Early recognition of problematic wine fermentations through multivariate data analyses

Multiway principal component analysis (MPCA) and multiway partial least squares (MPLS) were applied to unfolded fermentation data, and compared for early recognition of problematic behavior in wine fermentations, such as late onset, slow or stuck (premature termination of fermentation). Information from 17 industrial wine fermentations (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 problematic fermentation within 72 h can have significant economic impact on operating costs in a commercial winery.

► Multiway multivariate statistic was applied for early classification of wine fermentations.
► Information from industrial fermentations of Cabernet Sauvignon and thirty two variables were used.
► MPCA and MPLS were good options for detecting abnormal behaviors.
► MPLS was able to predict the 100% of abnormal batches at 72 h with classical measurements.