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.
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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 | 0.001 | 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 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".