step. Also, several studies have shown the SPME's abilities to be versatile and able to cover different aspects in the wine field, such as the origin and flavor characterization [8; 9; 10]; the off-flavor identification [11; 12]; the improvement of the fermentation process [13]; the ageing studies [14; 15]; the evaluation of the packaging's impact on the sensory profile [16]; and the influence of pedo-climatic conditions [17]. The development of the SPME method requires the setup of some fundamental parameters to obtain good extraction efficiency, and good results in terms of accuracy and repeatability. It is necessary to identify the optimal time and temperature conditions for the equilibration and extraction phases, to choose the most suitable fiber according to the analytes to be extracted, to determine the volume of the sample, and the amount of salt to be used. The availability of fibers with different adsorbent polymeric coatings makes SPME a very versatile technique suitable for the extraction of different classes of volatile compounds from different matrices. For specific applications, the choice of a suitable solid phase depends on the class of compounds to be analyzed [18]. Polydimethylsiloxane (PDMS)-coated fibers have shown very good extraction efficiency with non-polar compounds [19], as their coating consists of a non-polar material. However, in recent years, the use of coatings based on the combination of different adsorbent polymers including poly divinylbenzene (DVB) and carbowax (CAR), such as DVB/CAR/PDMS or CAR/PDMS, has gained popularity, since they can be used for the extraction of a wider range of analytes [20]. The growing interest in this SPME technique, especially focused on the wine headspace (HS), is demonstrated by an almost linear increase in the number of manuscripts related to SPME applications in wine, evidenced from a search on the Scopus database between 1998 and 2021 using the keywords \"HS-SPME/wine/volatile\", as shown in Figure 1.  
Generally, metabolomic analysis, and, in particular, the colatolomic approach, generates extremely large volumes of data. Traditionally, wine VOCs' data have been evaluated using a univariate approach, based on assessing the influence of a single variable on the overall aroma. This approach can provide useful information, but not specific indications on the relationships between the variables [21; 22]. In order to study complex matrices such as wine, characterized by the presence of different interactions among the various matrix components, multivariate analysis techniques are required. The latter can exploit and determine, beyond the classic covariance between variables, more complex functional relationships that can be used in different ways, depending on the specific research needs. Different goals require different chemometric techniques to be applied, e.g., for data exploration, classification, or curve resolution [23; 24]. Principal Component Analysis (PCA) and Discriminant Analysis (DA), in particular, have been extensively applied to characterize  
Figure 1: Number of published articles between 1998 and 2022 related to HS-SPME/wine/volatile. wines based on their volatile content [25; 26; 27; 28; 29]. Cluster Analysis (CA) has been used to categorize wines based on their volatile composition [30; 31]. Analysis Of Variance (ANOVA), along with PCA, CA, and DA, have been used to classify South African wines, according to cultivar based on volatile content [32]. Moreover, the volatile profile, in combination with unsupervised methods, like Hierarchical Clustering Analysis (HCA) and PCA, was exploited for the discrimination or differentiation of grape cultivars and wines [33]. [PERSON] and [PERSON] [34] employed multivariate statistics (Generalized Procrustes Analysis, GPA, and Partial Least Squares regression, PLS) in understanding wine flavor, whereas [PERSON] et al.