Composite Alginate–Ginger Oil Edible Coating for Fresh-Cut Pears
Bibliographic record
Abstract
Fresh-cut fruit is highly perishable due to damage to its external protective skin leading to the acceleration of chemical and biochemical activities, respiration rate, ethylene production, texture softening and moisture loss. Edible films and coatings can provide effective barrier properties to control respiration and transpiration of produce. Sodium alginate and ginger oil have been successfully employed as coating materials in several studies. This study focused on evaluating the effect of composite alginate and ginger-essential-oil-based edible coatings for controlling physiological and microbiological activity in fresh-cut pear during refrigerated storage. A 2% sodium alginate solution with 0.5% ginger oil as a herbal antimicrobial agent was used as coating material and a 2% calcium chloride dip was used for cross linking and firming. Coated cut fruit and control cut fruit were sealed in plastic containers and stored at 4 °C for two weeks. Respiration rate, color, texture, moisture loss and other quality parameters were evaluated during the storage. The coated fruit (both with and without ginger oil) had significantly better retention of product quality with no microbial spoilage up to 15 days as compared to the control fruit which spoiled within a week. The sodium alginate–ginger oil–calcium alginate formulation was recommended as a good composite coating for extending the refrigerated shelf-life of cut pears.
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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".