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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".