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

Background and AimsMetabolomic 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 ResultsThe analytical parameters for a 65-m divinylbenzene/polydimethylsiloxane (DVB/PDMS) and a 100-m 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. ConclusionsThe 65-m 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 StudyA 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.