Mass Spectrometry Identification of <i>N</i>-Chlorinated Dipeptides in Drinking Water
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide 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, 35 Cl/ 37 Cl 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".