Rapid HPLC-MS Method for the Simultaneous Determination of Tea Catechins and Folates
Bibliographic record
Abstract
An effective and rapid HPLC-MS method for the simultaneous separation of the eight most abundant tea catechins, gallic acid, and caffeine was developed. These compounds were rapidly separated within 9 min by a linear gradient elution using a Zorbax SB-C18 packed with sub 2 μm particles. This methodology did not require preparative and semipreparative HPLC steps. In fact, diluted tea samples can be easily analyzed using HPLC-MS as described in this study. The use of mass spectrometry detection for quantification of catechins ensured a higher specificity of the method. The percent relative standard deviation was generally lower than 4 and 7% for most of the compounds tested in tea drinks and tea extracts, respectively. Furthermore, the method provided excellent resolution for folate determination alone or in combination with catechins. To date, no HPLC method able to discriminate catechins and folates in a quick analysis has been reported in the literature.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".