Effect of solid‐state fermentation on the protein quality and volatile profile of pea and navy bean protein isolates
Bibliographic record
Abstract
Abstract Background and Objectives Fermentation has been increasingly used as a “clean” processing technique to modify proteins. The goal of this research was to assess the use of solid‐state fermentation (SSF) by Aspergillus oryzae on pea (pea protein isolate [PPI]) and navy bean protein isolates (NBPI) for different time periods (0–48 or 0−72 h, respectively) and its impact on their nutrition and volatile profile. Findings The SSF process resulted in higher total phenolic content and lower protein digestibility, and consequently, the protein quality was reduced for both pulses. The quantity of the volatile compounds initially present in the samples did not change substantially after SSF; however, many new compounds were identified in fermented PPI, which have been reported to have pleasant sensory properties. Conclusions The protein quality of PPI or NBPI was not improved by A. oryzae SSF; however, the results suggest the potential for using SSF to positively modify the volatile profile of PPI. Significance and Novelty The findings of this research strengthened our knowledge base of fermented pulse ingredients with the use of protein isolates and navy beans, narrowing the somewhat limited gap for fermentation application to high protein substrates.
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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".