Handling within run retention time shifts in two-dimensional chromatography data using shift correction and modeling

Enhancing gas chromatography-mass spectrometry resolution and pure analyte discovery using intra-chromatogram elution profile matching

Chemometric decomposition methods like multivariate curve resolution-alternating least squares (MCR-ALS) are often employed in gas chromatography-mass spectrometry (GC-MS) to improve analyte identification and quantitation. However, these methods can perform poorly for analytes with a low chromatographic resolution (Rs) and a high degree of spectral contamination from noise and background interferences. Thus, we propose a novel computational algorithm, termed mzCompare, to improve analyte identification and quantitation when coupled to MCR-ALS. The mzCompare method utilizes an underlying requirement that the retention time and peak shape between mass channels (m/z) of the same analyte should be similar. By discovering the selective m/z for a given analyte in a chromatogram, a pure elution profile can be generated and used as an equality constraint in MCR-ALS. The performance of the mzCompare methodology is demonstrated with both experimental and simulated chromatograms. Experimentally, unresolved analytes with a Rs as low as 0.05 could be confidently identified with mzCompare assisted MCR-ALS. Furthermore, application of the mzCompare algorithm to a complex aerospace fuel resulted in the discovery of 335 analytes, a 44 % increase compared to conventional peak detection methods. GC-MS simulations of target-interferent analyte pairs demonstrated that the performance of MCR-ALS deteriorated below a Rs of ∼0.25. However, mzCompare assisted MCR-ALS showed excellent identification and acceptable quantitative accuracy at a Rs of ∼0.02. These results show that the mzCompare algorithm can help analysts overcome modeling ambiguities resulting from the chemometric multiplex disadvantage.

PARAFAC2×N: Coupled decomposition of multi-modal data with drift in N modes

Analysis of GC×GC-TOFMS data for large numbers of poorly-resolved peaks, and for large numbers of samples remains an enduring problem that hinders the widespread application of the technique. For multiple samples, GC×GC-TOFMS data for specific chromatographic regions manifests as a 4^th order tensor of I mass spectral acquisitions, J mass channels, K modulations, and L samples. Chromatographic drift is common along both the first-dimension (modulations), and along the second-dimension (mass spectral acquisitions), while drift along the mass channel is for all practical purposes nonexistent. A number of solutions to handling GC×GC-TOFMS data have been proposed: these involve reshaping the data to make it amenable to either 2^nd order decomposition techniques based on Multivariate Curve Resolution (MCR), or 3^rd order decomposition techniques such as Parallel Factor Analysis 2 (PARAFAC2). PARAFAC2 has been utilised to model chromatographic drift along one mode, which has enabled its use for robust decomposition of multiple GC-MS experiments. Although extensible, it is not straightforward to implement a PARAFAC2 model that accounts for drift along multiple modes. In this submission, we demonstrate a new approach and a general theory for modelling data with drift along multiple modes, for applications in multidimensional chromatography with multivariate detection. The proposed model captures over 99.9% of variance for a synthetic data set, presenting an extreme example of peak drift and co-elution across two modes of separation.

Optimizing column-to-column retention time alignment in high-speed gas chromatography by combining retention time locking and correlation optimized warping

We examine and then optimize alignment of chromatograms collected on nominally identical columns using retention time locking (RTL), an instrumental alignment tool, and software-based alignment using correlation optimized warping (COW). For this purpose, three samples are constructed by spiking two sets of analytes into a base test mixture. The three samples are analyzed by high-speed gas chromatography with four nominally identical columns and identical separation conditions. The data is first analyzed without alignment, then using COW alone, then RTL alone, and finally with RTL followed by COW to correct the severe column-to-column misalignment. Principal component analysis (PCA) is used to investigate how well each alignment method clustered the chromatograms into the three sample classes via a scores plot without being compromised by the specific column(s) used. The degree-of-class separation (DCS) is used as a classification metric, measured as the Euclidian distance between the centroids of two clusters in PC space in the scores plot, normalized by their pooled variance. With no alignment, the average DCS between sample classes (DCS_sam) was 3.0, while the average DCS between the four nominally identical columns, i.e., column classes (DCS_col) was 76.1 (ideally the DCS_col should be 0), indicating the chromatograms were initially classified by the columns used. Using either COW or RTL alone also produced unsatisfactory results, with COW alone incorrectly aligning many peaks, leading to a DCS_sam of only 1.9 and DCS_col of 1.7, while RTL alone provided a DCS_sam of 4.7 and DCS_col of 4.2. Finally, using RTL followed by COW alignment, DCS_sam increased to 32.5, indicating successful classification by chemical differences between sample classes, while the DCS_col decreased to 0.4, indicating virtually no classification due to column-to-column differences, as desired. Thus, RTL provided a "first-order" correction of the initial retention mismatch observed for the nominally identical columns, while additional alignment via COW was required to optimize sample classification by PCA.

