Detection of Cyanotoxins in Algae Dietary Supplements
Bibliographic record
Abstract
Algae dietary supplements are marketed worldwide as natural health products. Although their proprieties have been claimed as beneficial to improve overall health, there have been several previous reports of contamination by cyanotoxins. These products generally contain non-toxic cyanobacteria, but the methods of cultivation in natural waters without appropriate quality controls allow contamination by toxin producer species present in the natural environment. In this study, we investigated the presence of total microcystins, seven individual microcystins (RR, YR, LR, LA, LY, LW, LF), anatoxin-a, dihydroanatoxin-a, epoxyanatoxin-a, cylindrospermopsin, saxitoxin, and β-methylamino-l-alanine in 18 different commercially available products containing Spirulina or Aphanizomenon flos-aquae. Total microcystins analysis was accomplished using a Lemieux oxidation and a chemical derivatization using dansyl chloride was needed for the simultaneous analysis of cylindrospermopsin, saxitoxin, and β-methylamino-l-alanine. Moreover, the use of laser diode thermal desorption (LDTD) and ultra-high performance liquid chromatography (UHPLC) both coupled to high resolution mass spectrometry (HRMS) enabled high performance detection and quantitation. Out of the 18 products analyzed, 8 contained some cyanotoxins at levels exceeding the tolerable daily intake values. The presence of cyanotoxins in these algal dietary supplements reinforces the need for a better quality control as well as consumer’s awareness on the potential risks associated with the consumption of these supplements.
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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 | 0.001 | 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 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".