Effect of Calcium Chloride and Hydrocooling on Postharvest Quality of Selected Vegetables
Bibliographic record
Abstract
Precooling and postharvest application of calcium chloride (CaCl2) on produce has positive effects in maintaining the produce quality during storage. However, there is variation in the response of the produce to different CaCl2 concentrations. As a result, there is need to establish optimal concentrations of calcium chloride that can extend postharvest life of targeted produce. Fresh good quality produce (tomatoes, carrots, courgettes and African eggplants) of uniform size and maturity were harvested and sampled into four portions. One was a control, hydrocooled with water only at 2±1 0C and the others were hydrocooled with water containing CaCl2 at 0.5%, 1.0% and 1.5%. After hydrocooling, tomatoes, African eggplants and courgettes were stored at 10 0C, while carrots were stored at 7 0C, all at 95% constant relative humidity, and sampled every two days for quality assessment. Weight loss, chilling injury, vitamin c and beta-carotene loss were reduced by application of calcium chloride. Titratable acidity decrease and increase in total soluble solids and specific sugars was also slowed by application of CaCl2.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".