Flexible Implementation of the Trilinearity Constraint in Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) of Chromatographic and Other Type of Data

Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) can analyze three-way data under the assumption of a trilinear model using the trilinearity constraint. However, the rigid application of this constraint can produce unrealistic solutions in practice due to the inadequacy of the analyzed data to the characteristics and requirements of the trilinear model. Different methods for the relaxation of the trilinear model data requirements have been proposed, like in the PARAFAC2 and in the direct non-trilinear decomposition (DNTD) methods. In this work, the trilinearity constraint of MCR-ALS is adapted to different data scenarios where the profiles of all or some of the components of the system are shifted (not equally synchronized) or even change their shape among different slices in one of their data modes. This adaptation is especially useful in gas and liquid chromatography (GC and LC) and in Flow Injection Analysis (FIA) with multivariate spectroscopic detection. In a first data example, a synthetic LC-DAD dataset is built to investigate the possibilities of the proposed method to handle systematic changes (shifts) in the retention times of the elution profiles and the results are compared with those obtained using alternative methods like ATLD, PARAFAC, PARAFAC2 and DNTD. In a second data example, multiple wine samples were simultaneously analyzed by GC-MS where elution profiles presented large deviations (shifts) in their peak retention times, although they still preserve the same peak shape. Different modelling scenarios are tested and the results are also compared. Finally, in the third example, sample mixtures of acid compounds were analyzed by FIA under a pH gradient and monitored by UV spectroscopy and also examined by different chemometric methods using a different number of components. In this case, however, the departure of the trilinear model comes from the acid base speciation of the system depending on the pH more than from the shifting of the FIA diffusion profiles.

Multilinear Mathematical Separation in Chromatography

Chromatography is a powerful and generally applicable method for the analytical separation and quantification of the chemical constituents in complex mixtures because chromatographic separation can provide high selectivity by isolating all analytes from interferences. Multiway analysis based on the multilinear model is an increasingly widely used method for interference-free and fast determination of the chemical constituents also in complex mixtures because multilinear mathematical separation can provide high selectivity by extracting the pure signal of the analyte from the mixed signal of a real sample. By combining chromatographic separation with mathematical separation, multiway calibration method, multiway standard additions method, and multiway internal standard method can be established. Chromatography assisted by multiway analysis can reduce the requirements for complete chromatographic separation, save elution time, and decrease the consumption of the mobile phase, particularly when the peak coelution problem is difficult to solve. This review presents the fundamentals and analytical applications of multilinear mathematical separation in chromatography.

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

Linking the morphological and metabolomic response of Lactuca sativa L exposed to emerging contaminants using GC × GC-MS and chemometric tools

The occurrence of contaminants of emerging concern (CECs) in irrigation waters (up to low μg L-1) and irrigated crops (ng g-1 in dry weight) has been reported, but the linkage between plant morphological changes and plant metabolomic response has not yet been addressed. In this study, a non-targeted metabolomic analysis was performed on lettuce (Lactuca sativa L) exposed to 11 CECs (pharmaceuticals, personal care products, anticorrosive agents and surfactants) by irrigation. The plants were watered with different CEC concentrations (0-50 µg L-1) for 34 days under controlled conditions and then harvested, extracted, derivatised and analysed by comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer (GC × GC-TOFMS). The resulting raw data were analysed using multivariate curve resolution (MCR) and partial least squares (PLS) methods. The metabolic response indicates that exposure to CECs at environmentally relevant concentrations (0.05 µg L-1) can cause significant metabolic alterations in plants (carbohydrate metabolism, the citric acid cycle, pentose phosphate pathway and glutathione pathway) linked to changes in morphological parameters (leaf height, stem width) and chlorophyll content.

Instrument parameters controlling retention precision in gradient elution reversed-phase liquid

The precision of retention time in RPLC is important for compound identification, for setting peak integration time windows and in fundamental studies of retention. In this work, we studied the effect of temperature (T), initial (ϕo) and final mobile phase (ϕf) composition, gradient time (tG), and flow rate (F) on the retention time precision under gradient elution conditions for various types of low MW solutes. We determined the retention factor in pure water ( [Formula: see text] ) and the solute-dependent solvent strength (S) parameters of Snyder's linear solvent strength theory (LSST) as a function of temperature for three different groups of solutes. The effect of small changes in the chromatographic variables (T, ϕo, ϕf, tG and F) by use of the LSST gradient retention equation were estimated. Peaks at different positions in the chromatogram have different sensitivities to changes in these instrument parameters. In general, absolute fluctuations in retention time are larger at longer gradient times. Drugs showed less sensitivity to changes in temperature compared to relatively less polar solutes, non-ionogenic solutes. Surprisingly we observed that fluctuations in temperature, mobile phase composition and flow rate had less effect on retention time under gradient conditions as compared to isocratic conditions. Overall temperature and the initial mobile phase composition are the most important variables affecting retention reproducibility in gradient elution chromatography.

