Post-harvest dip of enhanced freshness formulation to extend the shelf life of banana(Musa acuminata cv. Grand Naine) in India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it