Effects of Beer Based Marinades on the Plasmalogen Content and Composition of Grilled Ruminant Meats
Bibliographic record
Abstract
Plasmalogens are important phospholipids essential for maintaining cardiovascular and brain health. Ruminant meats are excellent dietary sources of plasmalogens. Globally, grilling remains a popular technique for preparing meats. However, little is known concerning how marination affects retention and quality of plasmalogens in grilled ruminant meats. Here we present information on effects of two unfiltered beer-based marinades infused with herbs and spices on plasmalogens in grilled beef and moose meats. Although total plasmalogen contents of marinated grilled meats were lower compared to unmarinated controls; compositionally, wheat ale- and India session ale-based marinades retained higher levels of PUFA plasmalogen PC (phosphatidylcholine) and PE (phosphatidylethanolamine) species enriched with ω3 and ω6 fatty acids in grilled moose meats. In grilled beef, significantly higher levels of plasmalogen PC species enriched with monounsaturated fatty acids (MUFA) and PUFA were retained by Wheat ale-based marinade. Furthermore, strong positive correlations were observed between antioxidants, polyphenols, oxygenated terpenes and plasmalogens retained in the marinated grilled meats which contrasted negative correlations with total oxidation status of the marinated grilled meats. These findings appear to suggest that the phenolics, oxygenated terpenes and antioxidants present in the beer-based marinades preserved these plasmalogens in marinated meats against degradation during grilling. In view of the benefits associated with plasmalogens and essential fatty acid consumption, marination of beef and moose meats with unfiltered beer-based marinades could be useful for retaining MUFA and PUFA-enriched plasmalogens, as well as preserving the nutritional quality of grilled beef and moose meats.
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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".