Wine Saccharomyces Yeasts for Beer Fermentation

Multiple studies in recent years have shown the potential of Saccharomyces wild yeasts to produce craft beers with new flavour profiles and other desirable properties. Yeasts isolated from food (wine, bread, kombucha…) have shown potential promise for application in brewing. The aim of this study is to evaluate the ability of 141 Saccharomyces yeast strains isolated from the Madrilenian agriculture (from grapes, must, wine, vineyard, and cellars) to produce a novel ale beer. Fermentation activity of the strains was compared against the commercial strain Saccharomyces cerevisiae Safale S-04. In addition to the other aspects such as melatonin production, thirty-three volatile compounds belonging to higher alcohols, esters, aldehydes/cetones, acids, lactones and phenolic groups, were analysed by GC for selection of the strains. Ten strains were finally chosen, among which the most relevant was the strain G 520 showing a higher production of esters, higher alcohols and acids compared with S-04. The apparent attenuation for this strain was lower than commercial strain, which translates into more residual sugars. Furthermore, G 520 was more capable of producing significantly higher amounts of melatonin studied by HPLC, as well as showing a higher antioxidant capacity. Consumer study showed that G 520 strain could be used to produce a potential beer that has a place in the current market.

Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

Fermentation is one of the most important stages in the entire brewing process. In fermentation, the sugars are converted by the brewing yeast into alcohol, carbon dioxide, and a variety of by-products which affect the flavour of the beer. Fermentation temperature profile plays an essential role in the progression of fermentation and heavily influences the flavour. In this paper, the fermentation temperature profile is optimised. As every process model contains experimentally determined parameters, uncertainty on these parameters is unavoidable. This paper presents approaches to consider the effect of uncertain parameters in optimisation. Three methods for uncertainty propagation (linearisation, sigma points, and polynomial chaos expansion) are used to determine the influence of parametric uncertainty on the process model. Using these methods, an optimisation formulation considering parametric uncertainty is presented. It is shown that for the non-linear beer fermentation model, the linearisation approach performed worst amongst the three methods, while second-order polynomial chaos worked the best. Using the techniques described below, a fermentation process can be optimised for ensuring high alcohol content or low fermentation time while ensuring the quality constraints. As we explicitly consider uncertainty in the process, the solution, even though conservative, will be more robust to parametric uncertainties in the model.

Domain Adaptation and Federated Learning for Ultrasonic Monitoring of Beer Fermentation

Beer fermentation processes are traditionally monitored through sampling and off-line wort density measurements. In-line and on-line sensors would provide real-time data on the fermentation progress whilst minimising human involvement, enabling identification of lagging fermentations or prediction of ethanol production end points. Ultrasonic sensors have previously been used for in-line and on-line fermentation monitoring and are increasingly being combined with machine learning models to interpret the sensor measurements. However, fermentation processes typically last many days and so impose a significant time investment to collect data from a sufficient number of batches for machine learning model training. This expenditure of effort must be multiplied if different fermentation processes must be monitored, such as varying formulations in craft breweries. In this work, three methodologies are evaluated to use previously collected ultrasonic sensor data from laboratory scale fermentations to improve machine learning model accuracy on an industrial scale fermentation process. These methodologies include training models on both domains simultaneously, training models in a federated learning strategy to preserve data privacy, and fine-tuning the best performing models on the industrial scale data. All methodologies provided increased prediction accuracy compared with training based solely on the industrial fermentation data. The federated learning methodology performed best, achieving higher accuracy for 14 out of 16 machine learning tasks compared with the base case model.

Kveik brewing yeasts demonstrate wide flexibility in beer fermentation temperature and flavour metabolite production and exhibit enhanced trehalose accumulation

Traditional Norwegian Farmhouse ale yeasts, also known as kveik, have captured the attention of the brewing community in recent years. Kveik were recently reported as fast fermenting thermo- and ethanol tolerant yeasts with the capacity to produce a variety of interesting flavour metabolites. They are a genetically distinct group of domesticated beer yeasts of admixed origin with one parent from the Beer 1 clade and the other unknown. While kveik are known to ferment wort efficiently at warmer temperatures, its range of fermentation temperatures and corresponding flavour metabolites produced, remain uncharacterized. In addition, the characteristics responsible for its increased thermotolerance remain largely unknown. Here we demonstrate variation in kveik strains at a wide range of fermentation temperatures and show not all kveik strains are equal in fermentation performance, flavour metabolite production and stress tolerance. Furthermore, we uncovered an increased capacity of kveik strains to accumulate intracellular trehalose, which likely contributes to its increased thermo- and ethanol tolerances. Taken together our results present a clearer picture of the future opportunities presented by Norwegian kveik yeasts and offer further insight into their applications in brewing.

Dry-Hop Creep Potential of Various Saccharomyces Yeast Species and Strains

Previous research has shown that hops contain enzymes able to hydrolyze unfermentable dextrins into fermentable sugars when added during the dry-hopping process. In the presence of live yeast, these additional fermentable sugars can lead to an over-attenuation of the beer; a phenomenon known as “hop creep”. This study attempts to analyze the effect of different Saccharomyces yeast species and strains on hop creep, with the intent to find an ability to mitigate the effects of dry-hop creep by using a specific yeast. Thirty different yeast species and strains were chosen from commercial and academic collections and propagated for pilot fermentations. Brews were performed at the Anheuser-Busch Research Brewery (1.8 hL, 10 °P, 20 IBU) at UC Davis and split to 40 L cylindroconical fermenters, with one fermenter in each yeast pair receiving 10 g/L Centennial hop pellets towards the end of fermentation. Standard analytical measurements were performed over the course of fermentation, with real degrees of fermentation (RDF) and extract measured on an Anton Paar alcolyzer. In order to preemptively determine the amount of hop creep to be experienced with each unknown fermentation, bench-top fermentations with 20 g/L dry-hops were performed concurrently and compared to the pilot scale fermentations. RDF was significantly higher (p < 0.01) on dry-hopped than non-dry-hopped fermentations beginning two days post dry-hopping to the end of fermentation, with the exceptions of SafAle™ BE-134, a S. cerevisiae var. diastaticus, and UCDFST 11-510, a S. mikatae. No apparent correlation between flocculation and increased RDF was shown in dry-hopped treatments. pH was significantly different between the dry-hopped and non-hopped fermentations (p < 0.05 one day post dry-hop, p < 0.01 for all subsequent days); this may have impacted on additional attenuation. No yeasts in this study indicated their use for mitigation of dry-hop creep, but this is a first look at beer fermentation for some of the chosen yeasts. The results also present a new perspective on how hop creep varies in fermentation.