Identifying microbial proteins and changes in proteome in spontaneously fermented pulse protein isolates
Bibliographic record
Abstract
Pulses are a sustainable source of plant-based proteins, but they often fall short in terms of sensory attributes and functionality. Fermentation has been investigated as a natural food processing method to address these limitations. Spontaneous fermentation, where native microflora grow without the addition of specific microbes, has been traditionally used in food processing by various cultures around the world. However, there is a knowledge gap regarding the changes that occur in protein composition during spontaneous fermentation. This study used capillary gel electrophoresis and liquid chromatography coupled to tandem mass spectrometry to examine the changes in protein size distribution, identify microbial proteins and understand proteome-level changes that occurred during the spontaneous fermentation of three protein isolate substrates: chickpea, faba bean, and lentil. The findings revealed that proteins from a variety of bacterial and fungal species were identified in all substrates, and the number and quantity of these microbial proteins increased during spontaneous fermentation. This rise in microbial protein content was associated with the hydrolysis of proteins from the pulse substrates, which could potentially alter the functionality of the protein ingredient. • First proteomics study of spontaneous fermentation of chickpea, lentil and faba bean. • Identification of diverse proteins produced by indigenous microflora. • Changes in the proteome of substrate and indigenous microflora during fermentation. • Identified variations between substrates in their indigenous microbial protein content.
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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) | 0.000 | 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 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".