Effect of fermentation time on the physicochemical and functional properties of pea protein‐enriched flour fermented by <i>Aspergillus oryzae</i> and <i>Aspergillus niger</i>
Bibliographic record
Abstract
Abstract Background and objectives Pea protein‐enriched flour (PPEF) was inoculated with Aspergillus oryzae NRRL 5,590 or Aspergillus niger NRRL 334 to obtain limited protein hydrolysis (0%–10%). Fermented PPEF was analyzed for surface properties, water hydration capacity (WHC), oil‐holding capacity (OHC), and nitrogen solubility and emulsification and foaming properties at pH values of 3.0, 5.0, and 7.0. Findings The surface charge of fermented PPEF increased over fermentation time for both fungi, whereas surface hydrophobicity decreased. In all samples, functionality (based on solubility, emulsification, and foaming) was greatest at pH 3.0 and 7.0 and lowest at pH 5.0. Fermented PPEF significantly decreased in solubility over fermentation time for both fungi, and in turn, the foaming properties were negatively impacted, whereas the emulsifying properties remained relatively unchanged. However, fermentation improved the water and oil binding properties of fermented PPEF; WHC increased from 1.46 to 2.03 g/g with A. oryzae, and OHC increased from 1.18 to 2.27 g/g with A. niger. Conclusions Fermentation of PPEF with A. oryzae or A. niger may be considered for applications requiring high WHC and OHC, respectively. Significance and novelty Fermented PPEF represents a novel protein material that should be further explored for specific food applications.
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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 | 0.001 | 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".