Comparison of the Postharvest Characteristics of Mango Fruits Produced under Contrasting Agro-Ecological Conditions and Harvested at Different Maturity Stages
Bibliographic record
Abstract
Mango (Mangifera indica) fruit production in Kenya occurs under diverse agro-ecological zones (AEZs). The different AEZs have variations in rainfall, sunlight, temperature, soils and cultural practices all of which affect fruit physiology and quality at harvest. Maturity stage at harvest also affects mango fruit quality and storage potential. In the present study, the effect of AEZs and fruit maturity stage on the postharvest shelf-life of mango variety apple was determined. The study was conducted over two seasons in 2011 and 2013 and the fruits were harvested from two different AEZs: Embu (high potential zone) and Makueni (low potential zone). The fruits were also harvested at two maturity stages based on flesh color. The fruits were selected for uniformity and allowed to ripen at ambient room conditions (Temperature; 25±1 oC and RH 60±5%) until a predetermined end stage. Five fruits were randomly sampled from each batch for determination of respiration, cumulative weight loss, total soluble solids, titratable acidity, ascorbic acid and mineral nutrients (potassium and magnesium). The results showed that ‘apple’ mango fruit quality was significantly affected by production location, maturity stage and the interaction between the two factors. Fruits harvested from Makueni and fruits harvested at early maturity stage (S1) had a longer shelf life (3 days more). Makueni fruits had significantly (p < 0.05) lower respiration rates accompanied with minimal weight loss. In addition, total soluble solids (TSS), ascorbic acid and mineral (magnesium) were significantly (p < 0.05) high in Makueni fruits while fruits from Embu were significantly (p < 0.05) high in total titratable acidity and potassium content. This study confirms profound variability in fruit quality as affected by the production location and harvest maturity.
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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".