Targeted analyte deconvolution and identification by four-way parallel factor analysis using three-dimensional gas chromatography with mass spectrometry data.

Comprehensive three-dimensional gas chromatography with time-of-flight mass spectrometry (GC3-TOFMS) creates an opportunity to explore a new paradigm in chemometric analysis. Using this newly described instrument and the well understood Parallel Factor Analysis (PARAFAC) model we present one option for utilization of the novel GC3-TOFMS data structure. We present a method which builds upon previous work in both GC3 and targeted analysis using PARAFAC to simplify some of the implementation challenges previously discovered. Conceptualizing the GC3-TOFMS instead as a one-dimensional gas chromatograph with GC × GC-TOFMS detection we allow the instrument to create the PARAFAC target window natively. Each first dimension modulation thus creates a full GC × GC-TOFMS chromatogram fully amenable to PARAFAC. A simple mixture of 115 compounds and a diesel sample are interrogated through this methodology. All test analyte targets are successfully identified in both mixtures. In addition, mass spectral matching of the PARAFAC loadings to library spectra yielded results greater than 900 in 40 of 42 test analyte cases. Twenty-nine of these cases produced match values greater than 950.

Comprehensive Three-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry.

Development of comprehensive, three-dimensional (3D) gas chromatography with time-of-flight mass spectrometric detection (GC3/TOFMS) is described. This instrument provides four dimensions (4D) of chemical selectivity and includes significant improvements to total selectivity (mass spectrometric and chromatographic), peak identification, and operational temperature range relative to previous models of the GC3 reported. The new instrumental design and data output are evaluated and illustrated via two samples, a 115-component test mixture and a diesel fuel spiked with several compounds, for the purpose of illustrating the chemical selectivity benefits of this instrumental platform. Useful approaches to visualize the 4D data are presented. The GC3/TOFMS instrument experimentally achieved total peak capacity, nc,3D, ranging from 5000 to 9600 (x̅ = 7000, s = 1700) for 10 representative analytes for 50 min separations with component dimensional peak capacities averaging 406, 3.6, and 4.9 for 1D, 2D, and 3D, respectively. Particularly, GC3/TOFMS achieved a combined 2D × 3D peak capacity ranging from 10 to 26 (x̅ = 17.6, s = 5.0), which is similar to what is achieved by 2D alone in a GC × GC operating at equivalent modulation period conditions. The analytical benefits of employing three varied chemical selectivities in the 3D separation coupled with TOFMS are illustrated through the separation and detection of 1,6-dichlorohexane and cyclohexyl isothiocyanate as part of the diesel fuel analysis.

Performance evaluation of tile-based Fisher Ratio analysis using a benchmark yeast metabolome dataset.

Performance of tile-based Fisher Ratio (F-ratio) data analysis, recently developed for discovery-based studies using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS), is evaluated with a metabolomics dataset that had been previously analyzed in great detail, but while taking a brute force approach. The previously analyzed data (referred to herein as the benchmark dataset) were intracellular extracts from Saccharomyces cerevisiae (yeast), either metabolizing glucose (repressed) or ethanol (derepressed), which define the two classes in the discovery-based analysis to find metabolites that are statistically different in concentration between the two classes. Beneficially, this previously analyzed dataset provides a concrete means to validate the tile-based F-ratio software. Herein, we demonstrate and validate the significant benefits of applying tile-based F-ratio analysis. The yeast metabolomics data are analyzed more rapidly in about one week versus one year for the prior studies with this dataset. Furthermore, a null distribution analysis is implemented to statistically determine an adequate F-ratio threshold, whereby the variables with F-ratio values below the threshold can be ignored as not class distinguishing, which provides the analyst with confidence when analyzing the hit table. Forty-six of the fifty-four benchmarked changing metabolites were discovered by the new methodology while consistently excluding all but one of the benchmarked nineteen false positive metabolites previously identified.

Observations on "orthogonality" in comprehensive two-dimensional separations.

