Twin-screw Extrusion Processing of Plant-Based Blends Using Graded Levels of High Protein Fermented Soybean Meal (FSBM)
Bibliographic record
Abstract
Fast-paced growth in global aquaculture has elevated concerns about the high costs of fish farming production and potential water pollution. Thus, finding eco-friendly and more sustainable alternative protein sources for fish diets is of vital importance to the industry. A twin-screw extrusion processing study was performed using three ingredient blends formulated with graded levels of high protein fermented soybean meal (FSBM) (0, 80% and 100% db fishmeal replacement) along with calculated amounts of other ingredients to meet the rainbow trout diets’ requirements. Increasing the FSBM content from 0% to 100% resulted in a substantial increase in brightness, greenness, and yellowness, and a decrease in bulk density, water absorption index, and unit density (UD) values of the extrudates by 12.5%, 73%, 30%, 7.3%, 27.5%, and 10%, respectively. Compared to the control diet (100% fishmeal-based), extrudate moisture contents increased by 15.2% and 22% for the diets containing 80 and 100% FSBM, respectively; although no change was observed by increasing FSBM from 80 to 100%. The highest water solubility index (WSI) was obtained for 80% FSBM; however, further increasing FSBM did not influence the WSI significantly. All extrudates exhibited low water activity and high pellet durability values (less than 0.5 and more than 99.5%, respectively). The most buoyant extrudates were obtained using total FSBM inclusion, with UD and expansion ratio values of nearly 660 kg/m3 and 1.3, respectively. Overall, results indicated that FSBM could be a promising alternative protein in vegetable-based blends.
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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".