Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 541583
Title ASCA: The Implementation of Design of Experiments Into Multivariate Modelling in Chemometrics
Author(s) Jansen, Jeroen; Engel, Jasper
Source Comprehensive Analytical Chemistry 82 (2018). - ISSN 0166-526X - p. 301 - 335.
Department(s) Biometris (PPO/PRI)
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) Chemometrics - Multivariate modelling

The untargeted metabolomics paradigm may be very helpful to reveal biochemical patterns in the multifactorial experiments in environmental analysis. Specific combinations between metabolites may be highly specific biomarkers for environmental and/or ecological change. However, such patterns need to be recovered from a background of many unrelated metabolites within a wealth of cooccurring environmental processes. The range of methods we present here, based around Analysis-of-Variance-Simultaneous Component Analysis (ASCA), have been specifically developed retrieve such patterns. They combine the merits of quantitatively describing the Design-of-Experiments that underlies an environmental study with the multivariate nature of metabolomics data. The ASCA toolbox has by now extended into a comprehensive and generic approach that allows analysis of, e.g., unbalanced data, quantitative significance of effects and of relevant biomarkers. The same approach can also be taken to analyse specific effects with respect to positive and negative controls, to reveal specific experimentally relevant deviations in metabolism. We show the ASCA results of a specific plant chemical ecology dataset, in which all glucosinolates within a wild cabbage were profiled upon induction of an ecological defence response. ASCA provides direct insight in the variability associated with different aspects of this response, and relatively recent extensions in data preprocessing reveal very clearly the metabolites that are most relevant to the response.

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