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Record W2477126737 · doi:10.1021/acs.analchem.5b01435

Untargeted Identification of Organo-Bromine Compounds in Lake Sediments by Ultrahigh-Resolution Mass Spectrometry with the Data-Independent Precursor Isolation and Characteristic Fragment Method

2015· article· en· W2477126737 on OpenAlexafffund
Hui Peng, Chunli Chen, David Saunders, Jianxian Sun, Song Tang, Garry Codling, Markus Hecker, Steve Wiseman, Paul D. Jones, An Li, Karl J. Rockne, John P. Giesy

Bibliographic record

VenueAnalytical Chemistry · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
FundersState Administration of Foreign Experts AffairsNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsWestern Economic Diversification CanadaChinese Academy of SciencesU.S. Environmental Protection Agency
KeywordsChemistryBromineMass spectrometryIonPolybrominated diphenyl ethersChromatographyResolution (logic)High resolutionAnalytical Chemistry (journal)Organic chemistryGeology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.479
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.260
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations90
Published2015
Admission routes2
Has abstractyes

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