Identification of Yeasts and Evaluation of their Distribution in Taiwanese Kefir and Viili Starters
Bibliographic record
Abstract
The objective of the present study was to investigate yeast communities in kefir grains and viili starters in Taiwan through conventional microbiological cultivation and polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). The DNA sequencing was used as a validity technique to ensure that all isolates within each group belonged to just one species, and to confirm the identified results of PCR-DGGE. Results indicated that a combination of conventional microbiological cultivation with PCR-DGGE and sequencing could successfully identify 4 yeast species from both types of cultures in Taiwan. Kluyveromyces marxianus, Saccharomyces turicensis, and Pichia fermentans were found in Taiwanese kefir grains with a distribution of 76, 22, and 2%, respectively, whereas Klu. marxianus, Saccharomyces unisporus and P. fermentans were identified in viili starters corresponding to 58, 11, and 31% of the total cell counts, respectively. Furthermore, the culture-independent method was applied to identify the yeast species using DGGE. Only 2 yeast species, Klu. marxianus and S. turicensis, were found in kefir grains and 2, Klu. marxianus and P. fermentans, in viili starters. These results suggest that in samples containing multiple species, PCR-DGGE may fail to detect some species. Sequences of yeast isolates reported in this study have been deposited in the GenBank database under accession nos. DQ139802, AF398485, DQ377652, and AY007920.
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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 | 0.000 |
| 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 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".