# Chemosensory Profile of South Tyrolean Pinot Blanc Wines: A Multivariate Regression Approach
## 2 Results  
### Statistical Analysis Approach  
For the statistical analysis, multivariate regression approaches were optimized and applied on three different datasets: (i) overall quality judgment and sensory descriptors, (ii) GC/MS data and olfactory descriptors, and (iii) HPLC data and visual and gustatory descriptors. This arrangement of the study was imposed after considering the parameters evaluated by the trained panel (visual, olfactory, and gustatory aspects, respectively). In the sensory dataset (_i_), a PLS1 model was built considering the overall quality judgment (objective assessment) as y-vector and the sensory descriptors as the X-matrix. In the GC/MS dataset (_ii_), repeated PLS1 were performed on each sensory descriptor as the y-dependent variable and the aroma compounds as the X-matrix. Alternatively, a unique PLS2 model was computed on all sensory descriptors as the Y-matrix and the aromas compounds as the X-matrix, to study the correlations between volatile compounds and sensory variables and their interaction. In the HPLC dataset (_iii_), a unique PLS2 model was applied to the sensory (gustatory and visual) descriptors as the Y-matrix and non-volatile compounds as X-matrix. For the GC-MS and HPLC data, PCR was computed with sensory data as Y-matrix and volatile compounds as X-matrix. The HPLC and GC matrices were reconstructed with a Nonlinear Iterative Partial Least Square (NIPALS) algorithm for predicting missing value in a PCA model [16]. In order to qualitatively explore the main variability sources of the model, the data have been initially studied using Principal Component Analysis (PCA) (Figure 1). Notably, the results from 3 to 9 months of storage in bottles were discussed in a previous paper [13] and have now been integrated up to 18 months of storage. Samples after 12 and 18 months of storage were interesting, as certain relevant parameters such as the measured overall quality judgment (supplementary variable projected onto the PC1 vs. PC2 loadings plot, Figure 1E,F) resulted higher at month 18 (W18 samples), although not significantly, and in particular for V1 (control vinification). Considering the volatile compounds (Figure 1A,B), the PCA models showed clusters by month and wine for most triplicates, although a particular trend over time or vnification could not be clearly observed for all triplets along any particular direction in the PC1 vs. PC2 Score plot. However, W18 samples were completely clustered at low PC1 and high PC2 values, indicating also that some \"re-convergence\" of V1 and V2 volatile profiles took place after the biggest spreading at month 6 (W6) and 12 (W12). To simplify the interpretation, considering particularly the direction separating W3 from W18, only some of the compounds of the volatile profile characterized the temporal evolution, e.g., ethyl 9-decenoate, 2,3-butanediol, isobutyl octanoate, ethyl nonanoate, diethyl succinate, isoamyl alcohol, phenylethyl alcohol and n-hexanol. Regarding the phenolic profile (Figure 1C,D), here the trends were clearly observable. PC1 separated neatly the two vnifications, whereas PC2 mostly represented temporal evolutions for both wines, as W3-6 were separated in PC2 from W9-18 for both wines along PC2 (the evolution proceeded from high to low score values). The variables in the loadings plot are reported as retention times, as many compounds could not be definitively and unambiguously identified by LC-MS analysis. A consistent temporal trend was not observed for either of the sensory profiles (Figure 1E,F), when considering the entire time range. To simplify the interpretation, when considering the differences between W3 and W18, the overall evolution mainly involved the \"apple\", \"saltiness\", \"warmness\", which increased overall and \"yellow colour\", \"olfactory intensity\", \"astringency\" and \"clarity\", which decreased from W3 to W18 only.