Banana leaf as packaging of lulo for different storage temperatures and the effects on postharvest characteristics
Bibliographic record
Abstract
In Colombia, the small and médium farmers are responsible for the production of nearly 45.000 t yr-1 of lulo (Solanum quitoense). However adequate and easy techniques for postharvest handling are not often available to be implemented by this sector of the producers. This research aimed to study banana (Musa paradisiaca) leaf as primary packaging to minimize the loss of quality of lulo stored at different temperatures. Chemical and physiological quality parameters were considered in the analysis of the maturation process. Loss weight, color changes in CIELAB coordinates, total titratable acidity (TTA), Young's modulus and firmness were measured to represent the fruit quality. Use of banana leaf as primary package show that weight losses and the color changes result of the ripening process were decreased. The color changes of lulo skin were significantly affected by storage temperature. To avoid changes in TTA, mechanical damage is not recommended. When the lulo fruits were packed with banana leaves, the Young's modulus and firmness values was higher. The results of this research allow the comparison of quality of lulo in the packaging proposal and the results of others researchers who use conventionally packaging like wood crates and carton packaging. The proposal packaging configuration (lulos packed with banana leaf in plastic crates of 80x60x20 cm) is an easy alternative to get and preserve the quality of lulo fruits for a longer storage time.
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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 | 0.001 | 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".