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5 May 2010 A non-negative matrix factorization algorithm for the detection of chemicals from an incomplete Raman library
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Raman spectroscopy has proven to be a powerful technique for the standoff identification of surface-deposited chemical agents. In the supervised detection framework, the measured Raman spectrum is compared to a reference library of known spectra. A well-known shortcoming of the supervised approach is that no comprehensive library exists, and when chemicals are present that are not contained in the reference library, the supervised algorithms may confuse those chemicals with library members. One way to deal with this problem is to use an unsupervised method such as nonnegative matrix factorization (NMF) to estimate both the constituent spectra and their relative quantities directly from a block of measured spectra. Chemical identification may then be performed by associating the extracted spectra with the reference library spectra. This two-stage NMF approach often fails because knowledge of the reference library was not used in extracting the spectra. We present a novel modification of NMF in which a subset of the extracted spectra are constrained to be equal to the known reference library. This method is shown to outperform the standard NMF approach and the common supervised identification algorithms when there are chemicals present that are not in the library. This algorithm is applicable to any problem in which a target is identified by comparing a block of measured data to a library of known constituent signatures.
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Ryan D. Palkki and Aaron D. Lanterman "A non-negative matrix factorization algorithm for the detection of chemicals from an incomplete Raman library", Proc. SPIE 7665, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XI, 766519 (5 May 2010);

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