Tile-based Fisher ratio analysis of comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC-TOFMS) data using a null distribution approach.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a versatile instrumental platform capable of collecting highly informative, yet highly complex, chemical data for a variety of samples. Fisher-ratio (F-ratio) analysis applied to the supervised comparison of sample classes algorithmically reduces complex GC × GC-TOFMS data sets to find class distinguishing chemical features. F-ratio analysis, using a tile-based algorithm, significantly reduces the adverse effects of chromatographic misalignment and spurious covariance of the detected signal, enhancing the discovery of true positives while simultaneously reducing the likelihood of detecting false positives. Herein, we report a study using tile-based F-ratio analysis whereby four non-native analytes were spiked into diesel fuel at several concentrations ranging from 0 to 100 ppm. Spike level comparisons were performed in two regimes: comparing the spiked samples to the nonspiked fuel matrix and to each other at relative concentration factors of two. Redundant hits were algorithmically removed by refocusing the tiled results onto the original high resolution pixel level data. To objectively limit the tile-based F-ratio results to only features which are statistically likely to be true positives, we developed a combinatorial technique using null class comparisons, called null distribution analysis, by which we determined a statistically defensible F-ratio cutoff for the analysis of the hit list. After applying null distribution analysis, spiked analytes were reliably discovered at ∼1 to ∼10 ppm (∼5 to ∼50 pg using a 200:1 split), depending upon the degree of mass spectral selectivity and 2D chromatographic resolution, with minimal occurrence of false positives. To place the relevance of this work among other methods in this field, results are compared to those for pixel and peak table-based approaches.

Modeling RP-1 fuel advanced distillation data using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and partial least squares analysis.

Recent efforts in predicting rocket propulsion (RP-1) fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as elucidating the relationships between fuel compositions and their properties. Herein, we study multidimensional chromatographic data obtained by comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC-TOFMS) to analyze RP-1 fuels. For GC × GC separations, RTX-Wax (polar stationary phase) and RTX-1 (non-polar stationary phase) columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes (alkanes, cycloalkanes, aromatics, etc.), providing a significant level of chemical compositional information. The GC × GC-TOFMS data were analyzed using partial least squares regression (PLS) chemometric analysis to model and predict advanced distillation curve (ADC) data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the ADC data. The PLS modeling correlates compositional information found in the GC × GC-TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The root-mean-square error of calibration (RMSEC) ranged from 0.1 to 0.5 °C, and was typically below ∼0.2 °C, for the PLS calibration of the ADC modeling with GC × GC-TOFMS data, indicating a good fit of the model to the calibration data. Likewise, the predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation (LOOCV) yielding root-mean-square error of cross-validation (RMSECV) ranging from 1.4 to 2.6 °C, and was typically below ∼2.0 °C, at each % distilled measurement point during the ADC analysis.

Methods of discovery-based and targeted metabolite analysis by comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection.

The investigation of naturally volatile and derivatized metabolites in biological tissues by comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) can provide highly complex and information-rich data for comprehensive metabolomics analysis. The addition of the second separation dimension with GC × GC provides additional chemical selectivity, and the fast scanning time of TOFMS offers benefits in chemical selectivity and overall peak capacity compared to traditional one-dimensional (1D) GC. Furthermore, methods of derivatization to facilitate volatility and thermal stability, the most prominent being the silylation of organic compounds, have extended the use of GC as an important metabolomics tool. The highly information-rich data from GC × GC-TOFMS benefits from sophisticated comprehensive targeted and nontargeted algorithmic software methods. Herein, we detail a robust derivatization and instrumental method for metabolomics analysis and provide a brief overview of possible methods for data analysis.

Enhancing gas chromatography-time of flight mass spectrometry data analysis using two-dimensional mass channel cluster plots.

