Determination of trace perchlorate in water: a simplified method for the identification of potential interferences
Bibliographic record
Abstract
Perchlorate contamination of water and food poses potential health risks to humans due to the possible interference of perchlorate with the iodide uptake into the thyroid gland. Perchlorate has been found in food and drinking, surface, or swimming pool waters in many countries, including the United States, Canada, France, Germany, and Switzerland, with ion chromatography (IC) being the preferred analytical method. The standardization of a robust ion chromatographic method is therefore of the high interest for public health and safety. This article summarizes the experiments and results obtained from analyzing untreated samples, considering the sample’s electrical conductance as guidance for direct sample injection as described in EPA 314.0. The suitability of ion chromatography with suppressed conductivity detection was tested for water samples in order to check the influence of matrix effects on the perchlorate signal of untreated samples. A sample injection volume of 750 μL was applied to the selected 2 mm IC column. The IC determination of perchlorate at low µg/L levels is challenged by the presence of high loads of matrix ions (e.g., chloride, nitrate, carbonate, and sulfate at 100 mg/L and above). Perchlorate recovery is impaired with the increasing matrix ion concentrations, and its chromatographic peak is asymmetric particularly at low perchlorate concentrations. The identification of the individual maximum concentration of interfering anions like chloride, nitrate, and sulfate that influence perchlorate recovery helps to reduce the number of sample preparation steps or an obligatory measurement of the electrical conductivity of the sample. Within the scope of this study, samples containing less than 125 mg/L of either anion did not need sample preparation. The identification of the maximum concentration of interfering anions like chloride, nitrate, and sulfate influencing perchlorate recovery provides a simplified alternative to the EPA 314.0 method. This approach reduces unnecessary sample preparation steps while allowing a reliable prognosis of possible interferences and maintaining result quality. This study was performed to support the development of a respective international standard, which is being established by the International Organization for Standardization (ISO). The results of the study are also intended to be used as guidance for interested laboratories to optimize the analytical workflow for trace perchlorate determination.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.002 |
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".