Comparison of dry (air classification) and wet fractionated pea protein on protein molecular structure and gelling properties
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Dry fractionation is an environmentally friendly and cost‐effective method to separate protein fractions. While most research has focused on the structure and functional properties of pea protein isolates (PPI), the information on dry fractionated pea protein (DFPP) is limited. This study compared DFPP (protein content (PC): 50.7%, insoluble fiber: 17%), to PPI (PC: 80.1%, insoluble fiber: 8.33%), in terms of the protein structure, solubility, and heat‐induced gelation. SDS‐Page, size‐exclusive chromatographic, and Fourier‐transform infrared spectrophotometer analysis indicated that DFPP contained the major protein components in pea, and their native structures were well maintained. Whereas for PPI, some proteins were lost during wet extraction, and partial protein unfolding and aggregation were observed. At neutral pH, DFPP showed significantly higher solubility (44.64 ± 0.55%) than PPI (12.09 ± 1.42%). Interestingly, DFPP showed good gelling capacity as reflected by lower gelling concentration and higher gel mechanical strength and elasticity compared to those made from PPI. The DFPP gels were twice higher in mechanical strength (7.71 ± 0.21 kPa) than that prepared from PPI at pH 7. Strong gels were also obtained for DFPP at pH 5. The gel morphology revealed phase separation between protein and polysaccharides by heating, with stick‐shaped fiber (10–25 μm) dispersed in the continuous protein networks. Eventually, the polysaccharides including fiber and starch helped strengthen the gel network by acting as fillers. This knowledge will help to expand the applications of DFPP as a gelling ingredient in food formulations, but also allow industry to benefit from the dietary fiber co‐exist in the protein to develop healthier food products using a holistic approach.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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