Quantitative Nontarget Analysis of CECs in Environmental Samples Can Be Improved by Considering All Mass Adducts.

Quantitative nontarget analysis (qNTA) for liquid chromatography coupled to high-resolution mass spectrometry enables a more comprehensive assessment of environmental samples. Previous studies have shown that correlations between a compound's ionization efficiency and a range of molecular descriptors can predict the compound's concentration within a factor of 5. In this study, the qNTA approach was further improved by considering all mass adducts instead of only the protonated ion. The model was based on a quantitative structure-property relationship (QSPR), including 216 contaminants of emerging concern (CECs), of which 80 exhibited adduct formation that accounted for >10% of the total peak intensity. When all mass adducts were included, the test set coefficient of determination improved to Q2 = 0.855 compared to Q2 = 0.670 when only the protonated ions were considered (test set median RF error factor 1.6). The inclusion of all adducts was also important to transfer the RF QSPR model reliably. It was assumed that RF variations are sequence-dependent; therefore, a second QSPR model for the prediction of the transferability factor was built for each sequence. For validation, samples were analyzed up to two years apart. The median prediction fold change was 1.74 for analytical standards (63 compounds) and 2.4 for enriched wastewater effluent samples (41 compounds), with 80% of the compounds predicted within a fold change of 2.4 and 3.3, respectively. The model was also validated on a second instrument, where 80% of the 26 compounds in wastewater effluent were predicted within a factor of 3.8.

Ultra-high-performance supercritical fluid chromatography-mass spectrometry for the analysis of organic contaminants in sediments.

A nontarget screening method was developed based on D-optimal designs for ultra-high performance supercritical fluid chromatography with positive and negative electrospray ionization mode mass spectrometry. A mixture of organic contaminants such as pesticides, steroids, surfactants, phenolic and fatty acids, and polycyclic aromatic hydrocarbon derivatives, was used for the optimization. An aprotic mixture of dichloromethane and acetone [3:1] performed overall best as the injection solvent. The highest peak capacities (n) were accomplished at the shallowest gradient (1%B/min), ammonium formate (n = 378 in negative ionization mode), or ammonium acetate (n = 327 in positive ionization mode) in methanol as the modifier. Capillary voltage, make-up solvent flow rate, water, and additive concentration were the most significant factors for improving peak intensity: higher peak intensities were obtained at lower additive concentrations (5mM ammonium formate), and with 5% water in positive ionization mode. Conversely, water had detrimental effects in negative ionization mode. The optimized method was used to quantify organic contaminants in 17 freshwater sediment samples from Copenhagen, Denmark. Out of 50 monitored contaminants, 35 were detected in at least one sample. Further, the method has a potential for target and nontarget screening analysis of organic contaminants in solid matrices.

Obtaining clean and informative mass spectra from complex chromatographic and high-resolution all-ions-fragmentation data by nonnegative parallel factor analysis 2.

A major challenge in processing of complex data obtained from chromatography hyphenated to mass spectrometry is to resolve chromatographically co-eluting compounds. In this study, we present a workflow for the resolution of ultra-high pressure liquid chromatography high-resolution mass spectrometry data obtained by the broadband data-independent acquisition MSE operation (UHPLCHRMSE). The workflow is based on a recently introduced algorithm for Parallel Factor Analysis 2 (PARAFAC2) that allows to enforce non-negativity on all the model coefficients. The workflow was tested on three sets of UHPLC-HRMSE measurements from a Lupinus angustifolius L. crop field study, which included plant tissue samples, soil samples and samples from drainage water as well as stream water close to the field. The three datasets included 93, 59, and 75 chromatographic runs in total for the plant, soil and water batches, respectively. Nonnegative-PARAFAC2 models were fitted on the summed high and low energy (HE and LE) traces on chromatographic intervals corresponding to spiked standard for the three sample sets independently. In soil and plant samples, 13 out of 14 spiked standards were resolved by NN-PARAFAC2 even in presence of chromatographic co-elution, and their mass spectral loadings could be matched to a reference spectrum. In contrast, only seven spiked standards were correctly resolved and matched for the water samples because a higher chromatographic baseline rendered the data noisier. The results show that the workflow we present can provide improved mass spectral selectivity for data-independent acquisition compared to using the raw mass spectra and can be used to match fragment ions from the HE trace, and precursor and adduct ions from the LE trace even in presence of co-eluting compounds.

