Untargeted Identification of Organo-Bromine Compounds in Lake Sediments by Ultrahigh-Resolution Mass Spectrometry with the Data-Independent Precursor Isolation and Characteristic Fragment Method
Bibliographic record
Abstract
While previous studies have found that unknown natural and synthetic organo-bromine compounds (NSOBCs) contributed more than 99% of the total organic bromine (Br) in the environment, there was no efficient method for untargeted screening to identify NSOBCs in environmental matrixes. A novel untargeted method for identifying NSOBCs, based on ultrahigh-resolution mass spectrometry (UHRMS) with the Q Exactive instrument was developed. This method included a data-independent precursor isolation and characteristic fragment (DIPIC-Frag) procedure to identify NSOBCs. A total of 180 successive 5-m/z-wide windows were used to isolate precursor ions. This resulted in a sufficient dynamic range and specificity to identify peaks of Br fragment ions for analysis. A total of 2520 peaks of NSOBC compounds containing Br were observed in sediments from Lake Michigan, United States. A new chemometric strategy which combined chromatographic profiles, isotopic peaks, precursor isolation window information, and intensities was used to identify precursor ions and chemical formulas for detecting NSOBCs. Precursor ions for 2163 of the 2520 NSOBCs peaks (86%) were identified, and chemical formulas for 2071 NSOBCs peaks (82%) were determined. After exclusion of isotopic peaks, 1593 unique NSOBCs were identified and chemical formulas derived for each. Most of the compounds identified had not been reported previously and had intensities which were 100- to 1000-fold greater than the congeners of polybrominated diphenyl ethers (PBDEs). In extracts of sediments, these compounds exhibited variations in intensities (<10(3) to ∼10(8)), m/z values (170.9438-997.5217), retention times on a C18 column (1.0-29.3 min), and the number of Br atoms (1-8). Generally, compounds with greater m/z values had longer retention times and greater numbers of Br atoms. Three compounds were used in a proof-of-concept experiment to demonstrate that structures of some of the screened NSOBCs could be further predicted by combining searching of database libraries and high-resolution MS(2) spectra.
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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 | 0.001 | 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".