Yeast population dynamics during spontaneous fermentation of icewine and selection of indigenous <scp><i>Saccharomyces cerevisiae</i></scp> strains for the winemaking in <scp>Qilian, China</scp>
Bibliographic record
Abstract
BACKGROUND: Icewine produced in China is becoming popular, but there is only limited knowledge available on the yeast population that occurs during fermentation and also on the selection of indigenous Saccharomyces cerevisiae strains for its production. In this work, we first investigated yeast species and the evolution of yeast population in spontaneous fermentations of icewine produced in the Qilian region of China and then analyzed the biodiversity and important enological properties of S. cerevisiae isolates. RESULTS: Seven species of five genera including S. cerevisiae, S. uvarum, Torulaspora delbrueckii, Hanseniaspora uvarum, Lachancea thermotolerans, Metschnikowia aff. fructicola and H. osmophila were identified by the colony morphology on Wallerstein Laboratory Nutrient medium and sequence analysis of the 26S rRNA gene D1/D2 domain. Saccharomyces cerevisiae, H. uvarum and L. thermotolerans were the dominant species, representing almost 87% of the total yeast isolates. Microvinification with seven preselected S. cerevisiae strains were performed on Vidal. All selected strains could complete fermentations, and the enochemical parameters were within the acceptable ranges of the wine industry. W5B3 produced higher amounts of ethyl hexanoate and ethyl octanoate than other strains. R3A10 was a low volatile acid producer and the corresponding icewine presented the highest values on some odorants including β-damascenone, 1-octen-3-ol, ethyl 2-methylbutyrate, and isoamyl alcohol. Vidal icewines fermented with R3A10, R3A16 and W5B3 were well accepted by the judges because of superior sensory quality. CONCLUSION: Three indigenous strains (R3A10, R3A16 and W5B3) could be used as starters and could potentially improve the regional character of the icewine in Qilian. © 2020 Society of Chemical Industry.
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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 | 0.001 |
| 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".