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

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 Doptimal 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.