Recent advancements in comprehensive two-dimensional separations with chemometrics

Comprehensive two-dimensional (2D) separations provide the analyst with a tremendous amount of complex data. In order to glean useful information from this complex data, advancements in commercially available software that implement chemometrics are currently available and continue to evolve. Future advancements will no doubt involve commercializing (or adapting) specialized, in-house chemometric techniques that are currently found only in the hands of technical experts and researchers in industry, government, and academia. In order to make timely advancements, future commercialization of novel chemometric techniques should involve collaborations among instrument software manufacturers, professional programmers, technical experts, and researchers. During the last decade, this field has seen a steady advancement from single analyte target analysis to comprehensive non-target analysis of entire multidimensional sample profiles (involving sample classification and/or data mining for discovery-based sample comparisons). The advancements in instrumentation and chemometric software tools have a tremendous impact in various applications: fuels, food, environmental, pharmaceuticals, metabolomics, etc. Most of the development has been for software to apply with gas chromatography-based instrumentation, such as comprehensive two-dimensional gas chromatography (GC x GC) and comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (GC x GC-TOF-MS). More recently there have been notable advancements in liquid-phase instrumentation as well.     

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.