Development of standard operating protocol for measurement of cassava root mealiness
Bibliographic record
Abstract
One of the major attributes of boiled cassava roots is its ability to soften within a short period, otherwise known as mealiness. This study aimed to establish and validate standard operating procedures for assessing the mealiness of boiled cassava roots. Twenty cassava genotypes, including landrace and improved varieties, were selected for the protocol development, with an additional ten genotypes used for validation. Following cooking, the cassava roots were evaluated for hardness and work done in extrusion using a texturometer equipped with a five-blade Ottawa cell probe. The same samples were assessed for sensory texture analysis using trained panelists for parameters such as softness and chewiness. Pearson's correlation analysis revealed significant positive correlations (p < 0.01) between sensory softness and instrumental texture measurements, as well as between softness and cooking time (p < 0.01, r = 0.94), and between chewiness and cooking time (p < 0.05, r = 0.81). Validation results confirmed significant correlations (p < 0.01) between cooking time, sensory softness, and chewiness. These findings suggest that cooking time can serve as a reliable indicator, closely associated with sensory attributes, in determining the mealiness of boiled cassava roots. This approach offers a practical, mid-throughput method for assessing cassava root mealiness, with implications for breeding improved varieties, farmers adoption, and consumer acceptance.
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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".