A novel data reduction and representation method for gas chromatography time-of-flight mass spectrometry (GC-TOFMS) is presented that significantly facilitates separation visualization and analyte peak deconvolution. The method utilizes the rapid mass spectral data collection rate (100 scans/s or greater) of current generation TOFMS detectors. Chromatographic peak maxima (serving as the retention time, tR) above a user specified signal threshold are located, and the chromatographic peak width, W, are determined on a per mass channel (m/z) basis for each analyte peak. The peak W (per m/z) is then plotted against its respective tR (with 10 ms precision) in a two-dimensional (2D) format, producing a cluster of points (i.e., one point per peak W versus tR in the 2D plot). Analysis of GC-TOFMS data by this method produces what is referred to as a two-dimensional mass channel cluster plot (2D m/z cluster plot). We observed that adjacent eluting (even coeluting) peaks in a temperature programmed separation can have their peak W vary as much as ∼10-15%. Hence, the peak W provides useful chemical selectivity when viewed in the 2D m/z cluster plot format. Pairs of overlapped analyte peaks with one-dimensional GC resolution as low as Rs ≈ 0.03 can be visually identified as fully resolved in a 2D m/z cluster plot and readily deconvoluted using chemometrics (i.e., demonstrated using classical least-squares analysis). Using the 2D m/z cluster plot method, the effective peak capacity of one-dimensional GC separations is magnified nearly 40-fold in one-dimensional GC, and potentially ∼100-fold in the context of comparing it to a two-dimensional separation. The method was studied using a 73 component test mixture separated on a 30 m × 250 µm i.d. RTX-5 column with a LECO Pegasus III TOFMS.

Correlation of rocket propulsion fuel properties with chemical composition using comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry followed by partial least squares regression analysis.

There is an increased need to more fully assess and control the composition of kerosene-based rocket propulsion fuels such as RP-1. In particular, it is critical to make better quantitative connections among the following three attributes: fuel performance (thermal stability, sooting propensity, engine specific impulse, etc.), fuel properties (such as flash point, density, kinematic viscosity, net heat of combustion, and hydrogen content), and the chemical composition of a given fuel, i.e., amounts of specific chemical compounds and compound classes present in a fuel as a result of feedstock blending and/or processing. Recent efforts in predicting fuel chemical and physical behavior through modeling put greater emphasis on attaining detailed and accurate fuel properties and fuel composition information. Often, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is employed to provide chemical composition information. Building on approaches that used GC-MS, but to glean substantially more chemical information from these complex fuels, we recently studied the use of comprehensive two dimensional (2D) gas chromatography combined with time-of-flight mass spectrometry (GC×GC-TOFMS) using a "reversed column" format: RTX-wax column for the first dimension, and a RTX-1 column for the second dimension. In this report, by applying chemometric data analysis, specifically partial least-squares (PLS) regression analysis, we are able to readily model (and correlate) the chemical compositional information provided by use of GC×GC-TOFMS to RP-1 fuel property information such as density, kinematic viscosity, net heat of combustion, and so on. Furthermore, we readily identified compounds that contribute significantly to measured differences in fuel properties based on results from the PLS models. We anticipate this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an improved approach to fuel formulation and specification for advanced engine cycles.

Tile-based Fisher-ratio software for improved feature selection analysis of comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry data.

Comprehensive two-dimensional (2D) gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) is a highly capable instrumental platform that produces complex and information-rich multi-dimensional chemical data. The data can be initially overwhelming, especially when many samples (of various sample classes) are analyzed with multiple injections for each sample. Thus, the data must be analyzed in such a way as to extract the most meaningful information. The pixel-based and peak table-based Fisher ratio algorithmic approaches have been used successfully in the past to reduce the multi-dimensional data down to those chemical compounds that are changing between the sample classes relative to those that are not changing (i.e., chemical feature selection). We report on the initial development of a computationally fast novel tile-based Fisher-ratio software that addresses the challenges due to 2D retention time misalignment without explicitly aligning the data, which is often a shortcoming for both pixel-based and peak table-based algorithmic approaches. Concurrently, the tile-based Fisher-ratio algorithm significantly improves the sensitivity contrast of true positives against a background of potential false positives and noise. In this study, eight compounds, plus one internal standard, were spiked into diesel at various concentrations. The tile-based F-ratio algorithmic approach was able to "discover" all spiked analytes, within the complex diesel sample matrix with thousands of potential false positives, in each possible concentration comparison, even at the lowest absolute spiked analyte concentration ratio of 1.06, the ratio between the concentrations in the spiked diesel sample to the native concentration in diesel.

