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.

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.

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%.

Utilizing a constant peak width transform for isothermal gas chromatography.

A computational approach to partially address the general elution problem (GEP), and better visualize, isothermal gas chromatograms is reported. The theoretical computational approach is developed and applied experimentally. We report a high speed temporally increasing boxcar summation (TIBS) transform that, when applied to the raw isothermal GC data, converts the chromatographic data from the initial time domain (in which the peak widths in isothermal GC increase as a function of their retention factors, k), to a data point based domain in which all peaks have the same peak width in terms of number of points in the final data vector, which aides in preprocessing and data analysis, while minimizing data storage size. By applying the TIBS transform, the resulting GC chromatogram (initially collected isothermally), appears with an x-axis point scale as if it were instrumentally collected using a suitable temperature program. A high speed GC isothermal separation with a test mixture containing 10 compounds had a run time of ∼25 s. The peak at a retention factor k ∼0.7 had a peak width of ∼55 ms, while the last eluting peak at k ∼89 (i.e., retention time of ∼22 s) had a peak width of ∼2000 ms. Application of the TIBS transform increased the peak height of the last eluting peak 45-fold, and S/N ∼20-fold. All peaks in the transformed test mixture chromatogram had the width of an unretained peak, in terms of number of data points. A simulated chromatogram at unit resolution, studied using the TIBS transform, provided additional insight into the benefits of the algorithm.

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.

Real-time target selection optimization to enhance alignment of gas chromatograms.

An improved method for real-time selection of the target for the alignment of gas chromatographic data is described. Further outlined is a simple method to determine the accuracy of the alignment procedure. The target selection method proposed uses a moving window of aligned chromatograms to generate a target, herein referred to as the window target method (WTM). The WTM was initially tested using a series of 100 simulated chromatograms, and additionally evaluated using a series of 55 diesel fuel gas chromatograms obtained with four fuel samples. The WTM was evaluated via a comparison to a related method (the nearest neighbor method (NNM)). The results using the WTM with simulated chromatograms showed a significant improvement in the correlation coefficient and the accuracy of alignment when compared to the alignments performed using the NNM. A significant improvement in real-time alignment accuracy, as assessed by a correlation coefficient metric, was achieved with the WTM (starting at ∼ 1.0 and declining to only ∼ 0.985 for the 100th sample), relative to the NNM (starting at ∼ 1.0 and declining to ∼ 0.4 for the 100th sample) for the simulated chromatogram study. The results determined when using the WTM with the diesel fuels also showed an improvement in correlation coefficient and accuracy of the within-class alignments as compared to the results obtained from the NNM. In practice, the WTM could be applied to the real-time analysis of process and feedstock industrial streams to enable real-time decision making from the more precisely aligned chromatographic data.

Chemometric analysis of gas chromatography-mass spectrometry data using fast retention time alignment via a total ion current shift function.

A critical comparison of methods for correcting severely retention time shifted gas chromatography-mass spectrometry (GC-MS) data is presented. The method reported herein is an adaptation to the piecewise alignment algorithm to quickly align severely shifted one-dimensional (1D) total ion current (TIC) data, then applying these shifts to broadly align all mass channels throughout the separation, referred to as a TIC shift function (SF). The maximum shift varied from (-) 5s in the beginning of the chromatographic separation to (+) 20s toward the end of the separation, equivalent to a maximum shift of over 5 peak widths. Implementing the TIC shift function (TIC SF) prior to Fisher Ratio (F-Ratio) feature selection and then principal component analysis (PCA) was found to be a viable approach to classify complex chromatograms, that in this study were obtained from GC-MS separations of three gasoline samples serving as complex test mixtures, referred to as types C, M and S. The reported alignment algorithm via the TIC SF approach corrects for large dynamic shifting in the data as well as subtle peak-to-peak shifts. The benefits of the overall TIC SF alignment and feature selection approach were quantified using the degree-of-class separation (DCS) metric of the PCA scores plots using the type C and M samples, since they were the most similar, and thus the most challenging samples to properly classify. The DCS values showed an increase from an initial value of essentially zero for the unaligned GC-TIC data to a value of 7.9 following alignment; however, the DCS was unchanged by feature selection using F-Ratios for the GC-TIC data. The full mass spectral data provided an increase to a final DCS of 13.7 after alignment and two-dimensional (2D) F-Ratio feature selection.