Revealing novel insights into the improvement of greenhouse tea quality through exogenous substance interventions using targeted and untargeted metabolomics and microbial community analyses
Bibliographic record
Abstract
Tea quality in greenhouse was certain gap with open air. Metabolites and foliar microorganisms were investigated under seaweed fertiliser (CF) and gibberellin (CH) treatments using sensory evaluation, HPLC, untargeted metabolomics, 16S rDNA, and Internal Transcribed Spacer. CF tea was mellow, less astringent, and of better quality compared to CH. Catechin, −(−)Epicatechin, and Epigallocatechin were notably lower in CF. Differentially accumulated metabolites (DAMs) were notably enriched in Flavonoid and Phenylpropanoid biosynthesis, both involved in Catechin synthesis. DAMs in these pathways appeared down-regulated in CF. The CF improved quality by down-regulating metabolites in Phenylpropanoid biosynthesis in conjunction with microbial community metabolism enriched in amino acid and secondary metabolite biosynthesis. Metabolite- microbial correlation analysis indicated that the highest correlation with phenylpropane pathway metabolites was in bacteria Variovorax and Pseudomonas , and in fungi Filobasidium . The study provides theoretical basis for regulating flavour quality of greenhouse tea. • CCF exhibited umami, mellow and fragrant and sweet flavour, while CH showed slightly astringent, mellow and thick • Catechin, −(−)Epicatechin, and Epigallocatechin flavour metabolites were notably lower in CF • Phenylpropanoid biosynthesis was identified as a key pathway driving the flavour differences • Strong correlations were observed between signature microorganisms and flavour substances • A greenhouse tea quality improvement measure was proposed
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".