Twin-screw Extrusion Processing of Vegetable-based Protein Feeds for Yellow Perch (Perca flavescens) Containing Distillers Dried Grains, Soy Protein Concentrate, and Fermented High Protein Soybean Meal
Bibliographic record
Abstract
Changing to alternative protein sources supports production of more economic aquafeeds. Two isocaloric (3.06 kcal/g) and isonitrogenous (40% db) experimental feeds for juvenile yellow perch were formulated with incorporation of fermented soybean meal (FSBM) and soy protein concentrate (SPC), each of which were at two levels (0 and 20% db), along with constant amounts of high protein distillers dried grains (DDG) (~30% db), and appropriate amounts of other ingredients. Using a pilot scale twin-screw extruder, feed production was performed in two replications for each diet at conditioner steam levels of 0.11 to 0.16 kg/min, extruder water of 0.11 to 0.19 kg/min, and screw speeds of 230 to 300 rpm. The effects of SPC and FSBM inclusion on extrudate physical properties were compared with those of a control diet (which contained 20% fishmeal and ~30% DDG). Inclusion of 20% FSBM and 20%SPC resulted in a substantial decrease in unit density by 9.2 and 24%, but an increase in lightness, greenness, yellowness, and expansion ratio of the extrudates by 7, 27, 14, 7, 17, 34, 15, and 16.5%, respectively. SPC inclusion led to a considerable increase in water absorption, thermal resistivity, and thermal diffusivity by 17.5, 6.3, and 17.6%, respectively, whereas no significant change was observed for these properties with incorporation of 20% FSBM. Additionally, all extruded products had high durability. Taken together, using ~30% DDG with20% FSBM or20% SPC as alternative protein sources resulted in viable extrudates with properties appropriate for yellow perch production. A future study investigating the effect of extrusion processing conditions on the production of complete vegetable-based protein feeds for yellow perch species would be appropriate.
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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 | 0.000 |
| 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) | 0.001 | 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".