Effect of Extrusion on the Functional and Pasting Properties of High-quality Cassava Flour (HQCF)
Bibliographic record
Abstract
Cassava is a rich source of starch and is used as a food ingredient and additive. In its natural state, cassava flour or starch cannot meet all functional requirements in food processing. This necessitates the modification of starch to meet specific functional requirements for products and processes. This study investigated the effect of different extrusion conditions, such as moisture content, screw speed, and temperature on the functional and pasting properties of high-quality cassava flour (HQCF). Particle size distribution, functional and pasting properties of the HQCF were determined. Moisture content (MC) had a significant effect on the properties of HQCF. Water absorption capacity (WAC) increased from 245% to 732%, swelling power (SP) increased from 3.4 g/g to 7.2 g/g, and water absorption index (WAI) increased from 3.0% to 3.3% after extrusion at 40% MC. While at lower MC levels, bulk density (BD) increased from 0.7g/ml to 0.8g/ml for the non-extruded and 30% MC; oil absorption capacity (OAC) from 215% to 253% for the non-extruded and 10% MC; water solubility index (WSI) from 6.0% to 56% for the non-extruded and 20% MC respectively. A positive correlation was observed between extrusion parameters and functional properties. The results suggest that manipulation of extrusion conditions can be used to modify HQCF for varied food 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 | 0.000 |
| 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".