Effects of Extrusion Processing on the Proximate Compositions of Aerial Yam (Dioscorea bulbifera)-soybean (Glycine max) Flour Blends Using Response Surface Methodology
Bibliographic record
Abstract
The impact of extrusion process parameters on the proximate compositions of aerial yam (Dioscorea bulbifera)-soybean (Glycine max) flour blends was investigated with the help of a laboratory scale single-screw extruder, and the flour blending ratio of 25% aerial yam: 75% soybean. Response surface methodology based on Box-Behken design at three factors, five levels of barrel temperature (95, 100, 105, 110, and115℃ ), screw speed (85, 100, 115, 130, and145rpm) and feed moisture (31, 33, 35, 37, and 39%) were used in 20 runs. Adequate and significant (p< 0.05) regression models describing the effects of process parameters on the product quality attributes with high regression coefficient, R2≥  0.9 were obtained, showing that the models can be used to navigate the design space. Results obtained showed that the proximate compositions ranged between 4.03± 0.002 and 5.90± 0.002% ash; 3.10± 0.001 and 7.02± 0.003% moisture content; 2.70± 0.001 and 4.67± 0.003% fibre; 24.57± 0.078 and 36.79± 0.004% protein; 11.39± 0.004 and 35.35± 0.011% lipid. The best extrusion condition combinations were 1150C barrel temperature, 115rpm screw speed and 35% feed moisture for ash content; 1000C barrel temperature, 130rpm screw speed and 33% feed moisture for moisture content; 1150C barrel temperature, 115rpm screw speed and 35% feed moisture for fibre content; 1050C barrel temperature, 145rpm screw speed and 35% feed moisture for protein content; 1050C barrel temperature, 85rpm screw speed and 35% feed moisture for lipids. Analysis of variance showed that only barrel temperature affected the ash composition significantly (p< 0.05). Barrel temperature, screw speed and feed moisture showed insignificant (p> 0.05) effect on moisture content, while fibre content was significantly (p< 0.05) affected by barrel temperature and screw speed. Protein and crude fat (lipid) contents were significantly (p< 0.05) affected by barrel temperature, screw speed and feed moisture.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".