The effect of temperature and period of storage on the nutritional composition of cassava
Bibliographic record
Abstract
Cassava (Manihot esculenta), a staple crop in many tropical regions, plays a critical role in global food security due to its high caloric content and adaptability to varying climatic conditions. Despite its widespread consumption, cassava's nutritional value is highly influenced by post-harvest handling, particularly temperature and storage duration. This study investigates the effect of different storage temperatures and periods on the nutritional composition of cassava, focusing on macronutrients (carbohydrates, proteins, and lipids), micronutrients (vitamins and minerals), and anti-nutritional factors. Using controlled laboratory experiments, cassava roots were stored at varying temperatures (ambient, refrigerated, and frozen) for periods ranging from one week to two months. Nutritional analyses were conducted at predefined intervals to assess changes in starch content, crude protein levels, vitamin C concentration, and cyanogenic glycosides. Results indicate that prolonged storage, particularly under high temperatures, significantly reduces starch and vitamin C content while increasing protein degradation. Conversely, freezing preserved most nutrients but was associated with minimal increases in anti-nutritional factors. These findings underscore the importance of temperature control in preserving cassava's nutritional quality during storage. The study provides actionable insights for cassava processing industries, farmers, and policymakers, emphasizing the need for optimized storage conditions to maintain the crop's nutritional integrity. Future research should explore the interplay between storage conditions and cassava-derived product quality, with a focus on scaling up findings for industrial applications.
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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 | 0.001 |
| 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 | 0.000 |
| 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".