Aquatic fulvic acid as a matrix for matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometric analysis
Bibliographic record
Abstract
Abstract Aquatic fulvic acids (AFAs) are demonstrated to be effective matrices for the analysis of various polar compounds (ranging from 100–1500 Da) by matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOFMS). The efficiency of AFA as a matrix is shown for a wide range of test compounds, including a number of carbohydrates, cyclodextrins and peptides, with typical detection limits of ∼10 µg mL −1 . The propensity of AFA to enhance ionization through protonation of peptides, and formation of sodium and potassium adducts of carbohydrates and polyethylene glycol, was noted. Differences were observed in the performances of the two AFA matrices used, a Suwannee River, International Humic Substances Society (IHSS) standard and a locally extracted fulvic acid (LFA). For example, in the analysis of carbohydrate standards, the use of the LFA matrix typically doubled the analyte ion signal intensities and resulted in signal‐to‐noise (S/N) ratios that were 2–4 times better than when the Suwannee River AFA matrix was used. AFA was also used in the analysis of real‐world samples without extraction or purification; cantaloupe juice and acetaminophen tablets were analyzed, and glucose and acetaminophen could easily be identified as respective components. When lower concentrations of fulvic acid were used in the presence of sugars, a reversal of roles was observed in which the sugars functioned as the matrix and significantly enhanced ionization of the AFA components, while ions associated with the sugars themselves were suppressed or absent. Effective as a matrix for a variety of analytes and widely available, AFA is an attractive environmentally friendly choice for use in MALDI applications. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".