Conjugation of gluten hydrolysates with glucosamine at mild temperatures enhances antioxidant and antimicrobial properties
Bibliographic record
Abstract
Gluten represents one of the principal by-products of the wheat starch industry. Peptides obtained by wheat hydrolysis can be used for specific functional and biological activities, albeit at relatively low yields. Although the Maillard reaction (glycation) is widely used to increase functionality of proteins, its main disadvantage is the production of undesirable compounds due to high processing temperature. In this research, functional and biologically active glycopeptides were obtained from gluten. Alcalase or Flavourzyme proteases were used to hydrolyse gluten protein, and the resulting peptides were conjugated with glucosamine by enzymatic glycosylation, using transglutaminase, or through glycation. Both reactions were performed at mild temperatures (25 or 37 °C). The formation of glycopeptides depended mostly on the glycation process, as demonstrated by MALDI-TOF-MS. The bioactivities of the conjugated hydrolysates were compared to the native hydrolysates. Although a reduction in the anti-ACE activity was detected, improved DPPH scavenging activity and enhanced antimicrobial activity against Escherichia coli were observed in the glycated Alcalase-derived hydrolysates and in the glycated Flavourzyme-derived hydrolysates, respectively. This study showed that mild conditions are an alternate approach to the traditional Maillard process conducted at elevated temperatures in creating conjugated gluten hydrolysates with enhanced bioactivities.
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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".