Enhancing the quality of lentil proteins via combination with whey proteins based on a dual process: a novel strategy through the incorporation of complexation and fermentation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In recent years, there has been a growing interest in developing a distinguished alternative to human consumption of animal-based proteins. The application of lentil proteins in the food industry is typically limited due to their poor solubility and digestibility. An innovative method of balancing lentil-whey protein (LP-WP) complexes with higher-quality protein properties was established to address this issue, which coupled a pH-shifting approach with fermentation treatment. The results showed that microorganisms in the water kefir influenced the quality of protein structures and enhanced the nutritional values, including increasing the total phenolic compounds and improving the flavor of fermented LP-WP complexes. The protein digestibility, pH values, microbial growth, total soluble solids, and total saponin and phenolic contents were hydrolyzed for 5 days at 25 °C. The FTIR spectrophotometer scans indicated significant ( P < 0.05) changes to the secondary protein structure components (random coil and α-helix). This study showed that combining pH-shifting with fermentation treatment improves lentil and whey proteins’ structure, protein quality, and nutritional benefits.
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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 | 0.001 |
| 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 it