Effect of enzymatic Maillard reaction conditions on physicochemical properties, nutrition, fatty acids composition and key aroma compounds of fragrant rapeseed oil
Bibliographic record
Abstract
Abstract BACKGROUND A new enzymatic hydrolysis‐based process inspired by the Maillard reaction can produce strong flavored, high‐value rapeseed oil that meets safety requirements. In the present study, the effect of reaction time (10–30 min) and temperature (130–160 °C) on the physicochemical properties, nutritional status, fatty acids composition and key aroma compounds of fragrant rapeseed oil (FRO) was investigated. RESULTS An increasing reaction time and temperature substantially decreased the total tocopherol, polyphenol and sterol contents of FRO, but increased benzo[a]pyrene content, as well as the acid and peroxide values, which did not exceed the European Union legislation limit. Among the volatile components, 2,5‐dimethyl was the main substance contributing to the barbecue flavor of FRO. The 150 °C for 30 min reaction conditions produced a FRO with a strong, fragrant flavor, with high total tocopherol (560.15 mg kg−1), polyphenol (6.82 mg kg−1) and sterol (790.65 mg kg−1) contents; acceptable acid (1.60 mg g−1) and peroxide values (4.78 mg g−1); and low benzo[a]pyrene (1.39 mg g−1) content. These were the optimal conditions for the enzymatic Maillard reaction, according to the principal component analysis. Furthermore, hierarchical cluster analysis showed that reaction temperature had a stronger effect on FRO than reaction time. CONCLUSION The optimal enzymatic Maillard reaction conditions for the production of FRO are heating at 150 °C for 30 min. These findings provide new foundations for better understanding the composition and flavor profile of FRO, toward guiding its industrial production. © 2023 Society of Chemical Industry.
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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 | 0.000 |
| 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".