LC–MS/MS Coupled with Chemometric Analysis as an Approach for the Differentiation of Fritillariae cirrhosae Bulbus and Fritillariae pallidiflorae Bulbus
Bibliographic record
Abstract
Fritillariae cirrhosae bulbus (FCB) is one of the most important traditional Chinese medicines (TCM) for the treatment of cough and phlegm. Due to increasing demand and the complexity of FCB’s botanical origin, various substitutes have appeared in the market, resulting in a major challenge to distinguish FCB and its substitutes (F. pallidiflorae bulbus, FPB). Therefore, discriminating FCB from FPB has becoming an urgent necessity. In this study, an ultra-high-performance liquid chromatography–electrospray ionization–tandem mass spectrometry (UPLC–ESI–MS/MS) method was developed for the simultaneous quantification of nine steroidal alkaloids (imperialine-3-β-D-glucoside, imperialine, verticine, verticinone, peimisine, yibeinoside A, delavine, delavinone, ebeidinone) within 8 min. According to the composition and content of the above nine compounds, multivariate chemometric analyses were applied for the classification of FCB and FPB. The quantitative results showed that there were both similarities and differences in the content of nine steroidal alkaloids between FCB and FPB, and it was difficult to directly distinguish these two species. Fortunately, with the aid of chemometric analyses, FCB and FPB were successfully differentiated by partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discrimination analysis (OPLS-DA) models based on the nine alkaloids’ content. Moreover, four compounds (yibeinoside A, ebeiedinone, delavinone and imperialine) were discovered as potential markers for the identification and differentiation of FCB and FPB. Additionally, compared to other studies, this work collected a large number of samples (49 batches of FCB and 17 batches of FPB) to ensure the reliability of the results. In conclusion, this work established a new approach for the authentication of FCB based on its active components, which provides a good reference for the quality control of FCB and will help us to understand the chemical composition differences between FCB and its adulterants further.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.000 | 0.000 |
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".