Enhanced functionality of fermented whey protein using water kefir
Bibliographic record
Abstract
This research is part of an effort to enhance fermented whey proteins’ quality and nutritional value. In this research, we investigated the feasibility of using a natural fermentation approach on whey proteins. We determined the protein digestibility, non-nutritive compounds (including phenolic compounds and saponins), and microbiological quality of water kefir-fermented whey proteins. Due to the water kefir-induced fermentation of whey proteins, the protein digestibility improved from 88.48 to 94.33%. The protein quality of fermented whey proteins has been modified after the water kefir fermentation, as observed with the changes in the secondary protein structure components, especially α-helices (decreased from 10.58 to 8.52%) and random coil (increased from 12.25 to 17.33%). In addition, their water solubility, surface hydrophobicity, and surface charge changed significantly (P < .05) during the fermentation time; 88.44 to 94.11%, 387.23 to 272.7 a.u. and −30.06 to −34.93 mV, respectively. Furthermore, water kefir is an excellent source of fermenting microorganisms, including lactic and acetic acid bacteria and yeast. Thus, the water kefir fermentation approach could be an effective, practical, non-thermal approach for altering whey 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".