Effect of Glutamate Accumulation During Sourdough Fermentation with <i>Lactobacillus reuteri</i> on the Taste of Bread and Sodium‐Reduced Bread
Bibliographic record
Abstract
NaCl is an important contributor to the taste and texture of bread; therefore, it is challenging to reduce NaCl in bread without compromising quality. This study investigated sensory properties of bread with sourdough fermented with Lactobacillus reuteri accumulating glutamate or γ‐aminobutyrate (GABA). Sourdough was fermented with the GABA‐producing L. reuteri 100‐23 and LTH5448 as well as the glutamate‐accumulating L. reuteri 100‐23ΔgadB and TMW1.106. A consumer panel detected significant differences in the taste of bread with 6% addition of sourdough fermented with glutamate‐ or GABA‐producing L. reuteri. Remarkably, this difference was also detected when GABA‐producing L. reuteri 100‐23 was compared with its glutamate‐producing isogenic mutant L. reuteri 100‐23ΔgadB. The intensity of the salty taste of sourdough bread produced with 1% (flour basis) salt was equivalent to the intensity of the salty taste of reference bread produced with 1.5% salt. A trained panel found that sourdough breads (1 or 2% NaCl flour base) had a higher sour and umami taste intensity when compared with reference bread with the same salt content. Bread produced with sourdough fermented with L. reuteri 100‐23ΔgadB consistently had a higher umami taste intensity when compared with other sourdough breads. Neither sourdough addition nor NaCl level influenced bread volume or texture. In conclusion, the use of sourdough fermented with glutamate‐accumulating lactobacilli allowed reduction of NaCl without adverse effects on the taste or other quality attributes of bread.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".