Browsing by Author "Pszczolkowski, Philippo"

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    Background for Enodiplomacy in Chile (1960-2010)
    (2023) Pszczolkowski, Philippo; Canon, Pablo; Castro, Amalia
    Chile began the process that would lead it to become a world wine power in the 1980s, something that was achieved thanks to the joint action of the industries, the technical professional sector, and the State. The objective of this study is to reveal the role played by enodiplomacy in this process and its main characteristics. The methodology of the "participant actor" associated with the social sciences is used, based on direct testimonies of the time and in-depth interviews. An evolution of the elaboration and appreciation of Chilean wine is observed, hierarchized by Chilean foreign policy, from the 1980s to 2010 as its maximum expression, which led it to position itself as a wine power. This positioning is achieved thanks to an active enodiplomacy displayed by the Chilean State, supported by academia and the technical world, which improved both the marketing and the status of Chilean wine in the country and the world. In this, two currents are distinguished: one governed by the French paradigm (dominant) and another, more recent, marked by the Hispanic Creole paradigm, which aims to revalue heritage varieties and products.
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    Using data mining techniques to predict industrial wine problem fermentations
    (ELSEVIER SCI LTD, 2007) Urtubia, Alejandra; Perez Correa, J. Ricardo; Soto, Alvaro; Pszczolkowski, Philippo
    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. (c) 2006 Elsevier Ltd. All rights reserved.