Metabolic variation in sea buckthorn berries during yearly natural fermentation via non-targeted metabolomics
Bibliographic record
Abstract
This study used UPLC-MS-based non-targeted metabolomics to explore the metabolic changes and identify potential targets in yearly fermented sea buckthorn berries from Mount Wutai over 1–4 years of natural fermentation. A total of 520 metabolites were identified, including 241 core differential metabolites and 311 feature-important metabolites during fermentation based on traditional analysis supplemented by random forest algorithm. Pathway enrichment analysis revealed that the annotated core differential metabolites were predominantly enriched in flavone and flavonol biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis, as well as phenylalanine metabolism. Important metabolic pathways network analysis suggested that astragalin, phenylalanine, and pyruvate may serve as potential targets for investigating variations in the flavonoid and flavonol biosynthesis pathways, as well as amino acid biosynthesis. Further studies are warranted to validate these targets in controlled fermentation systems. Thus, these findings provide comprehensive insights into the variation of bioactive components and time-dependent effects in sea buckthorn berries during 1–4 years of natural fermentation and offer potential strategic guidance for the targeted development of sea buckthorn berry-based functional products.
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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) | 0.001 | 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".