Improving Accuracy and Confidence of Chemical Identification by Gas Chromatography/Vacuum Ultraviolet Spectroscopy-Mass Spectrometry: Parallel Gas Chromatography, Vacuum Ultraviolet, and Mass Spectrometry Library Searches.
Ian G M AnthonyMatthew R BrantleyAdam R FloydChristina A GawTouradj SoloukiPublished in: Analytical chemistry (2018)
Chemical identification often relies on matching measured chemical properties and/or spectral "fingerprints" of unknowns against their precompiled libraries. Chromatography, absorption spectroscopy, and mass spectrometry are all among analytical approaches that provide chemical measurement databases amenable to library searching. Occasionally, using conventional single-library or single-domain searches can lead to misidentification of unknowns. To improve chemical identification, we present a tandem gas chromatography/vacuum ultraviolet-mass spectrometry (GC/VUV-MS) chemical identification approach that utilizes databases from GC, VUV spectroscopy, and mass spectrometry analyses for a "multidomain" library search. Using standard chemical mixtures as well as aroma compounds as test cases, we demonstrate that multidatabase library searches utilizing GC, VUV, and MS data results in fully correct identification of chemical mixtures examined here that could only be identified with a 69.2% or an 88.5% success rate with MS or VUV library searches alone, respectively. Additionally, we introduce a library- and data domain-independent metric for evaluating the confidence of library search results. Using multidomain library searches improves both the chemical assignment accuracy and confidence.
Keyphrases
- mass spectrometry
- gas chromatography
- liquid chromatography
- tandem mass spectrometry
- high resolution mass spectrometry
- high resolution
- high performance liquid chromatography
- capillary electrophoresis
- gas chromatography mass spectrometry
- solid phase extraction
- single molecule
- magnetic resonance
- electronic health record
- simultaneous determination
- computed tomography
- multiple sclerosis
- big data
- machine learning
- bioinformatics analysis
- solid state