Chemical identification under a poisson model for Raman spectroscopy

dc.contributor.advisor Lanterman, Aaron D.
dc.contributor.author Palkki, Ryan D. en_US
dc.contributor.committeeMember Blair, W. Dale
dc.contributor.committeeMember Citrin, David
dc.contributor.committeeMember McLaughlin, Steven
dc.contributor.committeeMember Vidakovic, Brani
dc.contributor.department Electrical and Computer Engineering en_US
dc.date.accessioned 2013-01-17T22:06:09Z
dc.date.available 2013-01-17T22:06:09Z
dc.date.issued 2011-11-14 en_US
dc.description.abstract Raman spectroscopy provides a powerful means of chemical identification in a variety of fields, partly because of its non-contact nature and the speed at which measurements can be taken. The development of powerful, inexpensive lasers and sensitive charge-coupled device (CCD) detectors has led to widespread use of commercial and scientific Raman systems. However, relatively little work has been done developing physics-based probabilistic models for Raman measurement systems and crafting inference algorithms within the framework of statistical estimation and detection theory. The objective of this thesis is to develop algorithms and performance bounds for the identification of chemicals from their Raman spectra. First, a Poisson measurement model based on the physics of a dispersive Raman device is presented. The problem is then expressed as one of deterministic parameter estimation, and several methods are analyzed for computing the maximum-likelihood (ML) estimates of the mixing coefficients under our data model. The performance of these algorithms is compared against the Cramer-Rao lower bound (CRLB). Next, the Raman detection problem is formulated as one of multiple hypothesis detection (MHD), and an approximation to the optimal decision rule is presented. The resulting approximations are related to the minimum description length (MDL) approach to inference. In our simulations, this method is seen to outperform two common general detection approaches, the spectral unmixing approach and the generalized likelihood ratio test (GLRT). The MHD framework is applied naturally to both the detection of individual target chemicals and to the detection of chemicals from a given class. The common, yet vexing, scenario is then considered in which chemicals are present that are not in the known reference library. A novel variation of nonnegative matrix factorization (NMF) is developed to address this problem. Our simulations indicate that this algorithm gives better estimation performance than the standard two-stage NMF approach and the fully supervised approach when there are chemicals present that are not in the library. Finally, estimation algorithms are developed that take into account errors that may be present in the reference library. In particular, an algorithm is presented for ML estimation under a Poisson errors-in-variables (EIV) model. It is shown that this same basic approach can also be applied to the nonnegative total least squares (NNTLS) problem. Most of the techniques developed in this thesis are applicable to other problems in which an object is to be identified by comparing some measurement of it to a library of known constituent signatures. en_US
dc.description.degree PhD en_US
dc.identifier.uri http://hdl.handle.net/1853/45935
dc.publisher Georgia Institute of Technology en_US
dc.subject Spectral unmixing en_US
dc.subject Minimum description length (MDL) en_US
dc.subject Nonnegative matrix factorization (NMF) en_US
dc.subject Classification en_US
dc.subject Iteratively reweighted least squares en_US
dc.subject Errors-in-variables (EIV) modeling en_US
dc.subject Detection en_US
dc.subject.lcsh Raman spectroscopy
dc.subject.lcsh Parameter estimation
dc.subject.lcsh Algorithms
dc.title Chemical identification under a poisson model for Raman spectroscopy en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Lanterman, Aaron D.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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