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

Automated optimization and construction of chemometric models based on highly variable raw chromatographic data

“…The use of total ion chromatograms (TICs) [18,19] or extracted ion chromatograms (EICs) [18,20] is relatively straightforward, but these approaches may eliminate useful information (using TICs results in the loss of detail in the mass spectral domain, and EICs impose the analyst's preconceived notions of variable relevance on the model). Other methods such as analysis of variance (ANOVA) [9,21,22] or discriminating variable (DIVA) tests [23] are more complex. These approaches calculate a metric by which variables can be ranked based on their perceived ability to distinguish between sample classes.…”

Comprehensive two-dimensional gas chromatography/time-of-flight mass spectrometry peak sorting algorithm

“…Recently, Pierce et al proposed an alignment algorithm that can correct the entire chromatogram in both GC dimensions [14]. The algorithm used a novel indexing scheme together with a piecewise retention time alignment algorithm [15][16][17], in which one chromatogram is used as the target and other chromatograms aligned to it. A chromatogram is defined as an M × N matrix where there are N units along the first column dimension and M units along the second column dimension.…”

Application of Chemometrics to the Interpretation of Analytical Separations Data

“…Initial efforts into the application of statistical and chemometric tools to chromatographic data were accomplished using data that were processed to provide a list of detected, integrated peak areas or heights (or the calibrated concentrations for known compounds). However, the trend in recent years has turned towards the direct chemometric interpretation of raw chromatographic signals (Watson et al, 2006;Johnson & Synovec, 2002). The reason for this trend is that many errors can occur during integration of raw signals (Asher et al, 2009;de la Mata-Espinosa et al, 2011b).…”

“…A variant of this algorithm can be used when MS data are available. In this case, the mass spectrum at the apex of each peak in the target signal is compared to the mass spectrum of each peak within a set window on the sample signal and peaks are matched if their spectra have a high enough match quality (Watson et al, 2006). A general scheme for peak alignment using this approach is described in Figure 4.…”