A Multi-Class Cyanobacterial Toxin Screening Method using Hydrophilic Interaction Liquid Chromatography with Tandem Mass Spectrometry (HILIC-MS/MS)
Bibliographic record
Abstract
Cyanobacteria can produce diverse classes of toxins including microcystins (MCs), anatoxins (ATXs), cylindrospermopsins (CYNs), and saxitoxins (STXs). These range in polarities and molecular weights, generally requiring multiple preparation and chromatographic techniques for evaluation. Here we present the development and validation of a HILIC-MS/MS screening method for the detection and quantitation of all the aforementioned toxin classes. Various solvents and sample-to-solvent ratios were investigated with an in-house blend of toxic cyanobacteria to develop a universal extraction method using 75% acetonitrile-water (0.1% formic acid). HILIC-MS/MS was used with gradient elution (35 min run time) with selected reaction monitoring (SRM) settings optimized on a triple quadrupole MS using positive/negative polarity switching. Retention time matching with standards and product ion ratios were assessed for identification. Validation included evaluation of the calibration models, precision, and detection limits (between 0.2 (ATX) and 7 (GTX4) ng/mL). Excellent recoveries (> 99%) were obtained using spiked algal extracts and in‑house reference materials. This method was applied to cultures, cyanobacterial dietary supplements, Canadian bloom and benthic algal samples, water, and shellfish samples, demonstrating suitability for screening and quantitation of all major cyanobacterial toxin classes in a range of sample types. Future applications include the characterization of cyanotoxin matrix reference materials.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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".