Submerged fermentation of lentil protein isolate and its impact on protein functionality, nutrition, and volatile profiles
Bibliographic record
Abstract
Fermentation of pulses as a clean processing technique has been reported to have a favorable impact on the functional and nutritional quality of the starting materials. Compared to commonly fermented pulses such as peas and chickpeas, limited information is available on the effect of fermentation on lentils, especially when using a high protein isolate (>80% protein) as compared to seeds or flours. Therefore, in the present work, lentil protein isolate was used as a feedstock for submerged fermentation with Aspergillus niger, Aspergillus oryzae, or Lactobacillus plantarum. After 48 h, the samples showed increased protein content with enhanced solubility and oil-holding capacity. Controlled fermentation, as opposed to spontaneous fermentation, maintained the high foaming capacity; however, all fermented samples had lower foam and emulsion stabilizing properties and reduced water-holding capacity compared to the control. The fermented proteins were also less digestible, possibly due to an increase in phenolics and saponins. New volatile compounds were identified in fermented samples that show promise for improved sensory attributes. Significant differences were observed in specific quality attributes depending on the microbial strain used. Further research is required to better understand the fermentative metabolism of microbial communities when provided high-protein lentil ingredients as growth substrates. PRACTICAL APPLICATION: Fermented lentil protein isolate has promising flavor profiles that may improve its sensory properties for food application.
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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 | 0.001 | 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 | 0.001 |
| 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".