## 5 Conclusions  
In this work, a robust framework was developed in order to compare different preprocessing techniques and a wide variety of regression methods for predicting three important enological parameters (related to grape's maturity) through hyperspectral imaging. In addition, the framework employed a Monte Carlo double cross-validation scheme to assess the prediction performance of each regression method, using its RMSE distribution and a preset key performance indicator (KPI) as comparison metrics. In terms of preprocessing, we have shown that the effect of SNV, MSC, and Savitzky\(-\)Golay first derivative on the regression performance was similar, whereas Savitzky\(-\)Golay second derivative provided poor results. Thus, Savitzky\(-\)Golay first derivative was the select preprocessing approach to make further comparisons between the regression methods. From all 16 regression methods tested, the best results were obtained with ridge regression (that belongs to the class of penalized regression methods), as it was the method that most often appeared with the highest performance. This indicates that the combination Savitzky\(-\)Golay first derivative and ridge regression can be a good choice to deal with the prediction of enological parameters based on the use of hyperspectral imaging technology. The wavelengths most contributing to explaining the variability of each enological parameter were also investigated, providing important information for the development of precision viticulture technology. However, future work should assess the performance of RR using a larger dataset (composed by different varieties and vintages) and compare it with the most often used methods from literature (e.g., PLS and ANN), as well as with recent deep learning approaches. The use of ensemble preprocessing methods should also be the subject of future research.  
Conceptualization, V.G., R.R., M.S.R. and P.M.-P.; methodology, V.G., R.R., M.S.R. and P.M.-P.; conventional laboratory analysis, A.M.-F.; software, V.G. and R.R.; validation, V.G. and R.R.; formal analysis, V.G. and R.R.; investigation, V.G., R.R. and M.S.R.; resources, M.S.R., A.M.-F. and P.M.-P.; writing--original draft preparation, V.G. and R.R.; writing--review and editing, V.G., R.R., M.S.R., A.M.-F. and P.M.-P.; supervision, M.S.R. and P.M.-P.; project administration, M.S.R. and P.M.-P.; funding acquisition, M.S.R. and P.M.-P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Funds from FCT, the Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 (CITAB); and by Portuguese-FCT (PD/BD/128272/2017), under the Agrichains Doctoral Program (PD/00122/2012).  
Not applicable.  
The authors acknowledge the support provided by Biosystems and Integrative Sciences Institute by FCT (BioSIJ; FCT/UIDB/04046/2020); and the support from the Chemical Process Engineering and Forest Products Research Centre (CIEPQPF), which is financed by national funds from FCT/MCTES (reference UID/EQU/00102/2019). The authors also gratefully acknowledge all resources and support provided by Symington Family Estates.  
The authors declare no conflict of interest.