Influence of coating developed from oligomer isolated from lac resin on post-harvest quality and shelf life of peaches (Prunuspersica L.)
Bibliographic record
Abstract
Large quantities of fresh fruits are produced that never reach the consumers due to heavy post-harvest losses, lack of storage, transportation care and less acceptable quality. These losses are not only concerned in terms of the revenue but it also concern in terms of health and life style of human being. To fulfill consumer demand and to avoid losses due to environmental variability, several methods are being utilized to increase the shelf life of fruits. Fruit coatings are considered as one of the widely used methods. This work investigates the effect of fruit coating developed from oligomer (P-104), isolated from lac resin on the quality of peach (Prunuspersica L.) when stored at room temperature (36-40C) and at refrigerator temperature (4-8C) temperature. Fruit quality was evaluated by measuring physiological weight loss, color and textural changes as well as microbiological evaluation at a regular interval of four days. When kept at room temperature, uncoated peaches remained fresh and microbiologically safe for 4 days only while shelf life of coated peaches increased to 12 days. Again the shelf life of coated peaches prolonged to 24 days when stored at refrigerator temperature. These results showed that the combined effect of coating and low storage temperature could improve the shelf life of peaches indicating the potential of this combination for fruits preservation.
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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".