Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC-HRMS).

This paper describes informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography (GCxGC) and high-resolution mass spectrometry (HRMS). GCxGC-HRMS analysis produces large data sets that are rich with information, but highly complex. The size of the data and volume of information requires automated processing for comprehensive cross-sample analysis, but the complexity poses a challenge for developing robust methods. The approach developed here analyzes GCxGC-HRMS data from multiple samples to extract a feature template that comprehensively captures the pattern of peaks detected in the retention-times plane. Then, for each sample chromatogram, the template is geometrically transformed to align with the detected peak pattern and generate a set of feature measurements for cross-sample analyses such as sample classification and biomarker discovery. The approach avoids the intractable problem of comprehensive peak matching by using a few reliable peaks for alignment and peak-based retention-plane windows to define comprehensive features that can be reliably matched for cross-sample analysis. The informatics are demonstrated with a set of 18 samples from breast-cancer tumors, each from different individuals, six each for Grades 1-3. The features allow classification that matches grading by a cancer pathologist with 78% success in leave-one-out cross-validation experiments. The HRMS signatures of the features of interest can be examined for determining elemental compositions and identifying compounds.

Peak pattern variations related to comprehensive two-dimensional gas chromatography acquisition.

Identifying compounds of interest for peaks in data generated by comprehensive two-dimensional gas chromatography (GC x GC) is a critical analytical task. Manually identifying compounds is tedious and time-consuming. An alternative is to use pattern matching. Pattern matching identifies compounds by matching previously observed patterns with known peaks to newly observed patterns with unidentified peaks. The fundamental difficulty of pattern matching comes from peak pattern distortions that are caused by differences in data acquisition conditions. This paper investigates peak pattern variations related to varying oven temperature ramp rate and inlet gas pressure and evaluates two types of affine transformations for matching peak patterns. The experimental results suggest that, over the experimental ranges, the changes in temperature ramp rate generate non-linear pattern variations and changes in gas pressure generate nearly linear pattern variations. The results indicate the affine transformations can largely remove the pattern variations and can be used for applications such as pattern matching and normalizing retention times to retention indices.

Correlations between pulp properties of eucalyptus clones and leaf volatiles using automated solid-phase microextraction.

Analysis of biogenic volatile organic compounds (BVOC) of 14 Eucalyptus clones has been performed using an automated headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography (GC)/ion trap mass spectrometry (ITMS) method. Correlations between pulp properties of Eucalyptus clones and the BVOC of their leaf headspaces were studied. The compounds alpha-terpineol and the sesquiterpene beta-eudesmol were positively correlated with S5, a property related to the hemicelluose content in the pulp. Qualitative results obtained with automated HS-SPME were sufficient to group together the same species and related hybrids through cluster analysis and were confirmed through principal component analysis. A preliminary separation of the essential oils of Eucalyptus dunnii through comprehensive two-dimensional gas chromatography (GC x GC) showed approximately 580 peaks compared to approximately 60 in a typical GC/ITMS first-dimension chromatogram. The potential of HS-SPME coupled to GC x GC to improve the separation of Eucalyptus volatiles and other plant essential oils looks extremely promising for new applications of unsupervised learning methods.

Image background removal in comprehensive two-dimensional gas chromatography.

This paper describes a new technique for removing the background level from digital images produced in comprehensive two-dimensional gas chromatography (GCxGC). Background removal is an important first step in the larger problem of quantitative analysis. The approach estimates the background level across the chromatographic image based on structural and statistical properties of GCxGC data. Then, the background level is subtracted from the image, producing a chromatogram in which the peaks rise above a near-zero mean background. After the background level is removed, further analysis is required to determine the quantitative relationship between the peaks and chemicals in the sample. The algorithm is demonstrated experimentally to be effective at determining and removing the background level from GCxGC images. The algorithm has several parametric controls and is incorporated into an interactive program with graphical interface for rapid and accurate detection of GCxGC peaks.