## 3 Results
### Sensory Analysis  
Figure 4 shows PCA plots constructed on the sensory data of all samples; 47% of the total variance was explained by the first two principal components. The PCA model showed greater dispersion than those of phenolic and volatile compounds. There were two main clusters: the first at T12, which correlated with the overall quality judgment scores (OQJ): a score provided by the panel on the overall quality of the wine, which was included here as an additional variable projected in the PCA). T12 also correlated with \"olfactory cleanness\", \"gustatory cleanness\", \"clarity\", \"burning\", and \"overall intensity\". It is worth noting that the samples stored on shelves dampened by natural magnetic levitation (treatment A.T12) are closest to the \"overall intensity\".  
Samples stored for shorter times (T0, T1, T3, and T6) formed a more diffuse cluster (in the remaining three quadrants of the diagram), showing a marked difference from T12 and greater heterogeneity. The T1 treatments were separated along PC2, and can be divided into two clusters, as treatments A and D correlated positively with \"woody\" aroma and flavour, \"dry fruit\" aroma, \"bitterness\" and \"astringency\". In contrast, treatments B and C for T1 correlated with higher \"spicy\" aroma and flavour, \"salty-sapidity\", \"sourness\", and \"colour intensity\". Time 3 treatments were separated along PC1, again into two clusters, the first of which comprised treatments B, C, and D, which were correlated with higher \"full-body/viscosity\", \"colour tonality\", \"sweetness\", and \"red fruit\" aroma and flavour scores. In contrast, treatment A.T3 correlated with the same variables as B.T1 and C.T1 described above. The T6 wine samples were also divided into two main clusters: one cluster included samples A and B, which were located close to B, C, and D at Time 3 and the second cluster consisted of samples C and D, which were correlated with certain negative sensory descriptors, such as \"sourness\", \"undergrowth\", and \"unpleasant\" odours and flavours.  
The entire sensory dataset from T0 to T12 was analysed by means of ANOVA and [PERSON]'s post hoc test to see which variables were most influenced by the treatment. Figure 5 highlights the variables that significantly differentiated the treatments, which were: red fruit aroma, unpleasant odours, bitterness, red fruit flavour, and overall quality judgment. It should be noted that time 0 (T0) was completely different from the other times and treatments and was included to better highlight the overall effect of the treatment over a 12-month period compared to the initial sample. The overall quality score of the wine at T0 was lower than at all other times (the wine was not yet ready for market). Interestingly, the overall quality scores of the treatment with damping (treatment A) was more similar to the control (treatment C) than the other treatments, showing that vibration damping applied to the control samples reduced the rate of evolution of the wine. As Figure 5 shows,  
Figure 4: Plots of observations and variables constructed from the sensory data. T0, T1, T3, T6, and T12 indicate the storage month (0, 1, 3, 6, 12) at which the samples were analysed. A, B, C, and D indicate the treatment (see Figure 1 for details). **(a)** Scores plot, **(b)** Loadings plot.  
treatment B scored the highest for the overall quality. This seems to be in contradiction with MFA in Figure 6 (see below), in which OQJ at T12 was anti-correlated with treatment B. In practice, microvibration treatment (B treatment) was generally useful to improve the quality of a wine that was not yet ready for the market, but a 12-month period of vibration was too long.