Density, thermal expansion coefficient, and rheological behaviour of meat extract under different temperatures and solids concentrations
Bibliographic record
Abstract
Meat extract is a product with a high aggregated value obtained by concentrating cooking broth from meat products. To optimize project design and processing, we determined experimental values of the density and rheological behaviour of meat extract. We evaluated the influences of temperature and solids concentration on the studied parameters. Different concentrations and temperatures were selected based on the main processing steps, ranging from 0–60 g soluble solids /100 g solution and 2–98 °C. The model best fitted to density was derived and a thermodynamic relation was applied to calculate the thermal expansion coefficient. Meat extract density had a linear dependence on temperature and quadratic dependence on solids content, while the thermal expansion coefficient remained approximately constant at 5.33 × 10 −4 m 3 · m −3 · K −1 . Concerning rheological analyses, meat extract had Newtonian behaviour from 1.5–20 g soluble solids /100 g solution at the temperature range studied. From 30–60 g soluble solids /100 g solution , the Power‐Law model was better fitted to the data and the consistency coefficient and flow behaviour index could be calculated. Both parameters were sensitive to changes in temperature and concentration. Apparent viscosity increased with increasing the meat extract concentration and lowering the temperature. The dependence of rheological parameters on temperature was expressed through an Arrhenius‐type equation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".