Antioxidant and enzymes inhibitory properties of Amaranth leaf protein hydrolyzates and ultrafiltration peptide fractions
Bibliographic record
Abstract
Amaranth leaf protein isolate (ALI) was hydrolyzed using four different proteases (alcalase, trypsin, pepsin, and chymotrypsin) followed by fractionation of the pepsin hydrolyzate (PH) into different sizes using ultrafiltration membrane. Gel permeation chromatography showed that all the hydrolyzates had smaller size peptides (<7 kDa) than the protein isolate (>32 kDa). The chymotrypsin hydrolyzate had higher contents of hydrophobic amino acid (44.95%) compared to alcalase (42.72%), pepsin (43.93%), and trypsin (40.95%) hydrolyzates. The PH had stronger DPPH, hydroxyl radical, and superoxide radical scavenging activities than the other protein hydrolyzates but weaker Ferric reducing antioxidant power and metal chelating activities when compared to the peptide fractions. The <1 kDa peptide fraction exhibited stronger DPPH, hydroxyl, and superoxide radicals scavenging activities than the higher molecular weight (>1 kDa) fractions. Fractionation of PH also resulted in enhanced inhibition of α-amylase and ACE activities but weaker α-glucosidase inhibition. PRACTICAL APPLICATIONS: ALI was hydrolyzed using four proteases to produce protein hydrolyzates. The most active of the hydrolyzate was then fractionated to produce fractions of different molecular sizes. The results of the analyses showed that the hydrolyzates and the fractions showed good antioxidant and enzyme inhibitory activities such as the inhibition of ACE, α-amylase, and glucosidase enzymes. The results suggest that the enzymatic hydrolyzates and peptide fractions could be used as ingredients in the nutraceutical and functional food industries to scavenge free radicals and inhibit angiotensin-converting enzyme activity.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".