Overcoming Analytical Challenges for the Detection of 27 Cyanopeptides Using a UHPLC-QqQ-MS Method in Fish Tissues
Bibliographic record
Abstract
The increasing occurrence of harmful cyanobacterial blooms in freshwater ecosystems poses important risks to aquatic organisms and human health due to the production of bioactive secondary metabolites such as cyanopeptides. While analytical methods for microcystins are well developed, there is a notable lack of validated protocols for the broader spectrum of cyanopeptides in biota. This study presents the development and validation of a robust UHPLC-QqQ-MS method for the simultaneous extraction, cleanup, and quantification of 27 cyanopeptides, including microcystins, anabaenopeptins, microginins, aeruginosins, aeruginoguanidine, and nodularin, in fish muscle, liver, and whole fish tissues. Comprehensive optimization was conducted to minimize matrix effects and analyte losses during every step of sample preparation. The method demonstrated generally high recoveries (28–98%), good precision (RSD < 20%), and sensitivity, with MQLs below 0.5 ng g−1 for most analytes. Microginins posed analytical challenges due to their amphiphilic structure, which contributed to significant losses during filtration and extraction; the reasoning is discussed. Application to wild fish collected after a mass mortality event revealed no detectable cyanopeptide contamination but confirmed the method’s suitability for comprehensive detection. This represents an important advancement in cyanopeptide analysis, offering a valuable tool for environmental risk assessment and food safety evaluation related to harmful cyanobacteria.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".