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IVES 9 IVES Conference Series 9 OENO IVAS 9 OENO IVAS 2019 9 Analytical developments from grape to wine, spirits : omics, chemometrics approaches… 9 Strategies for sample preparation and data handling in GC-MS wine applications

Strategies for sample preparation and data handling in GC-MS wine applications


It is often said that wine is a complex matrix and the chemical analysis of wine with the thousands of compounds detected and often measured is proof. New technologies can assist not only in separating and identifying wine compounds, but also in providing information about the sample as a whole. 

Information-rich techniques can offer a fingerprint of a sample (untargeted analysis), a comprehensive view of its chemical composition. Applying statistical analysis directly to the raw data can significantly reduce the number of compounds to be identified to the ones relevant to a particular scientific question. More data can equal more information, but also more noise for the subsequent statistical handling. 

Therefore, strategies to reduce the some of the data can already be applied at the chemical analysis stage without loss of information. 

Using GCMS as analysis tool, an experiment was designed to evaluate on one hand different sample preparation methods, and on the other hand data handling strategies for the results. Twenty-six commercial wines from three cultivars (Chenin Blanc, Chardonnay, Sauvignon Blanc) and two winemaking styles (with and without wood contact) were subjected to three types of sample preparation (liquid/liquid extraction with three solvents, SPE on two stationary phases, HS-SPME on four fibres) before injection into GCMS. The various chemistries and polarities of the extraction solvents and stationary phases used resulted in different types of compounds being extracted from the wines. 

The TIC data was exported as a continuous signal (the chromatogram itself), as integrated peaks identified by their RTs, and as a (RT_m/z, abundance) matrix. Each type of data was submitted to PCA to underscore any natural grouping in the data. OPLS-DA and S-plots were subsequently used to determine the signals associated to cultivar discrimination and style. The raw data was revisited, and MS spectra extracted for the signals of interest, leading to the identification of the drivers (ions/compounds) for cultivars and style. 

The strategies for sample preparation and data extraction were evaluated based on their feasibility and potential for data mining. Additionally, this type of work can be of further use as a basis for developing screening or targeted analyses, based on the groups of analytes extracted during various sample preparation procedures.


Publication date: June 23, 2020

Issue: OENO IVAS 2019

Type: Article


Astrid Buica, Cody Williams, Mpho Mafata, Andrei Medvedovici, Costel Sarbu, Lucky Mokwen 

Institute for Grape and Wine Sciences, Stellenbosch University, South Africa 
Department of Viticulture and Oenology, Stellenbosch University, South Africa 
Department of Analytical Chemistry, Faculty of Chemistry, University of Bucharest, Romania 
Department of Analytical Chemistry, Faculty of Chemistry and Chemical Engineering, University Babes-Bolyai, Cluj-Napoca, Romania 
Central Analytical Facility, Stellenbosch University, South Africa 

Contact the author


data mining, GCMS, sample preparation, untargeted analysis


IVES Conference Series | OENO IVAS 2019


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