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Reiss, R.* ; Gruber, B. ; Klingbeil, S.* ; Gröger, T.M. ; Ehlert, S.* ; Zimmermann, R.

Evaluation and application of gas chromatography - vacuum ultraviolet spectroscopy for drug- and explosive precursors and examination of non-negative matrix factorization for deconvolution.

Spectrochim. Acta A 219, 129-134 (2019)

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Since the introduction of a benchtop vacuum ultraviolet (VUV) absorption spectroscope with an increased wavelength range towards to the high energetic ultraviolet radiation, gas chromatography coupled to VUV has been proven a powerful tool in several fields of application such as petroleomics, permanent gas analytic, pesticide analytic and many more. In this study, the potential of GC-VUV for investigations was examined, focusing on drug-and explosive precursors as well as chemical warfare simulants. The ability of VUV absorption spectra to differentiate isomers is presented, among others for nitroaromatics. In addition, the limit of detection for target compounds was determined to 0.7 ng absolute on column. Furthermore, non-negative matrix factorization (NMF) was successfully implemented as alternative deconvolution approach and evaluated for the deconvolution of unknown substances. In comparison, the spectral library-based deconvolution was applied to a standard mixture and a simulated case study. The results reveal that the NMF is a useful additional tool for deconvolution because, unlike library-based deconvolution, it allows to investigate unknown substances as well.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Vuv ; Drugs ; Precursor ; Deconvolution ; Forensic Chemistry ; Isomeric Separation; Mass-spectrometry; Resolution; Spectra

ISSN (print) / ISBN 1386-1425

e-ISSN 1873-3557

Journal Spectrochimica acta, Part A

Quellenangaben Volume: 219, Pages: 129-134

Publisher Elsevier

Publishing Place Amsterdam [u.a.]

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Cooperation Group Comprehensive Molecular Analytics (CMA)