Metabolomic kinetics investigation of Camellia sinensis kombucha using mass spectrometry and bioinformatics approaches
Bibliographic record
Abstract
tea leaves, along with sucrose, utilizing a symbiotic consortium of bacteria and yeast cultures. Nonetheless, there exists a dearth of comprehensive information regarding the spectrum of metabolites that constitute this beverage. To explore this intricate system, metabolomics was used to investigate fermentation kinetics of Kombucha. For that, an experimental framework was devised to assess the impact of varying sucrose concentrations and fermentation temperatures over a ten-day period of kombucha fermentation. Following fermentation, samples were analyzed using an LC-QTOF-MS system and a distinctive metabolomic profile was observed. Principal component analysis was used to discriminate between metabolite profiles. Moreover, the identified compounds were subjected to classification using the GNPS platform. The findings underscore notable differences in compound class concentrations attributable to distinct fermentation conditions. Furthermore, distinct metabolic pathways were identified, specially some related to the biotransformation of flavonoids. This comprehensive investigation offers valuable insights into the pivotal role of SCOBY in driving metabolite production and underscores the potential bioactivity harbored within Kombucha.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".