From data to reliable conclusions: Identification and comparison of persistent micropollutants and transformation products in 37 wastewater samples by non-target screening prioritization.

In this study, micropollutants in wastewater effluents were prioritized by monitoring the composition of influent and effluent wastewater by liquid chromatography - high-resolution mass spectrometry (LCHRMS) non-target screening (NTS) analysis. The study shows how important data pre-processing and filtering of raw data is to produce reliable NTS data for comparison of compounds between many samples (37 wastewater samples) analyzed at different times. Triplicate injections were critical for reducing the number of false-positive detections. Intensity drift corrections within and between batches analyzed months apart made peak intensities comparable across samples. Adjustment of the feature detection threshold was shown to be important, due to large intensity variations for low abundance compounds across batches. When the threshold correction cut-offs, or the filtering of relevant compounds by the occurrence frequency, were too stringent, a high number of false positive transformation products (TPs) were reported. We also showed that matrix effect correction by internal standards can over- or under-correct the intensity for unknown compounds, thus the TIC matrix effect correction was shown as an additional tool for a retention time dependent matrix effect correction. After these preprocessing and filtering steps, we identified 78 prioritized compounds, of which 36 were persistent compounds, defined as compounds with a reduction in peak intensity between influent and effluent wastewater <50%, and 13 compounds were defined as TPs because they occurred solely in the effluent samples. Some examples of persistent compounds are 1,3-diphenylguanidine, amisulpride and the human metabolites from losartan, verapamil and methadone. To our knowledge, nine of the identified TPs have not been previously described in effluent wastewater. The TPs were derived from metoprolol, fexofenadine, DEET and losartan. The screening of all identified compounds in effluent samples from eight wastewater treatment plants (WWTPs) showed that potential drugs of abuse, anti-psychotic and anti-depressant drugs were predominant in the capital city region, whereas the anti-epileptic agents and agricultural pesticides were dominant in more rural areas.

Removal of volatile gasoline compounds by indoor potted plants studied by pixel-based fingerprinting analysis.

Indoor potted plants are able to remove volatile organic compounds (VOC) from air, but only few studies have investigated the removal of compounds in mixtures. Here, we present a non-targeted pixel-based fingerprinting analysis documenting the removal of a complex mixture of gasoline VOCs by Hedera helix under dynamic chamber conditions allowing for air exchange and continuous gasoline exposure. For 15 days, the entire potted plant was exposed to gasoline; subsequently, the epigeous plant parts were removed and the soil microcosm (i.e. soil, plant roots and microorganisms) was exposed to gasoline for another eight days. Quantitative analysis was performed for heptane, 3-methylhexane, toluene, ethylbenzene and m,p-xylenes, and the CHEMSIC method (CHEMometric analysis of Selected Ion Chromatograms) was used for non-targeted pixel-based fingerprinting analysis. The quantitative analysis demonstrated that the presence of potted plants or pots without epigeous plant parts led to a reduction of selected VOCs by 16.7-22.6%. The CHEMSIC method confirmed this and revealed that all gasoline VOCs were reduced in concentration when H. helix was present. The estimate for the total VOC removal was in the range of 11-32%. The removal was highest for samples where the epigeous plant parts were absent and compounds known to be hard to degrade by microorganisms such as dimethylcyclopentanes were removed the least compared to compounds more easily degraded by microorganisms such as heptane when epigeous plant parts were removed. All findings support the conclusion that the soil microcosm was the main responsible for the removal of VOCs.

