Modified Atmosphere Packaging Enhances the Effectiveness of Coolbot™ Cold Storage to Preserve Postharvest Quality of Mango Fruits
Bibliographic record
Abstract
Recently, the Coolbot™ technology was introduced to smallholder farmers in Kenya as a low-cost alternative to conventional cold rooms. The present study sought to establish the additive benefits of cold storage under Coolbot™ cold storage and modified atmosphere packaging (MAP) in mango fruits. The participatory study was conducted in Makueni County of Kenya between November 2014 and July 2015. The mango fruits (variety ‘Apple mango’) were harvested at mature green stage from commercial orchards owned by smallholder farmers. The fruits were selected for uniformity and randomly separated into four batches which were subjected to four different treatments (storage conditions). The treatments included fruits packaged using Activebag® MAP or not packaged and either stored in the Coolbot cold room or at ambient room conditions. A random sample was taken at regular intervals from each of the four storage environments and evaluated for ripening and quality related changes during storage. The parameters evaluated included physiological weight loss, respiration, firmness, color, sugars and vitamin C. Results showed that cold storage extended the shelf life of mango fruits by 23 days without MAP and 28 days with MAP, in comparison to storage at ambient room conditions. Slow ripening under cold storage (with and without MAP) was evidenced by lower rates of respiration, softening, color changes and sugars accumulation compared to ambient room conditions. In addition, cold-stored mango fruits maintained better nutritional quality as evidenced by higher vitamin C levels, 59.77 mg/100mL and 51.8 mg/100mL with and without MAP respectively at the end of storage (day 40 and 35). This was significantly higher (p<0.05) compared to 55.17 and 51.53 mg/100 mL vitamin C for MAP packed and unpacked fruits at the end of storage under ambient room conditions (day 12). The results demonstrate the additive benefit of MAP and cold storage to preserve postharvest quality and extend the shelf life of mango fruits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".