Comprehensive discovery of 13C labeled metabolites in the bacterium Methylobacterium extorquens AM1 using gas chromatography-mass spectrometry.

Herein, we report the identification of isotopically labeled metabolite peaks (or the lack of labeling) between sets of GC-MS data from Methylobacterium extorquens AM1. M. extorquens AM1 is one of the best-characterized model organisms for the study of C1 metabolism in methylotrophic bacteria, a diverse group of microbes that can use reduced one-carbon (C1) sources, such as methanol and methane as a sole source for both energy generation and carbon assimilation. Application of a match value (MV) based metric was used to rank the metabolite peaks in the data from those exhibiting the most mass spectral indications of labeling, to those not exhibiting any indications of labeling. The MV-based ranking corresponded well with analyst interpretation of the mass spectra. The MV-based method was initially demonstrated and validated using a mixture of 21 standards with data sets generated for mixtures at natural abundance, a mixture with 6 of the compounds labeled, and a 1:1 mixture of the natural abundance and labeled mixtures. Experimental data from TMS-derivatized extracts from the bacterium M. extorquens AM1 grown with natural abundance or (13)C-labeled methanol as the carbon source were analyzed. Of 131 peaks considered for the analysis of M. extorquens AM1, the 40 peaks ranked highest for indications of (13)C labeling were all found to be labeled, while those peaks ranked lower progressed from peaks for which labeling was uncertain, to a larger number of peaks that were clearly not labeled. The list of peaks determined to be labeled forms a library of compounds that are known to be labeled following the methanol metabolic pathway in M. extorquens AM1 that can be further investigated in future work, e.g. fluxomic studies.

High throughput analysis of atmospheric volatile organic compounds by thermal injection--isothermal gas chromatography--time-of-flight mass spectrometry.

Sixty one volatile organic compounds (VOCs) from a standard gas mixture were separated via isothermal gas chromatography coupled with time-of-flight mass spectrometry (GC-TOFMS) in a ≈ 35s separation time window (≈ 45 s separation). The VOCs in the standard gas mixture were selected based on the EPA TO-15 methodology. The high throughput separation was achieved with a relatively high total peak capacity (n(c) ≈ 114), by simultaneously minimizing both on-column and off-column peak width broadening. The on-column contributions to peak width broadening were minimized by taking into account and applying GC separation theory for the selection of column dimensions and carrier gas flow rate conditions. Both fast cryogenic focusing and re-injection of compounds (implemented via a commercially available thermal modulator and referred to herein as thermal injection (TI) and fast TOFMS detection (100 scans/s)) were applied to reduce off-column sources of peak width band broadening (sometimes referred to as off-column band broadening). Cryogenic focusing during TI and minimal band broadening-based dilution during separation resulted in preconcentration factors for the detected peaks ranging from 78 (1,4-dichlorobenzene) to 420 (propylene). Since the injected volume for preconcentration was 500 µl, and based on the detected noise levels at selected m/z for each analyte compound, the concentration limit of detection (LOD) ranged from 67 ppbv (parts per billion by volume) for propylene, to 4 ppbv for freon-12. While application of standard VOC analysis conditions leads to separation times typically ranging from ≈ 30 to 50 min, the isothermal GC-TOFMS method reported herein represents a 40-fold improvement in analysis time while maintaining peak capacity and detection sensitivity that is comparable to traditional GC-MS VOC analysis.

