Investigation for Pu-Erh Tea Contamination Caused by Mycotoxins in a Tea Market in Guangzhou
Bibliographic record
Abstract
Objective: The purpose of the present study is to provide raw data for the development of guidelines for tea production and management, as well as relevant health standards. To investigate the mycotoxin contamination in the wet stored Pu-erh tea in a tea market in Guangzhou, we measured the concentrations of aflatoxin B1 (AFB1), fumonisin B1 (FB1), deoxynivalenol (DON), and T-2 toxin in 70 tea samples.Methods: 70 samples of wet stored Pu-erh tea were randomly chosen in the market. Following crushing, brewing, and filtration of the samples, the contaminations of FB1, DON, or T-2 toxin were assayed by ELISA detection kits, and the contamination of AFB1 was measured by the IAC-HPLC method.Results: We found that all tea samples were safe regarding FB1 and T-2 toxin (safety limit, 1 mg/kg and 0.1×10-3mg/kg, respectively). However, 8 out of 70 samples displayed higher AFB1 concentrations compared to the safety limit(5×10-3 mg/kg). Surprisingly, 63 out of 70 samples have exceeded the safety limit for DON (1 mg/kg).Conclusion: Our survey was the first time to find AFB1 and DON contaminations in the wet stored Pu-erh tea in this tea market. Although the FB1 and T-2 toxin in these tea samples has not yet exceeded the safety limits, they were still detectable, which should cause more concern.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".