Evaluation of Post-harvest Losses and Shelf Life of Fresh Mango ( <i>Mangifera indica</i> L.) in Eastern Zone of Tanzania
Bibliographic record
Abstract
Post-harvest loss negatively impacts food security, nutrition and economic stability of farmers, exporters, traders and consumers. Experiments were conducted to assess the effects of post-harvest techniques on the shelf life of Apple and Palmer mango cultivars under different storage conditions. Post-harvest losses of these fruit along the supply chain were also evaluated. A two-factors factorial experiment with six replications was used for each cultivar. Post-harvest techniques included dipping of fruit in hexanal solution (0.02% v/v), calcium chloride solution (2% w/v), smoke treatments and untreated fruit. The fruit were then stored at two different storage conditions namely: ambient temperature (28 ± 2°C) and cold storage (18 ± 2°C). Shelf life data was analyzed by using R-software. Mean separation was done by using Tukey Honestly Significant Difference at (p ≤ 0.05). Results showed that the major sites of post-harvest losses were at harvest, transport, wholesale and retail stages of supply chain. Furthermore, post-harvest treatments of fruit with hexanal and calcium chloride significantly increased shelf life and reduced disease incidences compared to untreated control and smoke-treated fruit. Cold storage significantly increased shelf life of mango fruit compared to ambient storage. Therefore, hexanal, calcium chloride and cold storage are recommended to extend fruit shelf life, maintain fruit firmness and to reduce disease incidences in mango fruit.
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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) | 0.000 | 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".