Optimizing the selection process of yeast starter cultures by preselecting strains dominating spontaneous fermentations
Bibliographic record
Abstract
We propose an efficient and time-saving strategy for starter culture selection. Our approach is based on the accomplishment of 3 phases: (i) the selection of yeast strains dominating spontaneous fermentations, (ii) the selection among the dominant strains of those showing the best technological characteristics, and (iii) the final selection among good technological strains of those showing the desired qualitative traits. We applied this approach to wine fermentations, even though the same strategy has the potential to be employed for the selection of any type of starter culture. We isolated and identified yeast strains at the mid- and final stages of 6 spontaneous fermentations carried out in 3 different Spanish wineries. We identified all strains as Saccharomyces cerevisiae by restriction fragment length polymorphism of the ribosomal DNA internal transcribed spacer region, and subsequently distinguished each strain by analyzing the polymorphism of the inter-delta regions. Strains that were detected both at the mid- and final stages of the fermentation were considered dominant. Four dominant strains were finally selected and tested in pilot-scale fermentation, and their performance was compared with that of a commercial wine strain. All dominant strains showed good fitness and resulted suitable to be employed as starter cultures. One of the dominant strains isolated in this study is currently commercialized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".