Early recognition of problematic wine fermentations through multivariate data analyses
Marco Emparán, Ricardo Simpson, Sergio Almonacid, Arthur Teixeira, Alejandra Urtubia; Food Control, Volume 27, Issue 1, September 2012, Pages 248–253
Multiway principal component analysis (MPCA) and multiway partial least squares (MPLS) were applied to unfolded fermentationdata, and compared for earlyrecognition of problematic behavior in winefermentations, such as late onset, slow or stuck (premature termination of fermentation).
Information from 17 industrial winefermentations (batches) were used, consisting of measured values for 32 variables, consisting of sugars, density, alcohols, organic acids and nitrogen compounds (including all amino acids). Curve smoothing and curve fitting techniques were applied as necessary pre-treatment of the data. Then, MPCA and MPLS were applied to four different data sets with different combinations of variables to identify the principal components responsible for the problematic behavior.
Density, sugars, alcohols and selected organic acids were identified as the principal components.
The MPCA application detected only 67% of problematic batches in the data sets after 72 h into the fermentation process. Whereas, the MPLS application was able to predict all of the problematic batches (100%) using the same variables and at the same time into the fermentation process.
The ability to identify a problematicfermentation within 72 h can have significant economic impact on operating costs in a commercial winery.
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Published on 22/04/2013