Banana Vinegars Production Using Thermotolerant Acetobacter pasteurianus Isolated From Ivorian Palm Wine
Bibliographic record
Abstract
Vinegar or sour wine is a product of alcoholic and subsequent acetous fermentation of sugary precursors. Among acetic acid producing bacteria, only few genera (Acetobacter and Gluconobacter) are used in vinegar industry. In this paper, we intended to produce vinegar at 37 °C using two Acetobacter pasteurianus strains (S3 and S32). These species were isolated from palm (Elaeis guineensis) wine and presented potentialities for industrial vinegar production at 37 °C. Successive fermentations were carried up and semi-continuous acetous fermentation was performed to increase acid production. Concentrated bananas (Musa ssp.) juice (11°Brix) was fermented using Saccharomyces cerevisae within 7 days, yielding 6.4% alcohol. After fermentation, 60 and 58 g/L acetic acid were produced in vinegars obtained using S3 and S32 stains respectively in 34 days and 5 flow cycles. Malic and acetic acids were the most substantial acids produced in alcoholic juice with 5 631.473 and 2 833.055 mg/L respectively. Among the eight organic acids responsible for vinegars total acidity, acetic acid was major compound with 23 459.416 and 21 268.407 mg/L for S3 and S32 strains respectively. Alcohol and acetic acid fermentation efficiency were 90.9% and 85.39 - 87.63% respectively. All the results above showed that S3 and S32 strains revealed great potentialities for successful industrial vinegar production from overripe banana.
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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".