Postharvest treatments to reduce chilling injury symptoms in stored mangoes
Bibliographic record
Abstract
The market life of many fruits and vegetables can be extended through storage at low temperatures. Chilling injury (CI) is a major postharvest storage problem for tropical commodities. Storing these products at temperatures below their critical temperature may result in severe physiological disorders known as CI symptoms. Mangoes (Mangifera indica. L) are susceptible to CI when stored below 12 °C. Visual CI symptoms include uneven ripening, surface pitting, discoloration, shriveling and scalding. Research has been conducted to overcome these serious problems using various postharvest treatments such as hot water, methyl jasmonate (MJ) or diphenylamine (DPA) with some reduction of the incidence of CI symptoms in fruits and vegetables. Experiments were performed to assess and compare the potential of the above-mentioned postharvest treatments to reduce the CI symptoms on mango cv. Kent. The obtained results indicated that MJ- and DPA-treatments gave significantly greater percentage of marketable fruits. Experiments were also conducted with mangoes cv. Tommy Atkins treated with MJ and DPA before storing at low temperatures (1, 4, 7 and 10°C). The chemical treatments were successful at reducing CI symptoms of mangoes. Fruit decay was reduced during subsequent ripening. MJ-treated fruits had lower mass loss and higher total soluble solids (TSS) than the control treatment. The overall quality of MJ- and DPA-treated fruits was good with lower surface pitting and scalding compared with the control treatment. The best results were obtained at storage temperatures of 7 and 10°C. Both MJ and DPA postharvest treatments can reduce CI symptoms in mangoes cvs. Kent and Tommy Atkins when the mangoes are stored at below critical temperature.
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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) | 0.002 | 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".