Application of parallel computing to speed up chemometrics for GC×GC–TOFMS based metabolic fingerprinting
Parallel computing was tested regarding its ability to speed up chemometric operations for data analysis. A set of metabolic samples from a second hand smoke (SHS) experiment was analyzed with comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC × GC–TOFMS). Data was further preprocessed and analyzed. The preprocessing step comprises background correction, smoothing and alignment of the chromatographic signal. Data analysis was performed by applying t-test and partial least squares projection to latent structures discriminant analysis (PLS-DA). The optimization of the algorithm for parallel computing led to a substantial increase in performance. Metabolic fingerprinting showed a discrimination of the samples and indicates a metabolic effect of SHS.