Acid stress responses of <i>Lactobacillus amylovorus</i> and <i>Candida kefyr</i> isolated from fermented sorghum gruel and their application in food fermentation
Bibliographic record
Abstract
Exposure of lactic acid bacteria (LAB) and yeasts to adverse fluctuations during fermentation causes stress; consequently, microbes develop adaptive responses. In this study, we investigated the physiological and proteomic responses of LAB and yeast to acid stress and their application in food fermentation. The physiological and proteomic responses of Lactobacillus amylovorus LS07 and Candida kefyr YS12 to acid stress were measured using turbidimetry, SDS-PAGE, and LC-MS/MS. The technique previously reported by the Association of Official Analytical Chemists (AOAC) was employed to evaluate the physicochemical and organoleptic properties of sorghum gruel fermented using LAB and yeast alone and in combination as starter cultures and spontaneous fermentation as a control. The growth of L. amylovorus LS07 was optimal at pH 1.0, and that of C. kefyr YSI2 was optimal at pH 4. An increased intensity of 30S ribosomal protein S2 (L. amylovorus LS07) and 6-phosphogluconate dehydrogenase (C. kefyr YS12) was observed at pH 1 and 4, respectively, suggesting increased microbial metabolism, thereby reducing the stress. Sorghum gruel produced with combined starters had the highest crude protein (10.94%), iron content (0.0085%), and organoleptic acceptability (7.29), which was significantly different from the products produced with the single starters and the control. The combined starter (L. amylovorus LS07 and C. kefyr YSI2) adapted to stress yielded foods with improved sensory properties, minerals, and reduced anti-nutrient contents.
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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) | 0.000 | 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".