Mechanisms of molecular and structural interactions between lentil and quinoa proteins in aqueous solutions induced by pH recycling
Bibliographic record
Abstract
Summary There has been a growing interest in plant proteins due to their beneficial health effects, low cost and variety of applications in food industries. The low solubility of lentil proteins (LPs) is one of the significant factors that limit their use in food applications. Quinoa proteins (QPs), which have high water solubility, were combined with LPs at pH 12 to generate LP‐QP complexes to generate pH‐based soluble protein compounds. The LP‐QP complexes demonstrated a large surface charge with an increase solubilisation of the protein complexes by more than 85%, together with resistance to protein aggregation. The combination of LPs to QPs led to a significant increase ( P < 0.05) in unique tertiary and secondary protein structures as determined by the protein–protein interaction (PPI) technique involving pH recycling. Interactions between LPs and QPs affected the surface morphology of the protein complexes formed. Electrostatic interactions, hydrophobic forces and hydrogen bonding were indicated to play key roles in the PPIs. The capacity of pH cycling to illustrate the above protein interactions shows that this is a robust approach for assessing the emulsion and foaming properties of food proteins.
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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".