Mass Spectrometric Approaches for the Analysis of Phytosterols in Biological Samples
Bibliographic record
Abstract
Plant sterols (phytosterols) are important structural components of plant cellular membranes, and they play a major role during development and metabolism. They have health-associated benefits, especially in lowering blood cholesterol levels. Because of their many health claims, there is a growing interest in their analysis. Although various analytical strategies have been employed in analyzing phytosterols, chromatography linked to mass spectrometry (MS) is superior due to its sensitivity. Furthermore, specificity and selectivity are enhanced by utilizing tandem mass spectrometry (MS/MS). This article reviews the various mass spectrometric strategies used for the analysis of phytosterols. It highlights the applications and limitations associated with each MS strategy in various sample matrixes such as plant, human, animal, food, and dietary supplements. GC-MS was historically the method of choice for analysis; however, the derivatization step rendered it tedious and time-consuming. On the other hand, liquid chromatography coupled to MS (LC-MS) simplifies the analysis. Many ionization techniques have been used, namely, electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI), and atmospheric pressure photoionization (APPI). APCI showed superiority in terms of ion intensity and consistency in ion formation, primarily forming [M + H – H 2 O] + ions rather than [M + H] + . In addition, matrix assisted laser desorption ionization (MALDI) as well as ambient mass spectrometry such as direct analysis in real time (DART) have also been evaluated.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".