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…

Viticultural practices: past, present and future

Practices in viticulture have greatly evolved in the last five decades. There were three objectives: improvement in the quality of the products, reduction in the production costs through mechanization

Cultivation of grapes Chardonnay in soils with management practices biodynamic and conventional

The cultivation of grapes, can be accomplished with the use of different systems and practices of agricultural management, the choice of the system to be followed in the vineyard, depends on the conditions of available resources, these being: natural, economic, social, cultural and territorial. As well, it is relevant to know the characteristics of the soil of the vineyard. In the last decade, has been recurrent use of agricultural practices which date back to milinares traditions, with the aim of promoting a recovery of soil and lead the management of cultivation with less damage to the ecosystem. The study here, aimed to quantify the environmental impacts caused in the use of nutrients in conventional tillage and of grapes in the biodynamic agricultural properties in the state of Rio Grande do Sul- Brazil.

Evaluation of the agronomic performance of cvs. Syrah and tempranillo when grafted on a new series of rootstocks developed in spain

The choice of an adequate rootstock is a key tool to improve the performance of grapevine varieties in different ‘terroirs’, as rootstocks confer adaptation to soil characteristics

Effect of different winemaking practices on chemical composition, aroma profile and sensory perception of ribolla gialla sparkling wines

This study aims at evaluating the effects of different refermentation methods (Martinotti/Charmat vs. Classic) on the chemical composition, aroma profile and sensory characteristics of Ribolla Gialla sparkling wines; furthermore, certain winemaking practices (skin contact and use of pectolytic enzymes) were investigated considering the extraction of varietal aromas and aroma precursors. METHODS: Sparkling wines were produced at pilot-plant scale. Concerning refermentation methods, traditional Martinotti (MB – 30 days length), extended Martinotti (ML) with 4 months of aging on lees and Classic method (CL) with 11 months of aging on lees were compared; in a second trial, skin contact (MM), enzyme addition on must also subjected to maceration (ME), and enzyme addition on base wine (VE) were evaluated. All experimental trials were performed in triplicate. Basic chemical composition, varietal (terpenes and C13-norisoprenoids in free and bound form) and non-varietal aroma compounds were evaluated by LLE-GCMS analysis; finally, sensory analysis was also performed, by descriptive testing.

The taste of color: how grape anthocyanin fractions affect in-mouth perceptions

Anthocyanins are responsible for the red wine color and their ability to condense with tannins is considered as a contributor in astringency reduction. However, recent studies showed the possibility of anthocyanins to influence directly the in-mouth perception of wines.