Peptidomics Analysis of Soy Protein Hydrolysates—Antioxidant Properties and Mechanism of their Inhibition of the Oxidation of Palm Olein during Frying Cycles
Bibliographic record
Abstract
This study determined for the first time the structure of the peptides (i.e., peptidomics) in soy protein hydrolysates and elucidated their effects on an oil's oxidative stability during frying cycles. The oil investigated was palm olein during 0, 4, 8, and 12 frying cycles of plantain banana chips. Proteins were extracted and hydrolyzed with two proteases. Trypsin hydrolysate (HTRY) exhibited higher anti-radical activity (DPPH, 70.2%) than the control (unhydrolyzed proteins, 33.49%) and pepsin hydrolysate (HPEP, 46.1%) at 200 µg/mL. HPEP however showed a 4.6-fold greater reduction of ferric ions (FRAP) while also possessing a higher peroxyl radical scavenging ability (716 ± 30 µM Trolox Eq/g) than HTRY (38.5 ± 35 µM Trolox Eq/g). During oil oxidative stability tests, HPEP improved the oxidative stability of the palm olein oil after 8 and 12 frying cycles, characterized by lower concentrations of hydroperoxides, and carbonyl and volatile compounds. HTRY however exerteda pro-oxidant activity. Structural data from SDS-PAGE and tandem mass spectrometry showed that the mechanism for the greater activity of the pepsin hydrolysate occurred due to unique structural features and a higher percentage of short-chain peptides. This was justified by a 25, 31, and 48% higher contents of tryptophan, histidine, and methionine, respectively (important amino acids with hydrogen atom transfer and electron-donating capacities) in the peptides identified in the pepsin hydrolysate.
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 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".