The Establishment of Tandem Mass Spectrometric Fingerprints of Phytosterols and Tocopherols and the Development of Targeted Profiling Strategies in Vegetable Oils
Bibliographic record
Abstract
Phytosterols and tocopherols are essential for plant biochemistry, and they possess beneficial health effects for humans. Evaluating the tandem mass spectrometric (MS/MS) behavior of phytosterols and tocopherols is needed for the development of a qualitative and quantitative method for these biologically active plant metabolites. Herein, the MS/MS dissociation behavior of phytosterols and tocopherols is elucidated to establish generalized MS/MS fingerprints. MS/MS and multistage (MS 3 ) analysis revealed common fragmentation behavior among the four tested phytosterols, namely β-sitosterol, stigmasterol, campesterol, and brassicasterol. Similar analysis was conducted for the tocopherols (i.e., alpha (α), beta (β), gamma (γ), and delta (δ)). As such, a universal MS/MS fragmentation pathway for each group was successfully established for the first time. Based on the generalized MS/MS fragmentation behavior of phytosterols, diagnostic product ions were chosen for the development of profiling methods for over 20 naturally occurring phytosterols. A precursor ion scan-triggered-enhanced product ion scan (PIS-EPI) method was established. Due to enhanced chromatographic peaks, multiple ion monitoring-triggered-enhanced product ion scan (MIM-EPI) was employed for confirmation. The screening approach was applied successfully to identify blinded samples obtained from standard mixtures as well as sesame and olive oils. The oil samples contain other phytosterols, and their successful identification indicates that, the generalized MS/MS fragmentation behavior is applicable to various structures of phytosterols. A similar approach was attempted for tocopherols and was only hindered by the low concentration of these bioactive metabolites present in the oil samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".