Effects of 1-MCP on External Postharvest Qualities and Shelf Life of ‘Maha Chanok’ Mango Fruit
Bibliographic record
Abstract
‘Maha Chanok’ mango is an economic fruit crop widely cultivated commercially throughout Thailand. By nature, mango fruit has a rather limited storage life after harvest. 1-methylcyclopropene (1-MCP) has been accepted as a commercial substance to improve several fruit qualities. The objective of this research was to study the effects of 1-MCP on the external postharvest qualities and storage life on the ‘Maha Chanok’ mango fruit. The experiment was laid out in a Completely Randomized Design with three replicates, ten fruits per replicate. Mango fruit was fumigated with 1-MCP at three concentrations (1000, 1250, or 1500 nl l-1) and three fumigation periods (12, 18, or 24 h), compared with the control fruit. After treating, all treatments were stored under ambient temperature (27 °C, 80%R.H.). The following determinations were made every two days for assessment of fruit weight loss, firmness, chlorophyll content, decay incidence, and storage life. The results showed that fruit treated with 1500 nl l-1 1-MCP for 24 h had the maximal fruit firmness. For chlorophyll content, the results showed that fruit-treated with 1500 nl l-1 1-MCP for 12 h could effectively retain the highest chlorophyll contents. Furthermore, both the lowest fruit decay and the longest storage life of 12 days were achieved from the fruit treated with 1000 nl l-1 1-MCP for 12 h.
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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".