Physicochemical and Functional Properties of Membrane-Fractionated Heat-Induced Pea Protein Aggregates
Bibliographic record
Abstract
This study was carried out to investigate the effect of heat pre-treatment of pea proteins at different pH values on the formation of functional protein aggregates. A 10% (w/v) aqueous mixture of pea protein concentrate (PPC) was adjusted to pH 3.0, 5.0, 7.0, or 9.0 followed by heating at 100°C for 30 min, cooled and centrifuged. The supernatant was sequentially passed through 30 and 50 kDa molecular weight cut-off membranes to collect the <30, 30–50, and >50 kDa fractions. The >50 kDa fractions from pH 3.0 (FT3), 5.0 (FT5), 7.0 (FT7), and 9.0 (FT9) treatments had >60% protein content in contrast to the ≤20% for the <30 and 30–50 kDa fractions. Therefore, the >50 kDa fractions were collected and then compared to the untreated PPC for some physicochemical and functional properties. Protein aggregation was confirmed as the denaturation temperature for FT3 (124.30°C), FT5 (190.66oC), FT7 (206.33oC) and FT9 (203.17oC) was significantly (p < 0.05) greater than that of PPC (74.45oC). Scanning electron microscopy showed that FT5 had a compact structure like PPC while FT3, FT7, and FT9 contained a more continuous network. In comparison to PPC, the >50 kDa fractions showed improved solubility (>60%), oil holding capacity (~100%), protein content (~7%), foam capacity (>10%), foam stability (>7%), water holding capacity (>16%) and surface hydrophobicity (~50%). Least gelation concentration of PPC (18%), FT3 (25%), FT5 (22%), FT7 (22%), and FT9 (25%) was improved to 16, 18, 20, 16, and 18%, respectively, after addition of NaCl.
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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 | 0.001 |
| 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".