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…

NMR profiling of grape musts from some italian regions

With wine fraud, being a widespread problem [1], the need for more sophisticated and precise analytical methods of its detection remains ever persistent.

A climatic characterisation of the sub-Appellations in the Niagara Peninsula wine region

This study used climatic and topographic data to characterize the sub-appellations that have been recently delineated in the Niagara Peninsula viticulture area in order to assess their potential for ripening early to late season Vitis vinifera varieties. No major differences were found in the ripening-period mean temperatures, but major differences in the diurnal temperature ranges were observed.

Deciphering the color of rosé wines using polyphenol targeted metabolomics

The color of rosés wines is extremely diverse and a key element in their marketing. It is due to the presence of red anthocyanins extracted from grape skins and pigments formed from them and other wine constituents during wine-making.

Mobile device to induce heat-stress on grapevine berries

Studying heat stress response of grapevine berries in the field often relies on weather conditions during the growing season. We constructed a mobile heating device, able to induce controlled heat stress on grapes in vineyards. The heater consisted of six 150 W infrared lamps mounted in a profile frame. Heating power of the lamps could be controlled individually by a control unit consisting of a single board computer and six temperature sensors to reach a pre-set temperature. The heat energy applied to individual berries within a cluster decreases by the squared distance to the heat source, enabling the establishment of temperature profiles within individual clusters. These profiles can be measured by infrared thermography once a steady state has been reached. Radiant flux density received by a berry depending on the distance was calculated based on a view factor and measured lamp surface temperature and resulted to 665 Wm-2 at 7cm. Infrared thermography of the fruit surface was in good agreement with measurements conducted with a thermocouple inserted at epidermis level. In combination with infrared thermography, the presented device offers possibilities for a wide range of applications like phenotyping for heat tolerance in the field to proceed in the understanding of the complex response of plants to heat stress. Sunburn necrosis symptoms were artificially induced with the aid of the device for cv. Bacchus and cv. Sylvaner in the 2020 and 2021 growing season. Threshold temperatures for sunburn induction (LT5030min) were derived from temperature data of single berries and visual sunburn assessment, applying logistic regression. A comparison of threshold temperatures for the occurrence of sunburn necrosis confirmed the higher susceptibility of cv. Bacchus. The lower susceptibility of cv. Sylvaner did not seem to be related to its phenolic composition, rendering a thermoprotective role of berry phenolic compounds unlikely.

The role of vine trunk height in delaying grape ripening: insights for viticultural adaptation strategies

Global changes in temperature patterns necessitate the development of viticultural adaptation strategies. One promising approach involves modifying the training system and elevating trunk height. This study explored the potential of raising the vine trunk as an adaptive strategy to counteract the effects of increasing temperatures and delay ripening. Thermal conditions, radiation levels, and must composition were measured at different heights (10 and 150 cm) in a commercial vineyard of the minority variety Maturana Blanca, trained on a vertical cordon.