted from a previous review [145], reproduction is allowed according to STM Permissions Guidelines. Abbreviations: design of experiment (DoE), quality control (QC), identification (ID), robust principal components analysis (rPCA), hierarchical cluster analysis (HCA), unsupervised random forest (URF), fisher ratio (FR), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares (OPLS), support vector machines (SVM), random forest (RF).