Rapid fingerprint analysis for herbal polysaccharides using direct analysis in real‐time ionization mass spectrometry
Bibliographic record
Abstract
RATIONALE: Herbal polysaccharides have various potential medicinal values. Development of reliable analytical method for the fingerprint analysis of polysaccharides is critical for their quality assessment, origin identification, and authenticity evaluation. METHODS: Mechanochemical extraction (MCE) was used to extract polysaccharide components from different herbal species. Intact polysaccharides were then directly analyzed by direct analysis in real-time mass spectrometry (DART-MS). Standard addition method with isotope-labeled internal standard was used to quantify polysaccharide amounts directly from liquid extract. Multivariate data analysis was further conducted for species classification. RESULTS: The intact and large polysaccharides were decomposed into small fragment ions less than m/z 350 instantaneously using DART ion source. Different polysaccharides showed distinguished fingerprint DART-MS spectra using both individual and mixed herbal species. The liquid supernatant from MCE was validated to be used as direct sample for DART-MS analysis. Quantitation was successfully achieved for polysaccharide contents in Dendrobium officinale from different locations. CONCLUSIONS: A rapid fingerprint protocol in combination of MCE and DART-MS for herbal polysaccharides was developed. The whole process could be accomplished within a few minutes, from raw materials to final spectra, without requirements of pre-digestion and additional sample purification.
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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 | 0.001 |
| 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".