Comparison of Liquid Chromatography/Mass Spectrometry, ELISA, and Phosphatase Assay for the Determination of Microcystins in Blue-Green Algae Products
Bibliographic record
Abstract
More than 100 samples of blue-green algae products (consisting of Aphanizomenon, Spirulina, and unidentified blue-green algae) in the form of pills, capsules, and powders were collected from retail outlets from across Canada. The samples were extracted with 75% methanol in water and centrifuged to remove solids. Aliquots of the extracts along with spiked blank sample extracts were sent to each participating laboratory and independently analyzed for microcystins by enzyme-linked immunosorbent assay (ELISA), protein phosphatase inhibition assay, and by liquid chromatography-tandem mass spectrometry (LC-MS/MS) after sample cleanup using C18 solid-phase extraction. The results obtained by ELISA and LC-MS/MS agreed very well over a concentration range of about 0.5-35 microg/g. The colorimetric phosphatase results generally agreed with the other 2 methods. While the 2 biochemical assays measured total microcystin content compared with a standard of microcystin LR, the LC-MS/MS method measured specific microcystins (LA, LR, RR, YR) using external standards of these for identification and quantitation. Microcystin LR was found in all positive samples by LC-MS/MS. Microcystin LA was the only other microcystin found in the samples analyzed. These 2 microcystins represent essentially all the microcystins that were present in the extracts. Otherwise, the LC-MS/MS results would have been significantly lower than the results of the biochemical assays had other unknown microcystins been present.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".