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Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics - imarina:9282606

Autor/es de la URV:Brezmes Llecha, Jesús Jorge / Domingo Almenara, Xavier / Ramírez González, Noelia
Autor según el artículo:Domingo-Almenara X; Perera A; Ramírez N; Brezmes J
Direcció de correo del autor:xavier.domingo@urv.cat
noelia.ramirez@urv.cat
jesus.brezmes@urv.cat
Identificador del autor:0000-0002-7704-8550
Año de publicación de la revista:2016
Tipo de publicación:Journal Publications
Referencia de l'ítem segons les normes APA:Domingo-Almenara X; Perera A; Ramírez N; Brezmes J (2016). Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics. Computer Methods And Programs In Biomedicine, 130(), 135-141. DOI: 10.1016/j.cmpb.2016.03.007
Referencia al articulo segun fuente origial:Computer Methods And Programs In Biomedicine. 130 135-141
Resumen:Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner. We studied the performance of ICA-OSD by the quantification of 38 metabolites through a set of 20 Jurkat cell samples analyzed by GC×GC-MS. The quantification by ICA-OSD was compared with a supervised quantification by selective ions, and most of the R2 coefficients of determination were in good agreement (R2>0.90) while up to 24 cases exhibited an excellent linear relation (R2>0.95). We concluded that ICA-OSD can be used to resolve co-eluted compounds in GC×GC-MS. © 2016 Elsevier Ireland Ltd.
DOI del artículo:10.1016/j.cmpb.2016.03.007
Enlace a la fuente original:https://www.sciencedirect.com/science/article/abs/pii/S0169260715300511
Versión del articulo depositado:info:eu-repo/semantics/acceptedVersion
Acceso a la licencia de uso:https://creativecommons.org/licenses/by/3.0/es/
Departamento:Enginyeria Electrònica, Elèctrica i Automàtica
URL Documento de licencia:https://repositori.urv.cat/ca/proteccio-de-dades/
Áreas temáticas:Software
Saúde coletiva
Psicología
Odontología
Medicina iii
Medicina ii
Medicina i
Medical informatics
Matemática / probabilidade e estatística
Interdisciplinar
Health informatics
General medicine
Engineering, biomedical
Engenharias iv
Engenharias iii
Engenharias ii
Educação física
Computer science, theory & methods
Computer science, interdisciplinary applications
Computer science applications
Ciências biológicas ii
Ciências biológicas i
Ciências ambientais
Ciências agrárias i
Ciência da computação
Biotecnología
Palabras clave:Spectrometry
Procedures
Principal component analysis
Phosphoric acid
Orthogonal signals
Orthogonal signal deconvolution
Multivariate curve resolution
Multivariant analysis
Metabolomics
Mass spectrometry
Mass fragmentography
Lymphatic leukemia
Linear relation
Lactic acid
Jurkat cell line
Inositol
Independent component analysis
Human cell
Human
Glycerol
Gas chromatography/mass spectrometry
Gas chromatography-mass spectrometry
Gas chromatography
Erythritol
Data handling
Correlation coefficient
Controlled study
Comprehensive gas chromatography
Compound deconvolution
Chromatography
Chromatographic signals
Chromatographic analysis
Blind source separation
Biological samples
Bioinformatics
Automation
Automated resolution
Aspartic acid
Article
Apoptosis
Analytic method
Algorithms
Algorithm
multivariate curve resolution
independent component analysis
comprehensive gas chromatography
compound deconvolution
Entidad:Universitat Rovira i Virgili
Fecha de alta del registro:2024-11-09
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