Fully automated non-targeted GC-MS data analysis

Abstract

Non-targeted analysis is applied in many different domains of analytical chemistry such as metabolomics, environmental and food analysis. In contrast to targeted analysis, non-targeted approaches take information of known and unknown compounds into account, are inherently more comprehensive and give a more holistic representation of the sample composition. 

Besides chromatographic techniques coupled to high resolution mass spectrometry such as LC-HRMS, gas chromatography with unit resolution mass spectrometry is still regularly utilized for non-targeted profiling or fingerprinting. This is mainly due to high separation power of GC and a wide availability and low costs of quadrupole mass spectrometers. 

Although several non-targeted approaches have been developed, data processing still remains a serious bottleneck. Baseline correction, feature detection, and retention time alignment can be prone to errors and time-consuming manual corrections are often necessary. We therefore developed an automated strategy to non-targeted GC-MS data avoiding feature detection and retention time alignment. The novel automated approach includes segmentation of chromatograms along the retention time axis, multiway decomposition of transformed segments followed by a supervised machine learning pipeline based on gradient boosted tree classification on the decomposed tensor [1, 2]. 

In order to make this novel data analysis strategy available to scientists without programming background, we developed a convenient browser based application. For the here presented interactive browser application the open source Python packages Bokeh and HoloViews were used. The application will be online freely available soon. 

[1] J. Vestner, G. de Revel, S. Krieger-Weber, D. Rauhut, M. du Toit, A. de Villiers, Toward automated chromatographic fingerprinting: A non-alignment approach to gas chromatography mass spectrometry data. Acta Chimica Acta 911 (2016) 42-58 
[2] K. Sirén, U. Fischer, J. Vestner, Automated supervised learning pipeline for non-targeted GC-MS data analysis. Analytica Chimica Acta: X 1 (2019) 100005

DOI:

Publication date: June 19, 2020

Issue: OENO IVAS 2019

Type: Article

Authors

Jochen Vestner, Kimmo Sirén, Pierre Le Brun, Ulrich Fischer

Institute for Viticulture and Oenology, DLR Rheinpfalz, Breitenweg 71, D-67435 Neustadt, Germany
Institut National Supérieur des Sciences Agronomiques de l’Alimentation et de l’ Environnement, Agrosup Dijon, 6 boulevard Docteur Petitjean, 21000 Dijon, France
Department of Chemistry, University of Kaiserslautern, Erwin-Schroedinger-Strasse 52, D-67663 Kaiserslautern

Contact the author

Keywords

metabolomics, non-targeted, GC-MS, exploratory data analysis 

Tags

IVES Conference Series | OENO IVAS 2019

Citation

Related articles…

SENSORY EVALUATION OF WINE AROMA: SHOULD COLOR-DRIVEN DESCRIPTORS BE USED?

The vocabulary used to describe wine aroma is commonly organized according to color, raising the question of whether they reflect the reality of olfactory perception. Previous studies have assumed this convention of color-aroma matching, and have investigated color’s influence on the perception of aroma only in dyed white wine or in red wine from particular places of origin. Here 48 white and red varietal wines from around the world were evaluated in black glasses then in clear glasses by a panel of wine experts, who gave intensity ratings for aroma attributes commonly used by wine professionals. In black glasses, aromas conventionally associated with white wine were perceived in the red wines, and vice versa.

Conservation: the best valorisation strategy for wine growing areas

Terroir encompasses many elements, including environment, grapes and human inputs that together contribute to the final wine quality of a certain wine growing area.

CONTRIBUTION OF VOLATILE THIOLS TO THE AROMA OF RIESLING WINES FROM THREE REGIONS IN GERMANY AND FRANCE (RHEINGAU, MOSEL, AND ALSACE)

Riesling wines are appreciated for their diverse aromas, ranging from the fruity fresh characters in young vintages to the fragrant empyreumatic notes developed with aging. Wine tasters often refer to Riesling wines as prime examples showcasing terroir, with their typical aroma profiles reflecting the geographical provenance of the wine. However, the molecular basis of the distinctive aromas of these varietal wines from major Riesling producing regions in Europe have not been fully elucidated. In this study, new lights were shed on the chemical characterization and the sensory contribution of volatile thiols to Riesling wines from Rheingau, Mosel, and Alsace. First, Riesling wines (n = 46) from the three regions were collected and assessed for their aroma typicality by an expert panel.

Hanseniaspora uvarum and high hydrostatic pressure for improving wine aging on lees

Non-saccharomyces yeasts gained an increased interest in winemaking during the last decades, due to their ability to produce relevant amounts of polysaccharides. Moreover, a significant release of glutathione into the wine during fermentation was also observed with these strains, as well as an improvement of color stability and wine aroma profile. Valuable results have been obtained by hanseniaspora spp. concerning the release of polysaccharides and the production of acetic esters, mainly during fermentation.

Comparison of ancestral and traditional methods in the elaboration of sparkling wines; preliminary results

Top quality sparkling wines (SW) are mostly produced using the traditional method that implies a second fermentation into the bottle[1]. That is the case of sparkling wines of reputed AOC such as Champagne, Cava or Franciacorta. However, it seems that the first SW was elaborated using the ancestral method in which only one fermentation takes place[2]. That is the case of the classical SW from the AOC Blanquette de Limoux[3]. In both cases, SW age in the bottle during some time in contact with lees favoring yeast’s autolysis[4]. There is a lot of information about traditional method but only few exists about ancestral method. The aim of this work was to compare SW made by the ancestral method with SW made by the traditional method.