Optimization of Physical and Functional Properties of Sorghum-Bambara Groundnut Extrudates
Bibliographic record
Abstract
<p>Blends of sorghum and Bambara groundnut flour at 90:10, 80:20 and 70:30 ratios respectively, were extruded at 20%, 22.5% and 25% moisture levels and 120<sup>o</sup>C, 140<sup>o</sup>C and 160<sup>o</sup>C barrel temperatures using a single-screw extruder. Response surface methodology with central composite face-centered (CCF) design was used to model the viscosity, water absorption index (WAI) water solubility index (WSI), sectional expansion index (SEI), bulk density (BD), apparent specific volume (ASV) and mass flow rate of the extrudates. The viscosity, WAI, WSI, SEI, BD, ASV and MFR of extrudates varied from 8.38 to 18.78 Nsm-<sup>2</sup>, 5.30 to 6.21 g/g water, 10.5 to 21%, 2.55 to 5.1, 0.223 to 0.499 g/cm<sup>3</sup>, 4.15 to 6.55 cm<sup>3</sup>, and 1.19 to 2.3 g/s respectively. The R<sup>2</sup> values were 0.83, 0.71, 0.55, 0.61, 0.66, 0.77 and 0.61 respectively with a non-significant lack of fit; except for viscosity. Plots of residuals against fitted values showed that residuals were randomly scattered in all cases, thus validating the empirical models for the responses. The optimum amount of Bambara groundnut flour, feed moisture and extrusion temperature were established for viscosity, WAI, SEI, BD, ASV and MFR of the extrudates. Feed moisture had the most effect on extrudates functional and physical properties followed by extrusion temperature.<strong></strong></p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".