## 3 Results
### Regression Models
#### 3.4.4. Partial Least-Squares Regression (PLS-R) for the Olfactory Attributes of Pinot Noir Wines  
Nonetheless, for Pinot Noir wines, a PLS1 regression model was built by taking each sensory olfactory variables separately as the dependent Y-matrix and the volatile compounds as the independent X-matrix. The most correlating volatile compounds with the sensory olfactory attributes were considered for this model. Only the variables that showed predictive relevance are shown here, which were \"green bell pepper\" aroma, \"cherry\" aroma, \"licorice\" aroma, and \"fresh wood\" aroma.  
The PLS with two factors for \"green bell pepper\" aroma had a Q2 (cum) of 0.4, a R2X(cum) of 0.6, and a R2Y(cum) of 0.8. The model for \"cherry\" aroma had a Q2 (cum) of 0.2, a R2X(cum) of 0.6, and a R2Y(cum) of 0.7, which was also built with two components. The PLS model built with two factors for \"licorice\" aroma had a Q2 (cum) of 0.2, a R2X(cum) of 0.7, and a R2Y(cum) of 0.6. Lastly, for \"fresh wood\" aroma, the model was built with three factors and had a Q2 (cum) of 0.4, a R2X(cum) of 0.8, and a R2Y(cum) of 0.8.  
The VIP graph of the PLS1 model for each individual sensory attribute is shown in Supplementary File S1--Tables S19-S22. Figure 9A-D shows the regression graph of the predicted vs. the experimental data for \"green bell pepper\" aroma, \"cherry\" aroma, \"licorice\" aroma, and \"fresh wood\" aroma, respectively. The R2, standard deviation, RMSE, and MSE are reported for the above-mentioned olfactory variables used in the regression in the following Table 6.  
Figure 9: (**A–D**) The regression graphs for predicted vs. experimental data of “green-bell pepper” aroma, “cherry” aroma, “licorice” aroma, and “fresh wood” aroma of Pinot Noir wines.  
For \"green bell pepper\" aroma, the compounds \"III: isopentanol\", \"VIII: 1-hexanol\", \"XVI: benzaldehyde\", \"XXXIII: ethyl phenylacetate\", and \"XLIII: ethyl isopentyl succinate\" showed a positive influence in the regression model, while the compounds \"VI: furfuryl ethyl ether\", \"XI: acetic acid\", \"XIV: ethyl sorbate\", \"XXVIII: ethyl 9-decenoate\", \"XLII: trans-whiskey lactone \", \"XLIV: 4-ethylguaiacol\", and \"XLVII: 4-ethylphenol\" showed a negative effect. For \"cherry\" aroma, the compounds that showed a positive contribution in the model were \"VI: furfuryl ethyl ether\", \"VIII: 1-hexanol\", \"XVI: benzaldehyde\", \"XXVIII: ethyl 9-decenoate\", and \"XLVII: 4-ethylphenol\", while the compounds \"III: isopentanol\" and \"XXI: isoamyl lactate\" showed a negative effect. For \"licorice\" aroma, the compounds \"X: furfural\", \"XVI: benzaldehyde\", \"XXI: isoamyl lactate\", \"XXX: 4-tert-butylcyclohexanol\", \"XL: hexanoic acid\", \"XLI: benzyl alcohol\", and \"XLII: trans-whiskey lactone\" showed a positive effect, while the compounds with a negative influence were \"III: isopentanol\", \"VI: furfuryl ethyl ether\", \"XIV: ethyl sorbate\", \"XLIII: ethyl isopentyl succinate\", \"XLIV: 4-ethylguaiacol\", \"XLVII: 4-ethylphenol\", and \"XXIV: ethyl 2-fluoroate\" Lastly, for \"fresh wood\" aroma, very few volatile compounds had a positive influence on the model, but included the compounds \"XXVIII: ethyl 9-decenoate\", \"XXIV: ethyl 2-fluoroate\", and \"XXVIII: \(\beta\)-damascenone\", while compounds \"VIII: 1-hexanol\", \"XIV: ethyl sorbate\", \"XXX: 4-tert-butylcyclohexanol\", \"XL: hexanoic acid\", \"XLIII: ethyl isopentyl succinate,\" and \"XLVII: 4-ethylphenol\" had a negative effect.  
The full regression model is presented in Supplementary File S3--PN GBP PLS, PN CH PLS, PN LI PLS, and PN FW PLS, respectively.