Determination of icewine lipids by Ultra High Performance Liquid Tandem Chromatography Quadrupole Time-of-Flight Mass Spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
Backgroud: Icewine is a unique food in the world. Lipids in icewine are nutritious and healthy for humans. However, limited studies are available on the qualitative and quantitative analysis of icewine. Method: UPLC-QTOF-MS approach to study lipids in icewine. Bioinformatics strategies will expand the applications of lipidomics in food science,OPLS-DA) was performed to visualise group separation and recognized significantly transform metabolites. Results: In the present study, lipid molecules belonging to 5 classes were qualitatively and quantitatively analysed. The lipids studied were as track: 102 triacylglycerols (TAG), 18 free fatty acids (FFA), 5 diacylglycerols (DAG), 6 ceramides and sphingosine-1-phosphate (Cer), and 1 N-palmitoyl-D-erythro-sphingosylphosphorylcholine (SM). The Shangri-La icewine has higher TAG and FFA content than the Canadian icewine. However, in Canadian icewine samples, the DAG (16:0/16:1) content (398.26 μg/mL) was higher than that of Shangri-La icewine specimens (522.43 μg/mL). The SM (14:0) content in Canadian icewine was higher than that of Shangri-La icewine. Conclusion:UPLC-QTOF-MS is an effective method for detecting lipids in icewine samples. The primary fundamental lipids in icewine samples were TAG, FFA, DAG, Cer, and SM. Therefore, Shangri-La icewine is more nutritious for human health than Canadian icewine.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it