## 3 Results and Discussion
### Focus on Informative Targeted Analyte Signatures  
Of the group of analytes that were targeted, the class of alcohols provides information on LOX activity in ripened fruits, and on enzymatic reactions promoted by bacteria and molds. Their concentration factors (CFs), obtained from Equation (1), are calculated over SPME-TRIF, which is taken as the reference technique:  
\[CF_{i}=\frac{A_{i}\;dev\;x}{A_{1}SPME\;TRIF}, \tag{1}\]  
where \(A_{i}\) is the 2D-chromatographic area of the \(i\) analyte obtained by applying a certain sampling device/approach (_dev x_) and \(A_{i}\)_SPME TRIF_ is the analyte \(i\) chromatographic area resulting from the HS-SPME approach, which is taken as the reference technique. Results were visualized in the heat-map of Figure 4A; alcohols were, in general, better recovered by MMSE-ODS/GC and DHS-PDMS sampling, with the latter showing a mean CF of 120 and a median of 3.36. The alcohols that were recovered most by DHS-PDMS sampling were 2-methyl-1-propanol (CF 1548) and 1-tetradecanol (CF 557). Indeed, DHS shows lower CF values for the most volatile members of the linear series (i.e., ethanol, pentanol, hexanol, heptanol, octanol, and nonanol), most probably because of their breakthrough (see below for further comments). The two C6 unsaturated alcohols ((\(E\)) and (Z)-3-hexenol), with a high information potential being fundamental for their green  
Figure 3: Heat-map showing the analyte-response (absolute 2D-peak volume) distribution resulting from the different sampling approaches. Data were normalized by 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. note contribution to the overall flavor, were better recovered by all devices, except SPME-TRIF. It is worth noting that their relative abundance in the sample was so high that their detection by HS sampling was not generally limiting.phenylethyl alcohol, the aromatic member of this chemical class, showed the opposite tendency, being better enriched by SHS with HSSE-PDMS and dual-phase twisters HSSE-PDMS/CBP. Another interesting chemical class is that of unsaturated aldehydes; they are formed by the \(\beta\)-scission of unsaturated fatty-acid hydroperoxides, and their odor thresholds are generally lower than those of the saturated homologs. They have been described in the volatile fraction of defected oils [2] (rancid, moldy and fusty) and contribute with fatty and rancid notes. Figure 4B shows CF values within a sub-set of techniques for the most relevant members of this series. In this case, the HSSE approach gave higher average CFs than the other techniques. For (_E_)-2-octenal and (_E_)-2-nonenal, MMSE-ODS/GC reported CFs of 10 and 13, showing good selectivity, compared to SPME-TRIF, for these two analytes, which were connoted by very low OTs, i.e., 0.004 and 0.9 mg/kg. The results could also be examined by observing the relative concentration capacity of each device/approach toward selected analytes and by taking the most effective one as a reference. In a few words, a unit value was assigned to the most effective approach towards each single analyte and the ratios between analyte responses were calculated in a 0-1 range for the other sampling devices. Sampling selectivity was emphasized with this parameter, if calculated for homologs, therefore it is a parameter to be considered in method optimization. Results for saturated aldehydes, alcohols, and short-chain fatty acids are shown in histograms in Figure 5A-C. Saturated aldehydes, the series from C6 to C17, showed an interesting trend. The static headspace approaches (SPME-TRIF, MMSE-ODS/GC, and HSSE), better recover the most volatile members of the series (from C6 up to C12), provide higher amount of the accumulating polymer, and more uniform relative analyte recovery, although they do so to different extents.