Use of <i>Bacillus subtilis</i> to enrich isoflavone aglycones in fermented natto
Bibliographic record
Abstract
Abstract BACKGROUND: Natto is a food made by fermenting cooked soybeans with Bacillus subtilis. Soybean isoflavones are reported to provide many health benefits, including oestrogenic effects. However, isoflavone aglycones may be absorbed faster and in higher amounts in the human intestine than their glucosides. This study aimed to investigate the content of isoflavone components in commercial natto products as well as the use of B. subtilis strains to ferment cooked soybeans to produce a high level of isoflavone aglycones in natto. RESULTS: The content and composition of isoflavones in commercial natto products were predominantly (>76%) isoflavone glucosides. Fermentation of cooked soybeans with B. subtilis BCRC 14718 at 37 °C for 48 h was more effective in converting glucosides to aglycones than with other strains of B. subtilis, increasing the proportion of isoflavone aglycones from 12 to 68% of the total isoflavones in the fermented natto. The proportions of the isoflavone aglycones daidzein and genistein in cooked soybeans fermented with B. subtilis BCRC 14718 for 48 h increased from 6 to 54% and from 5 to 13% respectively. CONCLUSION: Bacillus subtilis BCRC 14718 incubated with cooked soybeans produces higher levels of isoflavone aglycones, which may enhance health benefits over traditional fermented natto. Copyright © 2008 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) | 0.001 | 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) | 0.001 | 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".