Field-Free Atmospheric Pressure Photoionization–Liquid Chromatography–Mass Spectrometry for the Analysis of Steroids within Complex Biological Matrices
Bibliographic record
Abstract
A comparison study is presented in which the relative performance of a new orthogonal geometry field-free atmospheric pressure photoionization (FF-APPI) source was evaluated against both electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) for the analysis of a small panel of clinically relevant steroids, spiked within various complex biological matrices. Critical performance factors like sensitivity and susceptibility to matrix effects were assessed using a simple, isocratic, high-throughput LC-MS workflow. FF-APPI was found to provide the best performance in terms of both sensitivity and detection limit for all of the steroids included in the survey. Order-of-magnitude sensitivity advantages were realized for some low polarity analytes including both estradiol and estrone. A robust linear regression, post extraction addition method was used to evaluate the relative impact of matrix effects upon each ionization method using protein precipitated human serum, plasma and Surine (simulated urine) as standard clinical matrices. Under conditions optimized for sensitivity, both the field-free APPI and APCI sources were found to provide similarly high resistance to matrix suppression effects, while ESI performance was impacted the most dramatically. For the prototype FF-APPI source, a strong relationship was established between optimizable source parameters and the degree of ion suppression observed. Through careful optimization of vaporization temperature and nebulizer gas pressure it was possible to significantly reduce or even eliminate the impact of matrix effects, even for high-throughput LC-MS methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".