From phyllosphere to fermentation: Impact of fermentation scale and temperature on sauerkraut fermentation
Bibliographic record
Abstract
The spontaneous fermentation of vegetables such as kimchi, pao cai and sauerkraut, relies on lactic acid bacteria (LAB) from the plant phyllosphere. Plant-associated LAB occur only at low abundance. This study investigated the impact of fermentation scale, temperature and cabbage source on the LAB diversity. Fermentations were conducted at a scale of 0.8 g to 30 g by immersion of chopped interior leaves of white cabbage obtained from two supermarkets, SF and SW, in 2.5 % (w/w) NaCl solution at 10 °C and 20 °C. Fermentations were monitored by pH determination, metabolite analysis and microbial cell counts, and by 16S rRNA gene amplicon sequencing. Fermentation of 0.8 g cabbage SF with 20 replicates per group supported growth of Enterobacterales but not of LAB. Cabbage SW was fermented by Leuconostoc spp., with an average relative abundance of 98.8 % after 28 d. In fermentations with 8 g cabbage, Lactiplantibacillus established as dominating fermentation microorganism. At the 30 g scale, Lactiplantibacillus dominated at 20 °C regardless of cabbage source, whereas Leuconostoc or Rahnella remained dominant at 10 °C. The organic acid concentrations and the metabolism of phenolic compounds differed depending on whether Lactiplantibacillus, Leuconostoc or Rahnella were the predominant fermentation microorganisms. Collectively, our data highlight that the establishment of LAB communities in sauerkraut fermentation is influenced by both the fermentation scale and temperature, underscoring the critical roles of Lactiplantibacillus at higher temperatures and Leuconostoc at lower temperatures.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".