Post-harvest dip of enhanced freshness formulation to extend the shelf life of banana(Musa acuminata cv. Grand Naine) in India
Bibliographic record
Abstract
A laboratory study was undertaken to determine the effects of a nano-emulsion carrying hexanal, an enhanced freshness formulation (EFF), as a post-harvest dip technology to minimize the post-harvest losses and to extend the shelf life of bananas. The banana fruits were harvested at three maturities (95%, 85%, and 75%), dipped or not dipped in the EFF, and studied under both ambient and reduced temperature storage conditions. During the experiments, the fruit’s physical, physiological, and biochemical parameters were periodically evaluated. The treated fruit had lower physiological loss of weight and higher firmness throughout the study period, regardless of maturity level at the start. Treated fruit had higher total soluble solids and total sugars, and less acidity indicating improved fruit quality during storage, in addition to an extended shelf life. High resolution imaging using scanning electron microscopy showed that EFF-treated fruit exhibited well maintained structural lenticels on the fruit skin and deposition of starch granules in the fruit pulp, regardless of maturity level at the start. Overall, the results clearly indicated that the EFF-treated banana fruit were delayed in the ripening process and had an extended shelf life of up to six days in ambient conditions and nine days in cold storage conditions. Post-harvest dipping using hexanal formulation is a potential technology that could be adopted in pack houses for domestic and export markets.
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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".