title: Hyperspectral image analysis for CARS, SRS, and Raman data.
abstract: In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masiaet al. 1 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 sampling2 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 & Sons, Ltd.
authors.affiliation: School of Physics and Astronomy Cardiff University The Parade Cardiff CF24 3AA UK.
authors.forename: Francesco
authors.initials: F
authors.lastname: Masia
authors.affiliation: School of Physics and Astronomy Cardiff University The Parade Cardiff CF24 3AA UK.
authors.forename: Arnica
authors.initials: A
authors.lastname: Karuna
authors.affiliation: School of Physics and Astronomy Cardiff University The Parade Cardiff CF24 3AA UK; School of Biosciences Cardiff University Museum Avenue Cardiff CF10 3AX UK.
authors.forename: Paola
authors.initials: P
authors.lastname: Borri
authors.affiliation: School of Physics and Astronomy Cardiff University The Parade Cardiff CF24 3AA UK.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Langbein
keywords: coherent Raman micro‐spectroscopy; hyperspectral image analysis; sparse sampling