Microbiota stratification and succession of amylase‐producing <i>Bacillus</i> in traditional Chinese Jiuqu (fermentation starters)
Bibliographic record
Abstract
BACKGROUND: Jiuqu are vital saccharifying and fermenting agents for Chinese fermented foods. Natural ventilation during Jiuqu fermentation causes changes in temperature, oxygen and moisture content, resulting in mass and heat gradients from the outer to inner areas of Jiuqu blocks. In the present study, microbiota stratification in Jiuqu was investigated by single molecule real-time sequencing and culture isolation. The contributors of Bacillus to amylase activity of Jiuqu and the dynamics of their biomass during Jiuqu fermentation were also analyzed. RESULTS: The dominant orders, genera and species between the inner and outer layers of Huangjiu qu (HJQ) were similar, although they displayed greater variance in two layers of Baijiu qu (BJQ). Bacillus possessed the highest diversity (including 27 species) in Jiuqu. Bacillus licheniformis, Bacillus altitudinis, Bacillus subtilis, Bacillus amyloliquefaciens and Bacillus megaterium were most prevalent in HJQ, whereas B. licheniformis, B. amyloliquefaciens and Bacillus cereus were dominant in BJQ. Isolates of B. amyloliquefaciens, B. subtilis and B. cereus exhibited high activities of amylase and glucoamylase. Quantification of Bacillus members possessing genes of α-amylase revealed that B. cereus and B. licheniformis were the most dominant microbes to secret α-amylase in Jiuqu and their biomass were increasing during Jiuqu fermentation. CONCLUSION: The present study demonstrates the microbial distribution in different layers of Jiuqu and clarifies the Bacillus species processing the activity of α-amylase. These results will help industries control the quality of Jiuqu by rationally selecting starters and optimizing their microbiota. © 2020 Society of Chemical Industry.
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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 | 0.001 | 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) | 0.001 | 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".