Barbara Jaskula-Goiris

Fermentation assays reveal differences in sugar and (off-) flavor metabolism across different Brettanomyces bruxellensis strains

Abstract Brettanomyces (Dekkera) bruxellensis is an ascomycetous yeast of major importance in the food, beverage and biofuel industry. It has been isolated from various man‐made ecological niches that are typically characterized by harsh environmental conditions such as wine, beer, soft drink, etc. Recent comparative genomics studies revealed an immense intraspecific diversity, but it is still unclear whether this genetic diversity also leads to systematic differences in fermentation performance and (off‐)flavor production, and to what extent strains have evolved to match their ecological niche. Here, we present an evaluation of the fermentation properties of eight genetically diverse B. bruxellensis strains originating from beer, wine and soft drinks. We show that sugar consumption and aroma production during fermentation are determined by both the yeast strain and composition of the medium. Furthermore, our results indicate a strong niche adaptation of B. bruxellensis, most clearly for wine strains. For example, only strains originally isolated from wine were able to thrive well and produce the typical Brettanomyces‐related phenolic off‐flavors 4‐ethylguaiacol and 4‐ethylphenol when inoculated in red wine. Sulfite tolerance was found as a key factor explaining the observed differences in fermentation performance and off‐flavor production. Sequence analysis of genes related to phenolic off‐flavor production, however, revealed only marginal differences between the isolates tested, especially at the amino acid level. Altogether, our study provides novel insights in the Brettanomyces metabolism of flavor production, and is highly relevant for both the wine and beer industry.

Including experimental uncertainty on the independent variables when modelling microbial dynamics: The combined effect of pH and acetic acid on the growth rate of E. coli K12

Modelling methods applied in predictive microbiology generally neglect the importance of uncertainty on the measurement of the independent variables. The Ordinary Least Squares (OLS) method that is commonly applied in predictive microbiology is only applicable if the experimental error on the inputs of the model are insignificant. However, this does not apply for many types of experimental measurements of the independent variables. Therefore, a parameter estimation method was adapted in this research for the estimation of the parameters of secondary models, taking into account uncertainty on the measurement of the influencing food characteristics. This parameter estimation method was based on the work of Stortelder (1996) and is referred to as the Weighted Total Least Squares method (WTLS). The method is formulised as an extension of the commonly used OLS method. Consequently the current WTLS method (i) is easily implemented using similar numerical methods, (ii) reduces to an OLS method when the measurement error on the model inputs is negligible and (iii) enables the evaluation of the accuracy of the model parameter estimates based on the same approximations.