Infrared Matrix-Assisted Laser Desorption/Ionization Orthogonal-Time-of-Flight Mass Spectrometry Employing a Cooling Stage and Water Ice As a Matrix
Bibliographic record
Abstract
Although water ice has been utilized in the past as a matrix for infrared matrix-assisted laser desorption/ionization mass spectrometry (IR-MALDI-MS), it has not found a wider use due to limitations in the analytical performance and technical demands on the employment of the necessary cooling stage. Here, we developed a temperature-controlled sample stage for use with an orthogonal time-of-flight mass spectrometer (MALDI-o-TOF-MS). The stage utilizes a combination of liquid nitrogen cooling and counterheating with a Peltier element. It allows adjustment of the sample temperature between ~-120 °C and room temperature. To identify optimal irradiation conditions for IR-MALDI with the water ice matrix, we first investigated the influence of excitation wavelength, varied between 2.7 and 3.1 μm, and laser fluence on the signal intensities of molecular substance P ions. These data suggest the involvement of transient melting of the ice during the laser pulse and primary energy deposition into liquid water. As a consequence, the best analytical performance is obtained at a wavelength corresponding to the absorption maximum of liquid water of about 2.94 μm. The current data significantly surpass the previously reported analytical features. The particular softness of the method is, for example, exemplified by the analysis of noncovalently bound holo-myoglobin and of ribonuclease B. This is also the first report demonstrating the analysis of an IgG monoclonal antibody (MW ~ 150 kDa) from a water ice matrix. Untypical for MALDI-MS, high charge states of multiply protonated species were moreover observed for some of the investigated peptides and even for lacto-N-fucopentaose II oligosaccharides. Using water ice as matrix is of particular interest for MALDI MS profiling and imaging applications since matrix-free spectra are produced. The MS and tandem MS analysis of metabolites directly from frozen food samples is demonstrated with the example of a strawberry fruit.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".