Some Functional Properties of Kafirin Extracted from a- High- Tannin Sorghum Cultivar
Bibliographic record
Abstract
Kafirin was extracted form a whole high tannins sorghum flour (Fetarita). Different functional properties of produced protein were studied and sorghum flour was used as a control. Results showed that the extracted kafirin has a significant low loose and packed bulk densities compared to the control sorghum flour. The porosity of the control (0.374 g/ml) significantly higher than the extracted kafirin (0.196 g/ml). The Carr index for the kafirin was 20.4%, while it was 39.2% for the sorghum flour Alkaline water retention, as well as water and oil holding capacity of the extracted protein were highly significant than the control, and the results showed that kafirin was highly viscous with a water holding capacity of 2.01 ml H2O/g protein, oil holding capacity of 2.10 ml oil/g protein. There is no significant differences between the emulsifying activity of the kafirin and the control, while the kafirin had high emulsifying stability than the control by 11%. No foam capacity and stability were observed for the control, while the kafirin showed 2.75% foam capacity with no foam stability reported. Results shows that the control sorghum flour took longer time (84.9 sec) to completely be wet in cold water than the extracted kafirin (25.0 sec). SEM results of the kafirin supports the development in functional properties.Keywords: Sorghum, Kafirin, Functional properties; SEM.
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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".