Impact of gum arabic and cactus mucilage as potential coating substances combined with calcium chloride treatment on tomato (<i>Solanum lycopersicum</i> L.) fruit quality attributes under ambient storage conditions
Bibliographic record
Abstract
The current work investigates the impact of postharvest applications of calcium chloride (CaCl2) in combination with either 10% gum arabic or 50% cactus mucilage on the quality parameters and storability of tomato fruit (Izmir variety), at ambient storage conditions [21 ± 1 °C, relative humidity (RH) 45% ± 2%]. The tomato fruits were dipped in 6% CaCl2 for 10 min and then coated with either 10% gum arabic or 50% cactus mucilage for 3 min. During the time-course of storage, the fruit attributes, such as fresh fruit weight loss, fruit decay, firmness, titratable acidity (TA), total soluble solids (TSS), and color development were assessed. The results revealed a significant (P ≤ 0.05) effect of fruit postharvest treatment with 6% CaCl2 for 10 min combined with postharvest coating treatments with either 10% gum arabic or 50% cactus mucilage for 3 min on reducing fruit fresh weight loss, and decay percent of fruits. Fruit firmness, TA, and TSS were preserved compared with untreated fruit. In addition, treated fruits took more time to develop from pink to full red compared with untreated fruit. Hence, coating with natural substances combined with CaCl2 preserved the overall quality parameters and enhanced the shelf life of tomato fruit even after three weeks of storage at ambient temperature.
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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".