OENO IVAS 2019 banner
IVES 9 IVES Conference Series 9 OENO IVAS 9 OENO IVAS 2019 9 Analytical developments from grape to wine, spirits : omics, chemometrics approaches… 9 Fully automated non-targeted GC-MS data analysis

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…

Melatonin priming retards fungal decay in postharvest table grapes 

Postharvest losses of fruits may reach in some cases 40% in developed countries. This food waste has a significant carbon footprint and makes a major contribution toward greenhouse gas emissions so sustainable postharvest strategies are being investigated.
Melatonin, a well-known mammalian neurohormone, has been investigated as a priming agent to slow down fungal decay progression in postharvest climacteric and some non-climacteric fruits. However, the molecular and metabolic mechanisms responsible for such enhancement of disease tolerance are largely unknown.

Read More

Soil and Climate Interactions with Grapevines

To test the hypothesis that soil type plays a minor role relative to that of vine vigor in the determination of yield, fruit composition and wine sensory attributes, 5 Chardonnay vineyards in the Niagara

Read More

The impact of different yeasts and harvest time on the wine quality of Beihong and Beimei (<I>V. vinifera x V. amurensis</I>)

Beihong and Beimei are two wine cultivars from ‘Muscat Hamberg’ (V. vinifera L.) and wild V. amurensis Rupr., which were released in China in 2008. Here，two enology practices were reported. Firstly, the impact of different yeasts including D254, GRE, K1, D21 and BDX on dry wine quality of Beihong and Beimei was investigated. For Beihong, among wines fermented by all yeasts, residual sugar content was the lowest, total anthocyanin and resveratrol contents were the highest in the wine by D254. However, the wine by D254 had lower titrable acid than those by the other yeasts except BDX.

Read More

Effect of biological control agents on grapevine rhizosphere microbiome and grapevine defenses

Plant diseases are a major obstacle to crop production. The main approaches to battle plant diseases, consist of synthetic chemicals to attack infecting pathogens. However, concerns are increasing about the effects of chemicals in the environment, leading to an increase in the use of biocontrol agents (BCAs), due to their assets, such as, antagonism, and competition. In this study, we tested the hypothesis that the introduction of Bacillus subtilis PTA-271 (Bs PTA-271) and Trichoderma atroviride SC1 (Ta SC1) produce distinctive modifications in the composition and network structure of the grapevine rhizosphere microbial community, as well as grapevine induced defenses.

Read More

Simulating berry sunburn in virtual vineyards

Context and purpose of the study. Berry sunburn in vineyards is a recurring disorder that can cause severe yield loss. As sunburn observations are often associated with heat waves, a link to climate change is likely.

Read More