Fast Quantification Without Conventional Chromatography, The Growing Power of Mass Spectrometry
Bibliographic record
Abstract
Mass spectrometry (MS) in hyphenated techniques is widely accepted as the gold standard quantitative tool in life sciences. However, MS possesses intrinsic analytical capabilities that allow it to be a stand-alone quantitative technique, particularly with current technological advancements. MS has a great potential for simplifying quantitative analysis without the need for tedious chromatographic separation. Its selectivity relies on multistage MS analysis (MS n ), including tandem mass spectrometry (MS/MS), as well as the ever-growing advancements of high-resolution MS instruments. This perspective describes various analytical platforms that utilize MS as a stand-alone quantitative technique, namely, flow injection analysis (FIA), matrix assisted laser desorption ionization (MALDI), including MALDI-MS imaging and ion mobility, particularly high-field asymmetric waveform ion mobility spectrometry (FAIMS). When MS alone is not capable of providing reliable quantitative data, instead of conventional liquid chromatography (LC)-MS, the use of a guard column (i.e., fast chromatography) may be sufficient for quantification. Although the omission of chromatographic separation simplifies the analytical process, extra procedures may be needed during sample preparation and clean-up to address the issue of matrix effects. The discussion of this manuscript focuses on key parameters underlying the uniqueness of each technique for its application in quantitative analysis without the need for a chromatographic separation. In addition, the potential for each analytical strategy and its challenges are discussed as well as improvements needed to render them as mainstream quantitative analytical tools. Overcoming the hurdles for fully validating a quantitative method will allow MS alone to eventually become an indispensable quantitative tool for clinical and toxicological studies.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".