Using data mining techniques to predict industrial wine problem fermentations

Winemakers currently lack the tools to identify early signs of undesirable fermentation behavior and so are unable to take possible mitigating actions. Data collected from tracking 24 industrial fermentations of Cabernet sauvignon were used in this study to explore how useful is data mining to detect anomalous behaviors in advance. A database held periodic measurements of 29 components that included sugar, alcohols, organic acids and amino acids. Owing to the scale of the problem, we used a two-stage classification procedure. First PCA was used to reduce system dimensionality while preserving metabolite interaction information. Cluster analysis (K-Means) was then performed on the lower-dimensioned system to group fermentations into clusters of similar behavior. Numerous classifications were explored depending on the data used. Initially data from just the first three days were assessed, and then the entire data set was used. Information from the first three days’ fermentation behavior provides important clues about the final classification. We also found a strong association between problematic fermentations and specific patterns found by the data mining tools. In short, data from the first three days contain sufficient information to establish the likelihood of a fermentation finishing normally. Results from this study are most encouraging. Data from many more fermentations and of different varieties needs to be collected, however, to develop a reliable and more broadly applicable diagnostic tool.