Determination of icewine lipids by Ultra High Performance Liquid Tandem Chromatography Quadrupole Time-of-Flight Mass Spectrometry
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".