Structural interactions of complex proteins with trehalose: mechanisms for improving the multilevel structure and functional properties of lentil-quinoa protein complexes
Bibliographic record
Abstract
This study aimed to explore the impact of novel method in improving the functionality and nutritional value of lentil-quinoa protein complexes. The method involved manipulating protein interactions through pH shifting and the addition of trehalose, ultimately enhancing water solubility, digestibility, and the functionality. Fluorescence, ultraviolet, and Fourier-transform spectroscopic techniques were applied to characterize the structural and molecular properties of the trehalose-conjugated lentil-quinoa protein complexes at various concentrations. After trehalose conjugation, statistically significant alterations (p < 0.05) in the tertiary and secondary protein structures and protein conformation were observed with trehalose-conjugated lentil-quinoa protein complexes at a trehalose concentration of 5% (w/w). The water solubility and digestibility of the latter conjugate-derived lentil and quinoa complex increased from 75.1 to 80% and 75 to 80.3%, respectively. Modifications in surface properties after conjugation involved significant (p < 0.05) changes in surface charge and hydrophobicity. Overall, combining the protein complexes with trehalose enhanced the digestibility and solubility of the protein complexes and improved their physical properties. The study highlights the potential of this approach to develop more sustainable and efficient plant-based protein sources for various food and nutritional applications in the food industry.
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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".