A peaklet-based generic strategy for the untargeted analysis of comprehensive two-dimensional gas chromatography mass spectrometry data sets

Comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) is a well-established key technology in analytical chemistry and increasingly used in the field of untargeted metabolomics. However, automated processing of large GC×GC-MS data sets is still a major bottleneck in untargeted, large-scale metabolomics. For this reason we introduce a novel peaklet-based alignment strategy. The algorithm is capable of an untargeted deterministic alignment exploiting a density based clustering procedure within a time constrained similarity matrix. Exploiting minimal (1)D and (2)D retention time shifts between peak modulations, the alignment is done without the need for peak merging which also eliminates the need for linear or nonlinear retention time correction procedures. The approach is validated in detail using data of urine samples from a large human metabolomics study. The data was acquired by a Shimadzu GCMS-QP2010 Ultra GC×GC-qMS system and consists of 512 runs, including 312 study samples and 178 quality control sample injections, measured within a time period of 22 days. The final result table consisted of 313 analytes, each of these being detectable in at least 75% of the study samples. In summary, we present an automated, reliable and fully transparent workflow for the analysis of large GC×GC-qMS metabolomics data sets.

Global spectral deconvolution based on non-negative matrix factorization in GC × GC-HRTOFMS

A global spectral deconvolution, based on non-negative matrix factorization (NMF) in comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry, was developed. We evaluated the ability of various instrumental parameters and NMF settings to derive high-performance detection in nontarget screening using a sediment sample. To evaluate the performance of the process, a NIST library search was used to identify the deconvoluted spectra. Differences of the instrumental scan rates (25 and 50 Hz) in deconvolution were evaluated and results show that a high scan rate enhanced the number of compounds detected in the sediment sample. A higher mass resolution in the range of 1,000 to 10,000 and a higher m/z precision in the deconvolution were needed to obtain an accurate mass database. After removal of multiple duplicate hits, which occurred in batch processes of NIST library search on the deconvolution result, 62 unique assignable spectra with a match factor ≥900 were obtained in the deconvoluted chromatogram from the sediment sample, including 54 spectra that were refined by the deconvolution. This method will help to detect and build up well-resolved reference spectra from various complex mixtures and will accelerate nontarget screening.

Post-incident monitoring to evaluate environmental damage from shipping incidents: chemical and biological assessments

Oil and chemical spills in the marine environment are an issue of growing concern. Oil exploration and exploitation is moving from the continental shelf to deeper waters, and to northern latitudes where the risk of an oil spill is potentially greater and may affect pristine ecosystems. Moreover, a growing number of chemical products are transported by sea and maritime incidents of hazardous and noxious substances (HNS) are expected to increase. Consequently, it seems timely to review all of the experience gained from past spills to be able to cope with appropriate response and mitigation strategies to combat future incidents. Accordingly, this overview is focused on the dissemination of the most successful approaches to both detect and assess accidental releases using chemical as well as biological approaches for spills of either oil or HNS in the marine environment. Aerial surveillance, sampling techniques for water, suspended particles, sediments and biota are reviewed. Early warning bioassays and biomarkers to assess spills are also presented. Finally, research needs and gaps in knowledge are discussed.

Plant metabolomics: resolution and quantification of elusive peaks in liquid chromatography-mass spectrometry profiles of complex plant extracts using multi-way decomposition methods

Previous studies on LC-MS metabolomic profiling of 127 F2 Barbarea vulgaris plants derived from a cross of parental glabrous (G) and pubescent (P) type, revealed four triterpenoid saponins (hederagenin cellobioside, oleanolic acid cellobioside, epihederagenin cellobioside, and gypsogenin cellobioside) that correlated with resistance of plants against the insect herbivore, Phyllotreta nemorum. In this study, for the first time, we demonstrate the efficiency of the multi-way decomposition method PARAllel FACtor analysis 2 (PARAFAC2) for exploring complex LC-MS data. PARAFAC2 enabled automated resolution and quantification of several elusive chromatographic peaks (e.g. overlapped, elution time shifted and low s/n ratio), which could not be detected and quantified by conventional chromatographic data analysis. Raw LC-MS data of 127 F2 B. vulgaris plants were arranged in a three-way array (elution time point×mass spectra×samples), divided into 17 different chromatographic intervals and each interval were individually modeled by PARAFAC2. Three main outputs of the PARAFAC2 models described: (1) elution time profile, (2) relative abundance, and (3) pure mass spectra of the resolved peaks modeled from each interval of the chromatographic data. PARAFAC2 scores corresponding to relative abundances of the resolved peaks were extracted and further used for correlation and partial least squares (PLS) analysis. A total of 71 PARAFAC2 components (which correspond to actual peaks, baselines and tails of neighboring peaks) were modeled from 17 different chromatographic retention time intervals of the LC-MS data. In addition to four previously known saponins, correlation- and PLS-analysis resolved five unknown saponin-like compounds that were significantly correlated with insect resistance. The method also enabled a good separation between resistant and susceptible F2 plants. PARAFAC2 spectral loadings corresponding to the pure mass spectra of chromatographic peaks matched well with experimentally recorded mass spectra (correlation based similarity >95%). This enabled to extract pure mass spectra of highly overlapped and low s/n ratio peaks.

