Integrated metabolomic and transcriptomic analysis reveals the molecular basis of flavor formation in fresh edible Chinese olive (Canarium album): Insights into flavonoid and amino acid metabolism
Bibliographic record
Abstract
Chinese olive is a characteristic fruit of southern China, exhibits significant flavor variation among cultivars. To investigate the molecular basis of flavor formation in fresh edible Chinese olive cultivars and identify the key metabolites and genes involved in this process, the metabolomics and transcriptomics were used to compare metabolite profiles and gene expressions among non-fresh edible (CY), sweet aftertaste (HG), and mild flavor (MF) cultivars. A total of 934 differentially accumulated metabolites were identified, primarily categorized into terpenoids, flavonoids, amino acids, lipids, sugars and alcohols, organic acids, and polyphenols. Compared with CY, flavonoid was significantly decreased in both HG and MF cultivars, while the levels of glutamic acid and aspartic acid were significantly increased in HG cultivars. Integrated metabolomic and transcriptomic analysis identified the candidate genes related to the biosynthesis of these compounds. The findings indicated that the reduction in flavonoid reduced astringency in fresh edible cultivars. Meanwhile, the increase in umami-related amino acids contributed to the sweet aftertaste of HG culitvars.
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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 | 0.000 | 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".