Forensic Investigations of Diesel Oil Spills in the Environment Using Comprehensive Two-Dimensional Gas Chromatography-High Resolution Mass Spectrometry and Chemometrics: New Perspectives in the Absence of Recalcitrant Biomarkers.

Forensic investigations of oil spills aim to find the responsible source(s) of the spill. Oil weathering processes change the chemical composition of the spilled oil and make the matching of oil spill samples to potential sources difficult. Diesel oil spill cases are more challenging, because biomarkers recalcitrant to long-term weathering are absent. We developed and tested a new method for the analysis and matching of diesel oil spills using two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC - HRMS) and 2D-CHEMSIC (2-Dimensional CHEMometric analysis of Selected Ion Chromatograms), an extension of the CHEMSIC method to GC × GC data. The 2D-CHEMSIC performs pixel-based analysis using chemometrics on concatenated sections of 2D extracted ion chromatograms to assess the overall chemical variability of the samples, with potential applications for matching spill-source pairs in forensic investigations. The method was tested on samples from a number of diesel oil spill cases, (i) distinguishing chemically similar source diesels, (ii) investigating weathering effects on spill samples to determine type and degree of weathering, and (iii) improving the matching of diesel oil spills affected by weathering. Positive matches for spill-source pairs were identified after excluding the signals from the hydrocarbons most susceptible to evaporation, and photo-oxidized spills were also matched due to the presence of unaffected hydrocarbons. Forensic diagnostics obtained by the 2D-CHEMSIC were validated by the conventional CEN-Tr method.

Evaluation of chromatographic conditions in reversed phase liquid chromatography-mass spectrometry systems for fingerprinting of polar and amphiphilic plant metabolites.

Metabolic fingerprinting is a relatively young scientific discipline requiring robust, yet flexible and fit-for-purpose analytical methods. Here, we introduce a simple approach to select reversed phase LC systems with electrospray MS detection for fingerprinting of polar and amphiphilic plant metabolites. The approach does not rely on isotopic labeling or biological origin of sample constituent and can also be used for non-biological matrices (e.g., oil or sewage sludge) or for other optimization purposes (e.g., mass spectrometric source parameterization). The LC systems varied in column chemistry and temperature, mobile phase pH/additive, gradient steepness/eluotropic strength, and electrospray mode of operation. The systems were evaluated based on the number of features detected using the matchedFilter algorithm from XCMS and the repeatability of this detection across analytical replicates. For negative ion mode detection, the best performances were obtained with an HSS T3 column operated at low pH, which produced a 3-fold increase in the number of reliable features extracted compared with the worst system. The best system for positive ion mode (i.e., the BEH C18 column operated at intermediate pH) only produced a 50 % increase in the number of reliable features. The data also indicate that baseline removal is unavoidable for reliable intensity estimations using peak areas, and that peak heights may be a more robust measure of intensity when baselines cannot be completely removed or in case of coelution, fronting or tailing.

A combined proteomics and metabolomics approach to assess the effects of gold nanoparticles in vitro.

Omics technologies, such as proteomics or metabolomics, have to date been applied in the field of nanomaterial safety assessment to a limited extent. To address this dearth, we developed an integrated approach combining the two techniques to study the effects of two sizes, 5 and 30 nm, of gold nanoparticles (AuNPs) in Caco-2 cells. We observed differences in cells exposed for 72 h to each size of AuNPs: 61 responsive (up/down-regulated) proteins were identified and 35 metabolites in the cell extract were tentatively annotated. Several altered biological pathways were highlighted by integrating the obtained multi-omics data with bioinformatic tools. This provided a unique set of molecular information on the effects of nanomaterials at cellular level. This information was supported by complementary data obtained by immunochemistry, microscopic analysis, and multiplexed assays. A part from increasing our knowledge on how the cellular processes and pathways are affected by nanomaterials (NMs), these findings could be used to identify specific biomarkers of toxicity or to support the safe-by-design concept in the development of new nanomedicines.

icoshift: An effective tool for the alignment of chromatographic data.

