Ascorbic Acid Assisted High Performance Liquid Chromatography Mass Spectrometry Differentiation of Isomeric <i>C</i>-Chloro- and <i>N</i>-Chloro-Tyrosyl Peptides in Water
Bibliographic record
Abstract
We report a new method of ascorbic acid assisted high performance liquid chromatography (HPLC) with high resolution tandem mass spectrometry (HRMS/MS) for the differentiation of isomeric N -chloro ( N -Cl) from phenol ring C -chloro ( C -Cl) peptides produced during chlorination of water. Using the specific reductive nature of ascorbic acid, we successfully identified the N -Cl isomers and C -Cl isomer, overcoming the difficulty that, due to lack of standards, these isomers cannot be separated by HPLC-HRMS. Using the new approach, we identified 36 new chlorinated products including mono-, di-, tri-, and tetra-Cl-tyrosyl dipeptides in the reaction mixture based on retention time, accurate mass, 35 Cl/ 37 Cl isotopic pattern, and characteristic MS/MS fragments. The method was further applied to investigate competitive reactions when mixed tyrosyl dipeptides were chlorinated. Tyrosyl histidine was the most reactive tyrosyl dipeptide in the mixture. The chlorinated products formed are identical when the dipeptides are chlorinated separately or as a mixture. The formation conditions and stability of the chlorinated products were also examined. With increasing chlorine dose, the number of chlorine substituents on the tyrosyl dipeptides increased from products with one/two to three/four Cl atoms. Most of the chlorinated products are stable for up to 9 days. By chlorination of tyrosyl dipeptides spiked into raw water, we projected that chlorinated tyrosyl dipeptides can form during treatment of raw water containing tyrosyl dipeptides even at low μg/L levels. This new method can be utilized for the discovery of a wide range of chlorinated peptide DBPs and the study of their formation and occurrence in 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".