Hyperspectral image analysis for CARS, SRS, and Raman data. (14th June 2015)

• Record Type: Journal Article
• Authors: Masia, Francesco; Karuna, Arnica; Borri, Paola; Langbein, Wolfgang; Joubert, Pierre; Lavorel, Bruno; Kiefer, Johannes; Moger, Julian
• Is Part Of: Journal of Raman spectroscopy. Volume 46:Number 8(2015:Aug.)
• Page Start: 727
• Page End: 734
• Publication Date: 2015-06-14
• Subjects: Raman spectroscopy -- Periodicals; 535.846
• DOI: 10.1002/jrs.4729
• Languages: English
• ISSNs: 0377-0486

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia <italic>et al</italic>. <xref ref-type="link" rid="jrs4729-bib-0001">1</xref> The HIA introduced a method to factorize the hyperspectral data into the product of component concentrations and spectra for quantitative analysis of the chemical composition of the sample. The enhancements shown here comprise (1) a spatial weighting to reduce the spatial variation of the spectral error, which improves the retrieval of the chemical components with significant local but small global concentrations; (2) a new selection criterion for the spectra used when applying sparse sampling<xref ref-type="link" rid="jrs4729-bib-0002">2</xref> to speed up sequential hyperspectral imaging; and (3) a filter for outliers in the data using singular value decomposition, suited e.g. to suppress motion artifacts. We demonstrate the enhancements on coherent anti‐Stokes Raman scattering, stimulated Raman scattering, and spontaneous Raman data. We provide the HIA software as executable for public use. © 2015 The Authors. Journal of Raman Spectroscopy published by John Wiley &amp; Sons, Ltd.</p> </abstract>