Targeted mass spectral ratio analysis: a new tool for gas chromatography-mass spectrometry.

An algorithm, referred to as targeted mass spectral ratio analysis (TMSRA) is presented whereby the ratios between intensities as a function of mass channel (m/z) of a target analyte mass spectrum are used to automatically determine which m/z are sufficiently pure to quantify the analyte in a sample gas chromatogram. The standard perfluorotributylamine (PFTBA) was used to evaluate the reproducibility of the collected mass spectra, which aided in selecting a mass spectral threshold for TMSRA application to a subsequent case study. Results with PFTBA suggested that a threshold of all m/z at or above 1% of the highest recorded m/z intensity should be included for targeted analysis. For the case study, 1-heptene was selected as the target analyte and n-heptane was selected as the interfering compound. These two compounds were chosen since their mass spectra are very similar. Chromatographic data containing a pure peak for these analytes were extracted, and mathematically added at various temporal offsets to generate various degrees of chromatographic resolution, R(s), for the purpose of evaluating algorithm performance, and indeed, TMSRA successfully quantified 1-heptene. At the higher R(s) studied (0.6 ≤ R(s) ≤ 1.5) a deviation within ± 1% and a RSD generally below 1% were achieved for 1-heptene quantification. As the R(s) decreased, the deviation and RSD both increased. At a R(s)=0, a deviation of ≈ 9% and a RSD of ≈ 9% were achieved.

Preliminary effects of real-world factors on the recovery and exploitation of forensic impurity profiles of a nerve-agent simulant from office media.

Dimethyl methylphosphonate (DMMP) was used as a chemical threat agent (CTA) simulant for a first look at the effects of real-world factors on the recovery and exploitation of a CTA's impurity profile for source matching. Four stocks of DMMP having different impurity profiles were disseminated as aerosols onto cotton, painted wall board, and nylon coupons according to a thorough experimental design. The DMMP-exposed coupons were then solvent extracted and analyzed for DMMP impurities by comprehensive 2D gas chromatography/mass spectrometry (GC×GC/MS). The similarities between the coupon DMMP impurity profiles and the known (reference) DMMP profiles were measured by dot products of the coupon profiles and known profiles and by score values obtained from principal component analysis. One stock, with a high impurity-profile selectivity value of 0.9 out of 1, had 100% of its respective coupons correctly classified and no false positives from other coupons. Coupons from the other three stocks with low selectivity values (0.0073, 0.012, and 0.018) could not be sufficiently distinguished from one another for reliable matching to their respective stocks. The results from this work support that: (1) extraction solvents, if not appropriately selected, can have some of the same impurities present in a CTA reducing a CTA's useable impurity profile, (2) low selectivity among a CTA's known impurity profiles will likely make definitive source matching impossible in some real-world conditions, (3) no detrimental chemical-matrix interference was encountered during the analysis of actual office media, (4) a short elapsed time between release and sample storage is advantageous for the recovery of the impurity profile because it minimizes volatilization of forensic impurities, and (5) forensic impurity profiles weighted toward higher volatility impurities are more likely to be altered by volatilization following CTA exposure.

Fast, high peak capacity separations in comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry.

