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Record W2790413290 · doi:10.1002/jms.4086

Evaluation of multiple alternative instrument platforms for targeted and non‐targeted dioxin and furan analysis

2018· article· en· W2790413290 on OpenAlexaff
Conner Stultz, Karl J. Jobst, Liad Haimovici, R. Benjamin Jones, Sladjana Besevic, Jonathan D. Byer, Kari L. Organtini, Terry Kolic, Eric J. Reiner, Frank L. Dorman

Bibliographic record

VenueJournal of Mass Spectrometry · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of TorontoMcMaster UniversityMinistry of the Environment, Conservation and Parks
FundersMinistry of Education, IndiaMinistry of Earth Sciences
KeywordsChemistryMass spectrometryGas chromatographyTriple quadrupole mass spectrometerFuranChromatographyDetection limitAnalytical Chemistry (journal)Gas chromatography–mass spectrometryTandem mass spectrometrySelected reaction monitoringOrganic chemistry

Abstract

fetched live from OpenAlex

The use of gas chromatography coupled to high-resolution magnetic sector mass spectrometers (GC-HRMS) is well established for dioxin and furan analysis. However, the use of gas chromatography coupled to triple quadrupole (MS/MS) and time of flight (TOF) mass spectrometers with atmospheric pressure ionization (API) and traditional electron ionization (EI) for dioxin and furan analysis is emerging as a viable alternative to GC-HRMS screening. These instruments offer greater versatility in the lab for a wider range of compound identification and quantification as well as improved ease of operation. The instruments utilized in this study included 2 API-MS/MS, 1 traditional EI-MS/MS, an API-quadrupole time of flight mass spectrometer (API-QTOF), and a EI-high-resolution TOF (EI-HRTOF). This study compared these 5 instruments to a GC-HRMS using method detection limit (MDLs) samples for dioxin and furan analysis. Each instrument demonstrated acceptable MDL values for the 17 chlorinated dioxin and furans studied. The API-MS/MS instruments provide the greatest overall improvement in MDL value over the GC-HRMS with a 1.5 to 2-fold improvement. The API-QTOF and EI-TOF demonstrate slight increases in MDL value as compared with the GC-HRMS with a 1.5-fold increase. The 5 instruments studied all demonstrate acceptable MDL values with no MDL for a single congener greater than 5 times that for the GC-HRMS. All 5 instruments offer a viable alternative to GC-HRMS for the analysis of dioxins and furans and should be considered when developing new validated methodologies.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.270
Teacher spread0.252 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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