Browsing by Author "Urtubia, Alejandra"

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    Exploring the applicability of MIR spectroscopy to detect early indications of wine fermentation problems
    (ELSEVIER SCI LTD, 2008) Urtubia, Alejandra; Perez correa, J. Ricardo; Pizarro, Francisco; Agosin, Eduardo
    In this study we explore the applicability of MIR technology to detect early indications of wine fermentation problems. An oenologist could improve the chances of a vinification process finishing optimally if anomalies are detected early. A comparative analysis of three fermentations with artificial musts was performed; one of normal behaviour, one subject to a temperature gradient, and the third deficient in assimilable nitrogen. We tracked each fermentation through changes in spectra in addition to changes in must composition. It was easier to detect anomalous behaviour by monitoring wine metabolite concentrations than through direct spectra analysis, nevertheless, calibrations needed to be derived from fermenting must samples and so cost more. All measured compounds (glucose, fructose, ethanol, glycerol, succinic and acetic acids) exhibited behavioural changes at 30 h of fermentation in nitrogen deficient musts. Temperature deviations were reflected in the anomalous behaviour of ethanol, glycerol, succinic acid and acetic acid. (c) 2007 Elsevier Ltd. All rights reserved.
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    Technical Feasibility of Glucose Oxidase as a Prefermentation Treatment for Lowering the Alcoholic Degree of Red Wine
    (2017) Valencia, Pedro; Espinoza, Karen; Ramírez, Cristián; Franco, Wendy; Urtubia, Alejandra
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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.