Direct Analysis in Real Time Mass Spectrometry for Characterization of Large Saccharides
Bibliographic record
Abstract
Polysaccharide characterization posts the most difficult challenge to available analytical technologies compared to other types of biomolecules. Plant polysaccharides are reported to have numerous medicinal values, but their effect can be different based on the types of plants, and even regions of productions and conditions of cultivation. However, the molecular basis of the differences of these polysaccharides is largely unknown. In this study, direct analysis in real time mass spectrometry (DART-MS) was used to generate polysaccharide fingerprints. Large saccharides can break down into characteristic small fragments in the DART source via pyrolysis, and the products are then detected by high resolution MS. Temperature was shown to be a crucial parameter for the decomposition of large polysaccharide. The general behavior of carbohydrates in DART-MS was also studied through the investigation of a number of mono- and oligosaccharide standards. The chemical formula and putative ionic forms of the fragments were proposed based on accurate mass with less than 10 ppm mass errors. Multivariate data analysis shows the clear differentiation of different plant species. Intensities of marker ions compared among samples also showed obvious differences. The combination of DART-MS analysis and mechanochemical extraction method used in this work demonstrates a simple, fast, and high throughput analytical protocol for the efficient evaluation of molecular features in plant polysaccharides.
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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 | 0.001 |
| 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) | 0.002 | 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".