PECTIN‐BASED EDIBLE COATING FOR SHELF‐LIFE EXTENSION OF ATAULFO MANGO
Bibliographic record
Abstract
ABSTRACT Mango is a commercial but highly perishable fruit, and therefore, a longer shelf life is necessary for its successful marketing and consumer satisfaction. This study aims at evaluating the effects of edible coating, based on pectin, on the quality and shelf‐life extension of mangoes. The coating formulations included different combinations of pectin, beeswax, sorbitol and monoglyceride. The fruits were coated and stored at room temperature along with uncoated controls. Samples tested were evaluated periodically for quality parameters, which included visual observation, weight loss, respiration rate, color, firmness, pH, soluble solids (SS), titrable acidity and extent of decay. The coated‐fruits reduced the rate of color development, texture softening, weight loss, CO2 evolution and acid production (only pH and SS increased) compared with the control. The shelf life of control sample was less than a week, whereas the coated fruits remained good for over 2 weeks, thereby offering a significant advantage. PRACTICAL APPLICATIONS The study evaluates the use of pectin‐based edible coating for extending the shelf life of mango. The formulation is a blend of hydrophilic and hydrophobic groups to provide controlled respiration and water vapor permeability. The properties of pectin films have been highlighted in a previous paper, and several applications based on the coating have been demonstrated. The present study demonstrates that modifications to the formulation were necessary in order to successfully apply it to mango. Mango is a popular commercial product with high heritability. Simple edible coatings that can help to improve the shelf life and marketability of the product will be of significant interest in the marketing of mango products.
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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".