Selection of Bacillus spp. as fermentation cultures for production of plant-based cheese analogues
Bibliographic record
Abstract
Bacillus species are beneficial fermentation microbes that exhibit useful technological traits including the expression of extracellular amylolytic and proteolytic enzymes and antimicrobial lipopeptides. In this study, strains of Bacillus spp. were screened through genome analysis and the effect of fermentation of plant-based cheese analogues with the acidification cultures Lactococcus lactis, Lactococcus cremoris and Leuconostoc mesenteroides, and the adjunct culture Lentilactobacillus. buchneri plus Bacillus spp. was investigated. Based on genome analyses of 9 strains of Bacillus spp., B. velezensis FUA2155, B. amyloliquefaciens FUA2153, and B. subtilis FUA2114 that harbor genes encoding for amylases and proteases and lipopeptide synthases were selected for fermentation of plant cheese. Bacillus strains exhibited metabolic activity during bean germination but were inactive after acidification of the cheese matrix. The strains prolonged the mould-free storage time of plant-based cheese analogues and enhanced proteolysis. Of the three strains, only B. velezensis FUA2155 contributed to accumulation of taste-active glutamate. Lt. buchneri accelerated the inactivation of Ln. mesenteroides and enhanced the prevention against fungal contaminants in plant-based cheese analogues with bacilli at the ripening condition rH of 0.78. Taken together, this study provides evidence that the use of proteolytic strains of Bacillus in combination with the acidification cultures Lc. lactis and Lc. cremoris associated with Ln. mesenteroides and adjunct culture Lt. buchneri improved the quality of fermented plant-based cheese analogues.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".