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.
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.
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