Increasing fruit weight and altering flavour of pitaya by supplementing blue light during fruit growth
Bibliographic record
Abstract
• Blue light increased the weight, firmness, and antioxidant activity of pitaya fruit. • Blue light had minor effects on primary metabolites but more pronounced effects on volatile compounds. • Blue light increased the accumulation of bioactive ingredients in the fruit’s peel. • The accumulation of flavor-associated volatile compounds, such as organic acids, esters, and terpenes in the pulp, was significantly altered. Supplemental light is often used in fruit production, but few studies have been conducted on pitaya. In this study, supplemental blue light was applied to pitaya for four hours each night in the field from flowering to fruit ripening to examine changes in peel and pulp physicochemical parameters and metabolites. Blue light treatment significantly increased fruit weight, improved fruit firmness by increasing pectin content and retarding hemicellulose degradation, and enhanced antioxidant enzyme activity. Blue light had minor effects on primary metabolites but more pronounced effects on volatiles. It is possible that by affecting alanine, aspartate and glutamate metabolism, blue light treatment resulted in significant fruit growth, improved fruit biotic resistance, increased accumulation of bioactive ingredients in the peel, and significantly altered the accumulation of flavor-associated volatile compounds, such as organic acids, esters and terpenes in the pulp. Our results provide an important reference for improving the yield and quality of pitaya production using supplemental light in the field.
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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 | 0.001 |
| 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".