Separation and Quantitation of the Stereoisomers of Ephedra Alkaloids in Natural Health Products Using Flow Injection-Electrospray Ionization-High Field Asymmetric Waveform Ion Mobility Spectrometry-Mass Spectrometry
Bibliographic record
Abstract
A method is described for the determination of ephedrine (E) and pseudoephedrine (PE) and their metabolites norephedrine (NE), norpseudoephedrine (NPE), methylephedrine (ME), and methylpseudoephedrine (MPE) alkaloids in natural health products by flow injection-electrospray ionization-high field asymmetric waveform ion mobility spectrometry-mass spectrometry (FI-ESI-FAIMS-MS). The determination of the six alkaloids requires the separation of diastereomic pairs of E-PE, NE-NPE, and ME-MPE. FAIMS was able to resolve/separate these isomeric pairs based on their gas-phase ion mobility differences. The FAIMS-based separation and detection approach has been tested on over-the-counter diet pills. Following the extraction of the tablets, either by pressurized fluid extraction developed in-house or with sonication, the ephedra alkaloids were quantified using a modified isotope dilution approach. Detection limits for the alkaloids ranged from 0.1 to 3 ng/mL, and a linear range of at least 2 orders of magnitude was observed for the six analytes. The throughput of the current configuration of the FI-ESI-FAIMS-MS system is 2 min/sample, which is significantly higher than conventional chromatographic approaches. The developed FI-ESI-FAIMS-MS method has been compared with a conventional LC-UV analysis, and good agreement has been found for the major alkaloids.
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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 | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".