Impact of in vitro gastrointestinal digestion on peptide profile and bioactivity of cooked and non-cooked oat protein concentrates
Bibliographic record
Abstract
Oat (Avena sativa) is one of the most cultivated and consumed cereals worldwide. Recognized among cereals for its high protein content (12%–24%), it makes it an excellent source of bioactive peptides, which could be modified during processes such as heating and gastrointestinal digestion (GID). This work aims to evaluate the impact of heat treatment on the proteolysis of oat proteins and on the evolution of antioxidant peptide released during in vitro static GID, in terms of comparative analysis between cooked oat protein concentrate (COPC) and non-heated oat protein concentrate (OPC) samples. The protein extraction method and cooking procedure used showed no detrimental effects on protein quality. After GID, the proportion of free amino acids/dipeptides (<0.2 kDa) reached >40% for both samples (OPC and COPC), thus producing peptides with low molecular weight and enhanced bioactivity. Furthermore, during GID, the amino acid profile showed an increase in essential, positively-charged, hydrophobic and aromatic amino acids. At the end of GID, the reducing power of OPC and COPC increased >0.3 and 8-fold, respectively, in comparison to the non-digested samples; while ABTS•+ and DPPH• showed a >20-fold increase. Fe2+ chelating capacity of OPC and COPC was enhanced >4 times; similarly, Cu2+ chelation showed a >19-fold enhancement for OPC and >10 for COPC. β-carotene bleaching activity was improved 0.8 times in OPC and >9 times in COPC; the oxygen radical antioxidant capacity assay increased 2 times in OPC and >4.7 times in COPC, respectively. This study suggests that OPC after cooking and GID positively influenced the nutritional and bioactive properties of oat peptides. Thus, COPC could be used as a functional food ingredient with health-promoting effects, as hydrothermal treatment is frequently used for this type of cereals.
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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) | 0.001 | 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".