Enhancing mango shelf life through natural preservation: Investigating the efficacy of polysaccharide edible coating and leaf extracts
Bibliographic record
Abstract
A sustainable solution to prevent the waste of fruits and vegetables from spoilage is the use of edible coatings or films. This research project aimed to create a fresh coating recipe that could effectively extend the shelf life of mangoes. The coating was composed of chitosan, glycerol, and gum Arabic mixed with the extract obtained from the extraction of Cleistocalyx operculatus plant. The prepared exact has a total polyphenol content of 17% and showed potent free radical scavenging abilities in a dose-dependent manner. The chitosan/gum Arabic/Glycerol/extract edible coatings were analyzed using SEM and FTIR spectroscopy, revealing a smooth and uniform coating with a well-integration of components. Coating the mangoes with this formulation resulted in significant improvements in their appearance, brightness, weight loss, firmness, titratable acidity, and CO2 respiration rate compared to uncoated samples. The optimal concentration of the extract in the coating was determined to be 0.25% w/w for the best protective performance. After 21-day storage at room temperature, uncoated mangoes were found to be rotten, while coated mangoes remained fresh.
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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) | 0.000 | 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 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".