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

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.

Identification of unknowns in industrial wastewater using offline 2D chromatography and non-target screening.

Industrial wastewater is characterised by a complex composition of trace organic compounds (TrOC) in a difficult matrix. The identification of unknown pollutants is of high interest. On the one hand to ensure protection of the environment by elucidating contaminations and on the other hand to protect the biological treatment step in the wastewater treatment plant (WWTP). Due to the high variability of the matrix, the identification of compounds of interest is very time consuming and often unsuccessful. To overcome this limitation, a prioritisation method was developed to identify so called 'known unknowns', i.e. compounds frequently detected but not identified, as prioritised compounds in industrial wastewater. The method based on an offline two-dimensional (offline 2D) liquid chromatography (LC) approach with ultra violet (UV) detection in the first and high-resolution mass spectrometry (HRMS) in the second dimension. As a proof of concept, an identification process of one 'known unknown' is described. The compound was identified as a dichlorodinitrophenol isomer by retention time in two dimensions, UV spectrum, exact mass, mass fragmentation and 1H- NMR. As prioritisation method, the offline 2D LC in combination with non-target analysis provides a powerful workflow to determine tentative structures of unknown organic compounds in industrial wastewater.

Comprehensive analysis of antagonistic endocrine activity during ozone treatment of hospital wastewater.

To reduce the discharge of micropollutants, advanced wastewater treatment methods were investigated in the last years. Estrogenic effects were found to be reduced by ozonation. These activities are usually measured using genetically modified cell-based tests. As these bioassays are representing a sum parameter, also inhibitory effects such as antagonistic effects need to be further investigated as they are potentially reducing the detected activities. Therefore, a direct comparison of chemical target analysis and biological equivalent concentrations measured by bioassays is often difficult. To investigate the fate of antagonistic activities and their role in mixtures with agonistic activities, two hospital wastewater treatment plants were studied after different treatment steps. Thereby highly enriched samples were analyzed by a combination of bioassays with chemical target and non-target analyses. In order to achieve an in-depth characterization of the antagonistic activities a fractionation of the enriched samples was performed. To identify relevant compounds an effect directed identification approach was used by combining high-resolution mass spectrometry and bioassays. The results showed a high reduction for estrogene and androgene activities. However, a constant antagonistic activity after membrane bioreactor and ozone treatment was observed. A reduction of the antagonistic activity was observed after passing an activated carbon filter. The fractionation approach showed a specific finger-print of each sample of the different treatment steps. Hereby we could show that the composition of agonistic and antagonistic active compounds is changing after each treatment step while the overall measured activity stays the same. Using fractionation and the combination of bioassays the number of relevant features detected by chemical non-target screening could be reduced by >85%. As a result the phosphorous flame retardant TCEP could be identified as anti-estrogene active. Future research should be done to identify more antagonistic active compounds and potentially active transformation products after ozone treatment.

A new method for the determination of peak distribution across a two-dimensional separation space for the identification of optimal column combinations.

For the identification of the optimal column combinations, a comparative orthogonality study of single columns and columns coupled in series for the first dimension of a microscale two-dimensional liquid chromatographic approach was performed. In total, eight columns or column combinations were chosen. For the assessment of the optimal column combination, the orthogonality value as well as the peak distributions across the first and second dimension was used. In total, three different methods of orthogonality calculation, namely the Convex Hull, Bin Counting, and Asterisk methods, were compared. Unfortunately, the first two methods do not provide any information of peak distribution. The third method provides this important information, but is not optimal when only a limited number of components are used for method development. Therefore, a new concept for peak distribution assessment across the separation space of two-dimensional chromatographic systems and clustering detection was developed. It could be shown that the Bin Counting method in combination with additionally calculated histograms for the respective dimensions is well suited for the evaluation of orthogonality and peak clustering. The newly developed method could be used generally in the assessment of 2D separations. Graphical Abstract ᅟ.

Online and splitless NanoLC × CapillaryLC with quadrupole/time-of-flight mass spectrometric detection for comprehensive screening analysis of complex samples.

A novel multidimensional separation system based on online comprehensive two-dimensional liquid chromatography and hybrid high-resolution mass spectrometry has been developed for the qualitative screening analysis and characterization of complex samples. The core of the system is a consistently miniaturized two-dimensional liquid chromatography that makes the rapid second dimension compatible with mass spectrometry without the need for any flow split. Elevated temperature, ultrahigh pressure, and a superficially porous sub-3-µm stationary phase provide a fast second dimension separation and a sufficient sampling frequency without a first dimension flow stop. A highly loadable porous graphitic carbon stationary phase is employed in the first dimension to implement large volume injections that help countervailing dilution caused by the sampling process between the two dimensions. Exemplarily, separations of a 99-component standard mixture and a complex wastewater sample were used to demonstrate the performance of the dual-gradient system. In the second dimension, 30 s gradients at a cycle time of 1 min were employed. One multidimensional separation took 80-90 min (~120 min including extended hold and re-equilibration in the first dimension). This approach represents a cost-efficient alternative to online LC × LC strategies working with conventionally sized columns in the rapid second dimension, as solvent consumption is drastically decreased and analytes still are detectable at environmentally relevant concentrations.