Effect of Surface Coatings on the Shelf life and Quality of Cassava
Bibliographic record
Abstract
Cassava (Manihot esculenta) is grown as an important dietary source of carbohydrates for communities in a number of African countries. However, Cassava is susceptible to postharvest physiological deterioration which affects its quality and leads to the unpalatability and unmarketability of roots after harvest. Edible surface coatings have been found to be effective in preserving the quality of various perishable food products. This study was undertaken with the objective of determining the best combinations and concentrations of both xanthan gum and guar gum capable as a technology for extending the shelf life of harvested cassava roots. Cassava (variety KME 1) was harvested at physiological maturity. The coating formulations used were: 1%, 1.5%, 2% guar gum, 1.5%, 2%, 2.5% xanthan gum, and 1%, 1.5%, and 2.5% xanthan guar/gum combination in the ratio of 1:1 with some roots left as control. Sampling was done at 2-day intervals for 20 days. The coated cassava showed lower respiration and ethylene production rates than the control samples while change in quality parameters; phenols, colour, flesh firmness, weight loss and dry matter content was significantly (P≤0.05) delayed in the coated samples. The results suggested that using 1.5% xanthan guar/gum as an edible coating, cassava shelf life can be extended by upto 20 days at 25 °C.
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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".