Computational Mass Spectrometry
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Conventional mass spectrometry sensing has isomorphic nature, which means measure the input mass spectrum abundance function by a resemble of delta function to avoid ambiguity. However, the delta function nature of traditional mass spectrometry sensing approach imposes trade-offs between mass resolution and throughput/mass analysis time. This dissertation proposes a new field of mass spectrometry sensing which combines both computational signal processing and hardware modification to break the above trade-offs. We introduce the concept of generalized sensing matrix/discretized forward model in mass spectrometry filed. The presence of forward model can bridge the cap between sensing system hardware design and computational sensing algorithm including compressive sensing, feature/variable selection machine learning algorithms, and stat-of-art inversion algorithms.
Throughout this dissertation, the main theme is the sensing matrix/forward model design subject to the physical constraints of varies types of mass analyzers. For quadrupole ion trap systems, we develop a new compressive and multiplexed mass analysis approach mutli Resonant Frequency Excitation (mRFE) ejection which can reduce mass analysis time by a factor 3-6 without losing mass spectra specificity for chemical classification. A new information-theoretical adaptive sensing and classification framework has proposed on quadrupole mass filter systems, and it can significantly reduces the number of measurements needed and achieve a high level of classification accuracy. Furthermore, we present a coded aperture sector mass spectrometry which can yield a order-of-magnitude throughput gain without compromising mass resolution compare to conventional single slit sector mass spectrometer.
DepartmentElectrical and Computer Engineering
Computational mass spectrometry
magnetic sector spectrometer
Quadrupole ion trap
Quadrupole mass filter
CitationChen, Evan Xuguang (2015). Computational Mass Spectrometry. Dissertation, Duke University. Retrieved from http://hdl.handle.net/10161/10452.
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