Selection of adjunct cultures for the ripening of plant cheese analogues
Bibliographic record
Abstract
Fermentation contributes to the taste and odor of plant cheeses. The selection of functional cultures for the fermentation of plant cheeses, however, is in its infancy. This study aimed to select lactic acid bacteria for ripening of soy and lupin cheese analogues. Bacillus velezensis and B. amyloliquefaciens were used for germination of seeds to produce proteolytic enzymes; Lactococcus lactis and Lactiplantibacillus plantarum served as primary acidifying cultures. Levilactobacillus hammesii, Furfurilactobacillus milii, or Lentilactobacillus buchneri were assessed as adjunct cultures for the ripening of plant cheese. Growth of bacilli was inhibited at low pH. Both Lc. lactis and Lp. plantarum were inactived during plant cheese ripening. Cell counts of Lv. hammesii remained stable over 45 d of ripening while Ff. milii and Lt. buchneri grew slowly. Sequencing of full length 16S rRNA genes confirmed that the inocula the plant cheeses accounted for more than 98% of the bacterial communities. HPLC analysis revealed that Lt. buchneri metabolized lactate to acetate and 1,2-propanediol during ripening. Bacilli enhanced proteolysis as measured by quantification of free amino nitrogen, and the release of glutamate. LC-MS/MS analysis quantified kokumi-active dipeptides. The concentrations of γ-Glu-Leu, γ-Glu-Ile, and γ-Glu-Ala, γ-Glu-Cys in unripened cheeses were increased by seed germination but γ-Glu-Phe was degraded. Lt. buchneri but not Lv. hammesii or Ff. milii accumulated γ-Glu-Val, γ-Glu-Ile or γ-Glu-Leu during ripening, indicating strain-specific differences. In conclusion, a consortium of bacilli, acidification cultures and adjunct cultures accumulates taste- and kokumi-active compounds during ripening of plant cheeses.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".