Preharvest zinc sulfate spray improves the storability of longan (<scp><i>Dimocarpus longan</i></scp> Lour.) fruits by protecting the cell wall components and antioxidants of pericarp
Bibliographic record
Abstract
Abstract BACKGROUND Zinc (Zn) fertilization has been reported to improve the quality and storability of many fruits, but the mechanism had not been systematically studied. In this study, the effect of preharvest 0.2% zinc sulfate (ZnSO4) spray on the storability of longan fruits was investigated. RESULTS The preharvest ZnSO4 spray did not significantly influence the quality but increased the Zn content in longan pericarp by 12.5‐fold. More importantly, the treatment significantly reduced the rotting rate, pericarp browning, and aril breakdown of longan fruits stored at room temperature and low temperature. Physiological and biochemical results indicated that the treatment resulted in higher water retention capacity and inhibited the degradation of cellulose, pectin, flavonoid, and phenolics of longan pericarp at the late stages of storage. Consistent with these results, the activity of cellulase, polygalacturonase, polyphenol oxidase, and lipoxygenase was significantly inhibited in the ZnSO4‐treated longan pericarp at the late stages of storage. CONCLUSION Higher Zn content in the ZnSO4‐treated longan pericarp might help to enhance the resistance against microbial infection, inhibit the hydrolysis of cell wall components, and thus effectively protected the cell wall components, maintained the cellular compartmentation, and prevented the phenolics and flavonoid from degradation by browning‐related enzymes. © 2018 Society of Chemical Industry
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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".