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Issue 16, 2012
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D-optimal design of an untargeted HS-SPME-GC-TOF metabolite profiling method

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Abstract

In recent times we have seen the development of many “-omics” technologies. One of the youngest is undoubtedly metabolomics, which aims to define the whole chemical fingerprint unique to each specific organism. The development and optimisation of an untargeted high-throughput method capable of investigating the volatile fraction of a biological system represents a crucial step for the success of such holistic approaches, and specific optimisation criteria must be developed in connection with suitable experimental designs. In this paper experimental designs (D-optimal) were applied for the first time as an automatic optimisation tool to an untargeted HS-SPME-GC-TOF method. In this case, optimal conditions correspond to a maximal number of detected features, in order to provide a fingerprint that is as complete as possible. The system under study is the grape berry. Four variables were considered: the type of fibre, extraction time, equilibration time and temperature. The results show that the D-optimal design methodology provides an easily interpretable assessment of experimental settings. This and other specific properties of the D-optimal design, such as the possibility to explicitly exclude certain experimental conditions, make it an extremely suitable strategy for method optimisation in untargeted metabolomics.

Graphical abstract: D-optimal design of an untargeted HS-SPME-GC-TOF metabolite profiling method

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Publication details

The article was received on 28 Dec 2011, accepted on 02 May 2012, published on 02 May 2012 and first published online on 02 May 2012


Article type: Paper
DOI: 10.1039/C2AN16309H
Citation: Analyst, 2012,137, 3725-3731
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    D-optimal design of an untargeted HS-SPME-GC-TOF metabolite profiling method

    B. Fedrizzi, S. Carlin, P. Franceschi, U. Vrhovsek, R. Wehrens, R. Viola and F. Mattivi, Analyst, 2012, 137, 3725
    DOI: 10.1039/C2AN16309H

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