Untargeted metabolomics and conventional quality characterization of rowanberry pomace ingredients in meatballs
Bibliographic record
Abstract
(2%-AC), its ethanolic extract (1%-E) and extraction residue (2%-R), were tested in meatball preparation. The meatballs with 1%-E demonstrated the highest in vitro radical scavenging capacity. In the case of 1%-E the pH of meatballs was significantly lower compared to the control sample (P = 0.0132) on the 5-day. The lowest cooking loss was achieved when the meatballs contained mainly fibre-rich 2%-R. The UHPLC method detected 184 metabolites, including strong antioxidants, such as chlorogenic acids, 3',4'-methylenedioxy-5,7-dimethylepicatechin, hyperin, isoquercitrin. The 1%-E was particularly effective against the development of unpleasant off-flavours caused by carbonyl compounds. Consistently, the decrease in lipid oxidation, indicated by reduced 7-dodecenal and 2,4-heptadienal contents, has been observed following the addition of rowanberry extract to meatballs. Metabolomics coupled with conventional quality evaluations provided a deeper understanding of the potential utilization and valorisation of different rowanberry pomace extracts as meat ingredients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".