The Interval Correlation Optimised Shifting algorithm (icoshift) has recently been introduced for the alignment of nuclear magnetic resonance spectra. The method is based on an insertion/deletion model to shift intervals of spectra/chromatograms and relies on an efficient Fast Fourier Transform based computation core that allows the alignment of large data sets in a few seconds on a standard personal computer. The potential of this programme for the alignment of chromatographic data is outlined with focus on the model used for the correction function. The efficacy of the algorithm is demonstrated on a chromatographic data set with 45 chromatograms of 64,000 data points. Computation time is significantly reduced compared to the Correlation Optimised Warping (COW) algorithm, which is widely used for the alignment of chromatographic signals. Moreover, icoshift proved to perform better than COW in terms of quality of the alignment (viz. of simplicity and peak factor), but without the need for computationally expensive optimisations of the warping meta-parameters required by COW. Principal component analysis (PCA) is used to show how a significant reduction on data complexity was achieved, improving the ability to highlight chemical differences amongst the samples.

The use of environmental metabolomics to determine glyphosate level of exposure in rapeseed (Brassica napus L.) seedlings.

Metabolic profiling in plants can be used to differentiate between treatments and to search for biomarkers for exposure. A methodology for processing Ultra-High-Performance Liquid Chromatography-Diode-Array-Detection data is devised. This methodology includes a scheme for selecting informative wavelengths, baseline removal, retention time alignment, selection of relevant retention times, and principal component analysis (PCA). Plant crude extracts from rapeseed seedling exposed to sublethal concentrations of glyphosate are used as a study case. Through this approach, plants exposed to concentrations down to 5 µM could be distinguished from the controls. The compounds responsible for this differentiation were partially identified and were different from those specific for high exposure samples, which suggests that two different responses to glyphosate are elicited in rapeseed depending on the level of exposure. The PCA loadings indicate that a combination of other metabolites could be more sensitive than the response of shikimate to detect glyphosate exposure.

A novel approach for characterization of polycyclic aromatic hydrocarbon (PAH) pollution patterns in sediments from Guanabara Bay, Rio de Janeiro, Brazil.

A novel multivariate method based on principal component analysis of pre-processed sections of chromatograms is used to characterize the complex PAH pollution patterns in sediments from Guanabara Bay, Brazil. Five distinct sources of 3- to 6-ring PAHs could be revealed. The harbour is the most contaminated site in the bay, its plume stretches in a South West to North East direction and the chemical profile indicates mainly pyrogenic sources mixed with a fraction of high-molecular-weight petrogenic PAHs. Rio São João de Meriti is the second largest source of PAHs, and introduces mainly a fraction of low-molecular-weight petrogenic PAHs from the western region of Rio de Janeiro. The sites close to the ruptured pipeline at the Duque de Caxias Refinery show a distinctive pollution pattern indicating a heavy petroleum fraction. The method also led to the identification of new potential indicator ratios also involving coeluting peaks (e.g., triphenylene and chrysene).

A Tucker model based approach for analysis of complex oil biodegradation data.

A novel method based on gas chromatography-mass spectrometry in selected ion monitoring mode (GC-MS/SIM) and Tucker models is developed to evaluate the effects of oil type, microbial treatments and incubation time on the biodegradation of petroleum hydrocarbons. The data set consists of sections of the m/z 180, 192 and 198 GC-MS/SIM chromatograms of oil extracts from a biodegradation experiment where four oil types were exposed to four microbial treatments over a period of one year. The chosen sections, which are specific to methylfluorenes, phenanthrenes and dibenzothiophenes, were combined in a 4-way array (incubation timexoil typextreatmentxcombined chromatographic retention times) that was analyzed using both principal component analysis and the Tucker model. Several conclusions could be reached: the light fuel oil was the least degradable of those tested, 2- and 3-methyl isomers were more easily degraded compared to the 4-methyl isomers, the mixture of surfactant producers and PAC degraders provided the most effective degradation and the largest part of the degradation occurred between 54 and 132 days.

