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