Synthesis of Taste-Active γ-Glutamyl Dipeptides during Sourdough Fermentation by <i>Lactobacillus reuteri</i>
Bibliographic record
Abstract
This study aimed to assess whether peptides influence the taste of sourdough bread. γ-Glutamyl dipeptides with known kokumi taste threshold, namely γ-Glu-Glu, γ-Glu-Leu, γ-Glu-Ile, γ-Glu-Phe, γ-Glu-Met, and γ-Glu-Val, were identified in sourdough by liquid chromatography-tandem mass spectrometry in MRM mode. γ-Glutamyl dipeptides were found in higher concentrations in sourdough fermented with Lactobacillus reuteri when compared to the chemically acidified controls. Proteolysis was an important factor for generation of γ-glutamyl dipeptides. Sourdoughs fermented with four strains of L. reuteri had different concentrations of γ-Glu-Glu, γ-Glu-Leu, and γ-Glu-Met, indicating strain-specific differences in enzyme activity. Buffer fermentations with L. reuteri confirmed the ability of the strains to convert amino acids to γ-glutamyl dipeptides as well as the strain-specific differences. Sensory evaluation of bread revealed that sourdough bread with higher concentrations of γ-glutamyl dipeptides ranked higher with respect to the taste intensity when compared to regular bread and type I sourdough bread. Sourdough breads fermented with L. reuteri LTH5448 and L. reuteri 100-23 differed with respect to the intensity of the salty taste; this difference corresponded to a different concentration of γ-glutamyl dipeptides. These results suggest a strain-specific contribution of γ-glutamyl dipeptides to the taste of bread. The use of sourdough fermented with glutamate and kokumi peptide accumulating lactobacilli improved the taste of bread without adverse effect on other taste or quality attributes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".