## 2 Results and Discussion
### Signal Processing and Chemometric Workflow  
The mass spectral fingerprint encompasses all the chemical information on the volatile fraction of an analysed sample, while diagnostic and informative fragments can be correlated to a compound or a class of compounds. The mass fingerprint is displayed on a plot that reports the mass fragments (_m/z_) within the selected mass range on the \(X\)-axis, and the ion abundances for the mass fragments on the \(Y\)-axis (Figure 3). The use of chemometrics to extract the significant and useful information from the complex data matrix, however, requires the profile to be precise, in particular when data monitoring is carried out over a long period and when a mathematical model for classification or correlation has to be generated. The chemometric tools adopted in this study were, in sequence: (i) Principal Component Analysis (PCA) to identify outliers; (ii) Partial Least Square Discriminant Analysis (PLS-DA) carried out on the sensory scaled samples (low-high score range) to identify the fragment ions that are most closely related to each sensory attribute; and (iii) Partial Least Square Regression (PLS) to correlate chemicals to sensory attributes, and to evaluate the ability of extracted chemical variables to predict sensory scores. The data processing work-flow is reported in Figure 4.  
The consistency of the SPME fibres over time was ensured by testing six fibres of the same lot with a text mixture, and selecting those whose responses could not be distinguished using ANOVA; their performance was periodically monitored using the same test mixture. Before chemometric processing, the matrix was cleaned of ground fragments that may have interfered with data elaboration, e.g., the fragments at _m/z_ = 44 (CO\({}_{2}\)), _m/z_ = 73, 133, 147 and 177 (system bleeding), and _m/z_ = 149 (derived from phthalates). The resulting data matrix was than subjected to internal normalisation vs. the most abundant ion (_m/z_ 43) and standardisation using Pareto scaling [39; 40]. The HS-SPME-MS pattern is informative because the intensity of each ion (_m/z_) derives from the contribution of all components that present that fragment in their ionisation pattern. Chemometric  
Figure 3: Analytical output signals of an Arabica roasted coffee sample from: (**a**) HS-SPME-MS-enose profile; (**b**) average HS-SPME-MS-enose mass spectral fingerprint that corresponds to the TIC data from MS-enose; (**c**) HS-SPME-GC-MS chromatogram; (**d**) average HS-SPME-GC-MS mass spectral fingerprint of the whole chromatographic profile. elaboration consists of a series of steps to \"extract\" significant information from the MS fingerprint for further sensory score prediction. The data matrix from the Pareto scaling was first submitted to an unsupervised exploratory investigation using principal component analysis (PCA) to detect sample outliers. The samples were found to be homogeneously distributed along the first three PCs with a cumulative explained variance of 83.15%, and the data indicated two populations of samples on the first PC that were related to the coffee species (Figure S1 in Supplementary Material). The fragment ions that derived from the volatile fraction therefore provided information on the chemical diversity of the investigated set of samples. A supervised Partial Least Square Discriminant Analysis (PLS-DA) was then applied to the reprocessed MS spectral fingerprints on selections of samples that had the highest and the lowest scores for each sensory attribute in order to extract ions that had a high impact on sample discrimination (low vs. high scores). A cross-validation (CV = 5) was set to run the PLS-DA. The variable importance for projection (VIPs) scores estimate the importance of each variable in the projection that was used in a PLS-DA model, and is often used to select variables. VIPs higher than 1 and with a low standard deviation were considered for the extraction of the relevant ions that described each sensory attribute.