The effects of hexanal incorporated composite material (HICM) made of banana fibre and polymerson extending the storage life of mango fruit(Mangifera indica L. var TEJC) in Sri Lanka
Bibliographic record
Abstract
Maintaining the quality of perishable commodities during storage and transportation remains a challenge to the fruit and vegetable industry in many countries. In order to minimize post-harvest loss, a hexanal incorporated composite material (HICM) was created using hexanal, banana fibre, polymeric materials, and biopolymers. Efficacy of the HICM was tested on mango variety TEJC. Trials were conducted over two consecutive fruit seasons in 2016 and 2017. Six fruits were packed in corrugated cardboard cartons and eight cartons per treatment (with and without HICM) were stored at 13.5°C ? 2 oC and 92% relative humidity. Quality observations for each treatment were recorded at 7-day intervals for 28 days. Higher retention of fruit firmness and marketability were observed in fruits packed with the HICM with 50% of the fruit marketable after 21 days storage at 13.5 oC ? 2 oC (p <.05). Control fruits were not marketable at 21 days. Qualitative headspace gas chromatography molecular spectroscopy (GCMS) analyses were executed to determine the stability of released hexanal and the fate of hexanal when absorbed into fruit. Hexyl esters and hexanoate esters were observed in the headspace of HICM-treated fruits. Results from this study indicate that HICM treatment could be used along with low temperature storage to promote the marketability of TEJC mangoes.
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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".