## 4 Data Processing
### Peak Annotation  
After peak detection, peak annotation can be performed using multiple factors. Assist in peak annotation: each peak has two retention times and a normal or high-resolution mass spectrum at the peak apex. Comparing the chromatographic retention time and MS information with a reference standard is always required to achieve level 1 annotation for VOC profiling [158]. Chromatographic peaks can be identified based on their retention times in the 1 st and 2 nd dimensions. Linear retention indices (LRI) can also be calculated based on analyte retention times and standard retention times. It can be used to cross-check the annotation with the retention time database. A 2D retention index system can be created by using PEG homologs together with \(n\)-alkanes [159] and it has recently been modified with the Lee index [160].The identification of unknown peaks can be aided by retention time and/or exponential prediction [161; 162]. In most cases, peak annotation requires a comparison of the electron ionization mass spectrum of the chromatographic peak with a standard mass spectrum from a spectral library (NIST, Wiley, etc.). When using high-resolution mass spectra, the mass-to-charge ratio calculation can also be the basis for identification. Many algorithms have been published to compare the mass spectrum of a chromatographic peak with the reference MS spectrum recorded in libraries. Their performances have been extensively studied [163; 164; 165; 166; 167; 168]. All this requires the simple setting of the appropriate match thresholds, all of which provide accurate annotation results. In most cases, errors in MS identification arise from a non-accurate acquisition of the signals due to analyte characteristic ions in the sample run. This issue affects most signals with low intensities and can be crucial for structural confirmation. In this context, a good alternative is the use of \"smart templates\", which are tools whose employment is made possible by prior knowledge of the sample and whose use simplifies peak matching [169]. Besides, most VOCs are common in most types of fermented drinks. For these reasons, it is not necessary to perform a complete, untargeted analysis. Building a library for beverage VOCs and matching their template to the chromatogram peak can dramatically reduce annotation errors. Moreover, in the event that a new compound is found, its template can be simply added to the current library and recognized in future data processing [170]. This target-guided data processing concept has been successfully applied to the imperfect signal correction of GC\(\times\)GC data [150].  
With a perfect chromatogram, the algorithm can reach 97% accuracy for peak annotation of known substances [169]. The remaining 3% can be solved with manual annotation. Manual identification does not need to go through all the peaks but only focuses on the important ones. The important peaks are the peak differences among the chromatogramsof different sample classes, which can be measured by the ANOVA, for example. The comparison of the chromatogram may be based on the mathematical features of chromatogram \"images\", preventing errors from peak detection and MS construction [171]. This approach is discussed in the next section, \"cross-class comparison\", named tile-based fisher ratio. Since manual annotation still plays an important role in metabolomics studies, tools with user-friendly interfaces that support customized peak picking, feature template definition, peak alignment, and result visualization are desired [172]. A tool has been developed to facilitate the user's evaluation of the peak annotation [173]. Still, GC\(\times\)GC-MS has its inherent limitations. Typical compounds of interest may elute at close retention times and have similar mass spectra. In such a case, the peak annotation problem cannot be solved by the data processing process. It is necessary to increase the data dimensions; for example, GC\(\times\)GC coupled with dual detectors [174].