Peak capacity production is substantially improved for two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) and applied to the fast separation of a 28 component liquid test mixture, and two complex vapor samples (a 65 component volatile organic compound test mixture, and the headspace of warm ground coffee beans). A high peak capacity is achieved in a short separation time by selecting appropriate experimental conditions based on theoretical modeling of on-column band broadening, and by reducing the off-column band broadening by applying a narrow, concentrated injection pulse onto the primary column using high-speed cryo-focusing injection (HSCFI), referred to as thermal injection. A long, relatively narrow open tubular capillary column (20 m, 100 µm inner diameter (i.d.) with a 0.4 µm film thickness to benefit column capacity) was used as the primary column. The initial flow rate was 2 ml/min (60 cm/s average linear flow velocity) which is slightly below the optimal average linear gas velocity of 83 cm/s, due to the flow rate constraint of the TOFMS vacuum system. The oven temperature programming rate was 30°C/min. The secondary column (1.8m, 100 µm i.d. with a 0.1 µm film thickness) provided a relatively high peak capacity separation, concurrent with a significantly shorter modulation period, P(M), than commonly applied with the commercial instrument. With this GC×GC-TOFMS instrumental platform, compounds in the 28 component liquid test mixture provided a ∼7 min separation (with a ∼6.5 min separation time window), producing average peak widths of ∼600 ms full width half maximum (FWHM), resulting in a peak capacity on the primary column of ∼400 peaks (at unit resolution). Using a secondary column with a 500 ms P(M), average peak widths of ∼20 ms FWHM were achieved, thus providing a peak capacity of 15 peaks on the second dimension. Overall, an ideal orthogonal GC×GC peak capacity of ∼6000 peaks (at unit resolution) was achieved (or a ß-corrected orthogonal peak capacity of ∼4400, at an average modulation ratio, M(R), of ∼2). This corresponds to an ideal orthogonal peak capacity production of ∼1000 peaks/min (or ∼700 peaks/min, ß-corrected). For comparison, standard split/split-less injection techniques with a 1:100 split, when combined with standard GC×GC conditions typically provide a peak capacity production of ∼100 peaks/min, hence the instrumental platform we report provides a ∼7-fold to 10-fold improvement.

High-speed cryo-focusing injection for gas chromatography: reduction of injection band broadening with concentration enrichment.

In order to maximize peak capacity and detection sensitivity of fast gas chromatography (GC) separations, it is necessary to minimize band broadening, and in particular due to injection since this is often a major contributor. A high-speed cryo-focusing injection (HSCFI) system was constructed to first cryogenically focus analyte compounds in a 6 cm long section of metal MXT column, and second, reinject the focused analytes by rapidly resistively heating the metal column via an in-house built electronic circuit. Since the cryogenically cooled section of column is small (∼750 nl) and the direct resistive heating is fast (∼6000 °C/s), HSCFI is demonstrated to produce an analyte peak with a 6.3 ms width at half height, w(1/2). This was achieved using a 1m long column with a 180 µm inner diameter (i.d.) operated at an absolute head pressure of 55 psi and an oven temperature of 60 °C, with a 10 V pulse applied to the metal column for 50 ms. HSCFI was also used to demonstrate the head space sampling and fast GC analysis of an aqueous solution containing six test analytes (acetone, methanol, ethanol, toluene, chlorobenzene, pentanol). Using Henry's law constants for each of the analytes, injected mass limits of detection (LODs) were typically in the low pg levels (e.g., 1.2 pg for acetone) for the high speed separation. Finally, to demonstrate the use of HSCFI with a complex sample, a gasoline was separated using a 20 m × 100 µm i.d. column and the stock GC oven for temperature programming, which provided a separation time of 200 s and an average peak width at the base of 440 ms resulting in a total peak capacity of 460 peaks (at unit resolution).

Review of chemometric analysis techniques for comprehensive two dimensional separations data.

Comprehensive two-dimensional (2D) separations, such as comprehensive 2D gas chromatography (GC×GC), liquid chromatography (LC×LC), and related instrumental techniques, provide very large and complex data sets. It is often up to the software to assist the analyst in transforming these complex data sets into useful information, and that is precisely where the field of chemometric data analysis plays a pivotal role. Chemometric tools for comprehensive 2D separations are continually being developed and applied as researchers make significant advances in novel state-of-the-art algorithms and software, and as the commercial sector continues to provide user friendly chemometric software. In this review, we build upon previous reviews of this topic, by focusing primarily on advances that have been reported in the past five years. Most of the reports focus on instrumental platforms using GC×GC with either flame ionization detection (FID) or time-of-flight mass spectrometry (TOFMS) detection, or LC×LC with diode array absorbance detection (DAD). The review covers the following general topics: data preprocessing techniques, target analyte techniques, comprehensive nontarget analysis techniques, and software for chemometrics in multidimensional separations.

