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Identification of lignin oligomers in Kraft lignin using ultra-high-performance liquid chromatography/high-resolution multiple-stage tandem mass spectrometry (UHPLC/HRMS(n))

Kraft lignin is the main source of technically produced lignin. For the development of valuable products based on Kraft lignin, its molecular structure is important. However, the chemical composition of Kraft lignin is still not well known. So far, the analysis of Kraft lignin by mass spectrometry (...

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Detalles Bibliográficos
Autores principales: Prothmann, Jens, Spégel, Peter, Sandahl, Margareta, Turner, Charlotta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6244760/
https://www.ncbi.nlm.nih.gov/pubmed/30306235
http://dx.doi.org/10.1007/s00216-018-1400-4
Descripción
Sumario:Kraft lignin is the main source of technically produced lignin. For the development of valuable products based on Kraft lignin, its molecular structure is important. However, the chemical composition of Kraft lignin is still not well known. So far, the analysis of Kraft lignin by mass spectrometry (MS) has been mainly focused on monomeric compounds. Previous MS studies on lignin oligomers (LOs) considered only synthesised LO standards and/or lignins produced by processes other than the Kraft process. Furthermore, published MS methods suffer from using high resolution only in the MS(1) stage in multiple-stage tandem MS methods. A high resolution in all MS(n) stages would provide more detailed information about LO fragmentation pathways. Since lignin samples are complex mixtures of a large number of similar phenolic compounds, the selection of tentative LOs in the MS data is challenging. In this study, we present a method for non-targeted analysis of LOs in Kraft lignin using ultra-high-performance liquid chromatography/high-resolution multiple-stage tandem mass spectrometry (UHPLC/HRMS(n)). A pre-selection strategy for LOs has been established based on a data-dependent neutral loss MS(3) method in combination with a principal component analysis-quadratic discriminant analysis classification model (PCA-QDA). The method was optimised using a design of experiments (DOE) approach. The developed approach improved the pre-selection of tentative LOs in complex mixtures. From 587 detected peaks, 36 peaks were identified as LOs. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00216-018-1400-4) contains supplementary material, which is available to authorized users.