## 1 Introduction  
Comprehensive two-dimensional gas chromatography (GC \(\times\) GC) is a multidimensional separation technique that enables the in-depth chemical characterization of the complex food volatilome [1]. It combines, in a single analytical platform, two separation dimensions with mass spectrometry, i.e., an orthogonal measurement principle that is fundamental for analyte identification and quantitation, and automated sample preparation. Such configured platforms deliver highly efficient profiling (detailed investigation of single molecular entities) with the intrinsic fingerprinting potential, and can provide accurate and informative cross-comparative analyses [1]. The chemical characterization of the olive-oil volatilome is a challenging, although fundamental, task that is part of the quality assessment process. The composition of the volatile fraction, also referred to as the chemical signature, is an informative and diagnostic tool for oil quality characterization and sensory qualification [2; 3; 4; 5]. Only a few of the considerable number of detectable volatiles are responsible for the positive and negative attributes that delineate olive oil sensory profiles. In fact, olive oil is, to date, the only food product whose sensory attributes are officially regulated by EU legislation, and standardized sensory assessment protocols [6; 7], in the form of smelling and tasting experiments, are run by constantly updated and trained panelists. Virgin olive oil is classified into three categories, extra-virgin (EV), virgin (V), and lampante oil, according to the presence/absence and the intensity of coded defects (i.e., fusty/muddy sediment, musty/humid/earthy, winey/vinegary, rancid) and the perception of the \"fruity\" taste. Improved separation power and detection sensitivity are needed to efficiently extract information on the presence of potent odorants, sometimes at trace and ultra-trace concentration levels. These features, if accompanied by a structured logic of elution for chemically correlated compounds, can provide highly confident and accurate chemical characterizations, while offering new perspectives to the important problems of quality and authenticity assessment [8]. GC \(\times\) GC has been adopted to characterize the olive-oil volatilome in studies that aim to define the volatile signatures of olives that differed in terms of variety, origin, and process technology [9; 10; 11] using both targeted and targeted/untargeted analyte distributions. A significant step ahead was made by [PERSON] et al. [2], who explored the 2D-patterns of volatiles, after headspace solid-phase microextraction (HS-SPME) sampling, to delineate the coded defects in the chemical signature of the oil (i.e., blueprint). Olive ripening and its impact on volatiles distribution and oil quality has been studied by [PERSON] et al. [12], who also introduced a systematic strategy for efficient untargeted and targeted investigations, which was based on pattern recognition by template matching; the strategy was defined as combined untargeted/targeted (UT) fingerprinting and has been recently extended to several other applications in the fields of food [13; 14; 15], and nutrimetabolomics [16]. All of the above-referenced studies have exploited HS-SPME as a sampling strategy; it combines the advantages of gas-phase extraction approaches with the possibility of achieving suitable enrichment factors that match method-sensitivity requirements [17; 18; 19; 20; 21; 22]. However, as the zeroth dimension of an analytical process [23], HS-SPME, and more generally any sampling procedure, may impact on method information potential by discriminating analytes in function of one of their specific characteristics (polarity, volatility, etc.).