Optimization of extruder cooking conditions for the manufacture of fish feeds using response surface methodology
Bibliographic record
Abstract
Abstract A composite blend consisting of sunflower cake, maize germ, wheat bran, fresh water shrimps and cassava flour was extruded using a single‐screw extruder to produce expanded fish feed pellets. The effects of temperature (80–120 °C), die diameter (2–4 mm), and feed pre‐conditioning time (50–150 s; steam 400 kPa) on properties of the pellets (expansion ratio, bulk density, floatability, durability, water absorption, water solubility, water stability, and in‐vitro protein digestibility) were investigated using response surface methodology. Regression equations describing the effect of each variable on the product responses were obtained. The pellets extruded using a factor combination of 120 °C extruder barrel temperature, 2 mm die diameter, and 100 s of feed pre‐conditioning time gave most desirable pellet floatability (100%), durability index (99%), expansion ratio (2.64), water absorption index (4.12), water solubility index (9.31), water stability (87%), bulk density (479 g/L), and in vitro protein digestibility (69.97%) with a composite desirability of 0.88. Practical applications Extrusion is a modern feed processing method whose use is fast gaining popularity among small feed processors in developing countries. However, extrusion is a process that involves many parameters that need to be optimized for desirable end properties. These findings guide fish feed manufacturers on the optimum conditions for single screw extruders for production of feeds with desirable properties especially for the fish types that are top feeders. In addition, the results offer important insights on how temperature, die diameter, and feed pre‐conditioning, may be manipulated to influence properties of extruded aquafeed when using simple low‐cost small‐scale extruders.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".