Beer spoilage caused by microorganisms, which is a major concern for brewers, produces undesirable aromas and flavors in the final product and substantial financial losses. To address this problem, brewers need easy-to-apply tools that inform them of beer susceptibility to the microbial spoilage. In this study, a growth/no growth (G/NG) binary logistic regression model to predict this susceptibility was developed. Values of beer physicochemical parameters such as pH, alcohol content (% ABV), bitterness units (IBU), and yeast-fermentable extract (% YFE) obtained from the analysis of twenty commercially available craft beers were used to prepare 22 adjusted beers at different levels of each parameter studied. These preparations were assigned as a first group of samples, while 17 commercially available beers samples as a second group. The results of G/NG from both groups, after artificially inoculating with one wild yeast and different lactic acid bacteria (LAB) previously adapted to grow in a...
A binary logistic regression model as a tool to predict craft beer susceptibility to microbial spoilage.
D. G. de Llano, Department of Food Biotechnology and Microbiology, Institute of Food Science Research, CIAL (CSIC-UAM), C/Nicolas Cabrera 9, 28049 Madrid, Spain. E-mail firstname.lastname@example.org
- Views Icon Views
- Share Icon Share
- Search Site
Rodriguez-Saavedra, M., Perez-Revelo, K., Valero, A., Moreno-Arribas, M. V., Llano, D. G. de; A binary logistic regression model as a tool to predict craft beer susceptibility to microbial spoilage.. IFIS Food and Health Sciences Database 2022; doi:
Download citation file: