Use of Bacillus spp. as beneficial fermentation microbes in baking
Bibliographic record
Abstract
The development of minimally processed baked goods is dependent on new "clean label" functional ingredients that allow substitution of additives without compromising quality. We investigated the use of fermentation with Bacillus spp. as a novel approach to improve bread quality. Bacillus velezensis FUA2155 and Bacillus amyloliquefaciens Fad WE ferments were prepared using white wheat flour, wheat bran or buckwheat, and were added at a level of 2.5-20 % to bread dough. Ropy spoilage of bread was controlled by sourdough addition at a level of 10 or 20 %. The volume of white wheat bread and wheat bran bread increased by 47.4 and 62.5 % respectively with 2.5 % Bacillus ferments. Bread shelf-life was prolonged by the Bacillus ferment only at higher dosages that also reduced bread volume. The use of unfermented or sourdough fermented buckwheat improved bread volume and delayed mould spoilage. The characterization of water-soluble polysaccharides from sourdoughs and Bacillus ferments revealed that solubilization of arabinoxylans contributed to the increase in volume after fermentation of wheat but not after fermentation of buckwheat. In conclusion, Bacillus fermentation can be used to improve bread quality, adding to the diversity of microbes that are suitable for baking applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.001 |
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".