The Effect of Sodium Alginate-Calcium Chloride Coating on the Quality Parameters and Shelf Life of Strawberry Cut Fruits
Bibliographic record
Abstract
Strawberry fruits have a short shelf life after harvesting due the physiological factors that enhances ripening such as respiration and transpiration. Sensory properties including color, texture, odor, and flavor are the main factors that makes fresh produce appealing to consumers, and they change very rapidly upon harvest. For this reason, quality preservation is essential during post-harvest handling and storage of strawberry fruits. Quality deterioration rates are higher in strawberry fruit cuts due to the mechanical damage and the loss of their natural protective barriers, resulting in an increase in moisture loss, respiration rates, and the deterioration of their sensory properties. The effect of a sodium alginate-calcium chloride edible coating on quality preservation and shelf life extension of strawberry cut fruits stored at 4 °C was studied. Control samples had mold growth initiated after one week of storage at 4 °C, while the coated fruit samples had a mold free shelf life extension for up to 15 days. The sodium alginate-calcium chloride edible coating was effective in reducing respiration and transpiration rates and delayed the increase of the pH and soluble solid content. Furthermore, the coating delayed surface mold growth for up to 15 days and preserved the sensory properties of the cut fruits such as color and texture.
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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".