InSituAnalyze: A Python Framework for Multicomponent Synchronous Analysis of Spectral Imaging.

InSituAnalyze: A Python Framework for Multicomponent Synchronous Analysis of Spectral Imaging. Spectral imaging is visualization of high precision and high sensitivity and suitable for analyzing the spatial distribution of complex materials. While providing rich and detailed information, it makes higher demands on feature extraction and information mining of high-dimensional data. For the convenience of further utilization, our research team has developed a Python framework for the multicomponent synchronous analysis of spectral imaging based on a characteristic band method and fast-NNLS algorithm, helping to handle spectrum data from complex samples and gaining semiquantitative information on the sample on the scale of pixel based on target components. With the help of the easy-to-use framework, users are leading to choose suitable pretreatment methods for images and spectra, extract spatial information on tissues/structures account of multispace, and conduct analysis on target components in an intuitive and timesaving way. The sophisticated functional architecture also makes the framework expedited to add algorithms and supported data formats. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Analytical chemistry Pubmed

InSituAnalyze: A Python Framework for Multicomponent Synchronous Analysis of Spectral Imaging.

Analytical chemistry, Volume 92 (1): 4 – Jan 8, 2020
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InSituAnalyze: A Python Framework for Multicomponent Synchronous Analysis of Spectral Imaging.

Analytical chemistry, Volume 92 (1): 4 – Jan 8, 2020

Abstract

Spectral imaging is visualization of high precision and high sensitivity and suitable for analyzing the spatial distribution of complex materials. While providing rich and detailed information, it makes higher demands on feature extraction and information mining of high-dimensional data. For the convenience of further utilization, our research team has developed a Python framework for the multicomponent synchronous analysis of spectral imaging based on a characteristic band method and fast-NNLS algorithm, helping to handle spectrum data from complex samples and gaining semiquantitative information on the sample on the scale of pixel based on target components. With the help of the easy-to-use framework, users are leading to choose suitable pretreatment methods for images and spectra, extract spatial information on tissues/structures account of multispace, and conduct analysis on target components in an intuitive and timesaving way. The sophisticated functional architecture also makes the framework expedited to add algorithms and supported data formats.
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DOI
10.1021/acs.analchem.9b03374

Abstract

Spectral imaging is visualization of high precision and high sensitivity and suitable for analyzing the spatial distribution of complex materials. While providing rich and detailed information, it makes higher demands on feature extraction and information mining of high-dimensional data. For the convenience of further utilization, our research team has developed a Python framework for the multicomponent synchronous analysis of spectral imaging based on a characteristic band method and fast-NNLS algorithm, helping to handle spectrum data from complex samples and gaining semiquantitative information on the sample on the scale of pixel based on target components. With the help of the easy-to-use framework, users are leading to choose suitable pretreatment methods for images and spectra, extract spatial information on tissues/structures account of multispace, and conduct analysis on target components in an intuitive and timesaving way. The sophisticated functional architecture also makes the framework expedited to add algorithms and supported data formats.

Journal

Analytical chemistryPubmed

Published: Jan 8, 2020

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