PECTIN‐BASED EDIBLE COATING FOR SHELF‐LIFE EXTENSION OF ATAULFO MANGO
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
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, CO 2 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.
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 | 0.001 | 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 it