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

Abstract

Identifying compounds of interest for peaks in data generated by comprehensive two-dimensional gas chromatography (GC × 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.

Profiling food volatiles by comprehensive two-dimensional ga schromatography coupled with mass spectrometry: Advanced fingerprinting approaches for comparative analysis of the volatile fraction of roasted hazelnuts (Corylus avellana L.) from different origins

“…The correspondence is established, if a peak is detected within the retention-times window around the corresponding transformed template peak, also showing an MS fragmentation pattern with a proper match factor [35,36]. The effectiveness of the algorithm adopted for the template transformation has been extensively discussed in previous work [35,37].…”

Evaluation of different internal‐diameter column combinations in comprehensive two‐dimensional gas chromatography in flavour and fragrance analysis

“…This approach has already been used in the petrochemical, fat and oil fields [35,36] and can be run with several methods. The template matching method consists of a pattern matching where all (or a selection of) the separated components of an unknown sample are compared to those of a reference pattern (i. e. the template) [37]. The two patterns are then submitted to a procedure matching the corresponding peaks in the template and in the target peak pattern, through their bidimensional retention times (1-D and 2-D), mass spectra and peak values or intensities.…”

Analysis of petroleum compositional similarity using multiway principal components analysis (MPCA) with comprehensive two-dimensional gas chromatographic data

“…This algorithm was later adjusted to align both dimensions [40]. Alternative methods include correlation-optimized shifting algorithms based on the inner-product correlation for local subregions [41]; windowed rank minimization alignment with interpolative stretching between windows using set anchor points [42]; as well as affine transformations that match peak patterns between a peak template and target peak pattern [43]. Other approaches include dynamic time warping and correlation optimized warping (COW), which work for a broad range of chromatograms [44][45][46].…”