# Chemosensory Profile of South Tyrolean Pinot Blanc Wines: A Multivariate Regression Approach
## 2 Results
### Visual and Gustatory Descriptors
In Figure S3, the PLS2 standardized coefficients plots for the visual and gustatory descriptors are reported. Figure 4B-G shows the regression graph for the different visual and gustatory sensory descriptors. As described in the Methods section, seven randomly extracted samples were chosen as the validation set in the PLS2 model. In order to have a predictive model, it is relevant that the validation sets are within the confidence interval (95%). Specifically, Figure 4B shows the regression for the yellow colour descriptor, which shows that 20% of the validation set outside of the confidence interval fix at 95%. Also the warmness is shown in Figure 4H with just 10% of validation set outside of the confidence interval. It is interesting to notice that the model for bitterness (Figure 4F) does not have outliers in validation. Instead, Figure 4C shows the regression for sweetness, which had 20% of the validation set outside of the confidence intervals. Figure 4G shows the regression for astringency that has 20% of the validation set outside of the confidence interval. Figure 5 shows the summary for calculated \(p\)-value and standardized coefficients of the volatile compound in the regression models, as shown in Figure 3. The compounds 12.9 (\(\lambda_{\text{MAX}}\) = 261 nm) and 16.8 (gallic acid) showed a similarity for yellow colour intensity, warmness, sourness, saltiness, bitterness, and astringency, except for sweetness. The compound 36.4 (ferratic acid) showed a positive effect for yellow colour intensity, warmness, sweetness, sourness, and bitterness except for astringency. As expected, compound 41.7 (a procyanidin dimer, \(\lambda_{\text{MAX}}\) = 278 nm) showed a significant negative effect except for astringency. This contrary effect was also shown by the 47.5 (\(\lambda_{\text{MAX}}\) = 290 nm) and 48.5 (\(\lambda_{\text{MAX}}\) = 290 nm and 317 nm) compounds. The 47.5 (\(\lambda_{\text{MAX}}\) = 290 nm) compound showed a positive effect in yellow colour intensity and astringency, and a negative effect on the other descriptors. Instead, the 48.5 (\(\lambda_{\text{MAX}}\) = 290 nm and 317 nm) compound showed a negative effect for yellow colour and astringency but a positive effect for warmness, sweetness, sourness, saltiness, and bitterness. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Statistic** & **Comp.1** & **Comp.2** & **Comp.3** & **Comp.4** & **Comp.5** & **Comp.6** \\ \hline Q\({}^{2}\) (cum) & 0.241 & 0.239 & 0.252 & 0.332 & 0.347 & 0.347 \\ R\({}^{2}\)Y (cum) & 0.352 & 0.480 & 0.553 & 0.681 & 0.728 & 0.761 \\ R\({}^{2}\)X (cum) & 0.180 & 0.378 & 0.634 & 0.703 & 0.786 & 0.862 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quality index (Q\({}^{2}\), R\({}^{2}\)Y, and R\({}^{2}\)X) for partial least square regression on visual and gustatory sensory descriptors. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Yellow Colour** & **Sweetness** & **Sourness** & **Salitness** & **Bitterness** & **Astringency** & **Warmness** \\ \hline R\({}^{2}\) & 0.778 & 0.679 & 0.876 & 0.869 & 0.745 & 0.532 & 0.846 \\ Std. deviation & 0.395 & 0.267 & 0.215 & 0.263 & 0.339 & 0.269 & 0.399 \\ RMSE & 0.329 & 0.222 & 0.179 & 0.220 & 0.283 & 0.225 & 0.333 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quality index (R\({}^{2}\), standard deviation, RMSE) for PLS on visual and gustatory sensory descriptors. Figure 4: Partial Least Square Regression with multiple dependent variables (PLS2) for gustoryand visual descriptors: (**A**) VIP (Variables Importance in Projection) at the component used for calculating the model; (**B** to **H**) Predicted/observed regression graph for visual and gustatory sensory descriptors (the confidence interval is set at 95%). Numbers of the X-axis are HPLC retention times of phenolics.