Simultaneous identification and quantification of bisphenol A and 12 bisphenol analogues in environmental samples using precolumn derivatization and ultra high performance liquid chromatography with tandem mass spectrometry
Bibliographic record
Abstract
A method for the identification and quantification of bisphenol A and 12 bisphenol analogues in river water and sediment samples combining liquid-liquid extraction, precolumn derivatization, and ultra high-performance liquid chromatography coupled with tandem mass spectrometry was developed and validated. Analytes were extracted from the river water sample using a liquid-liquid extraction method. Dansyl chloride was selected as a derivatization reagent. Derivatization reaction conditions affecting production of the dansyl derivatives were tested and optimized. All the derivatized target compounds were well separated and eluted in 10 min. Dansyl chloride labeled compounds were analyzed using a high-resolution mass spectrometer with electrospray ionization in the positive mode, and the results were confirmed and quantified in the parallel reaction monitoring mode. The method validation results showed a satisfactory level of sensitivity. Linearity was assessed using matrix-matched standard calibration, and good correlation coefficients were obtained. The limits of quantification for the analytes ranged from 0.005 to 0.02 ng/mL in river water and from 0.15 to 0.80 ng/g in sediment. Good reproducibility of the method in terms of intra- and interday precision was achieved, yielding relative standard deviations of less than 10.1 and 11.6%, respectively. Finally, this method was successfully applied to the analysis of real samples.
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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 | 0.000 |
| 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) | 0.000 | 0.000 |
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".