Determination of Aflatoxins in Medicinal Plants by High-Performance Liquid Chromatography–Tandem Mass Spectrometry
Bibliographic record
Abstract
PURPOSE: The intention of the proposed work is to study the presence of the aflatoxins B1, B2, G1 and G2 in medicinal plants, namely Mucuna pruriens, Delphinium denudatum and Portulaca oleraceae. METHODOLOGY: The aflatoxins were extracted, purified by immunoaffinity column chromatography and analysed by high-performance liquid chromatography-tandem quadrupole mass spectrometry with electrospray ionisation (HPLC-MS/MS). Fungal count was carried out in PDA media. RESULTS: A good linear relationship was found for AFB1, AFB2, AFG1 and AFG2 at 1-10 ppb (r>0.9995). The analyte accuracy under three different spiking levels was 86.7-108.1 %, with low per cent relative standard deviations in each case. The aflatoxins can be separated within 5 to7 min using an Agilent XDB C18-column. We found that AFB1 and AFB2 were in trace amounts below the detection limit in M. pruriens whilst they were not detected in D. denudatum. P. oleraceae was found to be contaminated with AFB1 and AFB2. AFG1 and AFG2 were not detected in M. pruriens, P. oleraceae and were below the detection limit in D. denudatum. This was consistent with very low numbers of fungal colonies observed after 6 hr of incubation. CONCLUSION: The analytical method developed is simple, precise, accurate, economical and can be effectively used to determine the aflatoxins in medicinal plants and therefore to control the quality of products. The aflatoxin levels in the plant extracts examined were related to the minimal fungal load in the medicinal plants examined.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".