Reducing chilling injury in 'Palmer' mangoes submitted to quarantine cold treatment
Bibliographic record
Abstract
Abstract BACKGROUND Treatment with low temperatures can be used to quarantine mangoes against fruit‐fly infestation. However, mangoes can develop chilling injury (CI) when stored at temperatures below 13 °C. We demonstrated that the immersion in polyol solutions can alleviate CI symptoms in ‘Palmer’ mangoes stored at 8 °C. These suggest that polyols can be used to reduce CI in mangoes during quarantine at low temperatures. Thus, we investigated the efficacy of applying 0.1% (v/v) glycerol, propylene glycol, or sorbitol to ‘Palmer’ mangoes subjected to cold treatment (1.0 °C) for 28 days. Mangoes were then ripened at 23 °C for 7 days. RESULTS Among these polyols, sorbitol was the most effective in alleviating CI for up to 14 days of cold treatment. Mangoes treated with sorbitol showed lower levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2), and reduced polyphenol oxidase (PPO) activity. These fruit also had elevated levels of ascorbate (AsA), especially in the epicarp, and increased superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) activity. CONCLUSION Sorbitol can reduce the CI, but to an unsatisfying level, and it should be combined with other treatments storage at low temperature. © 2022 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) | 0.002 | 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".