Author, as appears in the article.: Domingo-Almenara X; Perera A; Brezmes J
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Brezmes Llecha, Jesús Jorge / Domingo Almenara, Xavier
Keywords: Spectrometry Reproducibility Quantitative analysis Procedures Priority journal Orthogonal signals Orthogonal signal deconvolution Multivariate methods Multivariate curve resolution Multivariate analysis Moving window Metabolomics Mass spectrometry Mass fragmentography Least-squares analysis Least square analysis Ionization of gases Ionization Independent component analysis Impact ionization Gas chromatography-mass spectrometry Gas chromatography Electron impact-ionization Chromatography Chromatographic analysis Blood analysis Automation Automated resolution Article Alternating least squares Algorithms Algorithm multivariate curve resolution moving window metabolomics independent component analysis gas chromatography
Abstract: Gas chromatography–mass spectrometry (GC–MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC–MS can be resolved by taking advantage of the multivariate nature of GC–MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms. The automation of this segmentation process is a challenging task as it implies separating between informative data and noise from the chromatogram. This study demonstrates the capabilities of independent component analysis–orthogonal signal deconvolution (ICA–OSD) and multivariate curve resolution–alternating least squares (MCR–ALS) with an overlapping moving window implementation to avoid the typical hard chromatographic segmentation. Also, after being resolved, compounds are aligned across samples by an automated alignment algorithm. We evaluated the proposed methods through a quantitative analysis of GC–qTOF MS data from 25 serum samples. The quantitative performance of both moving window ICA–OSD and MCR–ALS-based implementations was compared with the quantification of 33 compounds by the XCMS package. Results shown that most of the R2 coefficients of determination exhibited a high correlation (R2 > 0.90) in both ICA–OSD and MCR–ALS moving window-based approaches. © 2016 Elsevier B.V.
Thematic Areas: Zootecnia / recursos pesqueiros Química Organic chemistry Nutrição Medicine (miscellaneous) Medicina veterinaria Medicina ii Medicina i Materiais Interdisciplinar Geociências General medicine Farmacia Engenharias ii Engenharias i Ciências biológicas iii Ciências biológicas ii Ciências biológicas i Ciências agrárias i Ciência de alimentos Chemistry, analytical Biotecnología Biodiversidade Biochemistry Biochemical research methods Analytical chemistry
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: xavier.domingo@urv.cat jesus.brezmes@urv.cat
Author identifier: 0000-0002-7704-8550
Record's date: 2024-10-26
Papper version: info:eu-repo/semantics/acceptedVersion
Link to the original source: https://www.sciencedirect.com/science/article/abs/pii/S0021967316314315
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Journal Of Chromatography a. 1474 145-151
APA: Domingo-Almenara X; Perera A; Brezmes J (2016). Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography–mass spectrometry-based metabolomics signals by multivariate methods. Journal Of Chromatography a, 1474(), 145-151. DOI: 10.1016/j.chroma.2016.10.066
Article's DOI: 10.1016/j.chroma.2016.10.066
Entity: Universitat Rovira i Virgili
Journal publication year: 2016
Publication Type: Journal Publications
Repositori URV