Crown ether dopant to reduce ion suppression and improve detection in the liquid microjunction surface sampling probe
Bibliographic record
Abstract
RATIONALE: Sodium and potassium are required in agar media for the growth of some microorganisms (e.g., marine bacteria). However, alkali cations are a significant source of contamination for mass spectrometry causing ion suppression and adduct formation. Conventionally, salts can be removed before mass spectrometric analysis with appropriate and often lengthy sample preparation. The direct mass spectrometric sampling of bacterial colonies grown on agar media seeks to minimize or eliminate sample preparation to improve workflow. However, this may exacerbate ion suppression and contamination since these metal cations will degrade spectral quality and limit the rapid profiling of microbial metabolites. Different approaches are needed to sequester sodium and potassium ions to minimize unwanted background interferences. Herein, we use crown ethers (CEs) in combination with a liquid microjunction surface sampling probe (LMJ-SSP) to directly sample the surface of the bacterial colonies from two marine bacteria species (Pseudoalteromonas rubra DSM6842 and Pseudoalteromonas tunicata DSM 14096). CEs (e.g., 18-crown-6 or 15-crown-5) are added to the carrier solvent of the LMJ-SSP, the chemical noise is reduced, and spectra are easier to interpret. METHODS: O (50:49.9%) with or without 0.01% CEs. Information-theoretic measures are employed to investigate qualitative changes between spectra before and after adding CEs. RESULTS: Our work demonstrates the capability of CEs to reduce background interferences within the direct profiling of bacterial colonies from agar plates. The data obtained from both P. rubra DSM6842 and P. tunicata DSM 14096 show that CEs can be used to mitigate the salty background and improve compound detection. CONCLUSION: Our approach can be implemented in natural product discovery using LMJ-SSP to allow fast and accurate detection of interesting/novel compounds.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".