Properties of Extrusion Processed Corn and Corn Coproducts
Bibliographic record
Abstract
As the world population continues to grow, the demand for human food and animal feed grows exponentially. Aquaculture is the food sector which has been growing at the greatest rate for several years. Because of the expense of fishmeal in aquaculture fees, an inexpensive protein source could be corn-based proteins. Although many studies have focused on the effects of extruding corn-based blends along with other supplement ingredients, few studies have focused on the extrusion of individual corn-based ingredients. This study examined physical effects of extrusion on distillers dried grains with soluble (DDGS) and corn. Specific objectives included determining moisture content, water activity, color, unit density, durability, water stability, floatability, and bulk density for each corn-based extrudate. Blends were prepared with three levels of moisture (15, 25, and 35% db), and extrusion conditions included three screw speeds (50, 75, and 100 rpm) and three barrel temperatures (100, 125, and 150°C). Results showed that as the moisture content increased, the water activity increased in the raw ingredients, and the moisture content of the extrudates increased. As the screw speed increased, the bulk density decreased in the extrudates, and the mass flow rate increased. As the temperature increased, the floatability of the extrudates increased, while the bulk density decreased. The amount of protein and starch content in the corn products affected the physical quality of the pellets, which is important in aquaculture feed development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".