Quantification of caffeoylquinic acids and triterpenes as targeted bioactive compounds of Centella Asiatica in extracts and formulations by liquid chromatography mass spectrometry
Bibliographic record
Abstract
(CA) is a culinary vegetable and well-known functional food that is widely used as a medicinal herb and dietary supplement. CA is rich in pentacyclic triterpenes (TTs), including asiaticoside (AS), madecassoside (MS) and the related aglycones asiatic acid (AA), madecassic acid (MA). Traditionally, TTs have been associated with the bioactivity and health promoting effect of CA. Recently, mono-caffeoylquinic acids (MonoCQAs) and di-caffeoylquinic acids (DiCQAs) have been found to contribute to the bioactivity of CA as well. This work reports an analytical strategy based on liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM-MS) for the simultaneous rapid and accurate quantification of 12 bioactive compounds in CA, namely AS, MS, AA, MA, 5-CQA, 4-CQA, 3-CQA, 1,3-DiCQA, 3,4-DiCQA, 1,5-DiCQA, 3,5-DiCQA, 4,5-DiCQA. Method selectivity, accuracy, precision, repeatability, robustness, linearity range, limit of detection (LOD), and limit of quantitation (LOQ) were validated. The validated LC-MRM-MS method has been successfully applied to quantify the 12 bioactive compounds in CA aqueous extracts and two related formulations: a standardized CA product (CAP) used in a phase I clinical trial and formulated CA rodent diets used in preclinical studies. The validated method allows us to support the standardization of CA products used for clinical trials and conduct routine LC-MRM-MS analyses of formulated preclinical diets to confirm correct levels of CA phytochemical markers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".