Evaluation of Nontarget Long-Term LC–HRMS Time Series Data Using Multivariate Statistical Approaches

• Published in: Analytical chemistry (Washington) Vol. 92; no. 18; pp. 12273 - 12281
• Main Authors: Purschke, Kirsten, Vosough, Maryam, Leonhardt, Juri, Weber, Markus, Schmidt, Torsten C
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 15.09.2020
• Subjects: Adducts; Analytical chemistry; Chemical composition; Chemistry; Chromatography; Chromatography, Liquid; Contaminants; Data analysis; Data interpretation; Data processing; Environmental Monitoring; Environmental science; Industrial wastes; Industrial wastewater; Liquid chromatography; Mass Spectrometry; Mass spectroscopy; Metabolomics; Models, Statistical; Molecular Structure; Multivariate Analysis; N-Methylpyrrolidone; Organic Chemicals - analysis; Organic compounds; Pollutants; Pollution monitoring; Principal components analysis; Scientific imaging; Spectroscopy; Statistical methods; Subtraction; Time dependence; Time series; Trends; Waste Water - chemistry; Wastewater analysis; Wastewater pollution; Wastewater treatment; Wastewater treatment plants; Water Pollutants, Chemical - analysis; Water pollution
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.0c01897

The use of liquid chromatography coupled with high-resolution mass spectrometry (LC–HRMS) has steadily increased in many application fields ranging from metabolomics to environmental science. HRMS data are frequently used for nontarget screening (NTS), i.e., the search for compounds that are not previously known and where no reference substances are available. However, the large quantity of data produced by NTS analytical workflows makes data interpretation and time-dependent monitoring of samples very sophisticated and necessitates exploiting chemometric data processing techniques. Consequently, in this study, a prioritization method to handle time series in nontarget data was established. As proof of concept, industrial wastewater was investigated. As routine industrial wastewater analyses monitor the occurrence of a limited number of targeted water contaminants, NTS provides the opportunity to detect also unknown trace organic compounds (TrOCs) that are not in the focus of routine target analysis. The developed prioritization method enables reducing raw data and including identification of prioritized unknown contaminants. To that end, a five-month time series for industrial wastewaters was utilized, analyzed by liquid chromatography–time-of-flight mass spectrometry (LC–qTOF-MS), and evaluated by NTS. Following peak detection, alignment, grouping, and blank subtraction, 3303 features were obtained of wastewater treatment plant (WWTP) influent samples. Subsequently, two complementary ways for exploratory time trend detection and feature prioritization are proposed. Therefore, following a prefiltering step, featurewise principal component analysis (PCA) and groupwise PCA (GPCA) of the matrix (temporal wise) were used to annotate trends of relevant wastewater contaminants. With sparse factorization of data matrices using GPCA, groups of correlated features/mass fragments or adducts were detected, recovered, and prioritized. Similarities and differences in the chemical composition of wastewater samples were observed over time to reveal hidden factors accounting for the structure of the data. The detected features were reduced to 130 relevant time trends related to TrOCs for identification. Exemplarily, as proof of concept, one nontarget pollutant was identified as N-methylpyrrolidone. The developed chemometric strategies of this study are not only suitable for industrial wastewater but also could be efficiently employed for time trend exploration in other scientific fields.