Particle filled protein-starch composites as the basis for plant-based meat analogues
Why this work is in the frame
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Bibliographic record
Abstract
Rapid swelling, high amylopectin starches including Thermally Inhibited (TI), Chemically Modified (CM), and Granular Cold- Swelling (GCS) were assessed for their supporting matrix forming potential and properties. Starches displayed identical calorimetric profiles with no endothermic events, and completely amorphous structure as judged by powder X-ray diffraction. However, they each provided different textural attributes. The starches were combined with pea protein isolate at a total concentration of 47%w/w (d.b.) to create a proteinacious supporting matrix. The starch protein matrix was then tested in a non-cold-set dough state as well as in a cold-set state after storage for 24h at 5oC. In the non-cold-set state, hardness increased with the addition of protein. CM was the softest dough and was difficult to work with, while TI and GCS were harder, with TI having the greatest resilience. Once cold-set, the textural properties changed, and GCS was not able to form a solid structure, instead remaining a viscoelastic dough. The hardness and storage modulus (G’) of TI and CM displayed a negative correlation with the addition of protein due to matrix disruption. However, the combination of TI starch and pea protein at a ratio of 70% starch and 30% protein in the dry fraction displayed a synergistic effect, with increased resilience, chewiness, and ductility. FTIR of TI starch and protein at the same 70:30 ratio provided further evidence for the existence of an interaction between pea protein and TI starch. The results support the use of TI rapid swelling starch and pea protein isolate as a supporting matrix for application in meat analogue systems.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it