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Solid‐phase microextraction: a powerful sample preparation tool prior to mass spectrometric analysis
Abstract
Sample preparation is an essential step in analysis, greatly influencing the reliability and accuracy of resulted the time and cost of analysis. Solid‐Phase Microextraction (SPME) is a very simple and efficient, solventless sample preparation method, invented by Pawliszyn in 1989. SPME has been widely used in different fields of analytical chemistry since its first applications to environmental and food analysis and is ideally suited for coupling with mass spectrometry (MS). All steps of the conventional liquid–liquid extraction (LLE) such as extraction, concentration, (derivatization) and transfer to the chromatograph are integrated into one step and one device, considerably simplifying the sample preparation procedure. It uses a fused‐silica fibre that is coated on the outside with an appropriate stationary phase. The analytes in the sample are directly extracted to the fibre coating. The SPME technique can be routinely used in combination with gas chromatography, high‐performance liquid chromatography and capillary electrophoresis and places no restriction on MS. SPME reduces the time necessary for sample preparation, decreases purchase and disposal costs of solvents and can improve detection limits. The SPME technique is ideally suited for MS applications, combining a simple and efficient sample preparation with versatile and sensitive detection. This review summarizes analytical characteristics and variants of the SPME technique and its applications in combination with MS. Copyright © 2004 John Wiley & Sons, Ltd.
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Metadata
- Authors
- Vas, G., & Vékey, K.
- Venue
- Journal of Mass Spectrometry
- Publisher
- Wiley
- Published
- 2004-03
- Volume
- 39
- Issue
- 3
- Pages
- 233-254
- ISSN
- 1076-5174 (ISSN-L)
- 1096-9888
- OpenAlex
- W1985216953
- Cited by
- 615
- References
- 117
- FWCI
- 24.78
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.