Soluble Pea Protein Aggregates Form Strong Gels in the Presence of κ-Carrageenan
Bibliographic record
Abstract
Pea protein has attracted attention as an alternative for soy protein, but its weaker gelling properties have limited applications in food formulations. In this study, heat-induced soluble pea protein aggregates were prepared in the first step, followed by the heat-induced gelation of the soluble pea protein aggregates in the presence of a small amount of κ-carrageenan. The mechanical property measurement indicated that the complex gel strength can be modulated by modifying the pea protein aggregate properties to achieve a compressive strength up to 14.15 kPa. In addition, such strong gels were achieved at a relatively low concentration of protein (7.5%) and κ-carrageenan (0.5%) and thus are advantageous for practical applications. The surface hydrophobicity, transmission electron microscopy, and Fourier-transform infrared spectroscopy characterizations suggest that pea protein particulate aggregates with hydrophobic patches on the surface can serve as the active building blocks to establish a homogeneous three-dimensional network of highly cross-linked structures with small pore size, thus leading to gels of superior mechanical strength when compared with gels prepared from pea protein isolate with κ-carrageenan. This research has provided a novel approach for structuring and texturization of plant-protein-based foods by using protein aggregates and contributed to the understanding of mechanism of gel formation from pea protein aggregates in the presence of κ-carrageenan.
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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".