Glucuronic Acid Rich Kombucha-fermented Pomegranate Juice
Bibliographic record
Abstract
This study is the first report using tea fungus “kombucha” to ferment natural pomegranate juice to produce a fermented beverage with high content of glucuronic acid, as a human health beneficial component. We profited the acetic acid bacteria and yeasts symbiotic layer, which is well known in producing pharmaceutical beverages with considerable released organic acids such as glucuronic acid. Also, we used the natural pomegranate juice with high amount of carbohydrate and acid, as a favourable substrate for the fermentation process. The yield of glucuronic acid production was monitored by cultivating natural pomegranate juices under the 17 optimized-combinations of three distinct sucrose concentrations, fermentation temperatures, and processing time. The combinations were designated by applying the statistical response surface methodology method. The maximum amount of glucuronic acid 17.074g/l determined in the media with 8g/l supplementary sucrose after 14 days fermentation at 37°C, using high-performance liquid chromatography. Along with glucuronic acid production, effect of the three factors - sugar concentration, processing temperature and time - was also examined on changes of five physical and chemical properties of the fermented pomegranate juices, including; pH value, remained sucrose and reducing sugar content, kombucha layer biomass, and total acidity. Within 14-day fermentation process, the pH values showed decrease, the layers’ mass presented considerable increase, and the total acid content increased in the beverages. Overall, obtained data suggested that natural pomegranate juice can be a potential candidate for further development as a functional beverage to support the maximum human daily intake of glucuronic acid (45mg for a 70kg adult).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".