By Cortez, P.; Teixeira, J.; Cerdeira, A.; Almeida, F.; Matos, T.; Reis, J.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Certiﬁcation and quality assessment are crucial issues within the wine industry. Currently, wine quality is mostly assessed by physico- chemical (e.g alcohol levels) and sensory (e.g. human expert evaluation) tests. In this paper, we propose a data mining approach to predict wine preferences that is based on easily available analytical tests at the certiﬁ- cation step. A large dataset is considered with white vinho verde samples from the Minho region of Portugal. Wine quality is modeled under a re- gression approach, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its do- main. Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selec- tion and that is guided by the sensitivity analysis. The support vector machine achieved promising results, outperforming the multiple regres- sion and neural network methods. Such model is useful for understand- ing how physicochemical tests affect the sensory preferences. Moreover, it can support the wine expert evaluations and ultimately improve the production.