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Record W1653885881 · doi:10.1021/acs.analchem.5b01850

Headspace versus Direct Immersion Solid Phase Microextraction in Complex Matrixes: Investigation of Analyte Behavior in Multicomponent Mixtures

2015· article· en· W1653885881 on OpenAlexafffund
Emanuela Gionfriddo, Érica A. Souza-Silva, Janusz Pawliszyn

Bibliographic record

VenueAnalytical Chemistry · 2015
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsUniversity of Waterloo
FundersEuropean Social FundNatural Sciences and Engineering Research Council of CanadaSigma-Aldrich Corporation
KeywordsAnalyteChemistrySolid-phase microextractionCoatingMatrix (chemical analysis)ChromatographyAnalytical Chemistry (journal)Mass spectrometryGas chromatography–mass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

This work aims to investigate the behavior of analytes in complex mixtures and matrixes with the use of solid-phase microextraction (SPME). Various factors that influence analyte uptake such as coating chemistry, extraction mode, the physicochemical properties of analytes, and matrix complexity were considered. At first, an aqueous system containing analytes bearing different hydrophobicities, molecular weights, and chemical functionalities was investigated by using commercially available liquid and solid porous coatings. The differences in the mass transfer mechanisms resulted in a more pronounced occurrence of coating saturation in headspace mode. Contrariwise, direct immersion extraction minimizes the occurrence of artifacts related to coating saturation and provides enhanced extraction of polar compounds. In addition, matrix-compatible PDMS-modified solid coatings, characterized by a new morphology that avoids coating fouling, were compared to their nonmodified analogues. The obtained results indicate that PDMS-modified coatings reduce artifacts associated with coating saturation, even in headspace mode. This factor, coupled to their matrix compatibility, make the use of direct SPME very practical as a quantification approach and the best choice for metabolomics studies where wide coverage is intended. To further understand the influence on analyte uptake on a system where additional interactions occur due to matrix components, ex vivo and in vivo sampling conditions were simulated using a starch matrix model, with the aim of mimicking plant-derived materials. Our results corroborate the fact that matrix handling can affect analyte/matrix equilibria, with consequent release of high concentrations of previously bound hydrophobic compounds, potentially leading to coating saturation. Direct immersion SPME limited the occurrence of the artifacts, which confirms the suitability of SPME for in vivo applications. These findings shed light into the implementation of in vivo SPME strategies in quantitative metabolomics studies of complex plant-based systems.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.099
GPT teacher head0.395
Teacher spread0.296 · 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 source (direct Gemma or distilled Codex), 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

Citations91
Published2015
Admission routes2
Has abstractyes

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