Potential of non-Saccharomyces yeast to produce non-alcoholic beer.

Recently, non-Saccharomyces yeast have become very popular in wine and beer fermentation. Their interesting abilities introduce novel aromatic profiles to the fermented product. In this study, screening of eight non-Saccharomyces yeast (Starmerella bombicola, Lindnera saturnus, Lindnera jadinii, Zygosaccharomyces rouxii, Torulaspora delbrueckii, Pichia kluyveri, Candida pulcherrima, and Saccharomycodes ludwigii) revealed their potential in non-alcoholic beer production. Conditions for non-alcoholic beer production were optimised for all strains tested (except T. delbrueckii) with the best results obtained at temperature 10 to 15 °C for maximum of 10 days. Starmerella bombicola, an important industrial producer of biosurfactants, was used for beer production for the first time and was able to produce non-alcoholic beer even at 20°C after 10 days of fermentation. Aromatic profile of the beer fermented with S. bombicola was neutral with no negative impact on organoleptic properties of the beer. The most interesting organoleptic properties were evaluated in beers fermented with L. jadinii and L. saturnus, which produced banana-flavoured beers with low alcohol content. This work confirmed the suitability of mentioned yeast to produce non-alcoholic beers and could serve as a steppingstone for further investigation.

Dual production of polyunsaturated fatty acids and beta-carotene with Mucor wosnessenskii by the process of solid-state fermentation using agro-industrial waste.

Solid-state fermentation is a technique employing microorganisms grown on a solid substrate in the absence of free water. The substrates used in this process are mostly waste from the agro-industry (brans, spent malt grains, distiller grains, etc.) that improves not only the economy of the process but also has positive effect on waste management problems. Zygomycetous fungi are not only able to grow in such conditions but also enrich fermented materials with various types of bioactive compounds. Mucor sp. strains have been identified as producers of gamma-linolenic acid and beta-carotene in submerged fermentation. The aim of the present study was to identify the best microbial producer of gamma-linolenic acid and beta-carotene among four different Mucor strains and to study the requirements for the dual production of these metabolites. Mucor wosnessenskii was identified as the most suitable producer of both metabolites. After optimization of the fermentation conditions, the highest yields obtained were 10.7 g of gamma-linolenic acid/kg of fermented product and 261.5 mg of beta-carotene/kg of fermented product. This yield of beta-carotene is the highest among the results published so far.

Multivariate analytics of chromatographic data: Visual computing based on moving window factor models.

Chromatography is one of the most versatile unit operations in the biotechnological industry. Regulatory initiatives like Process Analytical Technology and Quality by Design led to the implementation of new chromatographic devices. Those represent an almost inexhaustible source of data. However, the analysis of large datasets is complicated, and significant amounts of information stay hidden in big data. Here we present a new, top-down approach for the systematic analysis of chromatographic datasets. It is the goal of this approach to analyze the dataset as a whole, starting with the most important, global information. The workflow should highlight interesting regions (outliers, drifts, data inconsistencies), and help to localize those regions within a multi-dimensional space in a straightforward way. Moving window factor models were used to extract the most important information, focusing on the differences between samples. The prototype was implemented as an interactive visualization tool for the explorative analysis of complex datasets. We found that the tool makes it convenient to localize variances in a multidimensional dataset and allows to differentiate between explainable and unexplainable variance. Starting with one global difference descriptor per sample, the analysis ends up with highly resolute temporally dependent difference descriptor values, thought as a starting point for the detailed analysis of the underlying raw data.