Unraveling the regional specificities of Malbec wines from Argentina and Northern California
Hsieh Fushing, Olivia Lee, Constantin Heitkamp, Hildegarde Heymann, Susan E. Ebeler, Roger B. Boulton, Patrice Koehl
In any scientific settings, experiments are devices that are used to provide insight into the relationships between the features that control a system and the observations that are made on this system. Analyzing those relationships between features and observations for the set of objects under study can be complicated by possible additional relationships between the features themselves.
In this paper, we propose a new approach for performing the analysis of an experiment that relies on these relationships instead of trying to circumvent them. This new approach follows two steps. We first cluster the objects of the experiment using each feature independently. We then assign a distance between two features to be the mutual entropy of the clustering results they generate. The set of features is then clustered using this distance measure. The result of this clustering is a set of sub-groups of features, such that two features in the same group carry similar, i.e. synergetic information with respect to the objects of the experiment. The objects are then analyzed separately on the different sub groups of features, using the recently proposed Data Mechanics approach.
We have used this method to analyze the similarities and differences between Malbec wines from Argentina and California, as well as the similarities and differences between sub-regions of those two main wine producing countries. We report detection of groups of features that characterize the origins of the different wines included in the study.
All datas for this study: