# In-Depth Aroma and Sensory Profiling of Unfamiliar Table-Grape Cultivars
## Results
### Regression Model to Predict Liking Scores  
Obtaining consumer-liking scores from the market and sensory evaluations was not possible with the unfamiliar grapes due to their low yields, small plantings, and the fact that they were new to the market. To tackle this problem, we developed an orthogonal partial least squares (OPLS) model using the the popular cultivars [10] to relate the volatile contents (X) with the liking scores (Y). This model was used to predict the consumer-liking scores of unfamiliar cultivars. Although all three models functioned well (\(R^{2}\)Y, \(Q^{2}\)Y \(>\) 0.5), the values of \(R^{2}\)Y and \(Q^{2}\)Y were larger than those of the other two models. For model entailing the whole-grape berry, the \(R^{2}\) values (coefficients of correlation) were greater than those in the other two models. This indicated that the whole-berry model was better than that of either the skin or the pulp juice, and that the unfamiliar cultivars' liking scores could be predicted well using the popular cultivars' data. By excluding strong outliers with the aid of principal component analysis (PCA; Figure S3), an OPLS model was established for the volatiles of pulp juice, the skin, and the whole berry. Among these three (Table 1) models, the whole berry showed a much better fit and predictability \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attribute** & **No. of Latent Variable** & \(R^{2}\)**X** & \(R^{2}\)**Y** & \(Q^{2}\)**Y** & \(R^{2}\) & **RMSE** & **RMSEcv** \\ \hline Pulp juice & \(1+6+0\) & 0.693 & 0.919 & 0.799 & 0.9188 & 0.2016 & 0.2952 \\ Skin & \(1+5+0\) & 0.643 & 0.865 & 0.725 & 0.8645 & 0.2601 & 0.3484 \\ Grape berries & \(1+7+0\) & 0.753 & 0.942 & 0.834 & 0.9419 & 0.17215 & 0.2681 \\ \hline \hline \end{tabular}  
* \(R^{2}\)X—the ratio of the variance of the X data set; \(R^{2}\)Y—the ratio of the variance of the Y data set; \(Q^{2}\)Y—the cross-validated \(R^{2}\)Y; \(R^{2}\)—the coefficient of correlation; RMSE—not-mean-square error of estimation; RMSEC—not-mean-square error of cross-validation. \(R^{2}\)Y indicates the accuracy of model, whereas \(Q^{2}\)Y indicates its predictability.  
\end{table} 
Table 1: Summary of the results of orthogonal partial least squares (OPLS) model developed for pulp juice, skin, and whole-grape berries. Figure 4: Aromatic fingerprints of Heikuixiang. In the central pie chart, the outer band represents the primary aromatic series, while the interior band represents the berry components—skin (orange) and pulp juice (green). The concentric circles to the left, right, and top of the pie chart represent the fruity, floral, and sweet secondary aromatic series, respectively. In these circles, the outer radial sectors (deep colors) represent the skin, and the inner sectors (light colors) represent pulp juice. (\(R^{2}\)Y = 0.942, \(Q^{2}\)Y = 0.834) than the skin and the pulp juice. The model predicted liking scores with high precision, as evidenced by a root-mean-square error of cross-validation (RMSECV) score of 0.268, which represented about 5% of the full sensory score. The \(R^{2}\) and \(Q^{2}\) values of the original model were always higher than the corresponding \"permuted\" values. Furthermore, the permutation test showed the Q\({}^{2}\) intercept to be negative (Figure S3i). These data demonstrated that the model aptly fitted to whole berries, and it so happens that people generally consume whole berries, rendering the model as the ideal choice for predicting liking scores. To investigate the differences in liking scores among the cultivars, we performed ANOVA (Table S8), and found that the unfamiliar cultivar A received the lowest score, whereas Xiangyue (G) and Heikuixiang (P) obtained the highest scores (Figure 5a). The scores of other unfamiliar cultivars ranged from 3.5 to 4.5. Figure 5: The predicted aroma consumer liking and aroma intensity (OAVs) of all table grapes. (**a**) The predicted aroma consumer-liking scores of unfamiliar and popular cultivars.