New Approach for Chemometric Analysis of Mass Spectrometry Data

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10045/39135
Información del item - Informació de l'item - Item information
Título: New Approach for Chemometric Analysis of Mass Spectrometry Data
Autor/es: Marhuenda Egea, Frutos Carlos | Gonsálvez Álvarez, Rubén D. | Lledó Bosch, Belén | Ten Morro, Jorge | Bernabeu, Rafael
Grupo/s de investigación o GITE: Grupo de Fotoquímica y Electroquímica de Semiconductores (GFES) | Biotecnología
Centro, Departamento o Servicio: Universidad de Alicante. Departamento de Agroquímica y Bioquímica | Universidad de Alicante. Departamento de Biotecnología
Palabras clave: Chemometric analysis | Mass spectrometry data
Área/s de conocimiento: Bioquímica y Biología Molecular | Biología Celular
Fecha de publicación: 8-feb-2013
Editor: American Chemical Society
Cita bibliográfica: Analytical Chemistry. 2013, 85(6): 3053-3058. doi:10.1021/ac303255h
Resumen: The search of metabolites which are present in biological samples and the comparison between different samples allow the construction of certain biochemical patterns. The mass spectrometry (MS) methodology applied to the analysis of biological samples makes it possible for the identification of many metabolites. Each obtained signal (m/z) is characteristic of a particular metabolite. However, the mass data (m/z) interpretation is difficult because of the large amount of information that they contain. In this work, we present a relatively simple tool that allows us to deal with the whole of the mass information from the chemometric analysis. The statistical analysis is a key stage in order to identify the metabolites involved in a particular biochemical pattern. We transformed the mass data matrix in a vector. By having the data as a vector, it was possible to keep all the information and also avoid the signals overlapping, which is the major problem when the total ion chromatogram (TIC) is obtained. In the approach proposed here, the mass data (m/z) matrix was split in 100 different TIC in order to avoid the signal overlapping. The 100 chromatograms were concatenated in a vector. This vector, which can be plotted as a continuous (2D pseudospectrum), greatly simplifies for one to understand the subsequent dimensional multivariate analysis. To validate the method, 19 samples from two human embryos culture medium were analyzed by high-pressure liquid chromatography–mass spectrometry (HPLC–MS). Our methodology would be applied to the obtained raw data. Later on, a multivariate analysis was conducted using a robust principal components analysis interval (robPCA) and interval partial least squares algorithm (iPLS). The results obtained allow one to differentiate the two sample populations undoubtedly, although their composition was similar.
Patrocinador/es: This work has been supported by grants from Instituto Bernabeu (Grant INSTITUTOBERNABEU1-08I) and the University of Alicante (Grant UAUSTI09-08) Project.
URI: http://hdl.handle.net/10045/39135
ISSN: 0003-2700 (Print) | 1520-6882 (Online)
DOI: 10.1021/ac303255h
Idioma: eng
Tipo: info:eu-repo/semantics/article
Derechos: © 2013 American Chemical Society
Revisión científica: si
Versión del editor: http://dx.doi.org/10.1021/ac303255h
Aparece en las colecciones:INV - GFES - Artículos de Revistas
INV - GIDBT - Artículos de Revistas

Archivos en este ítem:
Archivos en este ítem:
Archivo Descripción TamañoFormato 
Thumbnail2013_Marhuenda_etal_AnalChem_final.pdfVersión final (acceso restringido)3,32 MBAdobe PDFAbrir    Solicitar una copia


Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.