Masia Francesco, Karuna Arnica, Borri Paola, Langbein Wolfgang
School of Physics and Astronomy Cardiff University The Parade Cardiff CF24 3AA UK.
School of Physics and Astronomy Cardiff University The Parade Cardiff CF24 3AA UK; School of Biosciences Cardiff University Museum Avenue Cardiff CF10 3AX UK.
J Raman Spectrosc. 2015 Aug;46(8):727-734. doi: 10.1002/jrs.4729. Epub 2015 Jun 14.
In this work, we have significantly enhanced the capabilities of the hyperspectral image analysis (HIA) first developed by Masia . 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.
在这项工作中，我们显著增强了由马西亚首次开发的高光谱图像分析（HIA）的功能。1 HIA引入了一种将高光谱数据分解为成分浓度和光谱乘积的方法，用于对样品的化学成分进行定量分析。此处展示的增强功能包括：（1）空间加权，以减少光谱误差的空间变化，这有助于提高对局部浓度显著但全局浓度较小的化学成分的检索；（2）在应用稀疏采样2时用于光谱的新选择标准，以加快顺序高光谱成像；（3）使用奇异值分解对数据中的异常值进行滤波，例如适用于抑制运动伪影。我们在相干反斯托克斯拉曼散射、受激拉曼散射和自发拉曼数据上展示了这些增强功能。我们提供HIA软件的可执行版本以供公众使用。© 2015作者。《拉曼光谱学杂志》由约翰·威利父子有限公司出版。
