Preparation of lentil and quinoa protein complexes through protein–protein interactions and water kefir–assisted fermentation to improve protein quality and functionality
Bibliographic record
Abstract
Introduction Industrial applications of lentil (LP) and quinoa (QP) proteins are limited due to their relatively poor water solubility. In this study, a combination of protein-protein interaction (PPI) and fermentation was used to improve the functionality and nutritional value of LP by conjugating them with QP. The reaction conditions between LP and QP for producing these conjugates were established. Methods The ratio of LP to QP was equal (50:50), and complexation was carried out at 25°C for 60 min. Fermentation of the solubilized LP-QP complexes (1%, w/v) for 5 days at 25°C with water kefir (5%, v/v) was carried out to enhance the protein quality and functionality of the LP-QP complexes. Results The combined technique significantly enhanced protein digestibility, decreased the proportion of α-helices in the protein structure in favor of random coil components, and improved the phenolic content of the LP-QP complexes. Digestibility increased to 87%, up from 76% for unfermented LP-QP. Moreover, the LP-QP complexes produced using the combined technique generated a highly nutritional protein with a reduced saponin content. Conclusion This research revealed that a combination of PPI and water kefir fermentation significantly enhances the nutritional and functional quality of LP, creating new opportunities for leveraging the growing popularity of plant-based proteins into high-value industrial applications.
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 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) | 0.001 | 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".