Pressurized Solvent Extraction with Ethyl Acetate and Liquid Chromatography—Tandem Mass Spectrometry for the Analysis of Selected Conazole Fungicides in Matcha
Bibliographic record
Abstract
The extraction of powdered nutraceuticals is challenging due to the low water content and high concentration of matrix components that can lead to significant matrix effects in liquid chromatography-positive ion electrospray ionization-tandem mass spectrometry (LC-ESI+-MS/MS). In this study we assess the feasibility of using pressurized solvent extraction with ethyl acetate to reduce the co-extraction of polar matrix components. Pigment attributed to chlorophyll was removed with in-cell clean-up utilizing Anasorb 747, Florisil®, and C18. Visible inspection of the extracts showed that pigment was removed from matcha, a powdered green tea sample. Pressurized solvent extraction with in-cell clean-up can be utilized to remove pigments from powdered samples such as nutraceuticals. Average matrix effect of the 32 target analytes that observed mass spectrometric signal suppression or soft MS signal enhancement was −41 ± 19% with the majority of analytes having a protonated molecular ion with m/z of 250 to 412. As generally moderate signal suppression was observed for conazole fungicides and structurally related compounds analyzed by LC-ESI+-MS/MS, it is recommended that matrix matched or standard addition calibration is used for quantitation. Catachins, other polyphenols, and caffeine are expected to contribute to the matrix effects observed in LC-ESI+-MS/MS. Diniconazole, fenbuconazole, and tebufenozide were the only target analytes with severe MS signal enhancement. Low levels (0.002–0.004 mg/kg) of prothioconazole-desthio and flusilazole were detected, along with trace levels of tebuthiuron in matcha.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".