期刊
APPLIED SPECTROSCOPY
卷 57, 期 11, 页码 1353-1362出版社
SAGE PUBLICATIONS INC
DOI: 10.1366/000370203322554509
关键词
hyperspectral Raman imaging; self-modeling curve resolution; SMCR; band-target entropy minimization; BTEM; factor analysis
资金
- NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [R01AR047969, P30AR046024] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DENTAL &CRANIOFACIAL RESEARCH [R29DE011530] Funding Source: NIH RePORTER
- NIAMS NIH HHS [P30 AR46024, R01 AR47969] Funding Source: Medline
- NIDCR NIH HHS [R29 DE11530] Funding Source: Medline
Band-target entropy minimization (BTEM) has been applied to extraction of component spectra from hyperspectral Raman images. In this method singular value decomposition is used to calculate the eigenvectors of the spectroscopic image data set. Bands in on-noise eigenvectors that would normally be used for recovery of spectra are examined for localized spectral features. For a targeted (identified) band, information entropy minimization or a closely related algorithm is used to recover the spectrum containing this feature from the non-noise eigenvectors, plus the next 5-30 eigenvectors, in which noise predominates. Tests for which eigenvectors to include are described. The method is demonstrated on one synthesized Raman image data set and two bone tissue specimens. By inclusion of small amounts of signal that would be unused in other methods, BTEM enables the extraction of a larger number of component spectra than are otherwise obtainable. An improvement in signal/noise ratio of the recovered spectra is also obtained.
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