The concept and definition of orthogonality in the context of comprehensive two-dimensional (2D) separations are interesting topics of active discussion. Over the years, several approaches have been taken to quantify the degree of orthogonality, primarily to serve as a metric to optimize (and compare) comprehensive 2D separations. Recently, a mathematical function was reported that is qualitatively instructive for the purpose of providing such a metric. However, the mathematical function has some quantitative shortcomings. Herein, we both explore and partially correct this function. The orthogonality metric, referred to previously and herein as the orthogonality, O, was mathematically related to the fraction of the 2D separation space occupied by compounds (i.e., fractional coverage) and the peak capacity, P, for one dimension of the 2D separation. The fractional coverage, f, is simply related to the percentage coverage, which is equal to 100%(f). Our main finding was that the values for O as a function of P for a given percentage coverage achieve a constant value at large P but deviate severely to lower O values at small P. For comprehensive 2D separations operated such that the second dimension is at small P, the findings we report have consequences for those who consider applying the O metric. Finally, it is discussed that the percentage coverage may be a better metric to gauge the extent to which the compounds in a given sample mixture have been disseminated in the 2D separation space.

Comprehensive three-dimensional gas chromatography with parallel factor analysis.

Development of a comprehensive, three-dimensional gas chromatograph (GC3) instrument is described. The instrument utilizes two six-port diaphragm valves as the interfaces between three, in-series capillary columns housed in a standard Agilent 6890 gas chromatograph fitted with a high data acquisition rate flame ionization detector. The modulation periods for sampling column one by column two and column two by column three are set so that a minimum of three slices (more commonly four or five) are acquired by the subsequent dimension resulting in both comprehensive and quantitative data. A 26-component test mixture and quantitative standards are analyzed using the GC3 instrument. A useful methodology for three-dimensional (3D) data analysis is evaluated, based on the chemometric technique parallel factor analysis (PARAFAC). Since the GC3 instrument produces trilinear data, we are able to use this powerful chemometric technique, which is better known for the analysis of two-dimensional (2D) separations with multichannel detection (e.g., GC x GC-TOFMS) or multiple samples (or replicates) of 2D data. Using PARAFAC, we mathematically separate (deconvolute) the 3D data "volume" for overlapped analytes (i.e., ellipsoids), provided there is sufficient chromatographic resolution in each of the three separation dimensions. Additionally, PARAFAC is applied to quantify analyte standards. For the quantitative analysis, it is demonstrated that PARAFAC may provide a 10-fold improvement in the signal-to-noise ratio relative to a traditional integration method applied to the raw, baseline-corrected data. The GC3 instrument obtains a 3D peak capacity of 3500 at a chromatographic resolution of one in each separation dimension. Furthermore, PARAFAC deconvolution provides a considerable enhancement in the effective 3D peak capacity.

Classification of high-speed gas chromatography-mass spectrometry data by principal component analysis coupled with piecewise alignment and feature selection.

A useful methodology is introduced for the analysis of data obtained via gas chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum at each retention time interval in which a mass spectrum was collected. Principal component analysis (PCA) with preprocessing by both piecewise retention time alignment and analysis of variance (ANOVA) feature selection is applied to all mass channels collected. The methodology involves concatenating all concurrently measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS separation into a row vector. All of the sample row vectors are incorporated into a matrix where each row is a sample vector. This matrix is piecewise aligned and reduced by ANOVA feature selection. Application of the preprocessing steps (retention time alignment and feature selection) to all mass channels collected during the chromatographic separation allows considerably more selective chemical information to be incorporated in the PCA classification, and is the primary novelty of the report. This methodology is objective and requires no knowledge of the specific analytes of interest, as in selective ion monitoring (SIM), and does not restrict the mass spectral data used, as in both SIM and total ion current (TIC) methods. Significantly, the methodology allows for the classification of data with low resolution in the chromatographic dimension because of the added selectivity from the complete mass spectral dimension. This allows for the successful classification of data over significantly decreased chromatographic separation times, since high-speed separations can be employed. The methodology is demonstrated through the analysis of a set of four differing gasoline samples that serve as model complex samples. For comparison, the gasoline samples are analyzed by GC-MS over both 10-min and 10-s separation times. The successfully classified 10-min GC-MS TIC data served as the benchmark analysis to compare to the 10-s data. When only alignment and feature selection was applied to the 10-s gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 10-s gasoline separations when the methodology was applied with all the m/z information. With ANOVA feature selection, chromatographic regions with Fisher ratios greater than 1500 were retained in a new matrix and subjected to PCA yielding successful classification for the 10-s separations.