Efficiency of Edible Coating from Locally Sourced Materials in Maintaining the Postharvest Quality of Belfast Tomatoes
Bibliographic record
Abstract
Edible coating technology has been proven to be an efficient and effective method of postharvest preservation. Especially in developing countries, edible coatings and other postharvest technologies are often limited by their high cost. The study aimed to assess the effect of edible coating materials prepared from inexpensive and locally available materials on the postharvest quality of Belfast tomatoes during storage. Different edible coating formulations [M1, M2, M3 and M4] were prepared by varying the concentration of orange peel powder [OP] [0,0.1,0.5,1%] in 10g/L Cassava Starch [CS] and 10g/L Chitosan [CH] coating solutions. Coated and control fruits were stored at 25°C for three weeks. Results showed that the coatings significantly [p<0.05] delayed the changes in weight loss, total Soluble solids, pH and colour compared to uncoated control fruits. At the end of the 3-week storage period, the control fruits recorded the highest weight loss, 25.58 ±1.73 % whiles M4 had the least, 15.14 ±0.30 %. M4 [CH+CS+1OP] significantly maintained the total soluble solids of the tomatoes which increased from 5.71° to 6.68°whiles the control tomatoes increased from 5.71° to 9.09° showing the effectiveness of the coating in maintaining the Total Soluble Solid (TSS) of the Belfast tomatoes. The coated samples also showed some resistance to the colour changes as well as the pH exhibiting the ability to delay the ripening rate in the tomatoes. The edible coating significantly improved the postharvest quality of the Belfast tomato and could have immense impact on other local tomato varieties.
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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".