## 3 Results and Discussion
### Sampling Information Potentials Based on Untargeted Data  
Smart-template-concept based pattern recognition [36], was used to study sampling effectiveness and 2D-pattern information potential. The template corresponds to the pattern of 2D-peaks and/or their corresponding graphic objects created over the 2D-peak contour of a reference image(s) (single or composite image) [16]. This template is then used to recognize similar peak patterns in an analyzed image(s) [37]. Template objects (2D-peak and/or graphic) carry various metadata such as retention times, \(I^{T}\), mass spectrum, compound name, compound group, informative ions and their relative ratios, additional constraint functions, and qualifier functions. Typical constraint functions are those that limit positive correspondence to analytes that show an MS-fragmentation pattern similarity above a fixed threshold, while qualifier functions may provide information about quality indicators, as calculated via scripts that are developed ad hoc. These functions enable highly specific cross-comparison of data, providing 2D-peak re-alignment across samples with high consistency. Figure 2: 2D pattern of the PGI Toscano EVO oil (**A**) and that of a reference oil from IOC for the fusty/muddy defect (**B**). Comparative visualization is rendered in (**C**) as colorized fuzzy ratio and analyte relative abundance in the two samples is highlighted by color-coding (green, red, and light-grey). For details see text. Peak-region features, introduced by [PERSON] and co-workers [38; 39], are template objects that give considerable assistance in compensating for temporal inconsistencies, detector fluctuations and highly variable sample compositions. They provide greater robustness than peak-features methods, while offering all the advantages of one-feature-to-one-analyte selectivity. The combination of both, 2D-peaks and peak-regions was adopted for combined untargeted and targeted fingerprinting--UT fingerprinting strategy [5; 12; 13; 16]. In UT fingerprinting, a group of reliable peaks, which positively match across all or most chromatograms in a set [40], is established and then used to re-align chromatograms [41], before their combination into a single-composite chromatogram. The composite chromatogram corresponds to the sum of the re-aligned datapoint responses in the 2D retention-time plane. It can therefore be treated as a regular 2D-chromatogram for peak detection and metadata extraction. The sub-set of reliable 2D-peaks and all the peak-regions extracted by peak outlines in the composite chromatogram are collected in a feature template, or consensus template, which covers the chemical dimensionality of the whole sample-set, and that is capable of capturing chemical variability with high specificity. The subset of known compounds can be completed from all detected analytes by filling their metadata fields (compound name, ion ratios, \(I^{T}\)); this subset--reported in Table 2--can be separately processed for the interpretation of results. A schematic of the UT fingerprinting process is illustrated in Supplementary Material--Supplementary Figure S1 together with some details on targeted and untargeted 2D peaks and peak-regions. In this specific application, UT fingerprinting is extremely useful since it enables a consistent re-alignment of detected features (UT peaks) when different sampling approaches are applied. In this context, the cross-comparative analysis aims at revealing 2D-pattern differences brought by the extraction techniques rather than those related to the different composition of a selection of samples. The distribution of about 1500 untargeted and targeted 2D-peak-regions is illustrated as a heat-map in Figure 3. Analyte responses (absolute 2D-peak volume) were normalized using the Z-score (i.e., mean subtraction and normalization to the standard-deviation) and clustered (hierarchical clustering--HC) based on Spearman rank correlation. For each sampling approach, three analytical replicates were computed.