## 4 Results and Discussion
### Sugar Content Analysis
Regarding the KSVR method, the authors of [12] reported RMSE values of 0.80 \({}^{\circ}\)Brix, using SVR with a radial basis function approach for the same dataset acquisition. However, the authors used a genetic algorithm followed by random search to determine the hyperparameters range and the best combination that led to the lowest RMSE. Furthermore, they employed a different splitting strategy where three samples were selected from each grape's harvest day and reserved for validation (by contrast, we randomly select 20% in each percentile). This might justify the difference in the obtained results and indicates that the data splitting step may have a significant impact on the results. Naturally, the splitting strategy employed should take into account the goals of the research being conducted. Nevertheless, in this work, the focus was on understanding how regression models perform under more strict conditions, where samples in the validation set do not require a counterpart sample in the training set collected on the same day. Furthermore, and as complementary information to the strategy designed for the proposed comparison framework (RMSE and KPI), the ratio of performance deviation (RPD) and range error rate (RER) values are provided in Table 3, in terms of the mean and respective 95% confidence intervals. Both RPD and RER normalize the RMSE values obtained by each model for the external validation sets against the standard deviation and range of their reference data, respectively. The best RPD and RER values were achieved for the RR method, with mean values of 3.32 and 13.83, respectively, indicating a good overall prediction ability of the RR model (following the guideline scale suggested by [39]). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**RPD**} & \multicolumn{2}{c}{**RER**} \\ \cline{2-5}  & **Mean** & **95\% CI** & **Mean** & **95\  
Taking into account the RR and PLSR approaches, with PLSR being the most employed in the literature for spectroscopic measurements in reflectance mode (Table 1), the RMSE results obtained forthe sugar content in the present work were better than those from [7; 8; 10; 15; 16]. On the other hand, the authors of [11] revealed results better than ours, but these authors used a significantly larger number of berries per sample compared with the six berries per sample used in this work. The use of a large number of berries reduces the sample variability, which have an impact on the RMSE. In terms of RPD and/or RER, the authors of [7; 8] did not show the values; however, in [15], the authors reported RPD values of 1.88 and 1.54 for Chardonnay and Viura varieties, respectively. Furthermore, the authors of [10] reported RPD and RER values of 4.06 and 13.89, respectively, for a Syrah variety and of 5.83 and 19.68 for a Cabernet Sauvignon variety, respectively; in [16], the RPD was 4.12, and the authors of [11] only mentioned that the RPD was acceptable (the authors did not disclose the value). Nevertheless, the authors of [8; 10; 16] also used a larger number of berries per sample in their studies, which might justify the better results obtained in terms of RPD and RER. In this work, the decision to use a small number of berries per sample (six) was due to the desire of some wineries to select the best berries from each bunch to produce specific high-quality wines, as stated in [7; 12; 13]. As RR was shown to be the best method, we analyzed the most important spectral regions for this model. Figure 7 presents the regression coefficients for the RR model, and important regions are characterized by higher magnitudes of the regression coefficients. One can notice by Figure 7 that the region near 900-1000 nm is rather important, as is the region near 750-800 nm. The spectral regions (400-500 nm) also had some important predictors, but they tended to be noisier.