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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 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".