In vitro antioxidant and antihypertensive properties of sesame seed enzymatic protein hydrolysate and ultrafiltration peptide fractions
Bibliographic record
Abstract
The objective of this study was to determine the in vitro antioxidant and antihypertensive potentials of sesame seed protein hydrolysate and its membrane ultrafiltration peptide fractions in comparison to the unhydrolyzed protein. Sesame seed protein isolate (SESPI) was prepared from the defatted sesame seed meal and then hydrolyzed using consecutive additions of pepsin and pancreatin to yield sesame protein hydrolysate (SESPH). The SESPH was subjected to membrane ultrafiltration consecutively to obtain fractions with peptide sizes of <1, 1-3, 3-5, and 5-10 kDa, respectively, which were then assayed for in vitro antioxidant and antihypertensive properties. The results showed that protein hydrolysis and fractionation led to significant (p < .05) increases in the content of hydrophobic amino acids. Radical scavenging and metal ion chelation were also significantly (p < .05) enhanced by these treatments. Inhibition of linoleic acid oxidation was stronger with the 1.0 mg/ml of sesame peptide samples in comparison to the mild inhibitory effect exhibited by the 0.5 mg/ml of samples. The <1 kDa peptide fraction was the most active inhibitor (81%) against angiotensin converting enzyme, whereas the bigger peptides (>3-5 and 5-10 kDa) were the most effective (75%-85% ) inhibitors against renin. These sesame products could be used as therapeutic agents in the development of health enhancing foods for the prevention and management of chronic diseases. PRACTICAL APPLICATIONS: Bioactive peptides have been produced from plant protein sources through in vitro enzymatic activities. Sesame seed peptides have demonstrated multifunctional potential to act as antioxidative and antihypertensive agents that could be utilized as ingredients for the development of novel functional foods and nutraceuticals.
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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".