## 2 Materials and Methods
### Sensory Analysis  
For the sensory analysis of Pinot Gris and Pinot Noir wines, three phases (defining the sensory attributes, rapid training, and RATA test) each consisting of one round table, two training sessions (1 h each), and two MRATA sessions (modified rate-all-that-apply [6; 21]) were performed. The sessions were held on different days for the different varieties. Cysensy, an SQL binding sensory analysis web software developed in collaboration with the Computer Science faculty of the Free University of Bozen/Bolzano, was used during the training and the MRATA sessions. The panelists were recruited on a voluntary basis and included students and staff of the Free University of Bozen/Bolzano (Italy). All panelists voluntarily agreed to participate and signed an informed consent form, which was obtained from all individual participants included in the sensory study. Training and sensory testing were conducted in the same room and at the same time for one month. To limit the impact of location and time on the reproducibility of the sensory analysis, these parameters were maintained for all sessions. The maximum quantity of alcohol/session was 32 mL/session, so the study was considered low risk for two reasons: the participants were instructed to spit the wine after the analysis, and the sample were commercial products available on the market. For these reasons, approval from an ethics committee was not required. The panelists for both Pinot Gris (ten people, 40% males, 60% females, aged between 25 \(\pm\) 2 years old) and Pinot Noir (eleven people, 55% females, 45% males, 25 \(\pm\) 2 years old) were recruited based on their interest in the project. In the round table session, they  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Origins** & **Zone** & **Climate Description** & \begin{tabular}{c} **Temperature Range** \\ **(Yearly/°C)** \\ \end{tabular}  & \begin{tabular}{c} **Rainfall** \\ **(Yearly/mm)** \\ \end{tabular}  & \ \begin{tabular}{c} **Climate** \\ **Classification** \\ \end{tabular}  \\ \hline \multirow{3}{*}{Hawkes Bay} & North Island, New Zealand & \begin{tabular}{c} Mild, generally warm, and \\ temperate with a significant \\ amount of rainfall during the \\ year [13] \\ \end{tabular}  & 7.2–19.5 \({}^{\circ}\)C & 59–110 mm (2–4 inches) & Cfb \\ \hline \multirow{3}{*}{Mendoza} & \multirow{3}{*}{Argentina} & Hot and clear summer, cold and cloudy winter, and dry all year [14] & \multirow{3}{*}{16.3 \({}^{\circ}\)C (61.4 \({}^{\circ}\)F)} & \multirow{3}{*}{492 mm (19.4 inches)} & \multirow{3}{*}{Cfb} \\ \cline{1-1} \cline{5-5}  
were asked to test each wine individually and to share descriptors of visual, olfactory, and gustatory (taste and flavor) attributes on an interactive web whiteboard (Jamboard, Google, Dublin, Ireland). After each sample, the panel leader and panelists discussed and selected the most appropriate shared attribute for each wine type, to create the final sensory lexicon. Then, the panelists were trained for those specific attributes (shown in Tables 4 and 5). For both varieties, the first training session was specific to the odor attributes. Panelists were trained with reference standards [22] or natural standards, if references were not available. The second training session was assigned to taste and flavor attributes along with those odor attributes that the panelists did not recognize at a percentage higher than 75% during the descriptor test of the first training session. A third training session was required for Pinot Noir, in which panelists were trained on flavors, on the remaining taste attributes (sweetness, warmness), and on two aromas not successfully recognized during the previous training sessions. Moreover, in addition to the two new taste attributes, panelists were further trained for astringency. The results of the training sessions are reported in Supplementary File S1--Tables S1-S4 for Pinot Gris and Tables S5-S10 for Pinot Noir.