European Farmhouse Brewing Yeasts Form a Distinct Genetic Group
Bibliographic record
Abstract
Abstract The brewing industry is constantly evolving, driven by the quest for novel flavors and fermentation characteristics that cater to evolving consumer preferences. This study explores the genetic and phenotypic diversity of European farmhouse yeasts, traditionally used in rural brewing practices and maintained outside of pure culture industrial yeast selection. We isolated landrace brewing yeast strains from diverse geographical locations across Europe, including Norway, Lithuania, Latvia, Russia, and also included African farmhouse brewing strains from Ghana. Our genomic analysis using long-read and short-read whole genome sequencing uncovered a genetically distinct group that diverges from industrial brewing yeasts. This group, which is closely related to ale brewing strains, is preliminarily named the “European Farmhouse” group and shows greater predicted admixture from Asian fermentation strains. Through genomic and phenotypic analyses, including flavor metabolite analysis via HS-GC-MS, sugar metabolite analysis via HPLC, and wort fermentation analysis, we found a broad spectrum of fermentation capabilities, from rapid and efficient fermentation to unique aroma and flavor compound profiles, potentially offering novel traits for brewing applications. This study highlights the importance of preservation of brewing cultural heritage knowledge and resources including yeast cultures. Key Points A large set of geographically diverse farmhouse brewing strains were characterized Norwegian and Baltic farmhouse brewing strains form a distinct genetic group Farmhouse strains show considerable diversity in fermentation and flavour formation
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".