Modeling Yeast in Suspension during Laboratory and Commercial Fermentations to Detect Aberrant Fermentation Processes

Abstract

Understanding yeast dynamics during fermentation is important for quality control, whether monitoring fermentation consistency or identifying aberrant events, such as premature yeast flocculation (PYF). Previous models of fermentation dynamics tend to be parameter rich and require large time series, which are rare in industry. This research investigates five simpler models to 1) describe fermentation dynamics, 2) refine quality control sampling regimes to improve model fit, and 3) identify PYF fermentations. The ability of these models to describe yeast dynamics was evaluated using model fitting with time series data and Akaike Information Criterion (AIC) model selection. Data simulated from large time series was used with this model fitting approach to improve sampling schedules without increasing sampling effort. Lastly, PYF was identified in fermentations of fungal-contaminated malt using linear discriminant analysis (LDA). For large data sets, a four-parameter extension of the normal curve performed best while smaller data sets were better described by the 2-parameter gamma model. Moving sampling effort nearer the population peak improved model fits. Lastly, all models detected PYF, however the two-parameter gamma model provided a simple metric for distinguishing PYF. This research provides guidelines on appropriate model use, improving sampling regimes, and identifying PYF.