Optimisation and validation of a high-throughput semi-quantitative solid-phase microextraction method for analysis of fermentation aroma compounds in metabolomic screening studies of wines

Abstract

Background and Aims: Metabolomic screening studies normally contain thousands of samples with each individual sample being thoroughly analysed for observed differences in multiple compounds. A comparative screen is often employed to narrow down the search field before undertaking an intensive quantitative search. This study optimised the parameters for two solid-phase microextraction (SPME) fibres recently reported to be optimum for the extraction of aroma compounds from a white wine and to create a validated comparative method with the optimised fibre for future metabolomic wine-screening studies. Methods and Results: The analytical parameters for a 65-μm divinylbenzene/polydimethylsiloxane (DVB/PDMS) and a 100-μm polydimethylsiloxane (PDMS) fibre were determined based on salt concentration, sample dilution, extraction time and extraction temperature for an extensive library of aroma compounds at a concentration similar to that found in commercial white wines. After optimisation, the best fibre was selected and a semi-quantitative high-throughput method was developed. This method was validated for 34 aroma compounds commonly found in wines, with similar results found in three media (model wine, spiked bag-in-box wine and a spiked laboratory-made wine) thus negating any potential matrix effect found when analysing different wines. Conclusions: The 65-μm PDMS/DVB fibre was the best for fermentation bouquet studies, and a newly devised method was developed for semi-quantitative high-throughput metabolomic screening studies involving 34 aroma compounds common to white wine fermentation bouquet. Significance of the Study: A semi-quantitative high-throughput method has been validated in a range of different wine media; it is fast and inexpensive and will find application in wine metabolomic studies as it allows one to narrow down the initial search field before employing the more expensive and time-consuming, traditional quantitative approach.