# Chemosensory Profile of South Tyrolean Pinot Blanc Wines: A Multivariate Regression Approach
## 2 Results
### Visual and Gustatory Descriptors  
The identification of several compounds had already been presented along with their fragmentation spectra in a previous report on the same Pinot Blanc wines [13]. This tentative identification, reported in Table S10, has been extended and obtained by off-line LC-MS analysis on a set of representative samples across the entire storage time. Peaks were expressed by their retention times, their MS1 _m/z_ values with an indication of the ionization mode (negative or positive) and the measured spectrum \(\lambda_{\text{MAX}}\) (nm) of the peak and the peak area (as Abs/mAU at 280 nm per retention time). The peak table has been aligned manually ensuring that each peak showed the same UV-Vis spectral features. Compounds with single \(\lambda_{\text{MAX}}\) at 285-275 nm could be associated tentatively to fluxan-3-ols and their oligomers (proundocyanities); the spectral profile of dihydroanvonols (flavanonol) with a higher maximum around 290-285 nm and a broad band of lower intensity located around 330 nm was applied to identify taxifolin and astilbin (taxifolin-3-O-rhameth); simple and derivated cinnamic acids presented clear features as a double maximum of relatively similar intensities between 280 nm and 315 nm (simple cinnamic acids) or around 290 nm and 325 nm (derivatives such as cinnamoyll tartrates e.g., catfaric acid); benzoic acids and their derivatives (e.g., esters) are characterized by \(\lambda_{\text{MAX}}\) at 270-260 nm. This classification allowed to suitably restrict the possible assignment of the observed peaks to a specific class. Using the complete peak list, multivariate regression models were built to explain visual and gustatory sensory descriptors. PLS2 was chosen as the best approach for gustatory and visual descriptors. The same data were also processed by PCR, which showed a worse prediction than PLS2. In Figure 4, the PLS2 regression for visual and gustatory descriptors is reported (yellow colour, sweetness, sourness, saltiness, bittermess, astringency, and warmness). The best PLS2 regression model was calculated at 6 components and the qualitative index Q2(cum),  
Figure 3: Summary of the calculated _p_-values and standardized coefficients of the volatile compounds in the regression models. The absolute length of the bars represents the significance expressed as \"\(1/p\)-value\"; consequently, the longer the bar is, the more significant is the effect of that compound. The colour and the direction of the bar indicate instead the sign of the standardized coefficient in the respective regression equation: Red = positive sign/correlation, Blue = negative sign/correlation. R\({}^{2}\)X (cum), and R\({}^{2}\)Ycum up to 6 components are reported in Table 2. In Figure 4A the VIP. The VIP describes the weight of the variables (HPLC peaks with assigned retention time) in the model; VIP were divided into two categories: the first included all the variables with a score higher than 1.2 (the most significant variables) (48.4, 47.4, 36.3, 23.3, 24, 51.8, 5.8, 21.9, 3.89 and 7.26), and the second category includes all the variables that are between 1.2 and 0.8 (26.1, 4.47, 17.3, 41.7, 16.7, 50.2, 22.2, 27.2, 12.8, 20.3, 6.5, 10.5 and 31.8). The most important phenolic compound was 48.5 (\(\lambda_{\text{MAX}}\) = 290 nm, 317 nm), the second most important was 47.5 (\(\lambda_{\text{MAX}}\) = 290 nm) and the third one was 36.4 (ferratic acid). Table 2 reports the quality index Q\({}^{2}\), R\({}^{2}\)Y, and R\({}^{2}\)X for each PLS2 model calculated at 1 to 6 components. The best PLS model was the one at 6 components, in fact increasing the number of components increased the cumulative Q2, R2X and R2Y indices but worsened the prediction quality. Table 3 shows the R\({}^{2}\) index, standard deviation, and RMSE with 6 components for the different sensory descriptors. These indices, in combination with the indices reported in Table 2, were used to select the best model.