Effect of Different Extrusion Processing Parameters on Physical Properties of Soy White Flakes and High Protein Distillers Dried Grains-Based Extruded Aquafeeds
Bibliographic record
Abstract
<p>Nutritionally balanced ingredient blends for catla (<em>Catla catla</em>), belonging to the family Cyprinidae, were extruded using single screw extruder. The extrusion was carried out at five levels of soy white flakes content (21%, 29%, 40%, 52%, and 59% db), five levels of moisture content (15, 19, 25, 31, and 35% db) and five levels of barrel temperature (100, 110, 125, 140, and 150 ºC) using three different die nozzles (having L/D ratios 3.33, 5.83, and 7.25). Blends with net protein content of 32.5% contains soy white flakes, along with high protein distillers dried grains (HP-DDG), corn flour, corn gluten meal, fish meal, vitamin, and mineral mix. A central composite rotatable design (CCRD) and response surface methodology (RSM) was used to investigate the significance of independent and interaction effects of the extrusion process variables on the extrudates physical properties namely pellet durability index, bulk density, water absorption and solubility indices and expansion ratio. Quadratic polynomial regression equations were developed to correlate the product responses and process variables as well as to obtain the response surfaces plots. The independent variables had significant (<em>P </em>&lt; 0.05) effects on physical properties of extrudates: (i) higher soy white flakes content increased the pellet durability index and water absorption index, but decreased the water solubility index, (ii) higher temperature decreased pellet durability index, bulk density and water solubility index, (iii) increased L/D ratio from 3.33 to 7.25 increased the pellet durability index, expansion ratio but decreased the bulk density of the extrudates.</p>
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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 | 0.001 | 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".