Comprehensive two-dimensional liquid chromatography — practical impacts of theoretical considerations. A review

A theory of comprehensive two-dimensional separations by liquid chromatographic techniques is overviewed. It includes heart-cutting and comprehensive two-dimensional separation modes, with attention to basic concepts of two-dimensional separations: resolution, peak capacity, efficiency, orthogonality and selectivity. Particular attention is paid to the effects of sample structure on the retention and advantages of a multi-dimensional HPLC for separation of complex samples according to structural correlations. Optimization of 2D separation systems, including correct selection of columns, flow-rate, fraction volumes and mobile phase, is discussed. Benefits of simultaneous programmed elution in both dimensions of LCxLC comprehensive separations are shown.

Experimental setup, modulation of the fraction collection and transfer from the first to the second dimension, compatibility of mobile phases in comprehensive LCxLC, 2D asymmetry and shifts in retention under changing second-dimension elution conditions, are addressed. Illustrative practical examples of comprehensive LCxLC separations are shown.

Semi-automated alignment and quantification of peaks using parallel factor analysis for comprehensive two-dimensional liquid chromatography-diode array detector data sets

Parallel factor analysis was used to quantify the relative concentrations of peaks within four-way comprehensive two dimensional liquid chromatography-diode array detector data sets. Since parallel factor analysis requires that the retention times of peaks between each injection are reproducible, a semi-automated alignment method was developed that utilizes the spectra of the compounds to independently align the peaks without the need for a reference injection. Peak alignment is achieved by shifting the optimized chromatographic component profiles from a three-way parallel factor analysis model applied to each injection. To ensure accurate shifting, components are matched up based on their spectral signature and the position of the peak in both chromatographic dimensions. The degree of shift, for each peak, is determined by calculating the distance between the median data point of the respective dimension (in either the second or first chromatographic dimension) and the maximum data point of the peak furthest from the median. All peaks that were matched to this peak are then aligned to this common retention data point. Target analyte recoveries for four simulated data sets were within 2% of 100% recovery in all cases. Two different experimental data sets were also evaluated. Precision of quantification of two spectrally similar and partially coeluting peaks present in urine was as good as or better than 4%. Good results were also obtained for a challenging analysis of phenytoin in waste water effluent, where the results of the semi-automated alignment method agreed with the reference LC-LC MS/MS method within the precision of the methods.

Capillary separations in metabolomics

Capillary-based separations offer increased resolution, low mass LOD and, in the case of MS, higher sensitivity. The chemical diversity and wide dynamic range of the metabolome requires systems that offer breadth and depth of analysis. In this review, we will highlight novel chemical innovations, technological advancements and various applications of capillary separations in the field of metabolomics.

The impact of sampling time on peak capacity and analysis speed in on-line comprehensive two-dimensional liquid chromatography

Comprehensive two-dimensional liquid chromatography (2DLC) offers a number of practical advantages over optimized one-dimensional LC in peak capacity and thus in resolving power. The traditional "product rule" for overall peak capacity for a 2DLC system significantly overestimates peak capacity because it neglects under-sampling of the first dimension separation. Here we expand on previous work by more closely examining the effects of the first dimension peak capacity and gradient time, and the second dimension cycle times on the overall peak capacity of the 2DLC system. We also examine the effects of re-equilibration time on under-sampling as measured by the under-sampling factor and the influence of molecular type (peptide vs. small molecule) on peak capacity. We show that in fast 2D separations (less than 1h), the second dimension is more important than the first dimension in determining overall peak capacity and conclude that extreme measures to enhance the first dimension peak capacity are usually unwarranted. We also examine the influence of sample types (small molecules vs. peptides) on second dimension peak capacity and peak capacity production rates, and how the sample type influences optimum second dimension gradient and re-equilibration times.