## 2 Results and Discussion
### Signal Processing and Chemometric Workflow
Table 2 shows the significant ions that were selected for each sensory attribute under investigation, together with their VIP value and standard deviation. Results show that the total number of relevant ions is different for each considered sensory attribute and, in particular, that sensory qualities share several ions Table 2 and Figure 5.  
Figure 4: Workflow of the chemical data processing used to obtain the regression model. A PLS model was built for each single sensory attribute to verify the relationship between the chemical ion fragments (_m/z_) (i.e., the chemical components of the volatile fraction) and the sensory profile, and to predict the sensory scores. Figure 5 shows that the ions selected from the PLS-DA and used to design the corresponding regression model for each sensory feature are involved in more than one feature. For example, the typical base fragment of organic acids _m/z_ 60 in coffee (the most abundant of them being acetic acid and 3-methyl butanoic acid), is depicted in 7/9 regression models, while _m/z_ 150/152, predominantly related to methoxy phenols, are only present in 4/9 regression models. This result is in agreement with results reported in [PERSON] et al., who underlined the importance of the co-participation of several volatiles in describing various sensory features in fifty three Arabica coffees [PERSON] et al. (2016). Moreover, the mass spectral fingerprint provides some information on the volatiles that characterise the samples. For example, _m/z_ 108 is mainly related to several alkylated pyrazines and pyrrole derivatives, _m/z_ 95/96 are associated to furfuryl products, and 135/137/150/152 are primarily related to methyl-ethyl pyrazine and methoxy phenols. The ability of the VIP-selected ions to describe the sensory characteristics of samples can be visualised for the most fruity and woody samples in the heat-map in Figure 6, which shows a clear discrimination between the samples with these two sensory profiles thanks to mass fingerprinting. The rows indicate the _m/z_ ions, and the columns the investigated samples. The colour scale varies from blue (low abundance) to red (high abundance). A hierarchical cluster analysis (HCA) of both the rows and columns shows that volatile distributions differ according to their normalised response across samples. Figure 6 highlights that the ion-intensity ratios across samples in place of the different quality volatiles is very effective to discriminate the two cluster of samples linked to their sensory peculiarity ([PERSON], 1974; [PERSON], 1974; [PERSON], 1974). The ions selected using PLS-DA were then used as independent variables to evaluate the relationship with sensory data and the ability to predict scores (dependent variables) of each sensory attribute by developing a specific and optimised regression model for each feature. All sensory notes have been modelled through a PLS algorithm; the evolution of each sensory note over the sample sets has been related a different number of variables (Table 2). The flexibility of the prediction model for each sensory attribute was evaluated in samples that covered a range of seasonality, origins and crops and then tested the models with an external test set (Table 3). Acid, Bitter, Spicy and (to a lesser extent) aromatic intensity and flowery PLS prediction models show good performance. The R2 values indicate that nearly more than 50% of the variance in the measured scores is explained by the models (i.e., the selected mass spectral fragments used to describe the model). This is quite a good result in consideration of the high variability of the training set. The goodness of the predictive capability is confirmed by the acceptable values of the root mean squared errors (RMSECV and RMSEP) reported for these attributes. The limit of acceptability for predicted values has been strictly fixed by the sensory  
Figure 5: The importance and occurrence of each selected mass fragment (_m/z_) in the partial least square (PLS) regression model of every sensory attribute. panel, in \(\pm 1\) score points.