Postharvest Application of Myo-Inositol Extends the Shelf-Life of Banana Fruit by Delaying Ethylene Biosynthesis and Improving Antioxidant Activity
Bibliographic record
Abstract
Banana fruits are harvested and then undergo rapid ripening and senescence, sharply limiting their shelf-life and marketability. Myo-inositol (MI) is an important regulator in ethylene production and reactive oxygen species (ROS) accumulation; however, its involvement in the postharvest ripening process of banana remains to be determined. This study found that postharvest application of MI could efficiently delay the fruit ripening and extend the time in which the luster, color, and hardness were maintained in two cultivars with contrasting storage characteristics, storable ‘Brazil’ and unstorable ‘Fenza No. 1’, when stored at room temperature (23 °C ± 2 °C). Moreover, physiological, metabolic, and gene expression analyses indicated that MI application improved MI metabolism and postponed ethylene biosynthesis and cell wall loosening. The decrease in ethylene production was associated with a reduction in the expression of ACS1 and ACO1 genes. MI treatment decreased the expressions of PL1/2, PG, and EXP1/7/8, which may account for the delay in softening. In addition, the application of MI could alleviate ROS-mediated senescence and cell membrane damage by promoting the activities of SOD, POD, and anti-O2− and decreasing PPO activity. This study shed light on the function of MI in regulating the postharvest ripening and senescence of bananas and provided an efficient strategy for extending shelf-life and reduce losses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".