Development of generic liquid chromatography-mass spectrometry methods using experimental design
Bibliographic record
Abstract
Standard approaches to development of liquid chromatography-mass spectrometry (LC-MS) methods, either ion-pairing or reversed-phase liquid chromatography, have been through trial and error or intentional variation of experimental factors. These approaches to method optimization fail to take into account interactions between experimental factors and therefore the results may not be optimal for the combination of experimental factors. Another approach to optimization is through the use of chemometrics. Chemometric approaches can be more efficient than trial and error or intentional variation because chemometrics make use of multivariate designs; experimental factors are varied simultaneously at the various levels. Therefore chemometrics can take into account interactions between factors. The goal of this study was to develop a generic ion-pair LC-MS method for the analysis of acidic compounds using a chemometric approach called design of experiments (DOE). Four acidic compounds which cover three classes of acidic functional groups: 1-naphthyl phosphate (1), 1-naphthalenesulfonic acid (2), 2-naphthalenesulfonic acid (3), and (1-naphthoxy)acetic acid (4) were used as model compounds to develop the generic method. This study illustrates that LC-MS conditions can be optimized efficiently with minimal amount of experimentation using a chemometric approach to experimental design.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".