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…

The current state and prospects for the development of viticulture and winemaking in Greece

Viticulture in Greece is the oldest, but in recent years there has been a reduction of areas intended for the production of wine products. The article contains data on viticulture in Greece. Over time, the land of Greek vineyards is fluctuating. There is a trend towards a decrease in areas in connection with the quota of products from the EU.

Adsorption of tetraconazole by organic residues and vineyard organically-amended soils 

Spain is the country with the largest wine-producing area in the EU and its productivity is largely controlled applying fungicides. However, residues of these compounds can move and contaminate surface and groundwater. The objective of this work was to evaluate the capacity of bioadsorbents from different origin to adsorb and immobilize tetraconazole by themselves or when applied as organic soil amendment, and to prevent soil and water contamination by this fungicide. The adsorption of tetraconazole by 3 organic residues: spent mushroom substrate (SMS), green compost (GC) and vine pruning sawdust (VP), as well as by vineyard soils unamended and amended individually with these residues at 1.5% (w/w) was evaluated using the batch equilibrium technique.

Influence of withering on valpolicella docgs grapes volatile composition

The blend of grapes used in the production of the four Valpolicella PDOs red wines, namely Valpolicella, Valpolicella Classico Superiore, Recioto della Valpolicella and Amarone della Valpolicella is quite unique, and includes two main varieties Corvina and Corvinone, and other minor varieties. To a very large extent all these grapes are only grown in the province of Verona. One of the main characteristics of Valpolicella is the use of grapes that are submitted to post-harvest withering. The aim of this study was therefore to evaluate the evolution of the free and glycosidically-bound volatile compounds in Corvina and Corvinone grapes under real production conditions.

Nucleophilic fraction to estimate the antioxidant activity of inactivated yeast derivates

Oxidation in wine is mostly related to the Michael addition of nucleophiles on two quinones formed from the oxidation of ortho-diphenols. In wine this mechanism is responsible for the increase of the yellow hue and aroma loss. Glutathione exerts its antioxidant activity throughout its competitive addition onto quinones, but many other compounds can have the same behavior: sulfanyl
compounds, amino acids, etc. Addition of yeast derivates during the winemaking process can increase the level of those nucleophilic compounds and then confer to the wine a higher resistance

Molecularly imprinted polymers: an innovative strategy for harvesting polyphenoles from grape seed extracts

Multiple sclerosis (MS) is a multifactorial autoimmune disease associating demyelination and axonal degeneration developing in young adults and affecting 2–3 million people worldwide. Plant polyphenols endowed with many therapeutic benefits associated with anti-inflammatory and antioxidant properties represent highly interesting new potential therapeutic strategies. We recently showed the safety and high efficiency of grape seed extract (GSE), a complex mixture of polyphenolics compounds comprising notably flavonoids and proanthocyanidins, in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS.