Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares

Multivariate data analysis techniques are ideal to decrypt chemical differences between anatomical features or tissue areas in hyperspectral images of biological samples. This protocol provides a user-friendly pipeline and graphical user interface (GUI) for data pre-processing and un-mixing of pixel...

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Authors: Felten, Judith, Hall, Hardy, Jaumot Soler, Joaquim, Tauler Ferré, Romà, Juan Capdevila, Anna de, Gorzsás, András
Format: article
Status:Versión aceptada para publicación
Publication Date:2015
Country:España
Institution:Universidad de Barcelona
Repository:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/127937
Online Access:https://hdl.handle.net/2445/127937
Access Level:Open access
Keyword:Anàlisi multivariable
Espectroscòpia d'infraroigs per transformada de Fourier
Espectroscòpia Raman
Processament d'imatges
Multivariate analysis
Fourier transform infrared spectroscopy
Raman spectroscopy
Image processing
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spelling Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squaresFelten, JudithHall, HardyJaumot Soler, JoaquimTauler Ferré, RomàJuan Capdevila, Anna deGorzsás, AndrásAnàlisi multivariableEspectroscòpia d'infraroigs per transformada de FourierEspectroscòpia RamanProcessament d'imatgesMultivariate analysisFourier transform infrared spectroscopyRaman spectroscopyImage processingMultivariate data analysis techniques are ideal to decrypt chemical differences between anatomical features or tissue areas in hyperspectral images of biological samples. This protocol provides a user-friendly pipeline and graphical user interface (GUI) for data pre-processing and un-mixing of pixel spectra into their contributing pure components by multivariate curve resolution-alternating least squares (MCR-ALS) analysis. The analysis considers the full spectral profile to identify the chemical compounds and to visualize their distribution across the sample to categorize chemically distinct areas. Results are rapidly achieved (usually less than 30 - 60 min/image) and are easy to interpret and evaluate both in terms of chemistry and biology, making the method generally more powerful than principal component analysis (PCA) or single band intensity heap maps. In addition, chemical and biological evaluation of the results by means of reference matching and segmentation maps (based on k-means clustering) are possible.Nature Publishing Group2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/127937Articles publicats en revistes (Enginyeria Química i Química Analítica)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésVersió postprint del document publicat a: https://doi.org/10.1038/nprot.2015.008Nature Protocols, 2015, vol. 10, p. 217-240https://doi.org/10.1038/nprot.2015.008(c) Felten, Judith et al., 2015info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1279372026-05-27T06:46:51Z
dc.title.none.fl_str_mv Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
title Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
spellingShingle Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
Felten, Judith
Anàlisi multivariable
Espectroscòpia d'infraroigs per transformada de Fourier
Espectroscòpia Raman
Processament d'imatges
Multivariate analysis
Fourier transform infrared spectroscopy
Raman spectroscopy
Image processing
title_short Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
title_full Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
title_fullStr Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
title_full_unstemmed Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
title_sort Vibrational spectroscopic image analysis of biological material using multivariate curve resolution - alternating least squares
dc.creator.none.fl_str_mv Felten, Judith
Hall, Hardy
Jaumot Soler, Joaquim
Tauler Ferré, Romà
Juan Capdevila, Anna de
Gorzsás, András
author Felten, Judith
author_facet Felten, Judith
Hall, Hardy
Jaumot Soler, Joaquim
Tauler Ferré, Romà
Juan Capdevila, Anna de
Gorzsás, András
author_role author
author2 Hall, Hardy
Jaumot Soler, Joaquim
Tauler Ferré, Romà
Juan Capdevila, Anna de
Gorzsás, András
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Anàlisi multivariable
Espectroscòpia d'infraroigs per transformada de Fourier
Espectroscòpia Raman
Processament d'imatges
Multivariate analysis
Fourier transform infrared spectroscopy
Raman spectroscopy
Image processing
topic Anàlisi multivariable
Espectroscòpia d'infraroigs per transformada de Fourier
Espectroscòpia Raman
Processament d'imatges
Multivariate analysis
Fourier transform infrared spectroscopy
Raman spectroscopy
Image processing
description Multivariate data analysis techniques are ideal to decrypt chemical differences between anatomical features or tissue areas in hyperspectral images of biological samples. This protocol provides a user-friendly pipeline and graphical user interface (GUI) for data pre-processing and un-mixing of pixel spectra into their contributing pure components by multivariate curve resolution-alternating least squares (MCR-ALS) analysis. The analysis considers the full spectral profile to identify the chemical compounds and to visualize their distribution across the sample to categorize chemically distinct areas. Results are rapidly achieved (usually less than 30 - 60 min/image) and are easy to interpret and evaluate both in terms of chemistry and biology, making the method generally more powerful than principal component analysis (PCA) or single band intensity heap maps. In addition, chemical and biological evaluation of the results by means of reference matching and segmentation maps (based on k-means clustering) are possible.
publishDate 2015
dc.date.none.fl_str_mv 2015
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/127937
url https://hdl.handle.net/2445/127937
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1038/nprot.2015.008
Nature Protocols, 2015, vol. 10, p. 217-240
https://doi.org/10.1038/nprot.2015.008
dc.rights.none.fl_str_mv (c) Felten, Judith et al., 2015
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Felten, Judith et al., 2015
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Nature Publishing Group
publisher.none.fl_str_mv Nature Publishing Group
dc.source.none.fl_str_mv Articles publicats en revistes (Enginyeria Química i Química Analítica)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869406533613256704
score 15,300724

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