MALDI-TOF MS-based analysis of dried seed proteins immobilized on filter paper
Bibliographic record
Abstract
Abstract Matrix-assisted laser-desorption and ionization time-of-flight mass spectroscopy (MALDI-TOF MS) is commonly used for the characterization of protein-containing biological samples. For this, we have previously developed sample-preparation methods that can be used for discrimination between Impatiens species and also between regional biotypes of Himalayan balsam (Impatiens glandulifera), initially using leaf samples and, more recently, using seed material. In the current article, we have developed a further MALDI-TOF MS-based method that can be used with seeds that uses only simple equipment and minimally hazardous reagents prior to storing and/or shipping dried seed proteins immobilized on filter paper for MALDI-TOF MS analysis. We have investigated I. glandulifera regional-biotype seeds originating from four different sites within the UK for which the parent plants differ in their susceptibility to the biological control agent Puccinia komarovii var. glanduliferae. Using a combination of time-course comparisons and principal-component analysis, we have demonstrated good MALDI-TOF MS spectral conservation, even after storage for 1 month at 35°C, of dried seed-protein samples immobilized on filter paper. This method may provide a further useful tool for the matching of biological control agents optimally to susceptible (regional) target-plant biotypes, and for seed characterization and/or identification in general.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".