Gas chromatography-mass spectrometry with chemometric analysis for determining ¹²C and ¹³C labeled contributions in metabolomics and ¹³C flux analysis.

A novel method for the analysis of nearly co-eluting ¹²C and ¹³C isotopically labeled metabolites has been developed and evaluated for gas chromatography coupled to mass spectrometry (GC-MS) data. The method utilizes parallel factor analysis (PARAFAC) with two-dimensional GC-MS data when sample replicates are aligned and stacked in series to create a three-dimensional data cube for mathematical peak deconvolution and ¹²C and ¹³C contribution isolation, with the intent of increasing the accuracy and precision of quantitative metabolomics and ¹³C flux analysis. The platform is demonstrated with ¹³C-labeled metabolite extracts, generated via biosynthesis, added as an internal standard to unlabeled ¹²C metabolites extracted from the methanol-utilizing bacterium Methylobacterium extorquens AM1. Eleven representative metabolites that are common targets for flux analysis were chosen for validation. Good quantitative accuracy and precision were acquired for a 5.00 µM known metabolite concentration (for the 11 metabolites), with an average predicted concentration of 5.07 µM, and a RSD range of 1.2-13.0%. This study demonstrates the ability to reliably deconvolute ¹²C-unlabeled and ¹³C-labeled contributions for a given metabolite. Additionally, using this chemical analysis platform, a dynamic flux experiment is presented in which the incorporation of ¹³C-labeled cell extract can be detected in the methane-utilizing bacterium Methylosinus trichosporium OB3b and measured temporally.

Fast, high peak capacity separations in gas chromatography-time-of-flight mass spectrometry.

Peak capacity production (i.e., peak capacity per separation run time) is substantially improved for gas chromatography-time-of-flight mass spectrometry (GC-TOFMS) and applied to the fast separation of complex samples. The increase in peak capacity production is achieved by selecting appropriate experimental conditions based on theoretical modeling of on-column band broadening, and by reducing the injection pulse width. Modeling to estimate the on-column band broadening from experimental parameters provided insight for the potential of achieving GC separations in the absence of off-column band broadening, i.e., the additional band broadening not due to the on-column separation process. To optimize GC-TOFMS separations collected with a commercial instrumental platform, off-column band broadening from injection and detection needed to be significantly reduced. Specifically for injection, a commercially available thermal modulator is adapted and applied (referred to herein as thermal injection) to provide a narrow injection pulse, while the TOFMS provided a data collection rate of 500 Hz, initially averaged to 100 Hz for data storage. The use of long, relatively narrow open tubular capillary columns and a 30 °C/min programming rate were explored for GC-TOFMS, specifically a 20 m, 100 µm inner diameter (i.d.) capillary column with a 0.4 µm film thickness to benefit column capacity, operated slightly below the optimal average linear gas velocity (at ~2 mL/min, due to the flow rate constraint of the TOFMS). Standard autoinjection with a 1:100 split resulted in an average peak width of ~1.2 s, hence a peak capacity production of 50 peaks/min. Metabolites in the headspace of urine were sampled by solid-phase microextraction (SPME), followed by thermal injection and a ~7 min GC separation (with a ~6 min separation time window), producing ~660 ms peak widths on average, resulting in a total peak capacity of ~550 peaks (at unit resolution) and a peak capacity production of ~90 peaks/min (~2-fold improvement relative to standard autoinjection with the 1:100 split). This total peak capacity production achieved is equivalent to, or greater than, that currently utilized in metabolomics studies using GC/MS, but with much slower separations, on the order of 40 to 60 min, corresponding to a 5-fold or greater GC/MS analysis throughput rate.

