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Data processing strategies for LC-HRMS based non-target analysis of organic micropollutants in aqueous matrices
A large variety of organic micropollutants (OMPs) are introduced into the aquatic environment and raise concerns due to their potential impact on ecosystems and human health. The high sensitivity and selectivity of liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) enable the screening of a broad range of OMPs at trace concentrations without restriction on predefined analytes. Thus, LC-HRMS based non-target screening (NTS) approaches are of increasing importance in water analysis as they provide the potential to identify formerly unknown compounds and obtain a more comprehensive overview of pollution loads. However, with NTS large amounts of data are recorded within each measurement making sophisticated data processing strategies necessary. The first task of a data processing workflow is a reliable extraction of analyte signals, so-called features, from raw data. After this step complex datasets with thousands of features are obtained. Subsequently, it is essential to reduce and prioritize features that are relevant to the studied research question. This thesis addresses several aspects of data processing strategies, focusing on both the feature extraction step and feature prioritization step based on multivariate chemometric methods. Nevertheless, high-quality measurement data are essential as a basis for the following data processing. A generic qualitative screening method was developed for an LC-HRMS analytical system. The sensitivity and selectivity to detect a broad range of OMPs at environmentally relevant concentrations and the stability of peak areas and retention times, enabling the comparison of several samples, were confirmed. On this basis, the importance of the feature extraction step was emphasized by first identifying weaknesses in the consistency of results obtained from different programs and secondly presenting an alternative chemometric-based approach. The comparability of feature extraction with four different commonly used open-source and commercial software tools ...
Data processing strategies for LC-HRMS based non-target analysis of organic micropollutants in aqueous matrices
A large variety of organic micropollutants (OMPs) are introduced into the aquatic environment and raise concerns due to their potential impact on ecosystems and human health. The high sensitivity and selectivity of liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) enable the screening of a broad range of OMPs at trace concentrations without restriction on predefined analytes. Thus, LC-HRMS based non-target screening (NTS) approaches are of increasing importance in water analysis as they provide the potential to identify formerly unknown compounds and obtain a more comprehensive overview of pollution loads. However, with NTS large amounts of data are recorded within each measurement making sophisticated data processing strategies necessary. The first task of a data processing workflow is a reliable extraction of analyte signals, so-called features, from raw data. After this step complex datasets with thousands of features are obtained. Subsequently, it is essential to reduce and prioritize features that are relevant to the studied research question. This thesis addresses several aspects of data processing strategies, focusing on both the feature extraction step and feature prioritization step based on multivariate chemometric methods. Nevertheless, high-quality measurement data are essential as a basis for the following data processing. A generic qualitative screening method was developed for an LC-HRMS analytical system. The sensitivity and selectivity to detect a broad range of OMPs at environmentally relevant concentrations and the stability of peak areas and retention times, enabling the comparison of several samples, were confirmed. On this basis, the importance of the feature extraction step was emphasized by first identifying weaknesses in the consistency of results obtained from different programs and secondly presenting an alternative chemometric-based approach. The comparability of feature extraction with four different commonly used open-source and commercial software tools ...
Data processing strategies for LC-HRMS based non-target analysis of organic micropollutants in aqueous matrices
Hohrenk-Danzouma, Lotta Laura (author) / Schmidt, Torsten C.
2023-03-29
Theses
Electronic Resource
English
| UB Braunschweig | 2021