Engineering

Statistics

Support vector machine

Variable (mathematics)

Economics

Data mining

Computer science

Certification

Food science

Business

Regression analysis

Biochemical engineering

Regression

White Wine

Mathematics

Management

Feature selection

Domain (mathematical analysis)

Mathematical analysis

Artificial intelligence

Chemistry

Wine

Artificial neural network

Machine learning

Decision Support Systems

We propose a data mining approach to predict human wine taste preferences that is based on easily available analytical tests at the certification step. A large dataset (when compared to other studies in this domain) is considered, with white and red vinho verde samples (from Portugal). Three regression techniques were applied, under a computationally efficient procedure that performs simultaneous variable and model selection. The support vector machine achieved promising results, outperforming the multiple regression and neural network methods. Such model is useful to support the oenologist wine tasting evaluations and improve wine production. Furthermore, similar techniques can help in target marketing by modeling consumer tastes from niche markets.

Modeling wine preferences by data mining from physicochemical properties