PARAFAC modeling of fluorescence excitation-emission spectra of fish bile for rapid en route screening of PAC exposure.

Polycyclic aromatic compound (PAC) metabolites in fish bile can be used as biomarkers for recent environmental exposure to PACs. Here, a novel method for rapid screening of nonhydrolyzed fish bile is presented. The method is based on excitation-emission fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) and may constitute an alternative to fixed wavelength fluorescence and synchronous fluorescence spectroscopy (SFS). PARAFAC was applied to excitation-emission matrices (EEMs) of bile samples of shorthorn sculpins and European eels collected in Greenland and Denmark. The EEMs were decomposed into a four-factor PARAFAC model. The comparison of the PARAFAC factors with the EEMs of PAC metabolites and amino acids suggests that two factors are related to PAC metabolites and two correspond to fluorescent residues of tryptophan and tyrosine in bile proteins. A new standardization procedure based on the mean of the scores for the biological factors was used to correct for feeding status and sample dilution and, upon such normalization, the score plots of PARAFAC factors showed a clear distinction between exposed and nonexposed fish. A good correlation was found between the factor scores and 1-hydroxypyrene equivalents determined by SFS for high contamination levels, whereas the sensitivity was better for the EEM method.

Practical aspects of chemometrics for oil spill fingerprinting.

Tiered approaches for oil spill fingerprinting have evolved rapidly since the 1990s. Chemometrics provides a large number of tools for pattern recognition, calibration and classification that can increase the speed and the objectivity of the analysis and allow for more extensive use of the available data in this field. However, although the chemometric literature is extensive, it does not focus on practical issues that are relevant to oil spill fingerprinting. The aim of this review is to provide a framework for the use of chemometric approaches in tiered oil spill fingerprinting and to provide clear-cut practical details and experiences that can be used by the forensic chemist. The framework is based on methods for initial screening, which include classification of samples into oil type, detection of non matches and of weathering state, and detailed oil spill fingerprinting, in which a more rigorous matching of an oil spill sample to suspected source oils is obtained. This review is intended as a tutorial, and is based on two examples of initial screening using respectively gas chromatography with flame ionization detection and fluorescence spectroscopy; and two of detailed oil spill fingerprinting where gas chromatography-mass spectrometry data are analyzed according to two approaches: The first relying on sections of processed chromatograms and the second on diagnostic ratios.

Dioxin screening in fish product by pattern recognition of biomarkers.

Two alternative, cost- and time-effective dioxin screening methods relying on two categories of potential lipid biomarkers were investigated. A dioxin range varying from 1.1 to 47.1 pg PCDD/F TEQ-WHO/g lipid using 64 fish meal samples was used for model calibration. The methods were based on multivariate models using either (1) fatty acid composition monitored by GC-FID or (2) fluorescence landscape signals analysed using the PARAFAC model and in both cases predicting dioxin content as pgPCDD/F TEQ-WHO/g lipid. In both cases, Partial Least Squares (PLS) regression was performed for predicting the dioxin content of a sample. The GC-FID data analyses was based on automatic peak alignment and integration, enabling extraction of the area of 140 peaks from the gas chromatograms, as opposed to the 31 fatty acids usually considered for fish oil characterisation. In addition to classic PLS employing the whole dataset for calibration, a two-step local PLS modeling approach was performed based upon an initial selection of k number of calibration samples providing the best match to the prediction sample using a so-called k Nearest Neighbors (kNN) approach, then followed by PLS calibration on these kNN selected samples for dioxin prediction. Fluorescence spectroscopy offers a promising non-invasive and ultra-rapid technique, with less than two minutes analysis time. However, fluorescence spectroscopy using the pattern recognition "kNN-PLS" yielded a correlation of 0.76 (r2) and a high root mean square error of prediction of 11.4 pg PCDD/F TEQ-WHO/g lipid. The predictions were improved when the PLS calibration was performed on all the sample with a root mean square error of prediction of 7.0 pg PCDD/F TEQ-WHO/g lipid. Unfortunately, these results failed to demonstrate the potential of fluorophore monitoring as a screening method. In contrast, the overall best screening performance was obtained with the fatty acid profile, when the kNN-PLS combination employed for pattern recognition (kNN) all the areas of the 140 detected peaks and the PLS regression used the areas of 46 selected peaks. This "kNN-PLS" prediction with three latent variables and based upon the 12 nearest neighbors selected out of the 64 x 2 fatty acid profiles (duplicate analyses), yielded a correlation of 0.85 (r2) and a root mean square error of prediction of 2.1 pg PCDD/F TEQ-WHO/g lipid and resulted in a total analysis time of one and half hour per sample.

