Improved Method for the Determination of Anatoxin-a and Two of Its Metabolites in Blue-Green Algae Using Liquid Chromatography with Fluorescence Detection
Bibliographic record
Abstract
Anatoxin-a, a neurotoxin produced by blue-green algae (BGA) species, can cause death to exposed organisms. In North America, BGA are harvested and sold as food supplements, some of which contain elevated levels of other algal toxins, such as microcystins. Concern that elevated levels of anatoxin-a also may be present in BGA food supplements has led to the development of a simple method to determine the presence of anatoxin-a in BGA. Some researchers have successfully analyzed this compound using liquid chromatography with fluorescence detection by forming a fluorescent derivative with 4-fluoro-7-nitrobenzofurazan (NBD-F) in water and phytoplankton extracts. With this method, the background noise is high in BGA extracts due to the presence of co-extractives. Addition of o-phthaldialdehyde (OPA) and mercaptoethanol to the extract before addition of the NBD-F resulted in the successful removal of primary amines from the background noise when the NBD-F derivatives were detected with fluorescence. Improved chromatograms were obtained when extracts were cleaned up in this manner, leading to a lower detection limit (approximately 50 microg/kg) for anatoxin-a. The detection limits obtained for the 2 degradation products dihydroanatoxin-a and epoxyanatoxin-a in BGA extracts were similarly low (55 and 65 microg/kg, respectively).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".