Investigation of the impact of brewing parameters on toxic element and rare earth element contamination in oolong tea
Bibliographic record
Abstract
Introduction: With the growing consumption of oolong tea, concerns regarding the leaching of toxic elements and rare earth elements (REEs) during brewing necessitate investigation. Methods: We analyzed 108 oolong teas of diverse origins and varieties. The concentrations of six toxic elements (including Pb, Cd, Al) and fifteen REEs were measured by ICP-MS. The effects of water temperature (90°C, 100°C) and brewing time (5 seconds to 2 hours) on leaching rates were systematically examined. Results: <0.05) the leaching of most elements. Tieguanyin tea contained the highest levels of Pb, Al, and REEs. Samples from Fujian province significantly exceeded safety standards for Pb and Al. Anomalously, the leaching rate of Cd was lower at 100°C than at 90°C, while the release of scandium (Sc) increased with temperature. Discussion: This study reveals that brewing conditions are critical for elemental migration. To minimize the intake of harmful substances, consumers are advised to shorten the brewing time. We also call for strengthened regulatory standards for toxic elements and REEs in tea. These findings provide a scientific basis for guiding safe tea consumption practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".