Relation of amino acid composition, hydrophobicity, and molecular weight with antidiabetic, antihypertensive, and antioxidant properties of mixtures of corn gluten and soy protein hydrolysates
Bibliographic record
Abstract
Abstract New mixed Alcalase‐hydrolysates were developed using corn gluten meal (CP) and soy protein (SP) hydrolysates, namely CPH, SPH, SPH30:CPH70, SPH70:CPH30, and SPH50:CPH50. Amino acid profile, surface hydrophobicity ( H 0 ), molecular weight (MW) distribution, antioxidant activity, angiotensin‐converting enzyme (ACE), α‐amylase, and α‐glucosidase inhibitory activities, and functional characteristics of hydrolysates were determined. Hydrolysis changed the amount of hydrophilic and hydrophobic amino acid composition and significantly increased the H 0 values of hydrolysates, especially for CPH. The DPPH radical scavenging activity (RSA) was higher for CPH, SPH30:CPH70, and SPH50:CPH50 than SPH and SPH70:CPH30. Moreover, SPH, SPH70:CPH30, and SPH50:CPH50 showed lower MW than CPH, and this correlated with the higher hydrophilicity, and ABTS and hydroxyl RSA values obtained for SPH and the mixed hydrolysates with predominantly SPH. SPH70:CPH30 exhibited higher ACE, α‐glucosidase, and α‐amylase inhibitory activities among all samples due to its specific peptides with high capacity to interact with amino acid residues located at the enzyme active site and also low binding energy. At 15% degree of hydrolysis, both SPH and CPH showed enhanced solubility at pH 4.0, 7.0 and 9.0, emulsifying activity, and foaming capacity. Taken together, SPH70:CPH30 displayed strong antioxidant, antihypertensive, and antidiabetic attributes, emulsifying activity and stability indexes, and foaming capacity and foaming stability, making it a promising multifunctional ingredient for the development of functional food products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".