Modeling Single-Screw Extrusion Processing Parameters and Resulting Extrudate Properties of DDGS-Based Nile Tilapia (Oreochromis niloticus) Feeds
Bibliographic record
Abstract
A single-screw laboratory extruder was used to conduct an L18 (22 ´ 36) Taguchi fractional factorial study of aquafeed processing. The ingredients were based on a formulation for nutritionally-balanced Nile tilapia diets containing distillers dried grains with solubles (DDGS) and soybean meal as the main protein sources, in addition to constant amounts of corn flour, whey, and fishmeal. The effects of three levels of DDGS (20, 30 and 40%), soybean meal (30, 40 and 50%), ingredient moisture content (20, 30 and 40% db), screw speed (100, 150 and 200 rpm), die dimension (L/D ratios of 5, 9 and 13), barrel temperature (80-100-100°C, 80-120-120°C and 80-140-140°C) and two levels of screw configuration (compression ratios of 2:1 and 3:1) on extrudate physical properties (moisture content, water activity, bulk density, unit density, expansion ratio, pellet durability index, water absorption and solubility indices, water stability, color) and extruder processing parameters (resulting temperatures, die pressure, extruder torque, mass flow rate, apparent viscosity, and specific mechanical energy) were determined. Data from raw materials, processing conditions, and extrudate properties were used to develop surface response curves and equations. However, predominantly low R2 values (< 0.5) only permitted linear relationships between some independent parameters and response variables. Regarding main effects, die pressure significantly decreased with higher DDGS levels, moisture content, temperature, lower die L/D, and higher screw compression. Expansion ratio decreased significantly with higher moisture content and lower die L/D. Significant differences in color were caused by changes in DDGS levels and moisture content. In summary, DDGS, moisture content, die dimension, and extrusion conditions had the biggest impact on most of the extrudate physical properties and processing conditions. Different combinations of these independent factors can be used to achieve desired extrudate physical properties and processing conditions.
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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 | 0.001 |
| 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".