Bibliographic record
Abstract
Botanicals are used in many countries for medicinal and general health-promoting purposes. Numerous natural occurrences of mycotoxins in botanicals and dried fruits have been reported. Aflatoxins or ochratoxin A (OTA) have been found in botanicals such as ginseng, ginger, liquorice, turmeric, and kava-kava in the USA, Spain, Argentina, India, and some other countries, while fumonisins have been found in medicinal wild plants in South Africa and in herbal tea and medicinal plants in Turkey. Zearalenone was identified in ginseng root. Dried fruits can be contaminated with aflatoxins, OTA, kojic acid, and, occasionally, with patulin or zearalenone. One main area of concern is aflatoxins in dried figs; bright greenish yellow fluorescence under ultraviolet light is associated with aflatoxin contamination. OTA in dried vine fruits (raisins, sultanas, and currants) is another concern. There are also reports of aflatoxins in raisins and OTA in dried figs, apricots, dried plums (prunes), dates, and quince. Maximum permitted levels in the European Union include 4 microg kg(-1) for total aflatoxins in dried fruit intended for direct consumption and 10 microg kg(-1) for OTA in dried vine fruit. This review discusses the occurrence of mycotoxins in botanicals and dried fruits and analytical issues such as sampling, sample preparation, and methods for analysis. Fungal contamination of these products, the influence of sorting, storage, and processing, and prevention are also considered.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".