Improved Antioxidant, Antihypertensive, and Antidiabetic Activities and Tailored Emulsion Stability and Foaming Properties of Mixture of Corn Gluten and Soy Protein Hydrolysates Via Enzymatic Processing and Fractionation
Bibliographic record
Abstract
Bioactive peptides and protein hydrolysates have gained considerable attention in the food industry and functional food markets due to their diverse health effects, including antioxidant, antihypertensive, and antidiabetic properties. This study aimed to produce combined soy and corn protein hydrolysates using Alcalase (Al), modification of Al-hydrolysates through sequential hydrolysis using Flavourzyme (Al-FL), cross-linking of Al-hydrolysates using microbial transglutaminase (MTGase) (Al-TG), and fractionation of Al-hydrolysates by ultrafiltration (UF) with molecular weight (MW) cut-off of 100 (Al-F4), 30 (Al-F3), 10 (Al-F2), and 2 kDa (Al-F1). Notably, the < 2 kDa fraction (Al-F1) showcased exceptional biological activities, including antioxidant (81.54% DPPH, 98.02% ABTS), antihypertensive (95.45%), and antidiabetic effects (44.72% α-glucosidase, 77.52% α-amylase), linked to its high hydrophobic amino acid content and low molecular weights (111 and 263 Da). Conversely, the higher molecular weight fraction (Al-TG) excelled in emulsion and foam stability, attributed to its balanced amino acid profile and larger peptides (1385-7057 Da). Our findings reveal that specific protein hydrolysate fractions, particularly Al-F1 and Al-TG, are promising for applications in food and pharmaceutical formulations due to their enhanced biological and functional properties.
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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) | 0.000 | 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 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".