Non-enzymatic browning in glucosamine and glucosamine-peptides reaction systems as a source of antioxidant and flavouring compounds
Bibliographic record
Abstract
Non-enzymatic browning is a common and complex reaction that occurs in everyday cooking-indeed, it is a crucial step in food processing. A typical browning process includes both the Maillard reaction and caramelization, which normally occur only at extreme temperatures to produce both desired and distinctive flavours and an intense brown colour in foods. On one hand, advanced stage Maillard reaction compounds canbe toxic, such as acrylamide and 5-hydroxymethylfurfural (5-HMF). And yet on the other hand, Maillard reaction compounds can possess bioactivities including taste enhancing, antioxidant and antimicrobial capacities; collectively these are known as Maillard reacted peptides (MRPs). This bizarre paradox among non-enzymatic browning reaction compounds has long deserved a more in depth investigation under controlled conditions. To achieve these positive bioactivities yet reduce the accumulation of the harmful compounds associated with the Maillard reaction, a research strategy was proposed to produce and understand MRPs at . lower temperatures. However, information on the antioxidant and sensorial properties MRPs at moderate temperatures is scarce. Glucosamine (GlcN) is an amino sugar recently revealed to be capable of triggering a fast Maillard reaction with protein at 25°C. Additionally, due to the presence of both a carbonyl and amino group wi~hin the same molecule, GlcN can form substantial dicarbonyls at 37°C-the precursors to desirable flavours. The main objective of this thesis was to study taste enhancement and antioxidant activity of GlcNpeptides in GlcN model systems. A total of 3 studies were designed representing the main building blocks of this project. The first study focused on the potential of GlcN to modifying protein hydrolysates at 25 and 37°C in a fish gelatin hydrolysates-GlcN model. Modification of the hydrolysates by GlcN was accomplished by two approaches: firstly, a Maillardbased glycation with GlcN, and secondly, an enzymatic glycosylation catalyzed by the transglutaminase (TGase ), condensing the primary amine group of GlcN with the carboxamide group of a glutamine residue in a peptide. GlcN-induced modification was achieved at both 25 and 37°C, and the antioxidant and antimicrobial activities were improved compared to native hydrolysates. The second study focused on the taste enhancing property of GlcN-modified hydrolysed meat proteins produced at37 and 50°C in the presence or absence of TGase. Samples were formulated into seasoning compositions and evaluated by untrained consumers. The meat protein hydrolysate was perceived as the saltiest (p<0.05) whereas the glycated hydrolysate produced at 50°C tended (p= 0.0593) to be the most savoury seasoning composition. This further confirmed the role of GlcN as an important component in modified hydrolysate by eliciting an umami taste, despite its inability to strike a balance between: eliciting saltiness and savouriness. The non-enzymatic browning of GlcN was further investigated in the third study. Chemical-physico changes and antioxidant activity were monitored in 3-level factorial models: in phosphate buffer versus ammonium hydroxide solution, at 40 versus 60°C, and incubated up to 48 h. Incubation at 40°C for 6 h produced a yellow-coloured caramel with the greatest levels of anti-radical activity and diacetyl-a volatile flavour compound of dicarbonyls. Overall, this thesis showed GlcN to be a reactive amino sugar capable of key rapid peptide reactions and self-modifications at moderate temperatures. Compounds from these GlcN-mediated non-enzymatic browning reactions not only represent important flavour and colour agents, but also showed promise as antimicrobials and antioxidants. A future paradigm shift is anticipated for GlcN, evolving from its current status as a health supplement to become a multi-functional food ingredient.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".