Author, as appears in the article.: Domingo-Almenara X; Perera A; Ramírez N; Cañellas N; Correig X; Brezmes J
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Brezmes Llecha, Jesús Jorge / Cañellas Alberich, Nicolau / Correig Blanchar, Francesc Xavier / Domingo Almenara, Xavier / Ramírez González, Noelia
Keywords: Urine level Urine Urea blood level Urea Tyrosine Trimethylsilyl derivative Threonine Serine Separation technique Regression analysis Proline Process optimization Process development Priority journal Principal component analysis Phenylalanine Orthogonal signals Orthogonal signal deconvolution Ornithine Nonhuman Multivariate curve resolution alternating least-squares Multivariate curve resolution Multivariate analysis Methionine Metabolomics Metabolome Measurement accuracy Mass spectrometry Mass fragmentography Leucine Least-squares analysis Least squares regression Ketoglutaric acids Isoleucine Inositol Independent components Independent component regression Independent component analysis(ica) Independent component analysis Humans Human Glycine Gas chromatography/mass spectrometry Gas chromatography-mass spectrometry Gas chromatography Cysteine Controlled study Compound deconvolution Citric acid Cholesterol Chemical compound Blood Blind source separation Aspartic acid Article Analytic method Amino acids Amino acid blood level Amino acid Alternating least square Alpha-ketoglutaric acid Algorithms Algorithm 2 oxoglutaric acid multivariate curve resolution metabolomics mass spectrometry independent component analysis compound deconvolution
Abstract: Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR. Those algorithms attempt to infer a model describing the observed data and, therefore, the least squares regression used in MCR assumes that the data is a linear combination of that model. However, due to the high complexity of real data, the construction of a model to describe optimally the observed data is a critical step and these algorithms should prevent the influence from outlier data. This study proves independent component regression (ICR) as an alternative for GC-MS compound identification. Both ICR and MCR though require least squares regression to correctly resolve the mixtures. In this paper, a novel orthogonal signal deconvolution (OSD) approach is introduced, which uses principal component analysis to determine the compound spectra. The study includes a compound identification comparison between the results by ICA-OSD, MCR-OSD, ICR and MCR-ALS using pure standards and human serum samples. Results shows that ICR may be used as an alternative to multivariate curve methods, as ICR efficiency is comparable to MCR-ALS. Also, the study demonstrates that the proposed OSD approach achieves greater spectral resolution accuracy than the traditional least squares approach when compounds elute under undue interference of biological matrices. © 2015 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 noelia.ramirez@urv.cat jesus.brezmes@urv.cat xavier.correig@urv.cat nicolau.canyellas@urv.cat
Author identifier: 0000-0002-7704-8550 0000-0002-6902-3054 0000-0003-4856-8132
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/S0021967315010122
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Journal Of Chromatography a. 1409 226-233
APA: Domingo-Almenara X; Perera A; Ramírez N; Cañellas N; Correig X; Brezmes J (2015). Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. Journal Of Chromatography a, 1409(), 226-233. DOI: 10.1016/j.chroma.2015.07.044
Article's DOI: 10.1016/j.chroma.2015.07.044
Entity: Universitat Rovira i Virgili
Journal publication year: 2015
Publication Type: Journal Publications