A Multi-Parameter, Predictive Model of Starch Hydrolysis in Barley Beer Mashes

Abstract

A key first step in the production of beer is the mashing process, which enables the solubilization and subsequent enzymatic conversion of starch to fermentable sugars. Mashing performance depends primarily on temperature, but also on a variety of other process parameters, including pH and mash thickness (known as the “liquor-to-grist” ratio). This process has been studied for well over 100 years, and yet essentially all predictive modeling efforts are alike in that only the impact of temperature is considered, while the impacts of all other process parameters are largely ignored. A set of statistical and mathematical methods collectively known as Response Surface Methodology (RSM) is commonly applied to develop predictive models of complex processes such as mashing, where performance depends on multiple parameters. For this study, RSM was used to design and test a set of experimental mash conditions to quantify the impact of four process parameters—temperature (isothermal), pH, aeration, and the liquor-to-grist ratio—on extract yield (total and fermentable) and extract composition in order to create a robust, yet simple, predictive model. In contrast to previous models of starch hydrolysis in a mash, a unique aspect of the model developed here was the quantification of significant parameter interaction effects, the most notable of which was the interaction between temperature and mash thickness (i.e., the liquor-to-grist ratio). This interaction had a sizeable impact on important mash performance metrics, such as the total extract yield and the fermentability of the resultant wort. The development of this model is of great future utility to brewery processing, as it permits the multi-parameter optimization of the mashing process.

Keywords

• mashing
• brewing
• modeling
• beer
• conversion
• enzyme