Study of the interdependency of the data sampling ratio with retention time alignment and principal component analysis for gas chromatography.

An in-depth study is presented to better understand how data reduction via averaging impacts retention alignment and the subsequent chemometric analysis of data obtained using gas chromatography (GC). We specifically study the use of signal averaging to reduce GC data, retention time alignment to correct run-to-run retention shifting, and principal component analysis (PCA) to classify chromatographic separations of diesel samples by sample class. Diesel samples were selected because they provide sufficient complexity to study the impact of data reduction on the data analysis strategies. The data reduction process reduces the data sampling ratio, S(R), which is defined as the number of data points across a given chromatographic peak width (i.e., the four standard deviation peak width). Ultimately, sufficient data reduction causes the chromatographic resolution to decrease, however with minimal loss of chemical information via the PCA. Using PCA, the degree of class separation (DCS) is used as a quantitative metric. Three "Paths" of analysis (denoted A-C) are compared to each other in the context of a "benchmark" method to study the impact of the data sampling ratio on preserving chemical information, which is defined by the DCS quantitative metric. The benchmark method is simply aligning data and applying PCA, without data reduction. Path A applies data alignment to collected data, then data reduction, and finally PCA. Path B applies data reduction to collected data, and then data alignment, and finally PCA. The optimized path, namely Path C, is created from Paths A and B, whereby collected data are initially reduced to fewer data points (smaller S(R)), then aligned, and then further reduced to even fewer points and finally analyzed with PCA to provide the DCS metric. Overall, following Path C, one can successfully and efficiently classify chromatographic data by reducing to a S(R) of ∼15 before alignment, and then reducing down to S(R) of ∼2 before performing PCA. Indeed, following Path C, results from an average of 15 different column length-with-temperature ramp rate combinations spanning a broad range of separation conditions resulted in only a ∼15% loss in classification capability (via PCA) when the loss in chromatographic resolution was ∼36%.

Experimental study of the quantitative precision for valve-based comprehensive two-dimensional gas chromatography.

For complex sample analysis, there is a need for multidimensional chromatographic instrumentation to be able to separate more compounds, often in shorter time frames. This has led to the development of comprehensive two-dimensional chromatographic instrumentation, such as comprehensive two-dimensional gas chromatography (GC × GC). Lately, much of the focus in this field has been on decreasing peak widths and, therefore, increasing peak capacity and peak capacity production. All of these advancements make it possible to analyze more compounds in a shorter amount of time, but the data still need to remain quantitative to address the needs of most applications. In this report, the relationship among the modulation ratio (M(R)), peak sampling phase (φ), retention time variation (Δt(R)), and how these parameters relate to quantitative analysis precision via the relative standard deviation (RSD) was studied experimentally using a valve-based GC × GC instrument. A wide range of the number of modulations across the first dimension peak width, that is, a M(R) range from ~1 to 10, was examined through maintaining an average first dimension peak width at the base, (1)w(b) of ~3 s and varying the second dimension separation run time from 300 to 2900 ms. An average RSD of 2.1% was experimentally observed at an average M(R) of 2, with a corresponding peak capacity production of ~1200 peaks/min possible. Below this M(R) the RSD quickly increased. In a long-term study of the quantitative precision at a M(R) of 2.5, using 126 replicate injections of a test mixture spanning ~35 h, the RSD averaged 3.0%. The findings have significant implications for optimizing peak capacity production by allowing the use of the longest second dimension run time, while maintaining quantitative precision.

Achieving high peak capacity production for gas chromatography and comprehensive two-dimensional gas chromatography by minimizing off-column peak broadening.

