Linear and Nonlinear Regimes of Electrospray Signal Response in Analysis of Urine by Electrospray Ionization-High Field Asymmetric Waveform Ion Mobility Spectrometry-MS and Implications for Nontarget Quantification
Bibliographic record
Abstract
Quantitative nontarget analysis is intended to provide a measurement of concentration of newly identified components in complex biological or environmental samples for which authentic or labeled standard do not exist. Electrospray ionization-high field asymmetric waveform ion mobility spectrometry-mass spectrometry (ESI-FAIMS-MS) has unique advantages that allowed us to develop a novel approach for quantification of nontarget analytes. In the nontarget analysis of urinary metabolites by ESI-FAIMS-MS, we find it practical and beneficial to analyze highly diluted urine samples. We show that urine extracts can be analyzed directly at very high dilutions (up to 20,000 times) by extending MS analysis times during slow FAIMS scanning. We explore the effects of sample dilution on ionization efficiency and ionization suppression in direct electrospray of complex sample matrixes. We consistently observe two distinct regimes in ESI operation related to the limited ionization capacity of this method. In the linear dynamic concentration range below the limiting ionization capacity, the analytical sensitivity of an analyte is constant and does not depend on matrix composition and concentration. Once the capacity of ESI is exceeded, all species exhibit log-log linearity in signal response. We show how quantification can be carried out using two different approaches, one for analytes which can be detected in the linear regime and another for those only detected in the suppression regime that overcomes the effects of ionization suppression. Our new insight into ionization suppression effects in ESI is of broad interest to anyone using ESI as an ionization technique for the MS analysis of complex samples.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".