Glutamine deamidation by cereal-associated lactic acid bacteria
Bibliographic record
Abstract
AIMS: It was the aim of our work to investigate glutamine deamidation by lactic acid bacteria isolated from cereal fermentations and to elucidate the ecological and technological relevance in baking of the conversion of glutamine to glutamate. METHODS AND RESULTS: Lactobacillus sanfranciscensis and Lact. reuteri were found to display glutaminase activity. The addition of glutamine to modified Man, Rogosa and Sharp medium increased the cell yields of Lact. sanfranciscensis, as well as the production of lactic and acetic acid. The final pH; however, was increased in the glutamine-containing medium. The addition of 47 mmol kg(-1) glutamate to chemically acidified doughs significantly changed the bread flavour. In sourdoughs with enhanced proteolytic activity, strain-dependent production of 27-120 mmol glutamate per kilogram sourdough was observed. CONCLUSIONS: Lactobacillus sanfranciscensis and Lact. reuteri converted glutamine into glutamate; this conversion improves the acid tolerance of lactobacilli and significantly influences wheat bread flavour. SIGNIFICANCE AND IMPACT OF THE STUDY: This paper illustrates the complex interaction of sourdough-lactobacilli with their environment: the flour provides substrates for metabolic activities that enable the lactobacilli to reach higher cell counts, and the produced metabolite may be one of the reasons why the flavour of fermented breads is different to the flavour of chemically acidified breads.
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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 | 0.000 |
| 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 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".