Measurement of Microcystin -LR in Water Samples Using Improved HPLC Method
Bibliographic record
Abstract
Microcystins are a group of toxic compounds produced by freshwater cyanobacteria and cause diseases. World Health Organization has recommended a concentration of 1 µg/l for Microcystin-LR (MC-LR) in potable water as guideline value. The high performance liquid chromatography (HPLC) followed by C18 analytical column and ultra violet detector for detection of MC-LR. In this regard, 5 different concentrations of MC-LR solutions were injected into HPLC. MC-LR was detected in 5.33 minute retention time and Calibration curve was achieved with R(2) = 0.988. Detection limit for this method was obtained by using acetonitrile solutions (32% and 55%) as a gradient run and a high silanol activity column equal to 0.02 µg /mL. Despite no acidic organic modifier being used in the mixture of solvents, the sensitivity of this method was appropriate for detection of MC-LR. Because of short retention time, reduction in number of solvents and high resolution and suitable sensitivity, this method is affordable and is fast for detection and determination of MC-LR in potable water.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".