Enhancing the performance of brewing yeasts

Impact of the genetic improvement of fermenting yeasts on the organoleptic properties of beer

The brewing industry has experienced a significant boom in recent years through the emergence of, on the one hand, craft breweries that produce beers with unique organoleptic characteristics, and, on the other hand, the brewing of a significant number of beers using hybridized or genetically modified microorganisms with the aim of improving both the brewing processes and the final products. This review covers the influence from yeast strains on the organoleptic properties of the final beers and also the main hybridization and genetic modification methods applied to such yeast strains with the aim of improving the sensory characteristics of the product obtained and/or the brewing process. Different approaches to the phenotypic modification of the yeasts used in beer brewing have arisen in recent years. These are dealt with in this work, with special emphasis on the methodology followed as well as on the effects of the same on the brewing process and/or on the final product.

Development of a novel SRAP-SCAR marker for rapid identification of lager and ale types in brewer's yeast

BACKGROUND

Beer is a globally consumed and universally popular beverage. According to the fermentation conditions of brewer's yeast, ale yeast and lager yeast are the two major varieties. Normal phenotypic and genotypic approaches are insufficient and time-consuming for identifying these two forms of yeast. Therefore, a method for the rapid and cost-effective identification of lager and ale-type brewer's yeasts is necessary.

METHODS AND RESULTS

In this study, we analysed the genetic diversity of 23 industrial brewer's yeasts from around the world using sequence-related amplified polymorphism (SRAP) markers and produced stable sequence characteristic amplification region (SCAR) markers. The specific DNA fragments identified by the SRAP marker were sequenced and primers were constructed; the resultant SCAR marker (757 bp) was then confirmed against the indicated brewer's yeast type.

CONCLUSION

The development of SRAP-SCAR marker is more economical, simple, and fast compared to morphological markers.

The Effects of Catabolism Relationships of Leucine and Isoleucine with BAT2 Gene of Saccharomyces cerevisiae on High Alcohols and Esters

This study sought to provide a theoretical basis for effectively controlling the content of higher alcohols and esters in fermented foods. In this work, isoleucine (Ile) or leucine (Leu) at high levels was used as the sole nitrogen source for a BAT2 mutant and its parental Saccharomyces. cerevisiae 38 to investigate the effects of the addition of amounts of Ile or Leu and BAT2 on the aroma components in the flavor profile using gas chromatography mass spectrometer (GC-MS). The results showed that 2-methyl-butyraldehyde, 2-methyl-1-butanol, and 2-methylbutyl-acetate were the products positively correlated with the Ile addition amount. In addition, 3-methyl-butyraldehyde, 3-methyl-1-butanol, and 3-methylbutyl-acetate were the products positively correlated with Leu addition amount. BAT2 deletion resulted in a significant decline in the yields of 2-methyl-butyraldehyde, 3-methyl-butyraldehyde,2-methyl-1-butanol, and 3-methyl-1-butanol, but also an increase in the yields of 2-methylbutyl-acetate and 3-methylbutyl-acetate. We speculated that BAT2 regulated the front and end of this metabolite chain in a feedback manner. Improved metabolic chain analyses, including the simulated energy metabolism of Ile or Leu, indicated that reducing the added amount of branched-chain amino acids, BAT mutation, and eliminating the role of energy cofactors such as NADH/NAD+ were three important ways to control the content of high alcohols and esters in fermented foods.

Modeling in Brewing—A Review

Beer production has over a thousand-year tradition, but its development in the present continues with the introduction of new technological and technical solutions. The methods for modeling and optimization in beer production through an applied analytical approach have been discussed in the present paper. For this purpose, the parameters that are essential for the main processes in beer production have been considered—development of malt blends, guaranteeing the main brewing characteristics; obtaining wort through the processes of mashing, lautering and boiling of wort; fermentation and maturation of beer. Data on the mathematical dependences used to describe the different stages of beer production (one-factor experiments, modeling of mixtures, experiment planning, description of the kinetics of microbial growth, etc.) and their limits have been presented, and specific research results of various authors teams working in this field have been cited. The independent variables as well as the objective functions for each stage have been defined. Some new trends in the field of beer production have been considered and possible approaches for their modeling and optimization have been highlighted. The paper suggests a generalized approach to describe the main methods of modeling and optimization, which does not depend on the beer type produced. The proposed approaches can be used to model and optimize the production of different beer types, and the conditions for their application should be consistent with the technological regimes used in each case. The approaches for modeling and optimization of the individual processes have been supported by mathematical dependencies most typical for these stages. Depending on the specific regimes and objectives of the study, these dependencies can be adapted and/or combined into more general mathematical models. Some new trends in the field of beer production have been considered and possible approaches for their modeling and optimization have been highlighted.

Recent innovations in the production of selected specialty (non-traditional) beers

Customer demand for product diversity is the key driving force for innovations in the brewing industry. Specialty beers are regarded as a distinct group of beers different from two major types, lagers and ales, without established definitions or boundaries. Specialty beers, including low- to no-alcohol beer, low carbohydrate beer, gluten-free beer, sour beer, probiotic beer, and enriched beer, are exclusively brewed and developed keeping in mind their functionality, the health and wellbeing of the consumer, and emerging market trends. Compared with conventional beer-brewing, the production of specialty beers is technologically challenging and usually requires additional process steps, unique microorganisms, and special equipment, which in turn may incur additional costs. In addition, the maintenance of quality and stability of the products as well as consumer acceptability of the products are major challenges to successful commercialization. A harmonious integration of traditional brewing practices and modern technological approaches may hold potential for future developments. In the present review, latest developments in the fermentative production of selected specialty beers are discussed.

Evaluation of genetic diversity and development of core collections of industrial brewing yeast using ISSR markers

Germplasm of industrial brewing yeast of the worldwide have a richer diversity, and various successes in improving the performance of brewing yeasts. However, they are limited in that they have relatively low odds of combining desirable traits in a correct manner. To improve germplasm resource preservation, management, and utilization efficiency. In this study, the genetic diversity of 35 industrial brewing yeasts were analyzed based upon inter simple sequence repeat (ISSR) markers, in which 151 out of 167 SSR loci (90.42%) were polymorphic between two or more strains. Three preliminary core collections were established using ISSR data, and based on three different strategies as follows: an advanced maximization (M) strategy, an allele preferred sampling (A) strategy, and a random sampling (R) strategy. Comparison of genetic parameters, including polymorphic information content, Nei's genetic diversity (H), effective allele number, observed allele number, Shannon's index (I), and principal coordinate analyses, confirmed that all the core collections accurately recapitulated the diversity of the initial germplasm. Considering the loci retention ratio and trait coverage efficiency, Core1 was considered the best core collection.