Effects of Steam, Moisture, and Screw Speed on Physical Properties of DDGS‐Based Extrudates
Bibliographic record
Abstract
ABSTRACT A fractional factorial design with a replicated central composite point was used to investigate the effects of extrusion processing on physical properties of distillers dried grains with solubles (DDGS) based aquafeeds using a twin‐screw extruder. Extrusion cooking trials were performed with a nutritionally balanced ingredient blend for Nile tilapia, with two levels of screw speed (350 and 450 rpm), two levels of extruder water (0.236 and 0.302 kg/min), and two levels of conditioner steam (0.1 and 0.15 kg/min). The central point was 400 rpm screw speed, 0.271 kg/min extruder water, and 0.12 kg/min conditioner steam. Effects of these processing conditions on extrudate characteristics were extensively analyzed and included moisture content, water activity, thermal properties, expansion ratio, unit density, bulk density, color, water stability, sinking velocity, water absorption and solubility indices, and pellet durability index. Increasing the extruder water and conditioner steam resulted in a 5.3% decrease and nearly 8.6% rise in mass flow rate, respectively. As screw speed increased from 350 to 400 rpm, water stability and water activity increased by 13 and 58%, respectively. Increasing extruder water from 0.236 to 0.302 kg/min led to a significant increase in water stability by 12.5% and decreases in water absorption index, water activity, and expansion ratio by 13, 21, and 5.5%, respectively. As conditioner steam increased from 0.1 to 0.15 kg/min, sinking velocity and water absorption index decreased by 25 and 15%, respectively. Increasing conditioner steam from 0.1 to 0.12 kg/min resulted in 20, 5.5, 10, and 3% decreases in moisture content of the extrudates, brightness ( L *), water stability, and expansion ratio, respectively. It also increased bulk density by 5.8% and unit density by 4.2%. Overall, all trials produced viable extrudates with properties appropriate for Nile tilapia feeding.
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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 | 0.000 | 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".