The result of this research is relevant to the wine science domain, helping in the understanding of how physicochemical characterization affects the final quality. In addition, this work can have an impact in the wine industry. At the certification phase and by Portuguese law, the sensory analysis has to be performed by human tasters. Yet, the evaluations are based in the experience and knowledge of the experts, which are prone to subjective factors. The proposed data-driven approach is based on objective tests and thus it can be integrated into a decision support system, aiding the speed and quality of the oenologist performance. For instance, the expert could repeat the tasting only if her/his grade is far from the one predicted by the DM model. In effect, within this domain the T = 1.0 distance is accepted as a good quality control process and, as
shown in this study, high accuracies were achieved for this tolerance. The model could also be used to improve the training of oenology students. Furthermore, the relative importance of the inputs brought interesting insights regarding the impact of the analytical tests. Since some variables can be controlled in the production process this information can be used to improve the wine quality. For instance, alcohol concentration can be increased or decreased by monitoring the grape sugar concentration prior to the harvest. Also, the residual sugar in wine could be raised by suspending the sugar fermentation carried out by yeasts. In future work, we intend to model preferences from niche and/or profitable markets (e.g. for a particular country by providing free wine tastings at supermarkets), aiming at the design of brands that match these market needs. We will also test other DM algorithms that specifically build rankers, such as regression trees