Mass spectrometric detection of proteins in non-aqueous media — The case of prion proteins in biodiesel
Bibliographic record
Abstract
Limitations in efficient extraction, minimization of media interferences, and suitable sample preparation methods pose significant challenges to the successful detection of protein traces in non-aqueous media. Here we present a filtration method, employing filter disks with embedded C8-modified silica particles, that allows the capture of proteins from non-aqueous sample volumes. The extraction process is followed by elution of the protein from the filter disk and by either direct mass spectrometric detection or tryptic digestion followed by peptide mapping and MS/MS fragmentation of protein-specific peptides. The method is applied to spiked biodiesel samples for the detection of prion proteins. The tryptic peptide with sequence YPGQGSPGGNR is specific for prion proteins and can be used for unambiguous identification. The developed extraction method has the potential application to be used for large-scale testing of protein impurities in non-aqueous media, for instance as a safety and quality control tool in the animal tallow-based biodiesel production process.Key words: protein detection, MALDI, non-aqueous media, filtration
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".