A non-targeted metabolomic approach based on reversed-phase liquid chromatography-mass spectrometry to evaluate coffee roasting process
Authors
Pérez Miguez, Raquel; Sánchez López, Elena; Plaza del Moral, Merichel; Castro Puyana, María; Marina Alegre, María LuisaIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/48127DOI: 10.1007/s00216-018-1405-z
ISSN: 1618-2642
Date
2018-10Affiliation
Universidad de Alcalá. Departamento de Química Analítica, Química Física e Ingeniería QuímicaBibliographic citation
Analytical and Bioanalytical Chemistry, 2018, v. 410, n. 30, p. 7859-7870
Keywords
Non-targeted metabolomics
Liquid chromatography
High-resolution mass spectrometry
Coffee beans
Roasting process
Project
info:eu-repo/grantAgreement/CAM//S2018%2FBAA-4393/ES/ESTRATEGIAS INTEGRADAS PARA LA MEJORA DE LA CALIDAD, LA SEGURIDAD Y LA FUNCIONALIDAD DE LOS ALIMENTOS: HACIA UNA ALIMENTACIÓN SALUDABLE/AVANSECAL-II
info:eu-repo/grantAgreement/UAH//CCG2015%2FEXP-032/ES/DESARROLLO DE UNA ESTRATEGIA METABOLÓMICA NO DIRIGIDA POR ELECTROFORESIS CAPILAR ACOPLADA A ESPECTROMETRÍA DE MASAS PARA LA CARACTERIZACIÓN DE ALIMENTOS DE ALTO CONSUMO
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Rights
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
© Springer
Access rights
info:eu-repo/semantics/openAccess
Abstract
In this work, a non-targeted metabolomics approach based on the use of reversed-phase liquid chromatography coupled to a high-resolution mass spectrometer has been developed to provide the characterization of coffee beans roasted at three different levels (light, medium, and dark). In this way, it was possible to investigate how metabolites change during the roasting process in order to identify those than can be considered as relevant markers. Twenty-five percent methanol was selected as extracting solvent since it provided the highest number of molecular features. In addition, the effect of chromatographic and MS parameters was evaluated in order to obtain the most adequate separation and detection conditions. Data were analyzed using both non-supervised and supervised multivariate statistical methods to point out the most significant markers that allow group discrimination. A total of 24 and 33 compounds in positive and negative ionization modes, respectively, demonstrated to be relevant markers; most of them were from the hydroxycinnamic acids family.
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