Chemical fingerprinting of petroleum biomarkers using time warping and PCA.

A new method for chemical fingerprinting of petroleum biomakers is described. The method consists of GC-MS analysis, preprocessing of GC-MS chromatograms, and principal component analysis (PCA) of selected regions. The preprocessing consists of baseline removal by derivatization, normalization, and alignment using correlation optimized warping. The method was applied to chromatograms of m/z 217 (tricyclic and tetracyclic steranes) of oil spill samples and source oils. Oil spill samples collected from the coastal environment in the weeks after the Baltic Carrier oil spill were clustered in principal components 1 to 4 with oil samples from the tank of the Baltic Carrier (source oil). The discriminative power of PCA was enhanced by deselecting the most uncertain variables or scaling them according to their uncertainty, using a weighted least squares criterion. The four principal components were interpreted as follows: boiling point range (PC1), clay content (PC2), carbon number distribution of sterols in the source rock (PC3), and thermal maturity of the oil (PC4). In summary, the method allows for analyses of chromatograms using a fast and objective procedure and with more comprehensive data usage compared to other fingerprinting methods.

Integrated methodology for forensic oil spill identification.

A new integrated methodology for forensic oil spill identification is presented. It consists of GC-MS analysis, chromatographic data processing, variable-outlier detection, multivariate data analysis, estimation of uncertainties, and statistical evaluation. The methodology was tested on four groups of diagnostic ratios composed of petroleum biomarkers and ratios within homologous PAH categories. Principal component analysis (PCA) was employed and enabled the simultaneous analysis of many diagnostic ratios. Weathering was taken into account by considering the sampling uncertainties estimated from replicate spill samples. Statistical evaluation ensured an objective matching of oil spill samples with suspected source oils as well as classification into positive match, probable match, and nonmatch. The data analysis is further refined if two or more source oils are classified as probable match by using weighted least squares fitting of the principal components, local PCA models, and additional information relevant to the spill case. The methodology correctly identified the source of two spill samples (i.e., crude oils from Oseberg East and Oseberg Field Centre) and distinguished them from closely related source oils.

Application of multi-way models to the time-resolved fluorescence of polycyclic aromatic hydrocarbons mixtures in water.

The time-resolved laser-induced fluorescence of a series of polycyclic aromatic compounds (PAHs) and mixtures of these latter in aqueous solution was measured by means of an apparatus equipped with optical fibers, which allows their real time in situ monitoring. The potential of such spectroscopic technique, yielding 4-way fluorescence data arrays, together with the application of multi-way models to the matricized data, was tested for the resolution of complex aqueous mixtures containing low concentrations of PAHs, as typical fluorescent pollutants in aquatic systems. PARAllel FACtors analysis was employed for the qualitative resolution of PAHs mixtures and for calculating the fluorescence lifetimes of single PAHs; n-way partial least squares analysis was applied for evaluating the concentration of the single PAHs in the aqueous mixtures.