RNA-Seq Reveals the Effect of Ethylene on the Volatile Organic Components (VOCs) of Cavendish Banana at Different Post-harvesting Stages
Bibliographic record
Abstract
Abstract Aroma serves as one of the decisive factors influencing the value of banana commodities. Most of characteristic volatile organic components (VOCs) are formed during post-harvesting. However, the changing of VOCs of banana at different post-harvesting stages remain ambiguous. In this study, the VOCs of Cavendish banana for the four typical post-harvesting stages (green stage/half of yellow stage/yellow ripening stage/over ripening stage) are clarified using headspace solid phase micro-extraction (HS-SPME), combined with gas chromatography-mass spectrometry (GC–MS). The results inferred that the relative content of branched-chain esters such as acetate and butyrate, which form the main contributors of aroma in bananas, is higher in the T2 and T3 stages. Further, RNA-Seq technology was employed to clarify the formation mechanism of banana aroma in the post-harvesting stage. TheMaTGL4gene of the linoleic acid metabolism pathway and theMaBCAT3andMaBCAT5genes of the valine, leucine and isoleucine degradation pathway in banana suggest the expression is active late in the ripening stage, and the upregulated expression of these genes is analogous to the formation of aroma components such as branched-chain esters and hexenal. The above results not only provide baseline data on the differences in physical and chemical properties of VOCs in various post-harvesting stages of banana production, but also provide theoretical guidance facilitating the subsequent improvement of the commercial value of bananas through genetic improvement.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".