Mass Spectrometry Identification of <i>N</i>-Chlorinated Dipeptides in Drinking Water
Bibliographic record
Abstract
Abstract We report the identification of N-chlorinated dipeptides as chlorination products in drinking water using complementary high-resolution quadrupole time-of-flight (QTOF) and quadrupole ion-trap mass spectrometry techniques. First, three model dipeptides, tyrosylglycine (Tyr-Gly), tyrosylalanine (Tyr-Ala), and phenylalanylglycine (Phe-Gly), reacted with sodium hypochlorite, and these reaction solutions were analyzed by QTOF. N-Cl-Tyr-Gly, N,N-di-Cl-Tyr-Gly, N-Cl-Phe-Gly, N,N-di-Cl-Phe-Gly, N-Cl-Tyr-Ala, and N,N-di-Cl-Tyr-Ala were identified as the major products based on accurate masses, 35Cl/37Cl isotopic patterns, and MS/MS spectra. These identified N-chlorinated dipeptides were synthesized and found to be stable in water over 10 days except N,N-di-Cl-Phe-Gly. To enable sensitive detection of N-chlorinated dipeptides in authentic water, we developed a high-performance liquid chromatography–tandem mass spectrometry (HPLC–MS/MS) method with multiple reaction monitoring (MRM) mode. N-Cl-Tyr-Gly, N,N-di-Cl-Tyr-Gly, N-Cl-Phe-Gly, N-Cl-Tyr-Ala, and N,N-di-Cl-Tyr-Ala along with their corresponding dipeptides were detected in authentic tap water samples. The dipeptides were clearly detected in the raw water, but the N-chlorinated dipeptides were at background levels. These results suggest that the N-chlorinated dipeptides are produced by chlorination. This study has identified N-chlorinated dipeptides as new disinfection byproducts in drinking water. The strategy developed in this study can be used to identify chlorination products of other peptides in drinking water.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".