By taking into consideration band broadening theory and using those results to select experimental conditions, and also by reducing the injection pulse width, peak capacity production (i.e., peak capacity per separation time) is substantially improved for one dimensional (1D-GC) and comprehensive two dimensional (GC×GC) gas chromatography. A theoretical framework for determining the optimal linear gas velocity (the linear gas velocity producing the minimum H), from experimental parameters provides an in-depth understanding of the potential for GC separations in the absence of extra-column band broadening. The extra-column band broadening is referred to herein as off-column band broadening since it is additional band broadening not due to the on-column separation processes. The theory provides the basis to experimentally evaluate and improve temperature programmed 1D-GC separations, but in order to do so with a commercial 1D-GC instrument platform, off-column band broadening from injection and detection needed to be significantly reduced. Specifically for injection, a resistively heated transfer line is coupled to a high-speed diaphragm valve to provide a suitable injection pulse width (referred to herein as modified injection). Additionally, flame ionization detection (FID) was modified to provide a data collection rate of 5kHz. The use of long, relatively narrow open tubular capillary columns and a 40°C/min programming rate were explored for 1D-GC, specifically a 40m, 180µm i.d. capillary column operated at or above the optimal average linear gas velocity. Injection using standard auto-injection with a 1:400 split resulted in an average peak width of ∼1.5s, hence a peak capacity production of 40peaks/min. In contrast, use of modified injection produced ∼500ms peak widths for 1D-GC, i.e., a peak capacity production of 120peaks/min (a 3-fold improvement over standard auto-injection). Implementation of modified injection resulted in retention time, peak width, peak height, and peak area average RSD%'s of 0.006, 0.8, 3.4, and 4.0%, respectively. Modified injection onto the first column of a GC×GC coupled with another high-speed valve injection onto the second column produced an instrument with high peak capacity production (500-800peaks/min), ∼5-fold to 8-fold higher than typically reported for GC×GC.

Development and application of a comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry method for the analysis of L-beta-methylamino-alanine in human tissue.

L-Beta-methylamino-alanine (BMAA) has been proposed as a worldwide contributor to neurodegenerative diseases, including Parkinson dementia complex (PDC) of Guam and Alzheimer's disease (AD). Recent conflicting reports of the presence of this amino acid in human brain from patients affected by these diseases have made it necessary to develop methods that provide unambiguous detection in complex samples. Comprehensive two-dimensional gas chromatography coupled with time-of-flight-mass-spectrometry analysis (GCxGC-TOFMS) followed by a targeted Parallel Factor Analysis (PARAFAC) deconvolution method has been used recently in metabolomic investigations to separate, identify, and quantify components of complex biological specimens. We have extended and applied this methodology to the toxicological problem of detecting BMAA in extracts of brain tissue. Our results show that BMAA can be isolated from closely eluting compounds and detected in trace amounts in extracts of brain tissue spiked with low levels of this analyte, ranging from 2.5ppb to 50ppb, with a limit of detection (LOD) of 0.7ppb. This new method was sufficiently sensitive to detect BMAA in cerebral extracts of mice fed BMAA. This optimized approach was then applied to analyze tissue from humans; however, no BMAA was detected in the brain extracts from controls or patients with PDC or AD. Our results demonstrate the application of multidimensional chromatography-mass spectrometry methods and computational deconvolution analysis to the problem of detecting trace amounts of a potential toxin in brain extracts from mice and humans.

Utilizing the third order advantage with isotope dilution mass spectrometry.

Akin to the standard addition method requiring only a single chromatographic injection, a robust isotope dilution mass spectrometry method is described. The (13)C labeled analyte at known concentration serves as the standard to quantify the unlabeled target analyte. Two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) provides a combined (12)C and (13)C analyte peak as part of the third order data cube. This combined peak can be isolated from interfering compounds and noise based on the "third order advantage" with parallel factor analysis (PARAFAC). The combined mass spectra are then mathematically resolved using classical least squares (CLS) providing a (12)C/(13)C ratio, thus absolute amounts of (12)C and (13)C. Good agreement between the prepared and determined concentration ratios for test analytes was achieved with further demonstration to real-world samples.